From 511df6ab32917570a94e96997b867e3ce46a7a33 Mon Sep 17 00:00:00 2001 From: sceckert Date: Thu, 6 Jun 2024 20:30:53 -0400 Subject: [PATCH] cleaned up notebook links --- data/VS-author-term_frequencies.csv | 5 + data/VS-title-term_frequencies.csv | 5 + data/list-of-Victorianist-journals.csv | 35 + notebooks/incompletes.csv | 1 - notebooks/jstor-data-and-analysis.ipynb | 29117 ++++++++-------------- notebooks/specialization.png | Bin 117404 -> 0 bytes notebooks/synchronic-journals.png | Bin 298830 -> 0 bytes 7 files changed, 9804 insertions(+), 19359 deletions(-) create mode 100644 data/VS-author-term_frequencies.csv create mode 100644 data/VS-title-term_frequencies.csv create mode 100644 data/list-of-Victorianist-journals.csv delete mode 100644 notebooks/incompletes.csv delete mode 100644 notebooks/specialization.png delete mode 100644 notebooks/synchronic-journals.png diff --git a/data/VS-author-term_frequencies.csv b/data/VS-author-term_frequencies.csv new file mode 100644 index 0000000..58b3924 --- /dev/null +++ b/data/VS-author-term_frequencies.csv @@ -0,0 +1,5 @@ +Author,1960s,1970s,1980s,1990s,2000s,2010s +Bronte,4.238258877434135,10.292524377031418,11.409395973154362,11.659513590844064,8.901251739,0.12217470983506415 +Dickens,25.42955326460481,29.902491874322862,30.285234899328863,25.894134477825464,25.17385257301808,25.65668906536347 +Eliot,21.534936998854526,23.61863488624052,29.194630872483224,22.031473533619454,23.92211405,22.052535125229078 +Hardy,15.349369988545247,23.185265438786566,20.80536912751678,15.021459227467812,9.805285118219748,11.48442272449603 \ No newline at end of file diff --git a/data/VS-title-term_frequencies.csv b/data/VS-title-term_frequencies.csv new file mode 100644 index 0000000..00e5988 --- /dev/null +++ b/data/VS-title-term_frequencies.csv @@ -0,0 +1,5 @@ +Title,1960s,1970s,1980s,1990s,2000s,2010s +Bleak House,4.238258877434135,4.8754062838569885,4.697986577181208,5.007153075822604,5.354659248956885,4.825901038485034 +David Copperfield,3.2073310423825885,3.5752979414951245,5.956375838926174,4.220314735336195,2.781641168289291,3.3598045204642637 +Great Expectations,3.436426116838488,3.5752979414951245,3.104026845637584,4.363376251788269,3.2684283727399164,2.8711056811240074 +Middlemarch,5.841924398625429,6.500541711809317,7.2147651006711415,5.865522174535051,5.632823365785813,6.536346976175931 \ No newline at end of file diff --git a/data/list-of-Victorianist-journals.csv b/data/list-of-Victorianist-journals.csv new file mode 100644 index 0000000..9eca322 --- /dev/null +++ b/data/list-of-Victorianist-journals.csv @@ -0,0 +1,35 @@ +Journal_Title +Victorian Studies + George Eliot - George Henry Lewes Studies + Nineteenth-Century Fiction + Nineteenth-Century Literature + Dickens Studies Annual + Victorian Literature and Culture + Victorian Review + The George Eliot George Henry Lewes Newsletter + Victorian Periodicals Review + Dickens Quarterly + Victorian Poetry + The Thomas Hardy Journal + The Gaskell Society Journal + The Gaskell Journal + Newsletter of the Victorian Studies Association of Western Canada + Dickens Studies Newsletter + Browning Institute Studies + Victorian Periodicals Newsletter + Carlyle Studies Annual + Conradiana + Tennyson Research Bulletin + The Conradian + The Hardy Society Journal + The Hardy Review + Studies in Browning and His Circle + Nineteenth-Century French Studies + The Wilkie Collins Journal + Carlyle Newsletter + The Wildean + Dickens Studies + Carlyle Annual + 19th-Century Music + The Trollopian + Conrad Studies \ No newline at end of file diff --git a/notebooks/incompletes.csv b/notebooks/incompletes.csv deleted file mode 100644 index 218a035..0000000 --- a/notebooks/incompletes.csv +++ /dev/null @@ -1 +0,0 @@ -,id,Author name,Year,Page number,Quotation from PDF,Quotation from JSONL full text,Ground-truth character indexes,Notes diff --git a/notebooks/jstor-data-and-analysis.ipynb b/notebooks/jstor-data-and-analysis.ipynb index 267f2ae..21e99db 100644 --- a/notebooks/jstor-data-and-analysis.ipynb +++ b/notebooks/jstor-data-and-analysis.ipynb @@ -6,7 +6,9 @@ "source": [ "# Analysis of Text Matching Data Generated from JSTOR Dataset \n", "\n", - "- [Generating *Middlemarch* chapter and book locations](#Generating-Middlemarch-chapter-and-book-locations)\n", + "- [Author and title references in *Victorian Studies*](#Author-and-title-references-in-Victorian-Studies)\n", + "- [*Middlemarch* statistics](#Middlemarch-statistics)\n", + " - [Generating *Middlemarch* chapter and book locations](#Generating-Middlemarch-chapter-and-book-locations)\n", "- [Statistics on our JSTOR dataset](#Statistics-on-our-dataset-of-JSTOR-matches)\n", " - [Data Dictionary for text matcher dataset](#Data-Dictionary-for-text-matcher-dataset)\n", " - [How many quotations do we have?](#How-many-quotations-do-we-have?)\n", @@ -19,24 +21,33 @@ " - [Quotation length statistics](#Quotation-Length-Statistics)\n", "- [Functions for extracting wordcounts, numbers of quotations for diachronic and synchronic analysis](#Functions-for-extracting-wordcounts,-numbers-of-quotations-for-diachronic-and-synchronic-analysis)\n", "- [Synchronic Analysis](#Synchronic-Analysis) \n", - " - [Quotations and Words Quoted Per Book in *Middlemarch*](#Quotations-Per-Book-in-Middlemarch)\n", - " - [Quotes and Words Quoted by Chapter in *Middlemarch*](#Number-of-Quotes-and-Words-Quoted-by-Chapter)\n", + " - [Quotations and words quoted per book in *Middlemarch*](#Quotations-and-words-quoted-per-book-in-Middlemarch)\n", + " - [Quotations and words quoted by chapter in *Middlemarch*](#Quotations-and-words-quoted-by-chapter-in-Middlemarch)\n", "- [Diachronic Analysis](#Diachronic-Analysis) \n", " - [*Middlemarch* diachronic analysis: quotations per book, by decade](#Middlemarch-diachronic-analysis:-quotations-per-book,-by-decade)\n", - " - [*Middlemarch* diachronic analysis: quotations per chapter,by decade](#Middlemarch-diachronic-analysis:-quotations-per-chapter,-by-decade)\n", + " - [*Middlemarch* diachronic analysis: quotations per chapter, by decade](#Middlemarch-diachronic-analysis:-quotations-per-chapter,-by-decade)\n", + " - [*Middlemarch* top 5 most frequently quoted chapters, line chart](#Middlemarch-top-5-most-frequently-quoted-chapters,-line-chart)\n", "- [*Middlemarch* chapter-specific analysis](#Middlemarch-chapter-specific-analysis)\n", " - [Chapter 15](#Chapter-15)\n", " - [Chapter 20](#Chapter-20)\n", - "- [*Middlemarch* Quotations, by Journal](#Middlemarch-Quotations,-by-Journal)\n", + "- [*Middlemarch* quotations, by journal](#Middlemarch-quotations,-by-journal)\n", " - [Descriptive statistics on journals in JSTOR dataset](#Descriptive-statistics-on-journals-in-JSTOR-dataset)\n", - " - [GE-GHLS, NLH, and ELH](#GE-GHLS,-NLH,-and-ELH)\n", - " - [NLH](#NLH)\n", - " - [ELH](#ELH)\n", - " - [Victorian Studies](#Victorian-Studies)\n", - "- [Middlemarch and Eliot in VS](#Middlemarch-and-Eliot-in-VS)\n", + " - [*Middlemarch* quotations per chapter, by journal, stacked bar chart](#Middlemarch-quotations-per-chapter,-by-journal,-stacked-bar-chart)\n", + " - [*George Eliot - George Henry Lewes Studies (GE-GHLS)*](#George-Eliot---George-Henry-Lewes-Studies-(GE-GHLS))\n", + " - [*Victorian Studies*](#Victorian-Studies)\n", + " - [All Victorianist journals](#All-Victorianist-journals)\n", + " - [Most distinctive words: Victorianist journals vs. non-Victorianist journals](#Most-Distinctive-Words:-Victorianist-Journals-vs.-Non-Victorianist-Journals) \n", "- [Evaluation](#Evaluation)\n", " - [ Generating samples of dataset for evaluating the precision and recall of text-matcher](#Generating-samples-of-dataset-for-evaluating-the-precision-and-recall-of-text-matcher)\n", - " - [Evaluation metrics](#Evaluation-metrics)\n" + " - [Link to evaluation metrics notebook](#Link-to-evaluation-metrics-notebook)\n", + "- [Figures for \"What We Quote: Disciplinary History and the Textual Atmospheres of *Middlemarch*\" essay](#Figures-for-\"What-We-Quote:-Disciplinary-History-and-the-Textual-Atmospheres-of-Middlemarch\"-essay)\n", + " - [Figure 1](#Figure-1)\n", + " - [Figure 2](#Figure-2)\n", + " - [Figure 3](#Figure-3)\n", + " - [Figure 4](#Figure-4)\n", + " - [Figure 5](#Figure-5)\n", + " - [Figure 6](#Figure-6)\n", + " - [Figure 7](#Figure-7)" ] }, { @@ -53,8 +64,8 @@ "import altair as alt\n", "#new viz library for single-column heatmap\n", "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set()\n", + "# import seaborn as sns\n", + "#sns.set()\n", "#from nltk.corpus import names\n", "from collections import Counter\n", "from matplotlib import pyplot as plt\n", @@ -67,223 +78,35 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Generating *Middlemarch* chapter and book locations \n", - "Here, we're using a Project Gutenberg text of *Middlemarch*, with one modification: the phrase \"Book 1\" has been moved to appear before the prelude, marking that the \"Prelude\" is indeed part of the text that appeared with Book 1." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "with open('../middlemarch.txt') as f: \n", - " mm = f.read()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "textALength = len(mm) " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "89" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get chapter locations\n", - "chapterMatches = re.finditer('PRELUDE|CHAPTER|FINALE', mm)\n", - "chapterLocations = [match.start() for match in chapterMatches]\n", - "chapterLocations.append(textALength) # Add one to account for last chunk. \n", - "len(chapterLocations)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4890" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get paragraph locations\n", - "paragraphMatches = re.finditer('\\n\\n', mm)\n", - "paragraphLocations = [match.start() for match in paragraphMatches]\n", - "paragraphLocations.append(textALength)\n", - "len(paragraphLocations)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0, 38, 250307, 481579, 681858, 915901, 1138247, 1364956, 1571148, 1793449]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get book locations\n", - "bookLocations = [match.start() for match in re.finditer('\\nBOOK', mm)]\n", - "bookLocations = [0] + bookLocations + [textALength] # Add one to account for last chunk.\n", - "bookLocations" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def getChapters(text): \n", - " chapters = []\n", - " for i, loc in enumerate(chapterLocations): \n", - " if i != len(chapterLocations)-1: \n", - " chapter = mm[loc:chapterLocations[i+1]]\n", - " chapters.append(chapter)\n", - " return chapters" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chapters = getChapters(mm)\n", - "chapterLengths = [len(chapter.split()) for chapter in chapters]\n", - "chapterLengthsSeries = pd.Series(chapterLengths)\n", - "chapterLengthsSeries.plot(kind='bar', title='Middlemarch Chapter Lengths')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def getParagraphs(text): \n", - " paragraphs = []\n", - " for i, loc in enumerate(paragraphLocations): \n", - " if i != len(paragraphLocations)-1: \n", - " paragraph = mm[loc:paragraphLocations[i+1]]\n", - " paragraphs.append(paragraph)\n", - " return paragraph" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "paragraphs = getParagraphs(mm)\n", - "paragraphLengths = [len(paragraph.split()) for paragraph in paragraphs]\n", - "paragraphLengthsSeries = pd.Series(paragraphLengths)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\n\\nHer finely touched spirit had still its fine issues, though they were\\nnot widely visible. Her full nature, like that river of which Cyrus\\nbroke the strength, spent itself in channels which had no great name on\\nthe earth. But the effect of her being on those around her was\\nincalculably diffusive: for the growing good of the world is partly\\ndependent on unhistoric acts; and that things are not so ill with you\\nand me as they might have been, is half owing to the number who lived\\nfaithfully a hidden life, and rest in unvisited tombs.\\n'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "paragraphs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Statistics on our dataset of JSTOR matches" + "## Author and title references in *Victorian Studies*\n", + "\n", + "- [Most frequent author references in *Victorian Studies*](#Most-frequent-author-references-in-Victorian-Studies)\n", + " - [Most frequent author references in *Victorian Studies*, line chart](#Most-frequent-author-references-in-Victorian-Studies,-line-chart)\n", + "\t- [Most frequent author references in *Victorian Studies*, line chart (color)](#Most-frequent-author-references-in-Victorian-Studies,-line-chart-(color))\n", + "- [Most frequent title references in *Victorian Studies*](#Most-frequent-title-references-in-Victorian-Studies)\n", + "\t- [Most frequent title references in *Victorian Studies*, line chart](#Most-frequent-title-references-in-Victorian-Studies,-line-chart)\n", + " - [Most frequent title references in *Victorian Studies*, line chart (color)](#Most-frequent-title-references-in-Victorian-Studies,-line-chart-(color))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Read in our ` text-matcher` JSTOR data \n", - "Here, we're reading in the output of our text-matcher on our JSTOR data (in JSON format)" + "### Most frequent author references in *Victorian Studies*" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# Briefly commenting out the old dataset\n", - "#df = pd.read_json('../../../Middlematch/hyperparameter-data/t2-c3-n2-m3-no-stops.json')\n", - "df = pd.read_json('../../../Middlematch/Hyperparameter-testing/hyperparameter-data/t2-c3-n2-m3-no-stops.json')" + "vs_authors_df = pd.read_csv(\"../data/VS-author-term_frequencies.csv\")" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -307,1058 +130,886 @@ " \n", " \n", " \n", - " creator\n", - " datePublished\n", - " docSubType\n", - " docType\n", - " id\n", - " identifier\n", - " isPartOf\n", - " issueNumber\n", - " language\n", - " outputFormat\n", - " ...\n", - " title\n", - " url\n", - " volumeNumber\n", - " wordCount\n", - " numMatches\n", - " Locations in A\n", - " Locations in B\n", - " abstract\n", - " keyphrase\n", - " subTitle\n", + " Author\n", + " 1960s\n", + " 1970s\n", + " 1980s\n", + " 1990s\n", + " 2000s\n", + " 2010s\n", " \n", " \n", " \n", " \n", " 0\n", - " [Rainer Emig]\n", - " 2006-01-01\n", - " book-review\n", - " article\n", - " http://www.jstor.org/stable/41158244\n", - " [{'name': 'issn', 'value': '03402827'}, {'name...\n", - " Amerikastudien / American Studies\n", - " 3\n", - " [eng]\n", - " [unigram, bigram, trigram]\n", - " ...\n", - " Review Article\n", - " http://www.jstor.org/stable/41158244\n", - " 51\n", - " 1109\n", - " 1\n", - " [[130022, 130046]]\n", - " [[6851, 6875]]\n", - " None\n", - " None\n", - " None\n", + " Bronte\n", + " 4.238259\n", + " 10.292524\n", + " 11.409396\n", + " 11.659514\n", + " 8.901252\n", + " 0.122175\n", " \n", " \n", " 1\n", - " [Martin Green]\n", - " 1970-01-01\n", - " book-review\n", - " article\n", - " http://www.jstor.org/stable/3722819\n", - " [{'name': 'issn', 'value': '00267937'}, {'name...\n", - " The Modern Language Review\n", - " 1\n", - " [eng]\n", - " [unigram, bigram, trigram]\n", - " ...\n", - " Review Article\n", - " http://www.jstor.org/stable/3722819\n", - " 65\n", - " 1342\n", - " 0\n", - " []\n", - " []\n", - " None\n", - " None\n", - " None\n", + " Dickens\n", + " 25.429553\n", + " 29.902492\n", + " 30.285235\n", + " 25.894134\n", + " 25.173853\n", + " 25.656689\n", " \n", " \n", " 2\n", - " [Richard Exner]\n", - " 1982-01-01\n", - " book-review\n", - " article\n", - " http://www.jstor.org/stable/40137021\n", - " [{'name': 'issn', 'value': '01963570'}, {'name...\n", - " World Literature Today\n", - " 1\n", - " [eng]\n", - " [unigram, bigram, trigram]\n", - " ...\n", - " Review Article\n", - " http://www.jstor.org/stable/40137021\n", - " 56\n", - " 493\n", - " 0\n", - " []\n", - " []\n", - " None\n", - " None\n", - " None\n", + " Eliot\n", + " 21.534937\n", + " 23.618635\n", + " 29.194631\n", + " 22.031474\n", + " 23.922114\n", + " 22.052535\n", " \n", " \n", " 3\n", - " [Ruth Evelyn Henderson]\n", - " 1925-10-01\n", - " research-article\n", - " article\n", - " http://www.jstor.org/stable/802346\n", - " [{'name': 'issn', 'value': '00138274'}, {'name...\n", - " The English Journal\n", - " 8\n", - " [eng]\n", - " [unigram, bigram, trigram, fullText]\n", - " ...\n", - " American Education Week--November 16-22; Some ...\n", - " http://www.jstor.org/stable/802346\n", - " 14\n", - " 2161\n", - " 0\n", - " []\n", - " []\n", - " None\n", - " None\n", - " None\n", + " Hardy\n", + " 15.349370\n", + " 23.185265\n", + " 20.805369\n", + " 15.021459\n", + " 9.805285\n", + " 11.484423\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " Author 1960s 1970s 1980s 1990s 2000s 2010s\n", + "0 Bronte 4.238259 10.292524 11.409396 11.659514 8.901252 0.122175\n", + "1 Dickens 25.429553 29.902492 30.285235 25.894134 25.173853 25.656689\n", + "2 Eliot 21.534937 23.618635 29.194631 22.031474 23.922114 22.052535\n", + "3 Hardy 15.349370 23.185265 20.805369 15.021459 9.805285 11.484423" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vs_authors_df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "vs_authors_df = vs_authors_df.melt(id_vars=[\"Author\"], \n", + " var_name = \"Decade\",\n", + " value_name=\"Value\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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4[Alan Palmer]2011-12-01research-articlearticlehttp://www.jstor.org/stable/10.5325/style.45.4...[{'name': 'issn', 'value': '00394238'}, {'name...Style4[eng][unigram, bigram, trigram]...Rejoinder to Response by Marie-Laure Ryanhttp://www.jstor.org/stable/10.5325/style.45.4...4511270[][]NoneNoneNone0Bronte1960s4.238259
..................................................................1Dickens1960s25.429553
5879[Michaela Giesenkirchen]2005-10-01research-articlearticlehttp://www.jstor.org/stable/27747183[{'name': 'issn', 'value': '15403084'}, {'name...American Literary Realism1[eng][unigram, bigram, trigram]...Ethnic Types and Problems of Characterization ...http://www.jstor.org/stable/277471833873491[[23799, 24121]][[41472, 41793]]NoneNoneNone2Eliot1960s21.534937
5880[Leon Botstein]2005-07-01miscarticlehttp://www.jstor.org/stable/4123220[{'name': 'issn', 'value': '00274631'}, {'name...The Musical Quarterly2[eng][unigram, bigram, trigram]...On the Power of Musichttp://www.jstor.org/stable/41232208815250[][]NoneNoneNone3Hardy1960s15.349370
5881[Linda M. Shires]2013-01-01research-articlearticlehttp://www.jstor.org/stable/24575734[{'name': 'issn', 'value': '10601503'}, {'name...Victorian Literature and Culture4[eng][unigram, bigram, trigram]...HARDY'S MEMORIAL ART: IMAGE AND TEXT IN \"WESSE...http://www.jstor.org/stable/2457573441107361[[173657, 173756]][[33963, 34061]]NoneNoneNone4Bronte1970s10.292524
5882[Edward H. Cohen]1990-07-01miscarticlehttp://www.jstor.org/stable/3827815[{'name': 'issn', 'value': '00425222'}, {'name...Victorian Studies4[eng][unigram, bigram, trigram]...Victorian Bibliography for 1989http://www.jstor.org/stable/382781533818190[][]NoneNoneNone5Dickens1970s29.902492
5883None1964-06-01miscarticlehttp://www.jstor.org/stable/2932781[{'name': 'issn', 'value': '00290564'}, {'name...Nineteenth-Century Fiction1[eng][unigram, bigram, trigram]...Volume Informationhttp://www.jstor.org/stable/2932781196940[][]NoneNoneNone6Eliot1970s23.618635
7Hardy1970s23.185265
8Bronte1980s11.409396
9Dickens1980s30.285235
10Eliot1980s29.194631
11Hardy1980s20.805369
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13Dickens1990s25.894134
14Eliot1990s22.031474
15Hardy1990s15.021459
16Bronte2000s8.901252
17Dickens2000s25.173853
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" ], "text/plain": [ - " creator datePublished docSubType docType \\\n", - "0 [Rainer Emig] 2006-01-01 book-review article \n", - "1 [Martin Green] 1970-01-01 book-review article \n", - "2 [Richard Exner] 1982-01-01 book-review article \n", - "3 [Ruth Evelyn Henderson] 1925-10-01 research-article article \n", - "4 [Alan Palmer] 2011-12-01 research-article article \n", - "... ... ... ... ... \n", - "5879 [Michaela Giesenkirchen] 2005-10-01 research-article article \n", - "5880 [Leon Botstein] 2005-07-01 misc article \n", - "5881 [Linda M. Shires] 2013-01-01 research-article article \n", - "5882 [Edward H. Cohen] 1990-07-01 misc article \n", - "5883 None 1964-06-01 misc article \n", - "\n", - " id \\\n", - "0 http://www.jstor.org/stable/41158244 \n", - "1 http://www.jstor.org/stable/3722819 \n", - "2 http://www.jstor.org/stable/40137021 \n", - "3 http://www.jstor.org/stable/802346 \n", - "4 http://www.jstor.org/stable/10.5325/style.45.4... \n", - "... ... \n", - "5879 http://www.jstor.org/stable/27747183 \n", - "5880 http://www.jstor.org/stable/4123220 \n", - "5881 http://www.jstor.org/stable/24575734 \n", - "5882 http://www.jstor.org/stable/3827815 \n", - "5883 http://www.jstor.org/stable/2932781 \n", - "\n", - " identifier \\\n", - "0 [{'name': 'issn', 'value': '03402827'}, {'name... \n", - "1 [{'name': 'issn', 'value': '00267937'}, {'name... \n", - "2 [{'name': 'issn', 'value': '01963570'}, {'name... \n", - "3 [{'name': 'issn', 'value': '00138274'}, {'name... \n", - "4 [{'name': 'issn', 'value': '00394238'}, {'name... \n", - "... ... \n", - "5879 [{'name': 'issn', 'value': '15403084'}, {'name... \n", - "5880 [{'name': 'issn', 'value': '00274631'}, {'name... \n", - "5881 [{'name': 'issn', 'value': '10601503'}, {'name... \n", - "5882 [{'name': 'issn', 'value': '00425222'}, {'name... \n", - "5883 [{'name': 'issn', 'value': '00290564'}, {'name... \n", - "\n", - " isPartOf issueNumber language \\\n", - "0 Amerikastudien / American Studies 3 [eng] \n", - "1 The Modern Language Review 1 [eng] \n", - "2 World Literature Today 1 [eng] \n", - "3 The English Journal 8 [eng] \n", - "4 Style 4 [eng] \n", - "... ... ... ... \n", - "5879 American Literary Realism 1 [eng] \n", - "5880 The Musical Quarterly 2 [eng] \n", - "5881 Victorian Literature and Culture 4 [eng] \n", - "5882 Victorian Studies 4 [eng] \n", - "5883 Nineteenth-Century Fiction 1 [eng] \n", - "\n", - " outputFormat ... \\\n", - "0 [unigram, bigram, trigram] ... \n", - "1 [unigram, bigram, trigram] ... \n", - "2 [unigram, bigram, trigram] ... \n", - "3 [unigram, bigram, trigram, fullText] ... \n", - "4 [unigram, bigram, trigram] ... \n", - "... ... ... \n", - "5879 [unigram, bigram, trigram] ... \n", - "5880 [unigram, bigram, trigram] ... \n", - "5881 [unigram, bigram, trigram] ... \n", - "5882 [unigram, bigram, trigram] ... \n", - "5883 [unigram, bigram, trigram] ... \n", - "\n", - " title \\\n", - "0 Review Article \n", - "1 Review Article \n", - "2 Review Article \n", - "3 American Education Week--November 16-22; Some ... \n", - "4 Rejoinder to Response by Marie-Laure Ryan \n", - "... ... \n", - "5879 Ethnic Types and Problems of Characterization ... \n", - "5880 On the Power of Music \n", - "5881 HARDY'S MEMORIAL ART: IMAGE AND TEXT IN \"WESSE... \n", - "5882 Victorian Bibliography for 1989 \n", - "5883 Volume Information \n", - "\n", - " url volumeNumber \\\n", - "0 http://www.jstor.org/stable/41158244 51 \n", - "1 http://www.jstor.org/stable/3722819 65 \n", - "2 http://www.jstor.org/stable/40137021 56 \n", - "3 http://www.jstor.org/stable/802346 14 \n", - "4 http://www.jstor.org/stable/10.5325/style.45.4... 45 \n", - "... ... ... \n", - "5879 http://www.jstor.org/stable/27747183 38 \n", - "5880 http://www.jstor.org/stable/4123220 88 \n", - "5881 http://www.jstor.org/stable/24575734 41 \n", - "5882 http://www.jstor.org/stable/3827815 33 \n", - "5883 http://www.jstor.org/stable/2932781 19 \n", - "\n", - " wordCount numMatches Locations in A Locations in B abstract \\\n", - "0 1109 1 [[130022, 130046]] [[6851, 6875]] None \n", - "1 1342 0 [] [] None \n", - "2 493 0 [] [] None \n", - "3 2161 0 [] [] None \n", - "4 1127 0 [] [] None \n", - "... ... ... ... ... ... \n", - "5879 7349 1 [[23799, 24121]] [[41472, 41793]] None \n", - "5880 1525 0 [] [] None \n", - "5881 10736 1 [[173657, 173756]] [[33963, 34061]] None \n", - "5882 81819 0 [] [] None \n", - "5883 694 0 [] [] None \n", - "\n", - " keyphrase subTitle \n", - "0 None None \n", - "1 None None \n", - "2 None None \n", - "3 None None \n", - "4 None None \n", - "... ... ... \n", - "5879 None None \n", - "5880 None None \n", - "5881 None None \n", - "5882 None None \n", - "5883 None None \n", - "\n", - "[5884 rows x 29 columns]" + " Author Decade Value\n", + "0 Bronte 1960s 4.238259\n", + "1 Dickens 1960s 25.429553\n", + "2 Eliot 1960s 21.534937\n", + "3 Hardy 1960s 15.349370\n", + "4 Bronte 1970s 10.292524\n", + "5 Dickens 1970s 29.902492\n", + "6 Eliot 1970s 23.618635\n", + "7 Hardy 1970s 23.185265\n", + "8 Bronte 1980s 11.409396\n", + "9 Dickens 1980s 30.285235\n", + "10 Eliot 1980s 29.194631\n", + "11 Hardy 1980s 20.805369\n", + "12 Bronte 1990s 11.659514\n", + "13 Dickens 1990s 25.894134\n", + "14 Eliot 1990s 22.031474\n", + "15 Hardy 1990s 15.021459\n", + "16 Bronte 2000s 8.901252\n", + "17 Dickens 2000s 25.173853\n", + "18 Eliot 2000s 23.922114\n", + "19 Hardy 2000s 9.805285\n", + "20 Bronte 2010s 0.122175\n", + "21 Dickens 2010s 25.656689\n", + "22 Eliot 2010s 22.052535\n", + "23 Hardy 2010s 11.484423" ] }, - "execution_count": 13, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df" + "vs_authors_df" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "df.to_csv('../data/matches.csv', encoding='utf-8')" + "vs_authors_df['Value'] = vs_authors_df['Value']* 0.01" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "# New for JSTOR 2022 data\n", - "df['year'] = pd.DatetimeIndex(df['datePublished']).year" + "line = alt.Chart(vs_authors_df, title=\"Frequency of author references in *Victorian Studies*\").mark_line().encode(\n", + " x=alt.X('Decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value')), \n", + " y=alt.Y('Value:Q', title=\"Percent of Documents\", axis=alt.Axis(labelAngle=0, format=\"%\")),\n", + " color=alt.Color('Author:O', scale=alt.Scale(scheme='greys'),legend=None),\n", + ")\n", + "\n", + "points = line.mark_point(filled=True).encode(\n", + " color=alt.Color('Author:O', scale=alt.Scale(scheme='greys')),\n", + " shape=alt.Shape('Author:O', scale=alt.Scale(range=[ 'circle', 'cross', 'square', 'triangle-right', 'diamond'])),\n", + " size=alt.Size('Author:O', legend=None, scale=alt.Scale(range=[200,200],domain=['Eliot', 'Dickens', 'Bronte', 'Hardy']))\n", + ")\n", + "\n", + "auth_chart = alt.layer(\n", + " line,\n", + " points\n", + ").resolve_scale(\n", + " color='independent',\n", + " shape='independent'\n", + ").properties(width=400).configure_legend(\n", + "titleFontSize=11,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Most frequent author references in *Victorian Studies*, line chart" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], "text/plain": [ - "0 2006\n", - "1 1970\n", - "2 1982\n", - "3 1925\n", - "4 2011\n", - " ... \n", - "5879 2005\n", - "5880 2005\n", - "5881 2013\n", - "5882 1990\n", - "5883 1964\n", - "Name: year, Length: 5884, dtype: int64" + "alt.LayerChart(...)" ] }, - "execution_count": 16, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# New for JSTOR 2022 data\n", - "df['year']" + "auth_chart#.save('Figure-1.png', ppi=300)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "df['Decade'] = df['year'] - (df['year'] % 10)\n", - "# df['Locations in A'] = df['matches'].apply(lambda x: x[1])\n", - "# df['NumMatches'] = df['matches'].apply(lambda x: x[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Dictionary for `text matcher` dataset\n", + "line = alt.Chart(vs_authors_df, title=\"Frequency of author references in *Victorian Studies*\").mark_line().encode(\n", + " x=alt.X('Decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value')), \n", + " y=alt.Y('Value:Q', title=\"Percent of Documents\", axis=alt.Axis(labelAngle=0, format=\"%\")),\n", + " color=alt.Color('Author:O', scale=alt.Scale(scheme='category20'),legend=None),\n", + ")\n", "\n", - "Our text-matcher dataset includes the following fields:" + "points = line.mark_point(filled=True).encode(\n", + " color=alt.Color('Author:O', scale=alt.Scale(scheme='category20')),\n", + " shape=alt.Shape('Author:O', scale=alt.Scale(range=[ 'circle', 'cross', 'square', 'triangle-right', 'diamond'])),\n", + " size=alt.Size('Author:O', legend=None, scale=alt.Scale(range=[200,200],domain=['Eliot', 'Dickens', 'Bronte', 'Hardy']))\n", + ")\n", + "\n", + "auth_chart_color = alt.layer(\n", + " line,\n", + " points\n", + ").resolve_scale(\n", + " color='independent',\n", + " shape='independent'\n", + ").properties(width=400).configure_legend(\n", + "titleFontSize=11,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "| Data field | Definition |\n", - "| :------------- | :------------- |\n", - "| 'Locations in A' | index characters for start and end locations for quoted text in source text (A) [Middlemarch], generated by text-matcher |\n", - "| 'Locations in B' | index characters for start and end locations for quoted text in the target text (B) [a given JSTOR text], generated by text-matcher |\n", - "| 'author' | author name supplied by JSTOR |\n", - "| 'coverdate'| date on cover, supplied by JSTOR |\n", - "| 'disc_name' | tags assigned to article, supplied by JSTOR |\n", - "| 'doi' | text (DOI), supplied by JSTOR |\n", - "| 'id' | unique identifier, supplied by JSTOR |\n", - "| 'jcode' | shortcode for journal, supplied by JSTOR |\n", - "| 'journal' | journal title, supplied by JSTOR |\n", - "| 'la' | language, supplied by JSTOR |\n", - "| 'no' | issue or number, supplied by JSTOR |\n", - "| 'numMatches'| number of matches, generated by text-matcher |\n", - "| 'pages' | page numbers, supplied by JSTOR |\n", - "| 'publisher_name' | supplied by JSTOR |\n", - "| 'sp' | starting page number, supplied by JSTOR |\n", - "| 'srcHtml' | HTML version of source citation (journal, Volume, issue, date) supplied by JSTOR |\n", - "| 'title' | title of article or piece of writing, supplied by JSTOR |\n", - "| 'topics' | subject headings, supplied by JSTOR |\n", - "| 'ty' | item type (fla = full length article; brv = book review; edi = opinion piece; nws OR mis) = other items, supplied by JSTOR |\n", - "| 'vo' | journal volume, supplied by JSTOR |\n", - "| 'year' | year of publication, supplied by JSTOR |\n", - "| 'Decade' | decade, generated by Middlematch team from 'year' |\n", - "| 'Quoted Words' | total number of words in all quotations in that text, generated by generated by Middlematch team from \"Locations in A' |\n", - "| 'Locations in A with Wordcounts' | list of pairs of index characters for start and end quote in the source text (A) [Middlemarch] and wordcount for each quoation, generated by generated by Middlematch team from \"Locations in A' |\n", - "| 'Wordcounts' | list of wordcounts for each matched quotation, generated by text-matcher | " + "### Most frequent author references in *Victorian Studies*, line chart (color)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "Index(['creator', 'datePublished', 'docSubType', 'docType', 'id', 'identifier',\n", - " 'isPartOf', 'issueNumber', 'language', 'outputFormat', 'pageCount',\n", - " 'pageEnd', 'pageStart', 'pagination', 'provider', 'publicationYear',\n", - " 'publisher', 'sourceCategory', 'tdmCategory', 'title', 'url',\n", - " 'volumeNumber', 'wordCount', 'numMatches', 'Locations in A',\n", - " 'Locations in B', 'abstract', 'keyphrase', 'subTitle', 'year',\n", - " 'Decade'],\n", - " dtype='object')" - ] - }, - "execution_count": 18, + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Functions for extracting wordcounts, numbers of quotations for diachronic and synchronic analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def diachronicAnalysis(df, decades=(1950, 2020), bins=chapterLocations, useWordcounts=True, normalize=True):\n", - " \"\"\" Turning on useWordcounts makes it so that it's weighted by wordcount. \n", - " Turning it off uses raw numbers of quotations. \"\"\"\n", - " decades = np.arange(decades[0], decades[1], 10)\n", - " # Make a dictionary of decades. \n", - " # Values are a list of locations. \n", - " decadeDict = {}\n", - " for i, row in df.iterrows():\n", - " decade = row['Decade']\n", - " locationsAndWordcounts = row['Locations in A with Wordcounts']\n", - " if decade not in decadeDict: \n", - " decadeDict[decade] = locationsAndWordcounts.copy()\n", - " else: \n", - " decadeDict[decade] += locationsAndWordcounts.copy()\n", - " # Grab the beginnings of quotes. \n", - " decadeStartsWeights = {decade: [(item[0][0], item[1]) \n", - " for item in loc] \n", - " for decade, loc in decadeDict.items()}\n", - " if useWordcounts: \n", - " decadesBinned = {decade: \n", - " np.histogram([loc[0] for loc in locations], \n", - " bins=bins,\n", - " weights=[loc[1] for loc in locations],\n", - " range=(0, textALength))[0]\n", - " for decade, locations in decadeStartsWeights.items() \n", - " if decade in decades}\n", - " else: \n", - " decadesBinned = {decade: \n", - " np.histogram([loc[0] for loc in locations], \n", - " bins=bins,\n", - " range=(0, textALength))[0]\n", - " for decade, locations in decadeStartsWeights.items() \n", - " if decade in decades}\n", - " decadesDF = pd.DataFrame(decadesBinned).T\n", - " #Normalize\n", - " if normalize: \n", - " decadesDF = decadesDF.div(decadesDF.max(axis=1), axis=0)\n", - " return decadesDF\n", - "\n", - "def countWords(locRange): \n", - " \"\"\" Counts words in middlemarch, given character ranges. \"\"\"\n", - " chunk = mm[locRange[0]:locRange[1]]\n", - " return len(chunk.split())\n", - "\n", - "def totalWords(locRangeSet): \n", - " \"\"\" Counts total words in a list of location ranges. \"\"\"\n", - " return sum([countWords(locRange) for locRange in locRangeSet]) \n", - " \n", - "def countsPerSet(locRangeSet): \n", - " \"\"\" Returns an augmented location range set that includes word counts. \"\"\"\n", - " return [(locRange, countWords(locRange))\n", - " for locRange in locRangeSet]\n", - " \n", - "def extractWordcounts(locsAndWordcounts): \n", - " \"\"\" \n", - " Takes pairs of location ranges and wordcounts, \n", - " and returns just the wordcounts. \n", - " \"\"\"\n", - " return [item[1] for item in locsAndWordcounts \n", - " if len(locsAndWordcounts) > 0]\n", - "\n", - "def synchronicAnalysis(df, bins=chapterLocations, useWordcounts=True): \n", - " locs = df['Locations in A'].values\n", - " locCounts = [(loc, countWords(loc)) for locSet in locs\n", - " for loc in locSet]\n", - " starts = [loc[0][0] for loc in locCounts]\n", - " counts = [loc[1] for loc in locCounts]\n", - " if useWordcounts: \n", - " binned = np.histogram(starts, bins=bins, \n", - " weights=counts, range=(0, textALength))\n", - " else: \n", - " binned = np.histogram(starts, bins=bins, \n", - " range=(0, textALength))\n", - " binnedDF = pd.Series(binned[0])\n", - " return binnedDF\n", - "\n", - "def plotDiachronicAnalysis(df, save=False, reverse=False): \n", - " ylabels = [str(int(decade)) for decade in df.index] + ['2020']\n", - " plt.pcolor(df, cmap='gnuplot')\n", - " plt.yticks(np.arange(len(df.index)+1), ylabels)\n", - " plt.gca().invert_yaxis()\n", - " plt.ylabel('Decade')\n", - " plt.xlabel('Chapter')\n", - " plt.gca().set_xlim((0, len(df.T)))\n", - " plt.colorbar(ticks=[])\n", - " if save: \n", - " plt.savefig('diachronic.png', bboxinches='tight', dpi=300, transparent=True)\n", - " plt.show()\n", - " \n", - "def plotSynchronicAnalysis(s, useWordcounts=True): \n", - " ax = s.plot(kind='bar')\n", - " ax.set_xlabel('Chapter')\n", - " if useWordcounts: \n", - " ax.set_ylabel('Number of Words Quoted')\n", - " else: \n", - " ax.set_ylabel('Number of Quotations')\n", - " \n", - "def plotSynchronicAnalysisHeatmap(s, useWordcounts=True): \n", - " vec1=synchronicAnalysis(df, useWordcounts=False)\n", - " fig, ax = plt.subplots()\n", - " sns.color_palette(\"magma\")\n", - " sns.heatmap([vec1])\n", - " ax.set_xlabel('Chapter')\n", - " ax.set_ylabel('Number of Quotations')\n", - " \n", - "def plotDiachronicAnalysisBubble(df, save=False, reverse=False):\n", - " ylabels = [str(int(decade)) for decade in df.index] + ['2020'] \n", - " alt.Chart(df).mark_circle().encode(\n", - " x='Chapter',\n", - " y='Decade',\n", - " size='sum(count):Q'\n", - ")" + "auth_chart_color" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Adding additional rows to DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "df['Quoted Words'] = df['Locations in A'].apply(totalWords)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "df['Locations in A with Wordcounts'] = df['Locations in A'].apply(countsPerSet)" + "### Most frequent title references in *Victorian Studies*" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "df['Wordcounts'] = df['Locations in A with Wordcounts'].apply(extractWordcounts)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "119747" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Verify that the diachronic wordcounts are the same as the synchronic wordcounts\n", - "decadeSums = diachronicAnalysis(df, decades=(1700, 2020), useWordcounts=True, normalize=False).sum(axis=1)\n", - "decadeSums.sum()" + "vs_titles_df = pd.read_csv(\"../data/VS-title-term_frequencies.csv\")" ] }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "119892" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chapterSums = synchronicAnalysis(df)\n", - "chapterSums.sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### How many quotations do we have?" - ] - }, - { - "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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Title1960s1970s1980s1990s2000s2010s
0Bleak House4.2382594.8754064.6979875.0071535.3546594.825901
1David Copperfield3.2073313.5752985.9563764.2203152.7816413.359805
2Great Expectations3.4364263.5752983.1040274.3633763.2684282.871106
3Middlemarch5.8419246.5005427.2147655.8655225.6328236.536347
\n", + "
" + ], "text/plain": [ - "3800" + " Title 1960s 1970s 1980s 1990s 2000s \\\n", + "0 Bleak House 4.238259 4.875406 4.697987 5.007153 5.354659 \n", + "1 David Copperfield 3.207331 3.575298 5.956376 4.220315 2.781641 \n", + "2 Great Expectations 3.436426 3.575298 3.104027 4.363376 3.268428 \n", + "3 Middlemarch 5.841924 6.500542 7.214765 5.865522 5.632823 \n", + "\n", + " 2010s \n", + "0 4.825901 \n", + "1 3.359805 \n", + "2 2.871106 \n", + "3 6.536347 " ] }, - "execution_count": 25, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sum([len(item) for item in df['Locations in A'].values])" + "vs_titles_df" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 13, "metadata": {}, + "outputs": [], "source": [ - "#### Total number of matches" + "vs_titles_df = vs_titles_df.melt(id_vars=[\"Title\"], \n", + " var_name = \"Decade\",\n", + " value_name=\"Value\")" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "allMatches = []\n", - "for group in df['Locations in A'].values: \n", - " for pair in group: \n", - " allMatches.append(pair)" + "vs_titles_df['Value'] = vs_titles_df['Value']* 0.01" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "3800" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 15, + "metadata": {}, + "outputs": [], "source": [ - "len(allMatches)" + "line = alt.Chart(vs_titles_df, title=\"Frequency of title references in *Victorian Studies*\").mark_line().encode(\n", + " x=alt.X('Decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value')), \n", + " y=alt.Y('Value:Q', title=\"Percent of Documents\", axis=alt.Axis(labelAngle=0, format=\"%\")),\n", + " color=alt.Color('Title:O', scale=alt.Scale(scheme='greys'),legend=None),\n", + ")\n", + "\n", + "points = line.mark_point(filled=True).encode(\n", + " color=alt.Color('Title:O', scale=alt.Scale(scheme='greys')),\n", + " shape=alt.Shape('Title:O', scale=alt.Scale(range=[ 'circle', 'cross', 'triangle-right', 'square','diamond'])),\n", + " size=alt.Size('Title:O', legend=None, scale=alt.Scale(range=[200,200],domain=['Bleak House', 'David Copperfield', 'Middlemarch','Great Expectations', ]))\n", + ")\n", + "\n", + "title_chart = alt.layer(\n", + " line,\n", + " points\n", + ").resolve_scale(\n", + " color='independent',\n", + " shape='independent'\n", + ").properties(width=400).configure_legend(\n", + "titleFontSize=11,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### How many total articles do we have? " + "### Most frequent title references in *Victorian Studies*, line chart" ] }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, + "execution_count": 16, + "metadata": { + "scrolled": true + }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total articles with 'Middlemarch' appearing somewhere in text or metadata:\n" - ] - }, { "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], "text/plain": [ - "5884" + "alt.LayerChart(...)" ] }, - "execution_count": 28, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"Total articles with 'Middlemarch' appearing somewhere in text or metadata:\")\n", - "len(df) # Total articles with \"Middlemarch\" mentioned somewhere" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Find only those with non-trivial quotations from Middlemarch: " + "title_chart#.save('Figure-2.png', ppi=300)" ] }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 1540.000000\n", - "mean 1991.488961\n", - "std 19.713886\n", - "min 1900.000000\n", - "25% 1980.000000\n", - "50% 1994.000000\n", - "75% 2007.000000\n", - "max 2022.000000\n", - "Name: year, dtype: float64" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "articlesWithMatches = df[df['Locations in A'].apply(lambda x: len(x) > 0)]\n", - "articlesWithMatches.year.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### How many articles with matches do we have? " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of articles with matches to text in 'Middlemarch':\n" - ] - }, - { - "data": { - "text/plain": [ - "1540" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(\"Number of articles with matches to text in 'Middlemarch':\")\n", - "articlesWithMatches['Locations in A'].count()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "George Eliot - George Henry Lewes Studies 106\n", - "Victorian Studies 78\n", - "Nineteenth-Century Fiction 68\n", - "PMLA 47\n", - "ELH 42\n", - " ... \n", - "Aevum 1\n", - "Chasqui 1\n", - "Imagined States 1\n", - "Literature and Medicine in the Nineteenth-Century Periodical Press 1\n", - "Charity Organisation Review 1\n", - "Name: isPartOf, Length: 300, dtype: int64" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "articlesWithMatches['isPartOf'].value_counts()[:300]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "article 1500\n", - "chapter 40\n", - "Name: docType, dtype: int64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "articlesWithMatches['docType'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1\n", - "9 1\n", - "17 16\n", - "19 3\n", - "21 7\n", - "Name: Wordcounts, dtype: int64" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "articlesWithMatches.Wordcounts.apply(len).head()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "# articlesWithMatches.to_json('../data/cleaned-matches.json')" + "line = alt.Chart(vs_titles_df, title=\"Frequency of title references in *Victorian Studies*\").mark_line().encode(\n", + " x=alt.X('Decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value')), \n", + " y=alt.Y('Value:Q', title=\"Percent of Documents\", axis=alt.Axis(labelAngle=0, format=\"%\")),\n", + " color=alt.Color('Title:O', scale=alt.Scale(scheme='category20'),legend=None),\n", + ")\n", + "\n", + "points = line.mark_point(filled=True).encode(\n", + " color=alt.Color('Title:O', scale=alt.Scale(scheme='category20')),\n", + " shape=alt.Shape('Title:O', scale=alt.Scale(range=[ 'circle', 'cross', 'triangle-right', 'square','diamond'])),\n", + " size=alt.Size('Title:O', legend=None, scale=alt.Scale(range=[200,200],domain=['Bleak House', 'David Copperfield', 'Middlemarch','Great Expectations', ]))\n", + ")\n", + "\n", + "title_chart_color = alt.layer(\n", + " line,\n", + " points\n", + ").resolve_scale(\n", + " color='independent',\n", + " shape='independent'\n", + ").properties(width=400).configure_legend(\n", + "titleFontSize=11,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### How many articles do we have published in each year? \n", - "Here, we're looking just at the 489 articles with matches" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DataTransformerRegistry.enable('default')" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# New for JSTOR 2022 dataset, because we have more data\n", - "alt.data_transformers.disable_max_rows()" + "### Most frequent title references in *Victorian Studies*, line chart (color)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1366,23 +1017,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ - "alt.Chart(...)" + "alt.LayerChart(...)" ] }, - "execution_count": 36, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(articlesWithMatches, title=\"Number of JSTOR articles with detected Middlemarch matches, by year\").mark_bar().encode(x='year:O', y='count()').properties(width=1000)" + "title_chart_color" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Number of non-matches in the 6,069-article dataset\n", - "One reason the 6,069-article original dataset is so much larger has to do with the way JSTOR ingests paratextual matter from journals. We found appearance of the word \"middlemarch\" in paratextual matter, which was systemattically titled in JSTOR. Here we define a quick function to count the number of appearances of article-like paratextual matter: \"front matter\", \"back matter\", \"table of contents\" and \"cover\".\n", - "\n", - "None of these titles are present in the smaller dataset of matches." + "## *Middlemarch* statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Nonmatches" + "### Generating *Middlemarch* chapter and book locations \n", + "Here, we're using the [Project Gutenberg text of *Middlemarch*](https://www.gutenberg.org/cache/epub/145/pg145.txt), with one modification: the phrase \"Book 1\" has been moved to appear before the prelude, marking that the \"Prelude\" is indeed part of the text that appeared with Book 1." ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "# Try to find out what articles contain no Middlemarch citations\n", - "articlesWithoutMatches = df[df['Locations in A'].apply(lambda x: len(x) == 0)]" + "with open('../middlemarch.txt') as f: \n", + " mm = f.read()" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "textALength = len(mm) " + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['Review Article',\n", - " 'Front Matter',\n", - " 'Back Matter',\n", - " 'Volume Information',\n", - " 'Summary of Periodical Literature',\n", - " 'Index',\n", - " 'Recent Studies in the Nineteenth Century',\n", - " 'Books Received',\n", - " 'List of Publications Received',\n", - " 'INDEX']" + "89" ] }, - "execution_count": 38, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "n = 10\n", - "articlesWithoutMatches['title'].value_counts()[:n].index.tolist()" + "# Get chapter locations\n", + "chapterMatches = re.finditer('PRELUDE|CHAPTER|FINALE', mm)\n", + "chapterLocations = [match.start() for match in chapterMatches]\n", + "chapterLocations.append(textALength) # Add one to account for last chunk. \n", + "len(chapterLocations)" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "count 4344\n", - "unique 2304\n", - "top Review Article\n", - "freq 1199\n", - "Name: title, dtype: object" + "4890" ] }, - "execution_count": 39, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# What is the most frequent name of articles with no citations?\n", - "articlesWithoutMatches['title'].describe()" + "# Get paragraph locations\n", + "paragraphMatches = re.finditer('\\n\\n', mm)\n", + "paragraphLocations = [match.start() for match in paragraphMatches]\n", + "paragraphLocations.append(textALength)\n", + "len(paragraphLocations)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 38, 250307, 481579, 681858, 915901, 1138247, 1364956, 1571148, 1793449]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "def isGarbage(itemTitle): \n", - " badTitles = ['front matter', 'back matter', 'table of contents', 'cover']\n", - " if itemTitle == None: \n", - " return False\n", - " for title in itemTitle: \n", - " for badTitle in badTitles: \n", - " if badTitle in title.lower(): \n", - " return True\n", - " return False" + "# Get book locations\n", + "bookLocations = [match.start() for match in re.finditer('\\nBOOK', mm)]\n", + "bookLocations = [0] + bookLocations + [textALength] # Add one to account for last chunk.\n", + "bookLocations" ] }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of'front matter', 'back matter', 'table of contents', 'cover' items in the 6069-article JSTOR dataset:\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(\"Number of'front matter', 'back matter', 'table of contents', 'cover' items in the 6069-article JSTOR dataset:\")\n", - "len(df[df.title.apply(isGarbage)]) # How many garbage items? " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Quotation Length Statistics" - ] - }, - { - "cell_type": "code", - "execution_count": 42, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ - "wordcounts = []\n", - "for countSet in df['Wordcounts'].values: \n", - " for count in countSet: \n", - " wordcounts.append(count)" + "def getChapters(text): \n", + " chapters = []\n", + " for i, loc in enumerate(chapterLocations): \n", + " if i != len(chapterLocations)-1: \n", + " chapter = mm[loc:chapterLocations[i+1]]\n", + " chapters.append(chapter)\n", + " return chapters" ] }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "scrolled": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 43, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -1621,276 +1242,85 @@ } ], "source": [ - "pd.Series(wordcounts).hist()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Average numbers of quoted words per item" + "chapters = getChapters(mm)\n", + "chapterLengths = [len(chapter.split()) for chapter in chapters]\n", + "chapterLengthsSeries = pd.Series(chapterLengths)\n", + "chapterLengthsSeries.plot(kind='bar', title='Middlemarch Chapter Lengths')" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 26, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 5884.000000\n", - "mean 20.375935\n", - "std 94.477822\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 4.000000\n", - "max 2138.000000\n", - "Name: Quoted Words, dtype: float64" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df['Quoted Words'].describe()" + "def getParagraphs(text): \n", + " paragraphs = []\n", + " for i, loc in enumerate(paragraphLocations): \n", + " if i != len(paragraphLocations)-1: \n", + " paragraph = mm[loc:paragraphLocations[i+1]]\n", + " paragraphs.append(paragraph)\n", + " return paragraph" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 27, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Statistics on average, min, and max number of quoted words per item:\n" - ] - }, - { - "data": { - "text/plain": [ - "count 1540.000000\n", - "mean 77.851948\n", - "std 172.172395\n", - "min 2.000000\n", - "25% 6.000000\n", - "50% 17.000000\n", - "75% 64.000000\n", - "max 2138.000000\n", - "Name: Quoted Words, dtype: float64" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "print('Statistics on average, min, and max number of quoted words per item:')\n", - "articlesWithMatches['Quoted Words'].describe()" + "paragraphs = getParagraphs(mm)\n", + "paragraphLengths = [len(paragraph.split()) for paragraph in paragraphs]\n", + "paragraphLengthsSeries = pd.Series(paragraphLengths)" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1540" + "'\\n\\nHer finely touched spirit had still its fine issues, though they were\\nnot widely visible. Her full nature, like that river of which Cyrus\\nbroke the strength, spent itself in channels which had no great name on\\nthe earth. But the effect of her being on those around her was\\nincalculably diffusive: for the growing good of the world is partly\\ndependent on unhistoric acts; and that things are not so ill with you\\nand me as they might have been, is half owing to the number who lived\\nfaithfully a hidden life, and rest in unvisited tombs.\\n'" ] }, - "execution_count": 46, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "len(df[df['Quoted Words'] > 0])" + "paragraphs" ] }, { - "cell_type": "code", - "execution_count": 47, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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mu5w3uBQeAKAi3HJh46VLl5Iks2fPHjXe2NiYzs7Oce+3vr6+Ii/Brq6uvv7fd9yZ2nt+cpKrYbyqp071/d3mfIe3P9/h7a16SlWu/cFnJruM20b/ZBfwFur/n0+k5urrk10GE1Fbm/T1TXYVTEBVf21+ePZyagaHUpNb6B8juDH+N3j7u5W/w6tV+eHr+sOPVT81aZg22VXcFMP504245dK3a9euJcmbgsG6urr09PSMe7/Tp0+fUF23usZ77kue3D3ZZTARvr/bn+/w9uc7vL35/gAAYNLdcvdsrKurS3L93o3/f729vamvr5+MkgAAAACAG3DLhY3Dl093dXWNGu/u7k5TU9NklAQAAAAA3IBbLmxcuHBhpk6dmpMnT46M9fT05OzZs2lpaZnEygAAAACAv88td8/G2trarFixIrt378706dMzZ86cfOELX8isWbOyfPnyyS4PAAAAABhD1dDQ0C33GKHBwcF88YtfzLFjx9Lb25uWlpY89thjueuuuya7NAAAAABgDLdk2AgAAAAA3H5uuXs2AgAAAAC3J2EjAAAAAFCEsBEAAAAAKELYCAAAAAAUIWwEAAAAAIoQNgIAAAAARdRMdgGMz+DgYPbu3ZsjR46kp6cnS5Ysybp169Lc3DzZpQE30cWLF/ORj3zkTeMbNmzIv/gX/yIdHR1pb2/PmTNnMm3atKxcuTKrV68eWad3QGXat29fXnjhhTz++OMjYyX6wY/bB3Dre6v+8Pu///s5duzYqHVNTU3ZtWtXEv0BKtXrr7+eL37xi/mrv/qrXL16NQsWLMgjjzySJUuWJPHbgXKEjbepffv25dChQ/nwhz+cpqam7N69O9u2bcvv/u7vpqbG1wqV6ty5c6mtrc1TTz2VqqqqkfE777wzr732WrZs2ZJ/8k/+SdavX5+//uu/ztNPP533vOc9aWtrS6J3QCU6ePBgnn322bS0tIyMlegHN7IP4Nb2Vv0huf574gMf+EBWrlw5MjZlyhsXvekPUJl27NiRy5cvZ+PGjZk+fXq+8pWvZOvWrfnUpz6V6dOn++1AMS6jvg319/fnwIEDWbNmTZYuXZpFixZl48aN6erqyvHjxye7POAmeumllzJ37tw0NjZm5syZI/+pq6vL4cOHU1tbm/Xr12fevHlpa2vLqlWrsn///iR6B1Sarq6ubNu2LXv27MncuXNHzZXoBz9uH8Ct6+/rD4ODgzl//nzuueeeUb8lpk+fnkR/gEr1ve99L9/+9rezbt26LFmyJHPnzs2v/uqvpqmpKV/72tf8dqAoYeNtqKOjI1evXk1ra+vIWENDQxYvXpxTp05NYmXAzfbSSy9l3rx5bzl3+vTptLS0pLq6emSstbU1Fy5cyOXLl/UOqDBnzpxJQ0NDnnjiidx7772j5kr0gx+3D+DW9ff1h1deeSV9fX1j/p7QH6Ayvec978lv/uZv5r3vfe/IWFVVVYaGhvL666/77UBRrpm7DV26dClJMnv27FHjjY2N6ezsnIySgHfISy+9lMbGxnz84x/PK6+8kubm5nzwgx/MT//0T+fSpUuZP3/+qPVNTU1Jks7OTr0DKsyyZcuybNmyt5wr0Q9+3D5mzJgx8Q8B3BR/X3946aWXUlVVlT/+4z/O888/nylTpuRnfuZn8qEPfSh33nmn/gAVqqGhIUuXLh019hd/8Rf5/ve/n/e9733Zs2eP3w4U48zG29C1a9eS5E33V6urq0tfX99klAS8A/r7+/PKK6/kypUr+dCHPpSPfexjueeee7J9+/a88MILuXbtWmpra0dtM/y6r69P74B3kRL94MftA7g9/e3f/m2qqqoyZ86cbNq0KWvXrs3zzz+fT3/60xkcHNQf4F3i9OnT2blz58g/TvjtQEnObLwN1dXVJbkePAz/nSS9vb2pr6+frLKAm6ympibt7e2prq4e+T/t9773vXn55Zfzv//3/37L0HD4dX19vd4B7yIl+sGP2wdwe1qzZk1WrVqVhoaGJMmCBQsyc+bM/NZv/Vb+5m/+Rn+Ad4FvfvObefLJJ3Pfffdl48aNSfx2oCxnNt6Ghk9b7urqGjXe3d09cooyUJnuuOOON/1r4YIFC3Lp0qXMnj073d3do+aG+0RTU5PeAe8iJfrBj9sHcHuqqqoaCRqHLViwIElGfk8k+gNUqq985St54okn8jM/8zP52Mc+NhIc+u1AScLG29DChQszderUnDx5cmSsp6cnZ8+eTUtLyyRWBtxM586dy9q1a3P69OlR42fOnMn8+fPT0tKS06dPZ3BwcGTuxIkTmTt3bmbMmKF3wLtIiX7w4/YB3J7+x//4H/nkJz85auxv/uZvkiTz5s3TH6CC/cmf/En+8A//MCtWrMjGjRtHncTgtwMlCRtvQ7W1tVmxYkV2796d5557LufOncuOHTsya9asLF++fLLLA26S+fPnZ8GCBfnc5z6X06dP5+WXX87nP//5fPe7380v/dIvpa2tLVeuXMnOnTtz/vz5HDt2LAcPHsyDDz6YRO+Ad5MS/eDH7QO4Pd1///359re/nS9/+cv53ve+l+effz47d+7M/fffn3nz5ukPUKEuXLiQ9vb2/OzP/mw+8IEP5Ic//GFeffXVvPrqq7ly5YrfDhRVNTQ0NDTZRfD2DQ4O5otf/GKOHTuW3t7etLS05LHHHstdd9012aUBN9EPf/jD7N69O88//3x6enry3ve+N7/yK78y8q+JL774Ytrb29PR0ZGZM2fmF37hF7JixYqR7fUOqEyf/exnc/HixTz++OMjYyX6wY/bB3Dre6v+8PWvfz1/9Ed/lJdffjkNDQ15//vfnw996EMjl1PqD1B5vvzlL2fPnj1vOffzP//z+chHPuK3A8UIGwEAAACAIlxGDQAAAAAUIWwEAAAAAIoQNgIAAAAARQgbAQAAAIAihI0AAAAAQBHCRgAAAACgCGEjAAAAAFCEsBEAAAAAKELYCAAAAAAUIWwEAAAAAIoQNgIAAAAARQgbAQAAAIAi/j890Gbk83vlEwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "articlesWithMatches['Quoted Words'].hist()" + "## Statistics on our dataset of JSTOR matches" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Average number of words per match, per item: \n", - "\n", - "Average number of words per match, per item: " + "### Read in our ` text-matcher` JSTOR data \n", + "Here, we're reading in the output of our text-matcher on our JSTOR data (in JSON format)" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 29, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average number of words per match, per item:\n" - ] - }, - { - "data": { - "text/plain": [ - "0 4.000000\n", - "9 23.000000\n", - "17 21.812500\n", - "19 22.333333\n", - "21 60.000000\n", - "Name: Wordcounts, dtype: float64" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "print('Average number of words per match, per item:')\n", - "articlesWithMatches['Wordcounts'].apply(np.mean).head()" + "df = pd.read_json('../data/t2-c3-n2-m3-no-stops.json') " ] }, { "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Statistics on average number of words, min/max per match, per item:\n" - ] - }, - { - "data": { - "text/plain": [ - "count 1540.000000\n", - "mean 24.711784\n", - "std 29.718071\n", - "min 2.000000\n", - "25% 6.000000\n", - "50% 15.000000\n", - "75% 32.000000\n", - "max 371.250000\n", - "Name: Wordcounts, dtype: float64" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('Statistics on average number of words, min/max per match, per item:')\n", - "articlesWithMatches['Wordcounts'].apply(np.mean).describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Synchronic Analysis\n", - "\n", - "In these next sections, we're going to look at the \"Book\" and chapter level quotation counts for the entire dataset of text-matcher data.\n", - "\n", - "- [Quotations and Words Quoted Per Book in Middlemarch](#Quotations-Per-Book-in-Middlemarch)\n", - " - [Number of words quoted per book in Middlemarch, bar chart](#Number-of-words-quoted-per-book-in-Middlemarch,-bar-chart)\n", - " - [Number of words quoted per book in Middlemarch, bubble chart](#Number-of-words-quoted-per-book-in-Middlemarch,-bubble-chart)\n", - " - [Number of quotations per book in Middlemarch, bar chart](#Number-of-quotations-per-book-in-Middlemarch,-bar-chart)\n", - " - [Number of quotations per book in Middlemarch, bubble chart](#Number-of-quotations-per-book-in-Middlemarch,-bubble-chart)\n", - "- [Quotes and Words Quoted by Chapter in Middlemarch](#Number-of-Quotes-(and-Words-Quoted)-by-Chapter)\n", - "\t- [Number of words quoted, by chapter in Middlemarch](#Number-of-words-quoted,-by-chapter-in-Middlemarch)\n", - "\t- [Number of quotations, by chapter in Middlemarch, bar chart](#Number-of-quotations,-by-chapter-in-Middlemarch,-bar-chart)\n", - "\t- [Number of quotations, by chapter in Middlemarch, bubble chart](#Number-of-quotations,-by-chapter-in-Middlemarch,-bubble-chart)\n", - "\t- [Normalized number of words quoted per chapter](#Normalized-number-of-words-quoted-per-chapter)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quotations Per Book in *Middlemarch*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Number of words quoted per book in *Middlemarch*" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 0\n", - "1 28112\n", - "2 32378\n", - "3 11351\n", - "4 10677\n", - "5 6938\n", - "6 6045\n", - "7 3882\n", - "8 20509\n", - "dtype: int64" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wordsQuotedPerBook = synchronicAnalysis(df, bins=bookLocations, useWordcounts=True)\n", - "wordsQuotedPerBook" - ] - }, - { - "cell_type": "code", - "execution_count": 51, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1914,1000 +1344,1036 @@ " \n", " \n", " \n", - " Number of Words Quoted\n", - " Book\n", + " creator\n", + " datePublished\n", + " docSubType\n", + " docType\n", + " id\n", + " identifier\n", + " isPartOf\n", + " issueNumber\n", + " language\n", + " outputFormat\n", + " ...\n", + " title\n", + " url\n", + " volumeNumber\n", + " wordCount\n", + " numMatches\n", + " Locations in A\n", + " Locations in B\n", + " abstract\n", + " keyphrase\n", + " subTitle\n", " \n", " \n", " \n", " \n", + " 0\n", + " [Rainer Emig]\n", + " 2006-01-01\n", + " book-review\n", + " article\n", + " http://www.jstor.org/stable/41158244\n", + " [{'name': 'issn', 'value': '03402827'}, {'name...\n", + " Amerikastudien / American Studies\n", + " 3\n", + " [eng]\n", + " [unigram, bigram, trigram]\n", + " ...\n", + " Review Article\n", + " http://www.jstor.org/stable/41158244\n", + " 51\n", + " 1109\n", + " 1\n", + " [[130022, 130046]]\n", + " [[6851, 6875]]\n", + " None\n", + " None\n", + " None\n", + " \n", + " \n", " 1\n", - " 28112\n", + " [Martin Green]\n", + " 1970-01-01\n", + " book-review\n", + " article\n", + " http://www.jstor.org/stable/3722819\n", + " [{'name': 'issn', 'value': '00267937'}, {'name...\n", + " The Modern Language Review\n", " 1\n", + " [eng]\n", + " [unigram, bigram, trigram]\n", + " ...\n", + " Review Article\n", + " http://www.jstor.org/stable/3722819\n", + " 65\n", + " 1342\n", + " 0\n", + " []\n", + " []\n", + " None\n", + " None\n", + " None\n", " \n", " \n", " 2\n", - " 32378\n", - " 2\n", + " [Richard Exner]\n", + " 1982-01-01\n", + " book-review\n", + " article\n", + " http://www.jstor.org/stable/40137021\n", + " [{'name': 'issn', 'value': '01963570'}, {'name...\n", + " World Literature Today\n", + " 1\n", + " [eng]\n", + " [unigram, bigram, trigram]\n", + " ...\n", + " Review Article\n", + " http://www.jstor.org/stable/40137021\n", + " 56\n", + " 493\n", + " 0\n", + " []\n", + " []\n", + " None\n", + " None\n", + " None\n", " \n", " \n", " 3\n", - " 11351\n", - " 3\n", + " [Ruth Evelyn Henderson]\n", + " 1925-10-01\n", + " research-article\n", + " article\n", + " http://www.jstor.org/stable/802346\n", + " [{'name': 'issn', 'value': '00138274'}, {'name...\n", + " The English Journal\n", + " 8\n", + " [eng]\n", + " [unigram, bigram, trigram, fullText]\n", + " ...\n", + " American Education Week--November 16-22; Some ...\n", + " http://www.jstor.org/stable/802346\n", + " 14\n", + " 2161\n", + " 0\n", + " []\n", + " []\n", + " None\n", + " None\n", + " None\n", " \n", " \n", " 4\n", - " 10677\n", + " [Alan Palmer]\n", + " 2011-12-01\n", + " research-article\n", + " article\n", + " http://www.jstor.org/stable/10.5325/style.45.4...\n", + " [{'name': 'issn', 'value': '00394238'}, {'name...\n", + " Style\n", " 4\n", + " [eng]\n", + " [unigram, bigram, trigram]\n", + " ...\n", + " Rejoinder to Response by Marie-Laure Ryan\n", + " http://www.jstor.org/stable/10.5325/style.45.4...\n", + " 45\n", + " 1127\n", + " 0\n", + " []\n", + " []\n", + " None\n", + " None\n", + " None\n", " \n", " \n", - " 5\n", - " 6938\n", - " 5\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 6\n", - " 6045\n", - " 6\n", + " 5879\n", + " [Michaela Giesenkirchen]\n", + " 2005-10-01\n", + " research-article\n", + " article\n", + " http://www.jstor.org/stable/27747183\n", + " [{'name': 'issn', 'value': '15403084'}, {'name...\n", + " American Literary Realism\n", + " 1\n", + " [eng]\n", + " [unigram, bigram, trigram]\n", + " ...\n", + " Ethnic Types and Problems of Characterization ...\n", + " http://www.jstor.org/stable/27747183\n", + " 38\n", + " 7349\n", + " 1\n", + " [[23799, 24121]]\n", + " [[41472, 41793]]\n", + " None\n", + " None\n", + " None\n", " \n", " \n", - " 7\n", - " 3882\n", - " 7\n", + " 5880\n", + " [Leon Botstein]\n", + " 2005-07-01\n", + " misc\n", + " article\n", + " http://www.jstor.org/stable/4123220\n", + " [{'name': 'issn', 'value': '00274631'}, {'name...\n", + " The Musical Quarterly\n", + " 2\n", + " [eng]\n", + " [unigram, bigram, trigram]\n", + " ...\n", + " On the Power of Music\n", + " http://www.jstor.org/stable/4123220\n", + " 88\n", + " 1525\n", + " 0\n", + " []\n", + " []\n", + " None\n", + " None\n", + " None\n", " \n", " \n", - " 8\n", - " 20509\n", - " 8\n", - " \n", - " \n", + " 5881\n", + " [Linda M. Shires]\n", + " 2013-01-01\n", + " research-article\n", + " article\n", + " http://www.jstor.org/stable/24575734\n", + " [{'name': 'issn', 'value': '10601503'}, {'name...\n", + " Victorian Literature and Culture\n", + " 4\n", + " [eng]\n", + " [unigram, bigram, trigram]\n", + " ...\n", + " HARDY'S MEMORIAL ART: IMAGE AND TEXT IN \"WESSE...\n", + " http://www.jstor.org/stable/24575734\n", + " 41\n", + " 10736\n", + " 1\n", + " [[173657, 173756]]\n", + " [[33963, 34061]]\n", + " None\n", + " None\n", + " None\n", + " \n", + " \n", + " 5882\n", + " [Edward H. Cohen]\n", + " 1990-07-01\n", + " misc\n", + " article\n", + " http://www.jstor.org/stable/3827815\n", + " [{'name': 'issn', 'value': '00425222'}, {'name...\n", + " Victorian Studies\n", + " 4\n", + " [eng]\n", + " [unigram, bigram, trigram]\n", + " ...\n", + " Victorian Bibliography for 1989\n", + " http://www.jstor.org/stable/3827815\n", + " 33\n", + " 81819\n", + " 0\n", + " []\n", + " []\n", + " None\n", + " None\n", + " None\n", + " \n", + " \n", + " 5883\n", + " None\n", + " 1964-06-01\n", + " misc\n", + " article\n", + " http://www.jstor.org/stable/2932781\n", + " [{'name': 'issn', 'value': '00290564'}, {'name...\n", + " Nineteenth-Century Fiction\n", + " 1\n", + " [eng]\n", + " [unigram, bigram, trigram]\n", + " ...\n", + " Volume Information\n", + " http://www.jstor.org/stable/2932781\n", + " 19\n", + " 694\n", + " 0\n", + " []\n", + " []\n", + " None\n", + " None\n", + " None\n", + " \n", + " \n", "\n", + "

5884 rows × 29 columns

\n", "" ], "text/plain": [ - " Number of Words Quoted Book\n", - "1 28112 1\n", - "2 32378 2\n", - "3 11351 3\n", - "4 10677 4\n", - "5 6938 5\n", - "6 6045 6\n", - "7 3882 7\n", - "8 20509 8" + " creator datePublished docSubType docType \\\n", + "0 [Rainer Emig] 2006-01-01 book-review article \n", + "1 [Martin Green] 1970-01-01 book-review article \n", + "2 [Richard Exner] 1982-01-01 book-review article \n", + "3 [Ruth Evelyn Henderson] 1925-10-01 research-article article \n", + "4 [Alan Palmer] 2011-12-01 research-article article \n", + "... ... ... ... ... \n", + "5879 [Michaela Giesenkirchen] 2005-10-01 research-article article \n", + "5880 [Leon Botstein] 2005-07-01 misc article \n", + "5881 [Linda M. Shires] 2013-01-01 research-article article \n", + "5882 [Edward H. Cohen] 1990-07-01 misc article \n", + "5883 None 1964-06-01 misc article \n", + "\n", + " id \\\n", + "0 http://www.jstor.org/stable/41158244 \n", + "1 http://www.jstor.org/stable/3722819 \n", + "2 http://www.jstor.org/stable/40137021 \n", + "3 http://www.jstor.org/stable/802346 \n", + "4 http://www.jstor.org/stable/10.5325/style.45.4... \n", + "... ... \n", + "5879 http://www.jstor.org/stable/27747183 \n", + "5880 http://www.jstor.org/stable/4123220 \n", + "5881 http://www.jstor.org/stable/24575734 \n", + "5882 http://www.jstor.org/stable/3827815 \n", + "5883 http://www.jstor.org/stable/2932781 \n", + "\n", + " identifier \\\n", + "0 [{'name': 'issn', 'value': '03402827'}, {'name... \n", + "1 [{'name': 'issn', 'value': '00267937'}, {'name... \n", + "2 [{'name': 'issn', 'value': '01963570'}, {'name... \n", + "3 [{'name': 'issn', 'value': '00138274'}, {'name... \n", + "4 [{'name': 'issn', 'value': '00394238'}, {'name... \n", + "... ... \n", + "5879 [{'name': 'issn', 'value': '15403084'}, {'name... \n", + "5880 [{'name': 'issn', 'value': '00274631'}, {'name... \n", + "5881 [{'name': 'issn', 'value': '10601503'}, {'name... \n", + "5882 [{'name': 'issn', 'value': '00425222'}, {'name... \n", + "5883 [{'name': 'issn', 'value': '00290564'}, {'name... \n", + "\n", + " isPartOf issueNumber language \\\n", + "0 Amerikastudien / American Studies 3 [eng] \n", + "1 The Modern Language Review 1 [eng] \n", + "2 World Literature Today 1 [eng] \n", + "3 The English Journal 8 [eng] \n", + "4 Style 4 [eng] \n", + "... ... ... ... \n", + "5879 American Literary Realism 1 [eng] \n", + "5880 The Musical Quarterly 2 [eng] \n", + "5881 Victorian Literature and Culture 4 [eng] \n", + "5882 Victorian Studies 4 [eng] \n", + "5883 Nineteenth-Century Fiction 1 [eng] \n", + "\n", + " outputFormat ... \\\n", + "0 [unigram, bigram, trigram] ... \n", + "1 [unigram, bigram, trigram] ... \n", + "2 [unigram, bigram, trigram] ... \n", + "3 [unigram, bigram, trigram, fullText] ... \n", + "4 [unigram, bigram, trigram] ... \n", + "... ... ... \n", + "5879 [unigram, bigram, trigram] ... \n", + "5880 [unigram, bigram, trigram] ... \n", + "5881 [unigram, bigram, trigram] ... \n", + "5882 [unigram, bigram, trigram] ... \n", + "5883 [unigram, bigram, trigram] ... \n", + "\n", + " title \\\n", + "0 Review Article \n", + "1 Review Article \n", + "2 Review Article \n", + "3 American Education Week--November 16-22; Some ... \n", + "4 Rejoinder to Response by Marie-Laure Ryan \n", + "... ... \n", + "5879 Ethnic Types and Problems of Characterization ... \n", + "5880 On the Power of Music \n", + "5881 HARDY'S MEMORIAL ART: IMAGE AND TEXT IN \"WESSE... \n", + "5882 Victorian Bibliography for 1989 \n", + "5883 Volume Information \n", + "\n", + " url volumeNumber \\\n", + "0 http://www.jstor.org/stable/41158244 51 \n", + "1 http://www.jstor.org/stable/3722819 65 \n", + "2 http://www.jstor.org/stable/40137021 56 \n", + "3 http://www.jstor.org/stable/802346 14 \n", + "4 http://www.jstor.org/stable/10.5325/style.45.4... 45 \n", + "... ... ... \n", + "5879 http://www.jstor.org/stable/27747183 38 \n", + "5880 http://www.jstor.org/stable/4123220 88 \n", + "5881 http://www.jstor.org/stable/24575734 41 \n", + "5882 http://www.jstor.org/stable/3827815 33 \n", + "5883 http://www.jstor.org/stable/2932781 19 \n", + "\n", + " wordCount numMatches Locations in A Locations in B abstract \\\n", + "0 1109 1 [[130022, 130046]] [[6851, 6875]] None \n", + "1 1342 0 [] [] None \n", + "2 493 0 [] [] None \n", + "3 2161 0 [] [] None \n", + "4 1127 0 [] [] None \n", + "... ... ... ... ... ... \n", + "5879 7349 1 [[23799, 24121]] [[41472, 41793]] None \n", + "5880 1525 0 [] [] None \n", + "5881 10736 1 [[173657, 173756]] [[33963, 34061]] None \n", + "5882 81819 0 [] [] None \n", + "5883 694 0 [] [] None \n", + "\n", + " keyphrase subTitle \n", + "0 None None \n", + "1 None None \n", + "2 None None \n", + "3 None None \n", + "4 None None \n", + "... ... ... \n", + "5879 None None \n", + "5880 None None \n", + "5881 None None \n", + "5882 None None \n", + "5883 None None \n", + "\n", + "[5884 rows x 29 columns]" ] }, - "execution_count": 51, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "wordsQuotedPerBook = pd.DataFrame(wordsQuotedPerBook, index=range(1,9), columns=['Number of Words Quoted'])\n", - "wordsQuotedPerBook['Book'] = range(1, 9)\n", - "wordsQuotedPerBook" + "df" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 31, "metadata": {}, + "outputs": [], "source": [ - "### Number of words quoted per book in *Middlemarch*, bar chart" + "df.to_csv('../data/matches.csv', encoding='utf-8')" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# New for JSTOR 2022 data\n", + "df['year'] = pd.DatetimeIndex(df['datePublished']).year" + ] + }, + { + "cell_type": "code", + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], "text/plain": [ - "alt.Chart(...)" + "0 2006\n", + "1 1970\n", + "2 1982\n", + "3 1925\n", + "4 2011\n", + " ... \n", + "5879 2005\n", + "5880 2005\n", + "5881 2013\n", + "5882 1990\n", + "5883 1964\n", + "Name: year, Length: 5884, dtype: int64" ] }, - "execution_count": 52, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(wordsQuotedPerBook, title=\"Number of Words Quoted, per Book in Middlemarch\").\\\n", - "mark_bar().encode(x=alt.X('Book:O', axis=alt.Axis(title=\"Book\", labelAngle=0)), y='Number of Words Quoted:Q').\\\n", - "properties(width=500)" + "# New for JSTOR 2022 data\n", + "df['year']" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 34, "metadata": {}, + "outputs": [], "source": [ - "### Number of quotations per book in *Middlemarch*" + "df['Decade'] = df['year'] - (df['year'] % 10)\n", + "# df['Locations in A'] = df['matches'].apply(lambda x: x[1])\n", + "# df['NumMatches'] = df['matches'].apply(lambda x: x[0])" ] }, { - "cell_type": "code", - "execution_count": 53, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 0\n", - "1 1036\n", - "2 880\n", - "3 334\n", - "4 311\n", - "5 251\n", - "6 224\n", - "7 142\n", - "8 622\n", - "dtype: int64" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "quotationsPerBook = synchronicAnalysis(df, bins=bookLocations, useWordcounts=False)\n", - "quotationsPerBook" + "### Data Dictionary for `text matcher` dataset\n", + "\n", + "Our text-matcher dataset includes the following fields:" ] }, { - "cell_type": "code", - "execution_count": 54, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Number of QuotationsBook
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" - ], - "text/plain": [ - " Number of Quotations Book\n", - "1 1036 1\n", - "2 880 2\n", - "3 334 3\n", - "4 311 4\n", - "5 251 5\n", - "6 224 6\n", - "7 142 7\n", - "8 622 8" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "quotationsPerBook = pd.DataFrame(quotationsPerBook, index=range(1,9), columns=['Number of Quotations'])\n", - "quotationsPerBook['Book'] = range(1, 9)\n", - "quotationsPerBook" + "| Data field | Definition |\n", + "| :------------- | :------------- |\n", + "| 'Locations in A' | index characters for start and end locations for quoted text in source text (A) [Middlemarch], generated by text-matcher |\n", + "| 'Locations in B' | index characters for start and end locations for quoted text in the target text (B) [a given JSTOR text], generated by text-matcher |\n", + "| 'author' | author name supplied by JSTOR |\n", + "| 'coverdate'| date on cover, supplied by JSTOR |\n", + "| 'disc_name' | tags assigned to article, supplied by JSTOR |\n", + "| 'doi' | text (DOI), supplied by JSTOR |\n", + "| 'id' | unique identifier, supplied by JSTOR |\n", + "| 'jcode' | shortcode for journal, supplied by JSTOR |\n", + "| 'journal' | journal title, supplied by JSTOR |\n", + "| 'la' | language, supplied by JSTOR |\n", + "| 'no' | issue or number, supplied by JSTOR |\n", + "| 'numMatches'| number of matches, generated by text-matcher |\n", + "| 'pages' | page numbers, supplied by JSTOR |\n", + "| 'publisher_name' | supplied by JSTOR |\n", + "| 'sp' | starting page number, supplied by JSTOR |\n", + "| 'srcHtml' | HTML version of source citation (journal, Volume, issue, date) supplied by JSTOR |\n", + "| 'title' | title of article or piece of writing, supplied by JSTOR |\n", + "| 'topics' | subject headings, supplied by JSTOR |\n", + "| 'ty' | item type (fla = full length article; brv = book review; edi = opinion piece; nws OR mis) = other items, supplied by JSTOR |\n", + "| 'vo' | journal volume, supplied by JSTOR |\n", + "| 'year' | year of publication, supplied by JSTOR |\n", + "| 'Decade' | decade, generated by Middlematch team from 'year' |\n", + "| 'Quoted Words' | total number of words in all quotations in that text, generated by generated by Middlematch team from \"Locations in A' |\n", + "| 'Locations in A with Wordcounts' | list of pairs of index characters for start and end quote in the source text (A) [Middlemarch] and wordcount for each quoation, generated by generated by Middlematch team from \"Locations in A' |\n", + "| 'Wordcounts' | list of wordcounts for each matched quotation, generated by text-matcher | " ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3800" + "Index(['creator', 'datePublished', 'docSubType', 'docType', 'id', 'identifier',\n", + " 'isPartOf', 'issueNumber', 'language', 'outputFormat', 'pageCount',\n", + " 'pageEnd', 'pageStart', 'pagination', 'provider', 'publicationYear',\n", + " 'publisher', 'sourceCategory', 'tdmCategory', 'title', 'url',\n", + " 'volumeNumber', 'wordCount', 'numMatches', 'Locations in A',\n", + " 'Locations in B', 'abstract', 'keyphrase', 'subTitle', 'year',\n", + " 'Decade'],\n", + " dtype='object')" ] }, - "execution_count": 55, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "quotationsPerBook['Number of Quotations'].sum()" + "df.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Number of quotations per book in *Middlemarch*, bar chart" + "### Functions for extracting wordcounts, numbers of quotations for diachronic and synchronic analysis" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 36, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], + "outputs": [], + "source": [ + "def diachronicAnalysis(df, decades=(1950, 2020), bins=chapterLocations, useWordcounts=True, normalize=True):\n", + " \"\"\" Turning on useWordcounts makes it so that it's weighted by wordcount. \n", + " Turning it off uses raw numbers of quotations. \"\"\"\n", + " decades = np.arange(decades[0], decades[1], 10)\n", + " # Make a dictionary of decades. \n", + " # Values are a list of locations. \n", + " decadeDict = {}\n", + " for i, row in df.iterrows():\n", + " decade = row['Decade']\n", + " locationsAndWordcounts = row['Locations in A with Wordcounts']\n", + " if decade not in decadeDict: \n", + " decadeDict[decade] = locationsAndWordcounts.copy()\n", + " else: \n", + " decadeDict[decade] += locationsAndWordcounts.copy()\n", + " # Grab the beginnings of quotes. \n", + " decadeStartsWeights = {decade: [(item[0][0], item[1]) \n", + " for item in loc] \n", + " for decade, loc in decadeDict.items()}\n", + " if useWordcounts: \n", + " decadesBinned = {decade: \n", + " np.histogram([loc[0] for loc in locations], \n", + " bins=bins,\n", + " weights=[loc[1] for loc in locations],\n", + " range=(0, textALength))[0]\n", + " for decade, locations in decadeStartsWeights.items() \n", + " if decade in decades}\n", + " else: \n", + " decadesBinned = {decade: \n", + " np.histogram([loc[0] for loc in locations], \n", + " bins=bins,\n", + " range=(0, textALength))[0]\n", + " for decade, locations in decadeStartsWeights.items() \n", + " if decade in decades}\n", + " decadesDF = pd.DataFrame(decadesBinned).T\n", + " #Normalize\n", + " if normalize: \n", + " decadesDF = decadesDF.div(decadesDF.max(axis=1), axis=0)\n", + " return decadesDF\n", + "\n", + "def countWords(locRange): \n", + " \"\"\" Counts words in middlemarch, given character ranges. \"\"\"\n", + " chunk = mm[locRange[0]:locRange[1]]\n", + " return len(chunk.split())\n", + "\n", + "def totalWords(locRangeSet): \n", + " \"\"\" Counts total words in a list of location ranges. \"\"\"\n", + " return sum([countWords(locRange) for locRange in locRangeSet]) \n", + " \n", + "def countsPerSet(locRangeSet): \n", + " \"\"\" Returns an augmented location range set that includes word counts. \"\"\"\n", + " return [(locRange, countWords(locRange))\n", + " for locRange in locRangeSet]\n", + " \n", + "def extractWordcounts(locsAndWordcounts): \n", + " \"\"\" \n", + " Takes pairs of location ranges and wordcounts, \n", + " and returns just the wordcounts. \n", + " \"\"\"\n", + " return [item[1] for item in locsAndWordcounts \n", + " if len(locsAndWordcounts) > 0]\n", + "\n", + "def synchronicAnalysis(df, bins=chapterLocations, useWordcounts=True): \n", + " locs = df['Locations in A'].values\n", + " locCounts = [(loc, countWords(loc)) for locSet in locs\n", + " for loc in locSet]\n", + " starts = [loc[0][0] for loc in locCounts]\n", + " counts = [loc[1] for loc in locCounts]\n", + " if useWordcounts: \n", + " binned = np.histogram(starts, bins=bins, \n", + " weights=counts, range=(0, textALength))\n", + " else: \n", + " binned = np.histogram(starts, bins=bins, \n", + " range=(0, textALength))\n", + " binnedDF = pd.Series(binned[0])\n", + " return binnedDF\n", + "\n", + "def plotDiachronicAnalysis(df, save=False, reverse=False): \n", + " ylabels = [str(int(decade)) for decade in df.index] + ['2020']\n", + " plt.pcolor(df, cmap='gnuplot')\n", + " plt.yticks(np.arange(len(df.index)+1), ylabels)\n", + " plt.gca().invert_yaxis()\n", + " plt.ylabel('Decade')\n", + " plt.xlabel('Chapter')\n", + " plt.gca().set_xlim((0, len(df.T)))\n", + " plt.colorbar(ticks=[])\n", + " if save: \n", + " plt.savefig('diachronic.png', bboxinches='tight', dpi=300, transparent=True)\n", + " plt.show()\n", + " \n", + "def plotSynchronicAnalysis(s, useWordcounts=True): \n", + " ax = s.plot(kind='bar')\n", + " ax.set_xlabel('Chapter')\n", + " if useWordcounts: \n", + " ax.set_ylabel('Number of Words Quoted')\n", + " else: \n", + " ax.set_ylabel('Number of Quotations')\n", + " \n", + "def plotSynchronicAnalysisHeatmap(s, useWordcounts=True): \n", + " vec1=synchronicAnalysis(df, useWordcounts=False)\n", + " fig, ax = plt.subplots()\n", + " sns.color_palette(\"magma\")\n", + " sns.heatmap([vec1])\n", + " ax.set_xlabel('Chapter')\n", + " ax.set_ylabel('Number of Quotations')\n", + " \n", + "def plotDiachronicAnalysisBubble(df, save=False, reverse=False):\n", + " ylabels = [str(int(decade)) for decade in df.index] + ['2020'] \n", + " alt.Chart(df).mark_circle().encode(\n", + " x='Chapter',\n", + " y='Decade',\n", + " size='sum(count):Q'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding additional rows to DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "df['Quoted Words'] = df['Locations in A'].apply(totalWords)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "df['Locations in A with Wordcounts'] = df['Locations in A'].apply(countsPerSet)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "df['Wordcounts'] = df['Locations in A with Wordcounts'].apply(extractWordcounts)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { "text/plain": [ - "alt.Chart(...)" + "119747" ] }, - "execution_count": 56, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(quotationsPerBook, title=\"Number of Quotations, per Book in Middlemarch\").\\\n", - "mark_bar().encode(x=alt.X('Book:O', axis=alt.Axis(title=\"Book\", labelAngle=0)), y='Number of Quotations:Q').\\\n", - "properties(width=500)" + "# Verify that the diachronic wordcounts are the same as the synchronic wordcounts\n", + "decadeSums = diachronicAnalysis(df, decades=(1700, 2020), useWordcounts=True, normalize=False).sum(axis=1)\n", + "decadeSums.sum()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 41, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "119892" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## Number of Quotes and Words Quoted by Chapter" + "chapterSums = synchronicAnalysis(df)\n", + "chapterSums.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Number of words quoted, by chapter in *Middlemarch*" + "### How many quotations do we have?" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "3800" ] }, + "execution_count": 42, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "plotSynchronicAnalysis(synchronicAnalysis(df))" + "sum([len(item) for item in df['Locations in A'].values])" ] }, { - "cell_type": "code", - "execution_count": 58, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "synchronicAnalysis(df, useWordcounts=True).to_csv('../papers/spring2017-middlemarch-paper/data/num-words-quoted-per-chapter.csv')" + "#### Total number of matches" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "wordsQuotedPerChapter = synchronicAnalysis(df, bins=chapterLocations, useWordcounts=True)" + "allMatches = []\n", + "for group in df['Locations in A'].values: \n", + " for pair in group: \n", + " allMatches.append(pair)" ] }, { "cell_type": "code", - "execution_count": 60, - "metadata": {}, + "execution_count": 44, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "text/html": [ - "
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Number of Words QuotedChapter
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162841
224122
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45134
.........
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" - ], "text/plain": [ - " Number of Words Quoted Chapter\n", - "0 3919 0\n", - "1 6284 1\n", - "2 2412 2\n", - "3 2915 3\n", - "4 513 4\n", - ".. ... ...\n", - "83 1000 83\n", - "84 180 84\n", - "85 1485 85\n", - "86 184 86\n", - "87 5219 87\n", - "\n", - "[88 rows x 2 columns]" + "3800" ] }, - "execution_count": 60, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "wordsQuotedPerChapter = pd.DataFrame(wordsQuotedPerChapter, index=range(0,88), columns=['Number of Words Quoted'])\n", - "wordsQuotedPerChapter['Chapter'] = range(0, 88)\n", - "wordsQuotedPerChapter" + "len(allMatches)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Number of quotations, by chapter in *Middlemarch*, bar chart" + "### How many total articles do we have? " ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 45, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total articles with 'Middlemarch' appearing somewhere in text or metadata:\n" + ] + }, { "data": { - "image/png": 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" + "5884" ] }, + "execution_count": 45, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "plotSynchronicAnalysis(synchronicAnalysis(df, useWordcounts=False), useWordcounts=False)" + "print(\"Total articles with 'Middlemarch' appearing somewhere in text or metadata:\")\n", + "len(df) # Total articles with \"Middlemarch\" mentioned somewhere" ] }, { - "cell_type": "code", - "execution_count": 62, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "quotationsPerChapter = synchronicAnalysis(df, bins=chapterLocations, useWordcounts=False)" + "Find only those with non-trivial quotations from Middlemarch: " ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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Number of QuotationsChapter
01590
11971
2892
31143
4404
.........
834083
841084
85485
862986
8718787
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88 rows × 2 columns

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" - ], "text/plain": [ - " Number of Quotations Chapter\n", - "0 159 0\n", - "1 197 1\n", - "2 89 2\n", - "3 114 3\n", - "4 40 4\n", - ".. ... ...\n", - "83 40 83\n", - "84 10 84\n", - "85 4 85\n", - "86 29 86\n", - "87 187 87\n", - "\n", - "[88 rows x 2 columns]" + "count 1540.000000\n", + "mean 1991.488961\n", + "std 19.713886\n", + "min 1900.000000\n", + "25% 1980.000000\n", + "50% 1994.000000\n", + "75% 2007.000000\n", + "max 2022.000000\n", + "Name: year, dtype: float64" ] }, - "execution_count": 63, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "quotationsPerChapter = pd.DataFrame(quotationsPerChapter, index=range(0,88), columns=['Number of Quotations'])\n", - "quotationsPerChapter['Chapter'] = range(0, 88)\n", - "quotationsPerChapter" + "articlesWithMatches = df[df['Locations in A'].apply(lambda x: len(x) > 0)]\n", + "articlesWithMatches.year.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Number of quotations, by chapter in *Middlemarch*, bar chart" + "### How many articles with matches do we have? " ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 47, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of articles with matches to text in 'Middlemarch':\n" + ] + }, { "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], "text/plain": [ - "alt.Chart(...)" + "1540" ] }, - "execution_count": 64, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(quotationsPerChapter, title=\"Number of Quotations, per Chapter in Middlemarch\").\\\n", - "mark_bar().encode(x=alt.X('Chapter:O', axis=alt.Axis(title=\"Chapter\", labelAngle=0, values=list(range(0, 87, 5)))), y='Number of Quotations:Q').\\\n", - "properties(width=800).configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - " labelFontSize=14,\n", - " titleFontSize=14)" + "print(\"Number of articles with matches to text in 'Middlemarch':\")\n", + "articlesWithMatches['Locations in A'].count()" ] }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "articlesWithMatches['isPartOf'].value_counts()[:300]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting vl-convert-python\n", - " Obtaining dependency information for vl-convert-python from https://files.pythonhosted.org/packages/df/42/690adbc3e4e162963d446a58e965e9e01022de7e9075f651c17611909c3b/vl_convert_python-1.4.0-cp37-abi3-macosx_10_12_x86_64.whl.metadata\n", - " Downloading vl_convert_python-1.4.0-cp37-abi3-macosx_10_12_x86_64.whl.metadata (5.2 kB)\n", - "Downloading vl_convert_python-1.4.0-cp37-abi3-macosx_10_12_x86_64.whl (26.9 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m26.9/26.9 MB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", - "\u001b[?25hInstalling collected packages: vl-convert-python\n", - "Successfully installed vl-convert-python-1.4.0\n" - ] + "data": { + "text/plain": [ + "article 1500\n", + "chapter 40\n", + "Name: docType, dtype: int64" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "!pip install vl-convert-python" + "articlesWithMatches['docType'].value_counts()" ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "9 1\n", + "17 16\n", + "19 3\n", + "21 7\n", + "Name: Wordcounts, dtype: int64" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "articlesWithMatches.Wordcounts.apply(len).head()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "alt.Chart(quotationsPerChapter).\\\n", - "mark_bar().encode(x=alt.X('Chapter:O', axis=alt.Axis(title=\"Chapter\", labelAngle=0, values=list(range(0, 87, 5)))), y='Number of Quotations:Q').\\\n", - "properties(width=800).configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - " labelFontSize=14,\n", - " titleFontSize=14).save('Figure-3.png', ppi=300)" + "# articlesWithMatches.to_json('../data/cleaned-matches.json')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Number of quotations, by chapter in *Middlemarch*, bar chart (sorted by frequency)" + "### How many articles do we have published in each year? \n", + "Here, we're looking just at the 1500 articles with matches" ] }, { "cell_type": "code", - "execution_count": 461, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], "text/plain": [ - "alt.Chart(...)" + "DataTransformerRegistry.enable('default')" ] }, - "execution_count": 461, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(quotationsPerChapter, title=\"Number of Quotations, per Chapter in Middlemarch\").\\\n", - "mark_bar().encode(x=alt.X('Chapter:O', sort='-y', axis=alt.Axis(labelExpr='\"Chap.\" + datum.value', values=(20, 15, 1, 87, 10, 2, 0, 3,19, 81))), y=alt.Y('Number of Quotations:Q', axis=alt.Axis(labelAngle=0))).\\\n", - "properties(width=800).configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - " labelFontSize=12,\n", - " titleFontSize=14\n", - ")" + "# New for JSTOR 2022 dataset, because we have more data\n", + "alt.data_transformers.disable_max_rows()" ] }, { "cell_type": "code", - "execution_count": 462, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -2915,23 +2381,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 462, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(quotationsPerChapter, title=\"Number of Quotations, per Chapter in Middlemarch\").\\\n", - "mark_bar().encode(x=alt.X('Chapter:O', sort='-y', axis=alt.Axis(labelExpr='\"Chap.\" + datum.value', values=())), y=alt.Y('Number of Quotations:Q', axis=alt.Axis(labelAngle=0))).\\\n", - "properties(width=800).configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - " labelFontSize=14,\n", - " titleFontSize=14\n", - ")" + "alt.Chart(articlesWithMatches, title=\"Number of JSTOR articles with detected Middlemarch matches, by year\").mark_bar().encode(x='year:O', y='count()').properties(width=1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Number of non-matches in the 6,069-article dataset\n", + "One reason the 6,069-article original dataset is so much larger has to do with the way JSTOR ingests paratextual matter from journals. We found appearance of the word \"middlemarch\" in paratextual matter, which was systemattically titled in JSTOR. Here we define a quick function to count the number of appearances of article-like paratextual matter: \"front matter\", \"back matter\", \"table of contents\" and \"cover\".\n", + "\n", + "None of these titles are present in the smaller dataset of matches." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Nonmatches" ] }, { "cell_type": "code", - "execution_count": 463, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ - "alt.Chart(quotationsPerChapter).\\\n", - "mark_bar().encode(x=alt.X('Chapter:O', sort='-y', axis=alt.Axis(labelExpr='\"Chap.\" + datum.value', values=())), y=alt.Y('Number of Quotations:Q', axis=alt.Axis(labelAngle=0))).\\\n", - "properties(width=800).configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - " labelFontSize=14,\n", - " titleFontSize=14\n", - ").save('Figure-4.png', ppi=300)" + "# Try to find out what articles contain no Middlemarch citations\n", + "articlesWithoutMatches = df[df['Locations in A'].apply(lambda x: len(x) == 0)]" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], "text/plain": [ - "alt.Chart(...)" + "['Review Article',\n", + " 'Front Matter',\n", + " 'Back Matter',\n", + " 'Volume Information',\n", + " 'Summary of Periodical Literature',\n", + " 'Index',\n", + " 'Recent Studies in the Nineteenth Century',\n", + " 'Books Received',\n", + " 'List of Publications Received',\n", + " 'INDEX']" ] }, - "execution_count": 67, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(quotationsPerChapter, title=\"Number of Quotations, per Chapter in Middlemarch\").\\\n", - "mark_bar().encode(x=alt.X('Chapter:O', sort='-y', axis=alt.Axis(labelExpr='\"Chap.\" + datum.value', values=(20, 15, 1, 87, 10, 2, 0, 3,19, 81))), y=alt.Y('Number of Quotations:Q', axis=alt.Axis(labelAngle=-90))).\\\n", - "properties(width=900).configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - " labelFontSize=12,\n", - " titleFontSize=14\n", - ")" + "n = 10\n", + "articlesWithoutMatches['title'].value_counts()[:n].index.tolist()" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], "text/plain": [ - "alt.Chart(...)" + "count 4344\n", + "unique 2304\n", + "top Review Article\n", + "freq 1199\n", + "Name: title, dtype: object" ] }, - "execution_count": 68, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(quotationsPerChapter, title=\"Number of Quotations, per Chapter in Middlemarch\").\\\n", - "mark_bar().encode(x=alt.X('Chapter:O', sort='-y', axis=alt.Axis(labelExpr='\"Chap.\" + datum.value', values=())), y=alt.Y('Number of Quotations:Q', axis=alt.Axis(labelAngle=-90))).\\\n", - "properties(width=900).configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - " labelFontSize=12,\n", - " titleFontSize=14\n", - ")" + "# What is the most frequent name of articles with no citations?\n", + "articlesWithoutMatches['title'].describe()" ] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 55, "metadata": {}, + "outputs": [], + "source": [ + "def isGarbage(itemTitle): \n", + " badTitles = ['front matter', 'back matter', 'table of contents', 'cover']\n", + " if itemTitle == None: \n", + " return False\n", + " for title in itemTitle: \n", + " for badTitle in badTitles: \n", + " if badTitle in title.lower(): \n", + " return True\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "scrolled": true + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of'front matter', 'back matter', 'table of contents', 'cover' items in the 6069-article JSTOR dataset:\n" + ] + }, { "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], "text/plain": [ - "alt.Chart(...)" + "0" ] }, - "execution_count": 69, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(quotationsPerChapter, title=\"Number of Quotations, per Chapter in Middlemarch\").\\\n", - "mark_bar().encode(x=alt.X('Number of Quotations:Q'), y=alt.Y('Chapter:O', sort='-x', axis=alt.Axis(title=\"Chapters, by frequency quoted\"))).\\\n", - "properties().configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - " labelFontSize=14,\n", - " titleFontSize=14\n", - ")" + "print(\"Number of'front matter', 'back matter', 'table of contents', 'cover' items in the 6069-article JSTOR dataset:\")\n", + "len(df[df.title.apply(isGarbage)]) # How many garbage items? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Quotation Length Statistics" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ - "quotationsPerParagraph = synchronicAnalysis(df, bins=paragraphLocations, useWordcounts=False)" + "wordcounts = []\n", + "for countSet in df['Wordcounts'].values: \n", + " for count in countSet: \n", + " wordcounts.append(count)" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 58, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series(wordcounts).hist()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Average numbers of quoted words per item" + ] + }, + { + "cell_type": "code", + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Number of QuotationsParagraph
000
101
202
303
404
.........
488424884
488524885
488624886
4887314887
4888604888
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4889 rows × 2 columns

\n", - "
" - ], "text/plain": [ - " Number of Quotations Paragraph\n", - "0 0 0\n", - "1 0 1\n", - "2 0 2\n", - "3 0 3\n", - "4 0 4\n", - "... ... ...\n", - "4884 2 4884\n", - "4885 2 4885\n", - "4886 2 4886\n", - "4887 31 4887\n", - "4888 60 4888\n", - "\n", - "[4889 rows x 2 columns]" + "count 5884.000000\n", + "mean 20.375935\n", + "std 94.477822\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 4.000000\n", + "max 2138.000000\n", + "Name: Quoted Words, dtype: float64" ] }, - "execution_count": 71, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "quotationsPerParagraph = pd.DataFrame(quotationsPerParagraph, index=range(0,4889), columns=['Number of Quotations'])\n", - "quotationsPerParagraph['Paragraph'] = range(0, 4889)\n", - "quotationsPerParagraph" + "df['Quoted Words'].describe()" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Statistics on average, min, and max number of quoted words per item:\n" + ] + }, + { + "data": { + "text/plain": [ + "count 1540.000000\n", + "mean 77.851948\n", + "std 172.172395\n", + "min 2.000000\n", + "25% 6.000000\n", + "50% 17.000000\n", + "75% 64.000000\n", + "max 2138.000000\n", + "Name: Quoted Words, dtype: float64" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Let's remove\n", - "nonzeroquotationsPerParagraph = quotationsPerParagraph[quotationsPerParagraph['Number of Quotations'] != 0]" + "print('Statistics on average, min, and max number of quoted words per item:')\n", + "articlesWithMatches['Quoted Words'].describe()" ] }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Paragraph
Number of Quotations
1548
2199
3111
443
539
621
716
813
919
106
117
123
1310
146
154
163
181
192
205
211
232
241
253
261
281
312
321
331
342
412
422
451
471
541
601
631
691
711
\n", - "
" - ], "text/plain": [ - " Paragraph\n", - "Number of Quotations \n", - "1 548\n", - "2 199\n", - "3 111\n", - "4 43\n", - "5 39\n", - "6 21\n", - "7 16\n", - "8 13\n", - "9 19\n", - "10 6\n", - "11 7\n", - "12 3\n", - "13 10\n", - "14 6\n", - "15 4\n", - "16 3\n", - "18 1\n", - "19 2\n", - "20 5\n", - "21 1\n", - "23 2\n", - "24 1\n", - "25 3\n", - "26 1\n", - "28 1\n", - "31 2\n", - "32 1\n", - "33 1\n", - "34 2\n", - "41 2\n", - "42 2\n", - "45 1\n", - "47 1\n", - "54 1\n", - "60 1\n", - "63 1\n", - "69 1\n", - "71 1" + "1540" ] }, - "execution_count": 73, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "nonzeroquotationsPerParagraph.groupby('Number of Quotations').count()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Number of quotations, by paragraph in *Middlemarch*, bar chart (sorted by frequency)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "#ax = nonzeroquotationsPerParagraph['Number of Quotations'].sort_values(ascending=False).plot(kind='bar',\\ title=\"Number of Middlemarch Quotations, by Paragraph, Sorted by Frequency\", figsize=(40,10))\n", - "#ax.set_xlabel('Paragraph')\n", - "#ax.set_ylabel('Number of Quotations')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Normalized number of words quoted per chapter" + "len(df[df['Quoted Words'] > 0])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], - "source": [ - "# Get the raw number of quotations per chapter\n", - "# synchronicAnalysis(df, useWordcounts=False).to_csv('../papers/spring2017-middlemarch-paper/data/num-quotations-per-chapter.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": { - "scrolled": true - }, "outputs": [ { "data": { "text/plain": [ - "Text(0, 0.5, 'Words Quoted, Normalized')" + "" ] }, - "execution_count": 76, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -3750,66 +2757,158 @@ } ], "source": [ - "# Adjusted for the number of words in each chapter\n", - "ax = (synchronicAnalysis(df) / chapterLengthsSeries).plot(kind='bar')\n", - "ax.set_xlabel('Chapter')\n", - "ax.set_ylabel('Words Quoted, Normalized')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Diachronic Analysis\n", - "\n", - "For the diachronic analysis, we examine book- and chapter-level data for quotations from *Middlemarch*. \n", - "\n", - "- [Middlemarch diachronic analysis: quotations per book, by decade](#Middlemarch-diachronic-analysis:-quotations-per-book,-by-decade)\n", - " - [Number of quotations per book, per decade (not normalized or weighted)](#Number-of-quotations-per-book,-per-decade-(not-normalized-or-weighted))\n", - " - [Number of quotations per book, per decade (normalized by decade)](#Number-of-quotations-per-book,-per-decade-(normalized-by-decade))\n", - " - [Number of quotations per book, per decade (normalized by decade and weighted by word count)](#Number-of-quotations-per-book,-per-decade-(normalized-by-decade-and-weighted-by-word-count))\n", - " - [Middlemarch quotations per book, per decade (normalized-and-weighted), heatmap](#Middlemarch-quotations-per-book,-per-decade-(normalized-and-weighted),-heat-map)\n", - " - [Middlemarch quotations per book, per decade (normalized-and-weighted), table bubble plots](#Middlemarch-quotations-per-book,-per-decade-(normalized-and-weighted),-table-bubble-plots)\n", - "- [ Middlemarch diachronic analysis: quotations per chapter,by decade](#Middlemarch-diachronic-analysis:-quotations-per-chapter,-by-decade)\n", - " - [Number of quotations per chapter, per decade (not normalized or weighted)](#Number-of-quotations-per-chapter,-per-decade-(not-normalized-or-weighted))\n", - " - [Number of quotations per chapter, per decade (normalized by decade and weighted by word count)](#Number-of-quotations-per-chapter,-per-decade-(normalized-by-decade-and-weighted-by-word-count))\n", - " - [Middlemarch quotations per chapter, per decade (normalized and weighted), heat map](#Middlemarch-quotations-per-chapter,-per-decade-(normalized-and-weighted),-heat-map)\n", - " - [Middlemarch quotations per chapter, per decade (normalized and weighted), table bubble plots](#Middlemarch-quotations-per-chapter,-per-decade-(normalized-and-weighted),-table-bubble-plots)" + "articlesWithMatches['Quoted Words'].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## *Middlemarch* diachronic analysis: quotations per book, by decade\n", + "### Average number of words per match, per item \n", "\n", - "We use three different methods to analyze quotations per book, by decade. First, we examine the raw counts of quotations per book, per decade. Because our corpus contains [varying numbers of JSTOR texsts per decade](#How-many-articles-do-we-have-published-in-each-year?), we've decided to also weigh by length of quotation and normalize per decade." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Number of quotations per book, per decade (not normalized or weighted)" + "Average number of words per match, per item: " ] }, { "cell_type": "code", - "execution_count": 77, - "metadata": { - "scrolled": false - }, + "execution_count": 63, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Number of quotations per book, per decade\n" + "Average number of words per match, per item:\n" ] }, { "data": { - "text/html": [ + "text/plain": [ + "0 4.000000\n", + "9 23.000000\n", + "17 21.812500\n", + "19 22.333333\n", + "21 60.000000\n", + "Name: Wordcounts, dtype: float64" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Average number of words per match, per item:')\n", + "articlesWithMatches['Wordcounts'].apply(np.mean).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Statistics on average number of words, min/max per match, per item:\n" + ] + }, + { + "data": { + "text/plain": [ + "count 1540.000000\n", + "mean 24.711784\n", + "std 29.718071\n", + "min 2.000000\n", + "25% 6.000000\n", + "50% 15.000000\n", + "75% 32.000000\n", + "max 371.250000\n", + "Name: Wordcounts, dtype: float64" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Statistics on average number of words, min/max per match, per item:')\n", + "articlesWithMatches['Wordcounts'].apply(np.mean).describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Synchronic Analysis\n", + "\n", + "In these next sections, we're going to look at the \"Book\" and chapter level quotation counts for the entire dataset of text-matcher data.\n", + "\n", + "- [Quotations and words quoted per book in *Middlemarch*](#Quotations-and-words-quoted-per-book-in-Middlemarch)\n", + " - [Number of words quoted per book in *Middlemarch*, bar chart](#Number-of-words-quoted-per-book-in-Middlemarch,-bar-chart)\n", + " - [Number of quotations per book in *Middlemarch*, bar chart](#Number-of-quotations-per-book-in-Middlemarch,-bar-chart)\n", + "- [Quotations and words quoted by chapter in *Middlemarch*](#Quotations-and-words-quoted-by-chapter-in-Middlemarch)\n", + "\t- [Number of words quoted, by chapter in *Middlemarch*](#Number-of-words-quoted,-by-chapter-in-Middlemarch)\n", + "\t- [Number of quotations, by chapter in *Middlemarch*, bar chart](#Number-of-quotations,-by-chapter-in-Middlemarch,-bar-chart)\n", + "\t- [Number of quotations, by chapter in *Middlemarch*, bar chart (ranked by frequency)](#Number-of-quotations,-by-chapter-in-Middlemarch,-bar-chart-(ranked-by-frequency))\n", + "\t- [Normalized number of words quoted per chapter](#Normalized-number-of-words-quoted-per-chapter)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quotations and words quoted per book in *Middlemarch*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Number of words quoted per book in *Middlemarch*" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 28112\n", + "2 32378\n", + "3 11351\n", + "4 10677\n", + "5 6938\n", + "6 6045\n", + "7 3882\n", + "8 20509\n", + "dtype: int64" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wordsQuotedPerBook = synchronicAnalysis(df, bins=bookLocations, useWordcounts=True)\n", + "wordsQuotedPerBook" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 82, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(booksNotNormalizedNotWeightedDiaDFMelted,\\\n", - " title=\"Middlemarch quotations per book, per decade (not weighted or normalized)\")\\\n", - ".mark_rect().encode(x=alt.X('book', type='ordinal', \n", - " axis=alt.Axis(labelAngle=0)), \n", - " y=alt.Y('decade', type='ordinal', sort='descending',\n", - " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", - " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations\"))).properties(width=500, height=300).configure_axis(\n", - " labelFontSize=14,\n", - " titleFontSize=14\n", - ").configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ")" + "alt.Chart(wordsQuotedPerBook, title=\"Number of Words Quoted, per Book in Middlemarch\").\\\n", + "mark_bar().encode(x=alt.X('Book:O', axis=alt.Axis(title=\"Book\", labelAngle=0)), y='Number of Words Quoted:Q').\\\n", + "properties(width=500)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Number of quotations per book in *Middlemarch*" ] }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 1036\n", + "2 880\n", + "3 334\n", + "4 311\n", + "5 251\n", + "6 224\n", + "7 142\n", + "8 622\n", + "dtype: int64" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quotationsPerBook = synchronicAnalysis(df, bins=bookLocations, useWordcounts=False)\n", + "quotationsPerBook" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 99, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(booksNotNormalizedNotWeightedDiaDFMelted,\\\n", - " title=\"Middlemarch quotations per book, per decade (not weighted or normalized)\")\\\n", - ".mark_rect().encode(x=alt.X('decade', type='ordinal',\n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", - " y=alt.Y('book', type='ordinal', sort='descending'), \n", - " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations (normalized)\"))).properties(width=500, height=300).configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - " labelFontSize=14,\n", - " titleFontSize=14\n", - ") " + "alt.Chart(quotationsPerBook, title=\"Number of Quotations, per Book in Middlemarch\").\\\n", + "mark_bar().encode(x=alt.X('Book:O', axis=alt.Axis(title=\"Book\", labelAngle=0)), y='Number of Quotations:Q').\\\n", + "properties(width=500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Number of quotations per book, per decade (normalized by decade)" + "## Quotations and words quoted by chapter in *Middlemarch*" ] }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "synchronicAnalysis(df, useWordcounts=True).to_csv('../papers/spring2017-middlemarch-paper/data/num-words-quoted-per-chapter.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Number of words quoted, by chapter in *Middlemarch*" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "wordsQuotedPerChapter = synchronicAnalysis(df, bins=chapterLocations, useWordcounts=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Quotations per book, per decade (normalized by decade):\n" - ] - }, { "data": { "text/html": [ @@ -4218,143 +3406,222 @@ " \n", " \n", " \n", + " Number of Words Quoted\n", + " Chapter\n", + " \n", + " \n", + " \n", + " \n", " 0\n", + " 3919\n", + " 0\n", + " \n", + " \n", " 1\n", + " 6284\n", + " 1\n", + " \n", + " \n", " 2\n", + " 2412\n", + " 2\n", + " \n", + " \n", " 3\n", + " 2915\n", + " 3\n", + " \n", + " \n", " 4\n", - " 5\n", - " 6\n", - " 7\n", - " 8\n", + " 513\n", + " 4\n", " \n", - " \n", - " \n", " \n", - " 1960\n", - " 0.0\n", - " 1.0\n", - " 0.890909\n", - " 0.309091\n", - " 0.418182\n", - " 0.418182\n", - " 0.163636\n", - " 0.218182\n", - " 0.981818\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 1970\n", - " 0.0\n", - " 1.0\n", - " 0.642384\n", - " 0.264901\n", - " 0.238411\n", - " 0.218543\n", - " 0.185430\n", - " 0.092715\n", - " 0.456954\n", + " 83\n", + " 1000\n", + " 83\n", " \n", " \n", - " 1980\n", - " 0.0\n", - " 1.0\n", - " 0.792350\n", - " 0.273224\n", - " 0.256831\n", - " 0.158470\n", - " 0.240437\n", - " 0.120219\n", - " 0.639344\n", + " 84\n", + " 180\n", + " 84\n", " \n", " \n", - " 1990\n", - " 0.0\n", - " 1.0\n", - " 0.914530\n", - " 0.294872\n", - " 0.363248\n", - " 0.277778\n", - " 0.166667\n", - " 0.128205\n", - " 0.572650\n", + " 85\n", + " 1485\n", + " 85\n", " \n", " \n", - " 2000\n", - " 0.0\n", - " 1.0\n", - " 0.867347\n", - " 0.306122\n", - " 0.295918\n", - " 0.239796\n", - " 0.255102\n", - " 0.117347\n", - " 0.408163\n", + " 86\n", + " 184\n", + " 86\n", " \n", " \n", - " 2010\n", - " 0.0\n", - " 1.0\n", - " 0.920904\n", - " 0.485876\n", - " 0.305085\n", - " 0.225989\n", - " 0.259887\n", - " 0.186441\n", - " 0.627119\n", + " 87\n", + " 5219\n", + " 87\n", " \n", " \n", "\n", + "

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" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 \\\n", - "1960 0.0 1.0 0.890909 0.309091 0.418182 0.418182 0.163636 0.218182 \n", - "1970 0.0 1.0 0.642384 0.264901 0.238411 0.218543 0.185430 0.092715 \n", - "1980 0.0 1.0 0.792350 0.273224 0.256831 0.158470 0.240437 0.120219 \n", - "1990 0.0 1.0 0.914530 0.294872 0.363248 0.277778 0.166667 0.128205 \n", - "2000 0.0 1.0 0.867347 0.306122 0.295918 0.239796 0.255102 0.117347 \n", - "2010 0.0 1.0 0.920904 0.485876 0.305085 0.225989 0.259887 0.186441 \n", + " Number of Words Quoted Chapter\n", + "0 3919 0\n", + "1 6284 1\n", + "2 2412 2\n", + "3 2915 3\n", + "4 513 4\n", + ".. ... ...\n", + "83 1000 83\n", + "84 180 84\n", + "85 1485 85\n", + "86 184 86\n", + "87 5219 87\n", "\n", - " 8 \n", - "1960 0.981818 \n", - "1970 0.456954 \n", - "1980 0.639344 \n", - "1990 0.572650 \n", - "2000 0.408163 \n", - "2010 0.627119 " + "[88 rows x 2 columns]" ] }, - "execution_count": 100, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Number of quotations (not weighted), normalized by decade(counts are scaled to the maximum value per decade)\n", - "booksNotWeightedDiaDF = diachronicAnalysis(df, decades=(1960, 2020), bins=bookLocations, useWordcounts=False, normalize=True).sort_index()\n", - "print('Quotations per book, per decade (normalized by decade):')\n", - "booksNotWeightedDiaDF" + "wordsQuotedPerChapter = pd.DataFrame(wordsQuotedPerChapter, index=range(0,88), columns=['Number of Words Quoted'])\n", + "wordsQuotedPerChapter['Chapter'] = range(0, 88)\n", + "wordsQuotedPerChapter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Number of quotations per book, per decade (normalized by decade and weighted by word count)" + "### Number of words quoted, by chapter in *Middlemarch*, bar chart" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 75, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Quotations per book, per decade (weighted by length of quotation and normalized by decade):\n" - ] - }, + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(wordsQuotedPerChapter, title=\"Number of Words Quoted, per Chapter in Middlemarch\").\\\n", + "mark_bar().encode(x=alt.X('Chapter:O', axis=alt.Axis(title=\"Chapter\", labelAngle=0, values=list(range(0, 87, 5)))), y='Number of Words Quoted:Q').\\\n", + "properties(width=800).configure_legend(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + " labelFontSize=14,\n", + " titleFontSize=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Number of quotations, by chapter in *Middlemarch*" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "quotationsPerChapter = synchronicAnalysis(df, bins=chapterLocations, useWordcounts=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -4376,188 +3643,133 @@ " \n", " \n", " \n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", - " 8\n", + " Number of Quotations\n", + " Chapter\n", " \n", " \n", " \n", " \n", - " 1960\n", - " 0.0\n", - " 0.567051\n", - " 1.000000\n", - " 0.229606\n", - " 0.354556\n", - " 0.481098\n", - " 0.068046\n", - " 0.166335\n", - " 0.467967\n", + " 0\n", + " 159\n", + " 0\n", " \n", " \n", - " 1970\n", - " 0.0\n", - " 1.000000\n", - " 0.839023\n", - " 0.271083\n", - " 0.264752\n", - " 0.206195\n", - " 0.148994\n", - " 0.121411\n", - " 0.555505\n", + " 1\n", + " 197\n", + " 1\n", " \n", " \n", - " 1980\n", - " 0.0\n", - " 0.877438\n", - " 0.946007\n", - " 0.268733\n", - " 0.287826\n", - " 0.156231\n", - " 0.250462\n", - " 0.099569\n", - " 1.000000\n", + " 2\n", + " 89\n", + " 2\n", " \n", " \n", - " 1990\n", - " 0.0\n", - " 0.816222\n", - " 1.000000\n", - " 0.208229\n", - " 0.418430\n", - " 0.176811\n", - " 0.106744\n", - " 0.078743\n", - " 0.528329\n", + " 3\n", + " 114\n", + " 3\n", " \n", " \n", - " 2000\n", - " 0.0\n", - " 1.000000\n", - " 0.964953\n", - " 0.528371\n", - " 0.265020\n", - " 0.223465\n", - " 0.289386\n", - " 0.079940\n", - " 0.463117\n", + " 4\n", + " 40\n", + " 4\n", " \n", " \n", - " 2010\n", - " 0.0\n", - " 0.748342\n", - " 1.000000\n", - " 0.519065\n", - " 0.374503\n", - " 0.152023\n", - " 0.190650\n", - " 0.188992\n", - " 0.539954\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 83\n", + " 40\n", + " 83\n", + " \n", + " \n", + " 84\n", + " 10\n", + " 84\n", + " \n", + " \n", + " 85\n", + " 4\n", + " 85\n", + " \n", + " \n", + " 86\n", + " 29\n", + " 86\n", + " \n", + " \n", + " 87\n", + " 187\n", + " 87\n", " \n", " \n", "\n", + "

88 rows × 2 columns

\n", "" ], "text/plain": [ - " 0 1 2 3 4 5 6 \\\n", - "1960 0.0 0.567051 1.000000 0.229606 0.354556 0.481098 0.068046 \n", - "1970 0.0 1.000000 0.839023 0.271083 0.264752 0.206195 0.148994 \n", - "1980 0.0 0.877438 0.946007 0.268733 0.287826 0.156231 0.250462 \n", - "1990 0.0 0.816222 1.000000 0.208229 0.418430 0.176811 0.106744 \n", - "2000 0.0 1.000000 0.964953 0.528371 0.265020 0.223465 0.289386 \n", - "2010 0.0 0.748342 1.000000 0.519065 0.374503 0.152023 0.190650 \n", + " Number of Quotations Chapter\n", + "0 159 0\n", + "1 197 1\n", + "2 89 2\n", + "3 114 3\n", + "4 40 4\n", + ".. ... ...\n", + "83 40 83\n", + "84 10 84\n", + "85 4 85\n", + "86 29 86\n", + "87 187 87\n", "\n", - " 7 8 \n", - "1960 0.166335 0.467967 \n", - "1970 0.121411 0.555505 \n", - "1980 0.099569 1.000000 \n", - "1990 0.078743 0.528329 \n", - "2000 0.079940 0.463117 \n", - "2010 0.188992 0.539954 " + "[88 rows x 2 columns]" ] }, - "execution_count": 101, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Weighted by wordcount (by the number of words in the quotation) and normalized by decade(counts are scaled to the maximum value per decade)\n", - "booksDiaDF = diachronicAnalysis(df, decades=(1960, 2020), bins=bookLocations, useWordcounts=True, normalize=True).sort_index()\n", - "print('Quotations per book, per decade (weighted by length of quotation and normalized by decade):')\n", - "booksDiaDF" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "booksDiaDF['decade'] = booksDiaDF.index" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [], - "source": [ - "booksMelted = booksDiaDF.melt(id_vars='decade', var_name='book')" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "# cut out erroneous \"book 0\" material (ie title page)\n", - "booksMelted = booksMelted[booksMelted.book != 0]" + "quotationsPerChapter = pd.DataFrame(quotationsPerChapter, index=range(0,88), columns=['Number of Quotations'])\n", + "quotationsPerChapter['Chapter'] = range(0, 88)\n", + "quotationsPerChapter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### *Middlemarch* quotations per book, per decade (normalized and weighted), heat map" + "### Number of quotations, by chapter in *Middlemarch*, bar chart" ] }, { "cell_type": "code", - "execution_count": 105, - "metadata": { - "scrolled": true - }, + "execution_count": 78, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 105, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(booksMelted,\\\n", - " title=\"Middlemarch quotations per book, per decade (weighted by length of quotation and normalized by decade)\")\\\n", - ".mark_rect().encode(x=alt.X('book', type='ordinal', \n", - " axis=alt.Axis(labelAngle=0)), \n", - " y=alt.Y('decade', type='ordinal', sort='descending', \n", - " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", - " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations (normalized)\"))).properties(width=500, height=300).configure_legend(\n", + "quotes_per_chap = alt.Chart(quotationsPerChapter, title=\"Number of Quotations, per Chapter in Middlemarch\").\\\n", + "mark_bar().encode(x=alt.X('Chapter:O', axis=alt.Axis(title=\"Chapter\", labelAngle=0, values=list(range(0, 87, 5)))), y='Number of Quotations:Q').\\\n", + "properties(width=800).configure_legend(\n", "titleFontSize=14,\n", "labelFontSize=14\n", ").configure_axis(\n", " labelFontSize=14,\n", - " titleFontSize=14\n", - ") " + " titleFontSize=14)\n", + "quotes_per_chap" ] }, { "cell_type": "code", - "execution_count": 106, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install vl-convert-python" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alt.Chart(quotationsPerChapter).\\\n", + "mark_bar().encode(x=alt.X('Chapter:O', axis=alt.Axis(title=\"Chapter\", labelAngle=0, values=list(range(0, 87, 5)))), y='Number of Quotations:Q').\\\n", + "properties(width=800).configure_legend(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + " labelFontSize=14,\n", + " titleFontSize=14).save('Figure-3.png', ppi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Number of quotations, by chapter in *Middlemarch*, bar chart (ranked by frequency)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -4641,23 +3881,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 106, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(booksMelted,\\\n", - " title=\"Middlemarch quotations per book, per decade (weighted by length of quotation and normalized by decade)\")\\\n", - ".mark_rect().encode(x=alt.X('book', type='ordinal', \n", - " axis=alt.Axis(labelAngle=0)), \n", - " y=alt.Y('decade', type='ordinal', sort='descending', \n", - " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", - " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations (normalized)\"))).properties(width=500, height=300).configure_legend(\n", + "ranked_freq_chap = alt.Chart(quotationsPerChapter, title=\"Number of Quotations, per Chapter in Middlemarch\").\\\n", + "mark_bar().encode(x=alt.X('Chapter:O', sort='-y', axis=alt.Axis(labelExpr='\"Chap.\" + datum.value', values=())), y=alt.Y('Number of Quotations:Q', axis=alt.Axis(labelAngle=0))).\\\n", + "properties(width=800).configure_legend(\n", "titleFontSize=14,\n", "labelFontSize=14\n", ").configure_axis(\n", " labelFontSize=14,\n", " titleFontSize=14\n", - ") " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Middlemarch quotations per book, per decade (normalized and weighted), table bubble plots" + ")\n", + "ranked_freq_chap#.save('Figure-4.png', ppi=300)" ] }, { "cell_type": "code", - "execution_count": 107, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "#alt.Chart(booksMelted, title=\"Middlemarch quotations per book, per decade (weighted by length of quotation and normalized by decade)\")\\\n", - "#.mark_circle().encode(\n", - "# x=alt.X('book:O', axis=alt.Axis(labelOverlap=True,\n", - "# labelAngle=0)), \n", - "# y='decade:O',\\\n", - "# size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations (normalized)\")))\\\n", - "#.properties(width=500, height=300).configure_legend(\n", - "#titleFontSize=10,\n", - "#labelFontSize=10)\n" - ] - }, - { - "cell_type": "markdown", + "execution_count": 80, "metadata": {}, - "source": [ - "## *Middlemarch* diachronic analysis: quotations per chapter, by decade" + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(quotationsPerChapter, title=\"Number of Quotations, per Chapter in Middlemarch\").\\\n", + "mark_bar().encode(x=alt.X('Number of Quotations:Q'), y=alt.Y('Chapter:O', sort='-x', axis=alt.Axis(title=\"Chapters, by frequency quoted\"))).\\\n", + "properties().configure_legend(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + " labelFontSize=14,\n", + " titleFontSize=14\n", + ")" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(quotationsPerChapter, title=\"Number of Quotations, per Chapter in Middlemarch\").\\\n", + "mark_bar().encode(x=alt.X('Chapter:O', sort='-y', axis=alt.Axis(labelExpr='\"Chap.\" + datum.value', values=(20, 15, 1, 87, 10, 2, 0, 3,19, 81))), y=alt.Y('Number of Quotations:Q', axis=alt.Axis(labelAngle=-90))).\\\n", + "properties(width=800).configure_legend(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + " labelFontSize=12,\n", + " titleFontSize=14\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "#alt.Chart(quotationsPerChapter).\\\n", + "#mark_bar().encode(x=alt.X('Chapter:O', sort='-y', axis=alt.Axis(labelExpr='\"Chap.\" + datum.value', values=())), y=alt.Y('Number of Quotations:Q', axis=alt.Axis(labelAngle=0))).\\\n", + "#properties(width=800).configure_legend(\n", + "#titleFontSize=14,\n", + "#labelFontSize=14\n", + "#).configure_axis(\n", + "# labelFontSize=14,\n", + "# titleFontSize=14\n", + "#).save('Figure-4.png', ppi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, "metadata": {}, + "outputs": [], "source": [ - "### Number of quotations per chapter, per decade (not normalized or weighted)" + "#alt.Chart(quotationsPerChapter, title=\"Number of Quotations, per Chapter in Middlemarch\").\\\n", + "#mark_bar().encode(x=alt.X('Chapter:O', sort='-y', axis=alt.Axis(labelExpr='\"Chap.\" + datum.value', values=())), y=alt.Y('Number of Quotations:Q', axis=alt.Axis(labelAngle=-90))).\\\n", + "#properties(width=900).configure_legend(\n", + "#titleFontSize=14,\n", + "#labelFontSize=14\n", + "#).configure_axis(\n", + "# labelFontSize=12,\n", + "# titleFontSize=14\n", + "#)" ] }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ - "# Raw quotation counts (not weighted by the number of words in the quoatation or normalized by decade)\n", - "# Turning on useWordcounts makes it so that it's weighted by wordcount. Turning it off uses raw numbers of quotations.\n", - "diaDFquoteOnly = diachronicAnalysis(df, decades=(1960, 2020), bins=chapterLocations, useWordcounts=False, normalize=False).sort_index()\n", - "diaDFquoteOnly.columns.name ='chapter'\n", - "diaDFquoteOnly.index.name = 'decade'" + "quotationsPerParagraph = synchronicAnalysis(df, bins=paragraphLocations, useWordcounts=False)" ] }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -4809,1134 +4228,659 @@ "\n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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4889 rows × 2 columns

\n", + "" + ], + "text/plain": [ + " Number of Quotations Paragraph\n", + "0 0 0\n", + "1 0 1\n", + "2 0 2\n", + "3 0 3\n", + "4 0 4\n", + "... ... ...\n", + "4884 2 4884\n", + "4885 2 4885\n", + "4886 2 4886\n", + "4887 31 4887\n", + "4888 60 4888\n", + "\n", + "[4889 rows x 2 columns]" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quotationsPerParagraph = pd.DataFrame(quotationsPerParagraph, index=range(0,4889), columns=['Number of Quotations'])\n", + "quotationsPerParagraph['Paragraph'] = range(0, 4889)\n", + "quotationsPerParagraph" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's remove\n", + "nonzeroquotationsPerParagraph = quotationsPerParagraph[quotationsPerParagraph['Number of Quotations'] != 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" ], "text/plain": [ - "chapter 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 \\\n", - "decade \n", - "1960 8 7 5 4 2 3 0 6 1 6 8 3 2 0 0 8 3 \n", - "1970 20 26 20 24 8 4 11 8 5 8 7 8 2 2 1 21 14 \n", - "1980 34 41 13 21 4 10 4 8 0 13 17 9 9 9 4 38 17 \n", - "1990 38 38 20 23 5 14 13 10 1 14 24 21 14 9 4 66 11 \n", - "2000 28 30 19 23 12 8 7 8 2 26 22 7 4 4 1 27 26 \n", - "2010 21 53 12 13 9 5 10 4 2 17 17 7 7 2 1 32 15 \n", - "\n", - "chapter 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 \\\n", - "decade \n", - "1960 3 0 1 20 11 3 2 1 1 1 7 3 0 1 1 0 0 \n", - "1970 1 3 9 27 8 11 0 3 1 3 14 12 3 1 3 0 0 \n", - "1980 2 6 18 37 9 5 11 2 0 2 8 7 12 3 4 1 0 \n", - "1990 10 11 34 41 15 12 1 6 4 6 27 5 8 1 6 1 4 \n", - "2000 2 4 26 45 12 23 9 7 3 2 7 4 5 5 4 4 10 \n", - "2010 4 10 24 39 21 15 7 20 2 0 20 22 5 1 7 2 0 \n", - "\n", - "chapter 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 \\\n", - "decade \n", - "1960 0 5 2 4 0 2 3 6 1 2 0 0 7 3 1 0 4 \n", - "1970 1 0 2 11 1 4 2 3 12 9 1 4 6 1 5 0 4 \n", - "1980 6 1 4 20 0 9 0 0 7 7 0 2 1 10 1 0 3 \n", - "1990 5 7 18 16 1 7 0 10 21 2 1 19 8 5 11 0 14 \n", - "2000 0 3 4 18 2 8 6 2 15 6 1 9 1 1 9 3 10 \n", - "2010 2 2 10 10 1 12 5 4 8 6 0 4 2 0 8 0 5 \n", - "\n", - "chapter 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 \\\n", - "decade \n", - "1960 1 2 3 1 0 3 0 1 0 3 1 0 1 2 0 2 0 \n", - "1970 1 1 1 4 2 5 2 8 1 3 2 1 1 3 1 0 0 \n", - "1980 5 0 0 10 4 4 0 11 2 2 6 5 1 9 4 2 1 \n", - "1990 1 2 2 2 4 3 1 10 3 4 7 5 6 3 5 1 2 \n", - "2000 1 1 5 10 4 7 5 4 1 8 8 3 5 0 0 2 0 \n", - "2010 8 0 7 1 6 7 0 15 0 7 8 2 7 5 0 1 2 \n", - "\n", - "chapter 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 \\\n", - "decade \n", - "1960 1 0 0 6 0 1 0 4 6 2 6 1 4 12 1 2 0 \n", - "1970 2 3 1 3 3 2 7 1 4 1 1 1 10 9 0 4 1 \n", - "1980 0 2 0 3 9 2 3 2 15 6 4 0 17 13 1 7 0 \n", - "1990 3 0 6 4 6 1 4 3 10 2 13 2 17 20 2 15 3 \n", - "2000 3 7 3 3 5 1 0 4 9 2 4 2 6 5 0 5 2 \n", - "2010 2 1 4 11 4 3 7 3 3 4 0 0 15 11 1 7 0 \n", - "\n", - "chapter 85 86 87 \n", - "decade \n", - "1960 0 6 9 \n", - "1970 0 3 22 \n", - "1980 4 3 31 \n", - "1990 0 3 33 \n", - "2000 0 6 29 \n", - "2010 0 2 51 " + " Paragraph\n", + "Number of Quotations \n", + "1 548\n", + "2 199\n", + "3 111\n", + "4 43\n", + "5 39\n", + "6 21\n", + "7 16\n", + "8 13\n", + "9 19\n", + "10 6\n", + "11 7\n", + "12 3\n", + "13 10\n", + "14 6\n", + "15 4\n", + "16 3\n", + "18 1\n", + "19 2\n", + "20 5\n", + "21 1\n", + "23 2\n", + "24 1\n", + "25 3\n", + "26 1\n", + "28 1\n", + "31 2\n", + "32 1\n", + "33 1\n", + "34 2\n", + "41 2\n", + "42 2\n", + "45 1\n", + "47 1\n", + "54 1\n", + "60 1\n", + "63 1\n", + "69 1\n", + "71 1" ] }, + "execution_count": 87, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "with pd.option_context(\"display.min_rows\", 6, \"display.max_rows\", 100, \\\n", - " \"display.max_columns\", 90, 'display.max_colwidth', 150):\n", - " display(diaDFquoteOnly)" + "nonzeroquotationsPerParagraph.groupby('Number of Quotations').count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Number of quotations, by paragraph in *Middlemarch*, bar chart (sorted by frequency)" ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ - "diaDFquoteOnly['decade'] = diaDFquoteOnly.index" + "#ax = nonzeroquotationsPerParagraph['Number of Quotations'].sort_values(ascending=False).plot(kind='bar',\\ title=\"Number of Middlemarch Quotations, by Paragraph, Sorted by Frequency\", figsize=(40,10))\n", + "#ax.set_xlabel('Paragraph')\n", + "#ax.set_ylabel('Number of Quotations')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normalized number of words quoted per chapter" ] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ - "diaDFquoteOnlyMelted = diaDFquoteOnly.melt(id_vars='decade')" + "# Get the raw number of quotations per chapter\n", + "# synchronicAnalysis(df, useWordcounts=False).to_csv('../papers/spring2017-middlemarch-paper/data/num-quotations-per-chapter.csv')" ] }, { "cell_type": "code", - "execution_count": 112, - "metadata": {}, + "execution_count": 90, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], "text/plain": [ - "alt.Chart(...)" + "Text(0, 0.5, 'Words Quoted, Normalized')" ] }, - "execution_count": 112, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "#Chart with raw quotations\n", - "alt.Chart(diaDFquoteOnlyMelted, title=\"Middlemarch quotations per chapter, per decade (not weighted or normalized)\")\\\n", - ".mark_rect().encode(x=alt.X('chapter', title=\"Chapter\", type='ordinal', \n", - " axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", - " y=alt.Y('decade', title=\"Decade\", type='ordinal', sort='descending', \n", - " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", - " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", - ".properties(width=1000, height=300).configure_axis(\n", - " labelFontSize=14,\n", - " titleFontSize=14\n", - ").configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ") " - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ + }, { "data": { - "text/html": [ - "\n", - "\n", - "
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"text/plain": [ - "alt.Chart(...)" + "
" ] }, - "execution_count": 113, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "#Chart with raw quotations\n", - "alt.Chart(diaDFquoteOnlyMelted, title=\"Middlemarch quotations per chapter, per decade (not weighted or normalized)\")\\\n", - ".mark_rect().encode(x=alt.X('decade',title=\"Decade\", type='ordinal', sort='ascending', axis=alt.Axis(labelAngle=0, \n", - " labelExpr='datum.value + \"s\"')), \n", - " y=alt.Y('chapter', title=\"Chapter\", type='ordinal', sort='descending', axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", - " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", - ".properties(width=300, height=1000).configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ") " + "# Adjusted for the number of words in each chapter\n", + "ax = (synchronicAnalysis(df) / chapterLengthsSeries).plot(kind='bar')\n", + "ax.set_xlabel('Chapter')\n", + "ax.set_ylabel('Words Quoted, Normalized')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### *Middlemarch* top 5 most frequently quoted chapters, quotation counts by decade" + "# Diachronic Analysis\n", + "\n", + "For the diachronic analysis, we examine book- and chapter-level data for quotations from *Middlemarch*. \n", + "\n", + "- [*Middlemarch* diachronic analysis: quotations per book, by decade](#Middlemarch-diachronic-analysis:-quotations-per-book,-by-decade)\n", + " - [Number of quotations per book, per decade (not normalized or weighted)](#Number-of-quotations-per-book,-per-decade-(not-normalized-or-weighted))\n", + " - [*Middlemarch* quotations per book, per decade (not normalized or weighted), heatmap](#Middlemarch-quotations-per-book,-per-decade-(not-normalized-or-weighted),-heat-map)\n", + " - [Number of quotations per book, per decade (normalized by decade)](#Number-of-quotations-per-book,-per-decade-(normalized-by-decade))\n", + " - [Number of quotations per book, per decade (normalized by decade and weighted by word count)](#Number-of-quotations-per-book,-per-decade-(normalized-by-decade-and-weighted-by-word-count))\n", + " - [*Middlemarch* quotations per book, per decade (normalized and weighted), heatmap](#Middlemarch-quotations-per-book,-per-decade-(normalized-and-weighted),-heat-map)\n", + "- [ *Middlemarch* diachronic analysis: quotations per chapter, by decade](#Middlemarch-diachronic-analysis:-quotations-per-chapter,-by-decade)\n", + " - [Number of quotations per chapter, per decade (not normalized or weighted)](#Number-of-quotations-per-chapter,-per-decade-(not-normalized-or-weighted))\n", + " - [*Middlemarch* quotations per chapter, per decade (not normalized or weighted), heat map](#Middlemarch-quotations-per-chapter,-per-decade-(not-normalized-or-weighted),-heat-map)\n", + " - [Number of quotations per chapter, per decade (normalized by decade and weighted by word count)](#Number-of-quotations-per-chapter,-per-decade-(normalized-by-decade-and-weighted-by-word-count))\n", + " - [*Middlemarch* quotations per chapter, per decade (normalized and weighted), heat map](#Middlemarch-quotations-per-chapter,-per-decade-(normalized-and-weighted),-heat-map)\n", + " - [*Middlemarch* top 5 most frequently quoted chapters, line chart](#Middlemarch-top-5-most-frequently-quoted-chapters,-line-chart)\n", + " - [*Middlemarch* top 5 most frequently quoted chapters, line chart (color)](#Middlemarch-top-5-most-frequently-quoted-chapters,-line-chart-(color)) \n", + " - [*Middlemarch* top 5 most frequently quoted chapters (normalized and weighted), line chart](#Middlemarch-top-5-most-frequently-quoted-chapters-(normalized-and-weighted),-line-chart)\n", + " - [*Middlemarch* top 5 most frequently quoted chapters (normalized and weighted), line chart (color)](#Middlemarch-top-5-most-frequently-quoted-chapters-(normalized-and-weighted),-line-chart-(color))" ] }, { - "cell_type": "code", - "execution_count": 114, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## *Middlemarch* diachronic analysis: quotations per book, by decade\n", + "\n", + "We use three different methods to analyze quotations per book, by decade. First, we examine the raw counts of quotations per book, per decade. Because our corpus contains [varying numbers of JSTOR texsts per decade](#How-many-articles-do-we-have-published-in-each-year?), we've decided to also weigh by length of quotation and normalize per decade." + ] + }, + { + "cell_type": "markdown", "metadata": {}, + "source": [ + "### Number of quotations per book, per decade (not normalized or weighted)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "scrolled": false + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of quotations per book, per decade\n" + ] + }, { "data": { "text/html": [ - "\n", - "\n", - "
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\n", + "" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8\n", + "1960 0 55 49 17 23 23 9 12 54\n", + "1970 0 151 97 40 36 33 28 14 69\n", + "1980 0 183 145 50 47 29 44 22 117\n", + "1990 0 234 214 69 85 65 39 30 134\n", + "2000 0 196 170 60 58 47 50 23 80\n", + "2010 0 177 163 86 54 40 46 33 111" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "alt.Chart(diaDFquoteOnlyMelted.loc[diaDFquoteOnlyMelted['chapter'].isin([0, 1, 15, 20, 87])], title=\"Middlemarch quotations per chapter, per decade (not weighted or normalized)\")\\\n", - ".mark_line().encode(y=alt.Y('value:Q', title=\"Number of Quotations\", axis=alt.Axis(labelAngle=0)), \n", - " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", - " strokeDash=\"chapter:O\",\n", - " color=alt.Color('chapter:O', scale=alt.Scale(scheme='category20b'),legend=alt.Legend(title=\"Chapter\")))\\\n", - ".properties(width=500).configure_legend(\n", - "titleFontSize=11,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ")" + "# Raw quotation counts (not weighted by the number of words in the quoatation or normalized by decade)\n", + "# Turning on useWordcounts makes it so that it's weighted by wordcount. Turning it off uses raw numbers of quotations.\n", + "booksNotNormalizedNotWeightedDiaDF = diachronicAnalysis(df, decades=(1960, 2020), bins=bookLocations,\\\n", + " useWordcounts=False, normalize=False).sort_index()\n", + "print('Number of quotations per book, per decade')\n", + "booksNotNormalizedNotWeightedDiaDF" ] }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "booksNotNormalizedNotWeightedDiaDF['decade'] = booksNotNormalizedNotWeightedDiaDF.index" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "booksNotNormalizedNotWeightedDiaDFMelted = booksNotNormalizedNotWeightedDiaDF.melt(id_vars='decade', var_name='book')" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "# cut out erroneous \"book 0\" material (ie title page)\n", + "booksNotNormalizedNotWeightedDiaDFMelted = booksNotNormalizedNotWeightedDiaDFMelted[booksNotNormalizedNotWeightedDiaDFMelted.book != 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "booksNotNormalizedNotWeightedDiaDFMeltedExport = booksNotNormalizedNotWeightedDiaDFMelted.rename(columns={\"value\": \"Number of Quotations\"})\n", + "# To export a CSV, uncomment the line below\n", + "# booksNotNormalizedNotWeightedDiaDFMeltedExport.to_csv(\"Middlemarch-quotations-per-book-per-decade-not-weighted-or-normalized.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Middlemarch* quotations per book, per decade (not normalized or weighted), heat map" + ] + }, + { + "cell_type": "code", + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -5944,23 +4888,23 @@ "text/html": [ "\n", "\n", - "
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" ], "text/plain": [ - " decade chapter value\n", - "0 1960 0 8\n", - "1 1970 0 20\n", - "2 1980 0 34\n", - "3 1990 0 38\n", - "4 2000 0 28\n", - ".. ... ... ...\n", - "523 1970 87 22\n", - "524 1980 87 31\n", - "525 1990 87 33\n", - "526 2000 87 29\n", - "527 2010 87 51\n", - "\n", - "[528 rows x 3 columns]" + " 0 1 2 3 4 5 6 7 \\\n", + "1960 0.0 1.0 0.890909 0.309091 0.418182 0.418182 0.163636 0.218182 \n", + "1970 0.0 1.0 0.642384 0.264901 0.238411 0.218543 0.185430 0.092715 \n", + "1980 0.0 1.0 0.792350 0.273224 0.256831 0.158470 0.240437 0.120219 \n", + "1990 0.0 1.0 0.914530 0.294872 0.363248 0.277778 0.166667 0.128205 \n", + "2000 0.0 1.0 0.867347 0.306122 0.295918 0.239796 0.255102 0.117347 \n", + "2010 0.0 1.0 0.920904 0.485876 0.305085 0.225989 0.259887 0.186441 \n", + "\n", + " 8 \n", + "1960 0.981818 \n", + "1970 0.456954 \n", + "1980 0.639344 \n", + "1990 0.572650 \n", + "2000 0.408163 \n", + "2010 0.627119 " ] }, - "execution_count": 402, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "top5 = []\n", - "if diaDFquoteOnlyMelted.loc[diaDFquoteOnlyMelted['chapter'].isin([0, 1, 15, 20, 87]):\n", - " then top5.add['Yes'],\n", - " else: \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 403, - "metadata": {}, - "outputs": [], - "source": [ - "top5 = diaDFquoteOnlyMelted[\"chapter\"].where(diaDFquoteOnlyMelted[\"chapter\"].isin([0, 1, 15, 20, 87]), other=\"Other\")" + "# Number of quotations (not weighted), normalized by decade(counts are scaled to the maximum value per decade)\n", + "booksNotWeightedDiaDF = diachronicAnalysis(df, decades=(1960, 2020), bins=bookLocations, useWordcounts=False, normalize=True).sort_index()\n", + "print('Quotations per book, per decade (normalized by decade):')\n", + "booksNotWeightedDiaDF" ] }, { - "cell_type": "code", - "execution_count": 407, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "diaDFquoteOnlyMelted['top5'] = top5" + "### Number of quotations per book, per decade (normalized by decade and weighted by word count)" ] }, { "cell_type": "code", - "execution_count": 472, + "execution_count": 99, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quotations per book, per decade (weighted by length of quotation and normalized by decade):\n" + ] + }, { "data": { "text/html": [ + "
\n", + "\n", - "
\n", - "" ], "text/plain": [ - "alt.LayerChart(...)" + "alt.Chart(...)" ] }, - "execution_count": 472, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "color = alt.condition(alt.datum.top5 == 'top5',\n", - " alt.Color('chapter:O', legend=None),\n", - " alt.value('gainsboro')\n", - " )\n", - "\n", - "line = alt.Chart(diaDFquoteOnlyMelted.loc[diaDFquoteOnlyMelted['chapter'].isin([0, 1, 15, 20, 87])], title=\"Middlemarch quotations per chapter, per decade (not weighted or normalized)\")\\\n", - ".mark_line().encode(\n", - " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", - " y=alt.Y('value:Q', title=\"Number of Quotations\", axis=alt.Axis(labelAngle=0)),\n", - " color=alt.Color('chapter:O', scale=alt.Scale(scheme='greys'), legend=None,),\n", - " \n", - ")\n", - "\n", - "points = line.mark_point(filled=True).encode(\n", - " color=alt.Color('chapter:O', scale=alt.Scale(scheme='greys')),\n", - " shape=alt.Shape('chapter:O', legend=alt.Legend(title=\"Chapter\"), scale=alt.Scale(range=['square', 'circle', 'cross','triangle-right', 'diamond'])),\n", - " size=alt.Size('chapter', legend=None, scale=alt.Scale(range=[200,200],domain=['0', '1', '15', '20', '87']))\n", - ")\n", - "\n", - "greyed = alt.Chart(diaDFquoteOnlyMelted.loc[~diaDFquoteOnlyMelted['chapter'].isin([0, 1, 15, 20, 87])])\\\n", - ".mark_line().encode(\n", - " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", - " y=alt.Y('value:Q', title=\"Number of Quotations\", axis=alt.Axis(labelAngle=0)),\n", - " color=color, \n", - ")\n", - "\n", - "alt.layer(\n", - " greyed,\n", - " line,\n", - " points,\n", - ").resolve_scale(\n", - " color='independent',\n", - " shape='independent'\n", - ").properties(width=400).configure_legend(\n", - "titleFontSize=11,\n", - "labelFontSize=14\n", - ").configure_axis(\n", + "alt.Chart(booksMelted,\\\n", + " title=\"Middlemarch quotations per book, per decade (weighted by length of quotation and normalized by decade)\")\\\n", + ".mark_rect().encode(x=alt.X('book', type='ordinal', \n", + " axis=alt.Axis(labelAngle=0)), \n", + " y=alt.Y('decade', type='ordinal', sort='descending', \n", + " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", + " color=alt.Color('value', legend=alt.Legend(title=\"Amount Quoted\"))).properties(width=500, height=300).configure_legend(\n", "titleFontSize=14,\n", "labelFontSize=14\n", - ")" + ").configure_axis(\n", + " labelFontSize=14,\n", + " titleFontSize=14\n", + ") " ] }, { - "cell_type": "code", - "execution_count": 473, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "color = alt.condition(alt.datum.top5 == 'top5',\n", - " alt.Color('chapter:O', legend=None),\n", - " alt.value('gainsboro')\n", - " )\n", - "\n", - "line = alt.Chart(diaDFquoteOnlyMelted.loc[diaDFquoteOnlyMelted['chapter'].isin([0, 1, 15, 20, 87])])\\\n", - ".mark_line().encode(\n", - " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", - " y=alt.Y('value:Q', title=\"Number of Quotations\", axis=alt.Axis(labelAngle=0)),\n", - " color=alt.Color('chapter:O', scale=alt.Scale(scheme='greys'), legend=None,),\n", - " #strokeDash=alt.StrokeDash(\"chapter:O\"),\n", - ")\n", - "\n", - "points = line.mark_point(filled=True).encode(\n", - " color=alt.Color('chapter:O', scale=alt.Scale(scheme='greys')),\n", - " shape=alt.Shape('chapter:O', legend=alt.Legend(title=\"Chapter\"), scale=alt.Scale(range=['square', 'circle', 'cross','triangle-right', 'diamond'])),\n", - " size=alt.Size('chapter', legend=None, scale=alt.Scale(range=[200,200],domain=['0', '1', '15', '20', '87']))\n", - ")\n", - "\n", - "greyed = alt.Chart(diaDFquoteOnlyMelted.loc[~diaDFquoteOnlyMelted['chapter'].isin([0, 1, 15, 20, 87])])\\\n", - ".mark_line().encode(\n", - " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", - " y=alt.Y('value:Q', title=\"Number of Quotations\", axis=alt.Axis(labelAngle=0)),\n", - " #color=alt.Color('chapter:O', scale=alt.Scale(scheme='greys', ), legend=None,), \n", - " color=color \n", - ")\n", - "\n", - "\n", - "chart = alt.layer(\n", - " greyed,\n", - " line,\n", - " points,\n", - ").resolve_scale(\n", - " color='independent',\n", - " shape='independent'\n", - ").properties(width=400).configure_legend(\n", - "titleFontSize=11,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ")\n", - "\n", - "\n", - "chart.save('Figure-6.png', ppi=300)" + "## *Middlemarch* diachronic analysis: quotations per chapter, by decade" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Number of quotations per chapter, per decade (normalized by decade and weighted by word count)" + "### Number of quotations per chapter, per decade (not normalized or weighted)" ] }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ - "# Weighted by wordcount (by the number of words in the quoatation) and normalized by decade(counts are scaled to the maximum value per decade)\n", - "diaDF = diachronicAnalysis(df, decades=(1960, 2020), bins=chapterLocations, useWordcounts=True, normalize=True).sort_index()\n", - "diaDF.columns.name = 'chapter'\n", - "diaDF.index.name = 'decade'" + "# Raw quotation counts (not weighted by the number of words in the quoatation or normalized by decade)\n", + "# Turning on useWordcounts makes it so that it's weighted by wordcount. Turning it off uses raw numbers of quotations.\n", + "diaDFquoteOnly = diachronicAnalysis(df, decades=(1960, 2020), bins=chapterLocations, useWordcounts=False, normalize=False).sort_index()\n", + "diaDFquoteOnly.columns.name ='chapter'\n", + "diaDFquoteOnly.index.name = 'decade'" ] }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 105, "metadata": {}, "outputs": [ { @@ -6613,671 +5767,608 @@ " \n", " \n", " 1960\n", - " 0.119514\n", - " 0.119514\n", - " 0.266106\n", - " 0.068161\n", - " 0.012138\n", - " 0.022409\n", - " 0.000000\n", - " 0.104575\n", - " 0.008403\n", - " 0.267974\n", - " 0.230626\n", - " 0.036415\n", - " 0.074697\n", - " 0.000000\n", - " 0.000000\n", - " 0.621849\n", - " 0.272642\n", - " 0.020542\n", - " 0.000000\n", - " 0.005602\n", - " 1.000000\n", - " 0.372549\n", - " 0.053221\n", - " 0.019608\n", - " 0.013072\n", - " 0.005602\n", - " 0.004669\n", - " 0.183940\n", - " 0.298786\n", - " 0.000000\n", - " 0.006536\n", - " 0.006536\n", - " 0.000000\n", - 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" ], "text/plain": [ - "chapter 0 1 2 3 4 5 6 \\\n", - "decade \n", - "1960 0.119514 0.119514 0.266106 0.068161 0.012138 0.022409 0.000000 \n", - "1970 0.224055 0.575258 0.584192 0.393814 0.107904 0.061856 0.360825 \n", - "1980 0.437037 0.772391 0.164310 0.377778 0.032997 0.249832 0.106397 \n", - "1990 0.514066 0.436061 0.150043 0.271952 0.046462 0.135550 0.196505 \n", - "2000 0.452062 0.739689 0.176754 0.371184 0.052491 0.179968 0.151044 \n", - "2010 0.212549 0.984737 0.198417 0.158282 0.049180 0.044658 0.136235 \n", - "\n", - "chapter 7 8 9 10 11 12 13 \\\n", - "decade \n", - "1960 0.104575 0.008403 0.267974 0.230626 0.036415 0.074697 0.000000 \n", - "1970 0.115464 0.158076 0.087973 0.079725 0.226804 0.063918 0.004124 \n", - "1980 0.175758 0.000000 0.105051 0.212121 0.160269 0.084175 0.092256 \n", - "1990 0.068201 0.013640 0.075021 0.249361 0.264706 0.227195 0.072890 \n", - "2000 0.069095 0.009641 0.302625 0.400107 0.269416 0.035351 0.028923 \n", - "2010 0.038440 0.016959 0.239118 0.295647 0.077445 0.100057 0.022046 \n", - "\n", - "chapter 14 15 16 17 18 19 20 \\\n", - "decade \n", - "1960 0.000000 0.621849 0.272642 0.020542 0.000000 0.005602 1.000000 \n", - "1970 0.002749 0.627491 0.340893 0.004811 0.012371 0.219244 1.000000 \n", - "1980 0.061953 0.801347 0.377104 0.035690 0.099663 0.400673 0.918519 \n", - "1990 0.033674 1.000000 0.237425 0.109122 0.113811 0.511509 0.777494 \n", - "2000 0.006427 0.470809 0.305838 0.020889 0.101768 0.539904 1.000000 \n", - "2010 0.010741 0.613906 0.273036 0.039570 0.159412 0.349915 0.733748 \n", - "\n", - "chapter 21 22 23 24 25 26 27 \\\n", - "decade \n", - "1960 0.372549 0.053221 0.019608 0.013072 0.005602 0.004669 0.183940 \n", - "1970 0.134021 0.204811 0.000000 0.061168 0.002062 0.009622 0.381443 \n", - "1980 0.242424 0.073401 0.235690 0.010774 0.000000 0.006061 0.151515 \n", - "1990 0.290708 0.093777 0.008525 0.073316 0.028986 0.060102 0.259165 \n", - "2000 0.146224 0.476165 0.212641 0.217461 0.061061 0.006963 0.111944 \n", - "2010 1.000000 0.207462 0.033352 0.662521 0.023742 0.000000 0.348785 \n", - "\n", - "chapter 28 29 30 31 32 33 34 \\\n", - "decade \n", - "1960 0.298786 0.000000 0.006536 0.006536 0.000000 0.000000 0.000000 \n", - "1970 0.229553 0.094845 0.003436 0.041924 0.000000 0.000000 0.032990 \n", - "1980 0.084848 0.296970 0.047138 0.022222 0.026263 0.000000 0.064646 \n", - "1990 0.084825 0.064791 0.001705 0.034527 0.002131 0.057118 0.193095 \n", - "2000 0.252276 0.194965 0.062132 0.055169 0.089984 0.431173 0.000000 \n", - "2010 0.446580 0.118146 0.002826 0.105144 0.028830 0.000000 0.033917 \n", - "\n", - "chapter 35 36 37 38 39 40 41 \\\n", - "decade \n", - "1960 0.106443 0.048553 0.156863 0.000000 0.030812 0.045752 0.320261 \n", - "1970 0.000000 0.062543 0.188316 0.007560 0.058419 0.018557 0.107216 \n", - "1980 0.041751 0.042424 0.253872 0.000000 0.415488 0.000000 0.000000 \n", - "1990 0.201194 0.185848 0.176897 0.003410 0.078005 0.000000 0.162830 \n", - "2000 0.027317 0.023032 0.258168 0.018211 0.103910 0.088913 0.044992 \n", - "2010 0.044658 0.186546 0.163934 0.009045 0.356699 0.183154 0.196721 \n", - "\n", - "chapter 42 43 44 45 46 47 48 \\\n", - "decade \n", - "1960 0.123249 0.024276 0.000000 0.000000 0.326797 0.047619 0.026144 \n", - "1970 0.329210 0.197938 0.004124 0.031615 0.074914 0.013058 0.151890 \n", - "1980 0.125926 0.127273 0.000000 0.008081 0.004040 0.191919 0.006061 \n", - "1990 0.355499 0.014493 0.002131 0.151321 0.026002 0.057545 0.164535 \n", - "2000 0.286020 0.084092 0.002142 0.121585 0.008570 0.002142 0.167649 \n", - "2010 0.102318 0.066704 0.000000 0.054268 0.065574 0.000000 0.055964 \n", - "\n", - "chapter 49 50 51 52 53 54 55 \\\n", - "decade \n", - "1960 0.000000 0.228758 0.007470 0.053221 0.414566 0.002801 0.000000 \n", - "1970 0.000000 0.071478 0.004124 0.018557 0.059107 0.087285 0.065979 \n", - "1980 0.000000 0.098990 0.076094 0.000000 0.000000 0.151515 0.152189 \n", - "1990 0.000000 0.121910 0.016198 0.004689 0.014493 0.017903 0.034101 \n", - "2000 0.045528 0.118372 0.003749 0.063739 0.099625 0.133905 0.152651 \n", - "2010 0.000000 0.108536 0.109666 0.000000 0.057660 0.003957 0.079141 \n", + "chapter 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 \\\n", + "decade \n", + "1960 8 7 5 4 2 3 0 6 1 6 8 3 2 0 0 8 3 \n", + "1970 20 26 20 24 8 4 11 8 5 8 7 8 2 2 1 21 14 \n", + "1980 34 41 13 21 4 10 4 8 0 13 17 9 9 9 4 38 17 \n", + "1990 38 38 20 23 5 14 13 10 1 14 24 21 14 9 4 66 11 \n", + "2000 28 30 19 23 12 8 7 8 2 26 22 7 4 4 1 27 26 \n", + "2010 21 53 12 13 9 5 10 4 2 17 17 7 7 2 1 32 15 \n", "\n", - "chapter 56 57 58 59 60 61 62 \\\n", - "decade \n", - "1960 0.102708 0.000000 0.015873 0.000000 0.014006 0.024276 0.000000 \n", - "1970 0.103093 0.012371 0.121649 0.003436 0.009622 0.046048 0.003436 \n", - "1980 0.019529 0.000000 0.204040 0.028283 0.032323 0.191919 0.041751 \n", - "1990 0.018329 0.001705 0.082268 0.038363 0.027280 0.092498 0.033674 \n", - "2000 0.151580 0.046599 0.065345 0.016069 0.023032 0.257097 0.082485 \n", - "2010 0.107971 0.000000 0.234596 0.000000 0.148672 0.053703 0.022046 \n", + "chapter 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 \\\n", + "decade \n", + "1960 3 0 1 20 11 3 2 1 1 1 7 3 0 1 1 0 0 \n", + "1970 1 3 9 27 8 11 0 3 1 3 14 12 3 1 3 0 0 \n", + "1980 2 6 18 37 9 5 11 2 0 2 8 7 12 3 4 1 0 \n", + "1990 10 11 34 41 15 12 1 6 4 6 27 5 8 1 6 1 4 \n", + "2000 2 4 26 45 12 23 9 7 3 2 7 4 5 5 4 4 10 \n", + "2010 4 10 24 39 21 15 7 20 2 0 20 22 5 1 7 2 0 \n", "\n", - "chapter 63 64 65 66 67 68 69 \\\n", - "decade \n", - "1960 0.020542 0.022409 0.000000 0.074697 0.000000 0.007470 0.000000 \n", - "1970 0.017869 0.226804 0.004811 0.000000 0.000000 0.070790 0.021306 \n", - "1980 0.012795 0.129293 0.055219 0.006734 0.003367 0.000000 0.030976 \n", - "1990 0.020034 0.005968 0.057971 0.002558 0.004689 0.029838 0.000000 \n", - "2000 0.042849 0.000000 0.000000 0.025174 0.000000 0.073380 0.041778 \n", - "2010 0.114754 0.057660 0.000000 0.005653 0.026003 0.029960 0.010175 \n", + "chapter 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 \\\n", + "decade \n", + "1960 0 5 2 4 0 2 3 6 1 2 0 0 7 3 1 0 4 \n", + "1970 1 0 2 11 1 4 2 3 12 9 1 4 6 1 5 0 4 \n", + "1980 6 1 4 20 0 9 0 0 7 7 0 2 1 10 1 0 3 \n", + "1990 5 7 18 16 1 7 0 10 21 2 1 19 8 5 11 0 14 \n", + "2000 0 3 4 18 2 8 6 2 15 6 1 9 1 1 9 3 10 \n", + "2010 2 2 10 10 1 12 5 4 8 6 0 4 2 0 8 0 5 \n", "\n", - "chapter 70 71 72 73 74 75 76 \\\n", - "decade \n", - "1960 0.000000 0.265173 0.000000 0.004669 0.000000 0.014939 0.130719 \n", - "1970 0.009622 0.017869 0.051546 0.023368 0.523024 0.002749 0.131959 \n", - "1980 0.000000 0.088215 0.144108 0.245791 0.058586 0.024242 0.292929 \n", - "1990 0.069480 0.064791 0.065217 0.022592 0.060529 0.054135 0.110401 \n", - "2000 0.047134 0.026245 0.046599 0.005892 0.000000 0.059454 0.333690 \n", - "2010 0.207462 0.192764 0.041266 0.022046 0.209158 0.018089 0.107405 \n", + "chapter 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 \\\n", + "decade \n", + "1960 1 2 3 1 0 3 0 1 0 3 1 0 1 2 0 2 0 \n", + "1970 1 1 1 4 2 5 2 8 1 3 2 1 1 3 1 0 0 \n", + "1980 5 0 0 10 4 4 0 11 2 2 6 5 1 9 4 2 1 \n", + "1990 1 2 2 2 4 3 1 10 3 4 7 5 6 3 5 1 2 \n", + "2000 1 1 5 10 4 7 5 4 1 8 8 3 5 0 0 2 0 \n", + "2010 8 0 7 1 6 7 0 15 0 7 8 2 7 5 0 1 2 \n", "\n", - "chapter 77 78 79 80 81 82 83 \\\n", - "decade \n", - "1960 0.017740 0.161531 0.039216 0.183007 0.225957 0.003735 0.065359 \n", - "1970 0.035052 0.010309 0.013058 0.327148 0.111340 0.000000 0.037113 \n", - "1980 0.103704 0.064646 0.000000 0.360269 0.303030 0.010101 0.085522 \n", - "1990 0.048167 0.126598 0.043905 0.298380 0.255754 0.022592 0.217818 \n", - "2000 0.069630 0.065345 0.026781 0.246920 0.042314 0.000000 0.051955 \n", - "2010 0.028265 0.000000 0.000000 0.287733 0.192764 0.044658 0.079706 \n", + "chapter 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 \\\n", + "decade \n", + "1960 1 0 0 6 0 1 0 4 6 2 6 1 4 12 1 2 0 \n", + "1970 2 3 1 3 3 2 7 1 4 1 1 1 10 9 0 4 1 \n", + "1980 0 2 0 3 9 2 3 2 15 6 4 0 17 13 1 7 0 \n", + "1990 3 0 6 4 6 1 4 3 10 2 13 2 17 20 2 15 3 \n", + "2000 3 7 3 3 5 1 0 4 9 2 4 2 6 5 0 5 2 \n", + "2010 2 1 4 11 4 3 7 3 3 4 0 0 15 11 1 7 0 \n", "\n", - "chapter 84 85 86 87 \n", - "decade \n", - "1960 0.000000 0.0 0.022409 0.228758 \n", - "1970 0.004811 0.0 0.008247 0.408935 \n", - "1980 0.000000 1.0 0.015488 0.571717 \n", - "1990 0.013214 0.0 0.009804 0.364024 \n", - "2000 0.021960 0.0 0.035886 0.479914 \n", - "2010 0.000000 0.0 0.004522 0.805540 " + "chapter 85 86 87 \n", + "decade \n", + "1960 0 6 9 \n", + "1970 0 3 22 \n", + "1980 4 3 31 \n", + "1990 0 3 33 \n", + "2000 0 6 29 \n", + "2010 0 2 51 " ] }, "metadata": {}, @@ -7287,37 +6378,37 @@ "source": [ "with pd.option_context(\"display.min_rows\", 6, \"display.max_rows\", 100, \\\n", " \"display.max_columns\", 90, 'display.max_colwidth', 150):\n", - " display(diaDF)" + " display(diaDFquoteOnly)" ] }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ - "diaDF['decade'] = diaDF.index" + "diaDFquoteOnly['decade'] = diaDFquoteOnly.index" ] }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 107, "metadata": {}, "outputs": [], "source": [ - "diaMelted = diaDF.melt(id_vars='decade')" + "diaDFquoteOnlyMelted = diaDFquoteOnly.melt(id_vars='decade')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### *Middlemarch* quotations per chapter, per decade (normalized and weighted), heat map" + "### *Middlemarch* quotations per chapter, per decade (not normalized or weighted), heat map" ] }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -7325,23 +6416,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 122, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(diaMelted, title=\"Middlemarch quotations per chapter, per decade (normalized by decade and weighted by word count)\")\\\n", - ".mark_rect().encode(x=alt.X('chapter', title=\"Chapter\", type='ordinal', axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", - " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", + "#Chart with raw quotations\n", + "alt.Chart(diaDFquoteOnlyMelted, title=\"Middlemarch quotations per chapter, per decade (not weighted or normalized)\")\\\n", + ".mark_rect().encode(x=alt.X('chapter', title=\"Chapter\", type='ordinal', \n", + " axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", + " y=alt.Y('decade', title=\"Decade\", type='ordinal', sort='descending', \n", " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", - " color=alt.Color('value', legend=alt.Legend(title=\"Amount Quoted\")))\\\n", - ".properties(width=1000, height=300).configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", + " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", + ".properties(width=1000, height=300).configure_axis(\n", + " labelFontSize=14,\n", + " titleFontSize=14\n", + ").configure_legend(\n", "titleFontSize=14,\n", "labelFontSize=14\n", ") " @@ -7416,34 +6509,7 @@ }, { "cell_type": "code", - "execution_count": 312, - "metadata": {}, - "outputs": [], - "source": [ - "alt.Chart(diaMelted, )\\\n", - ".mark_rect().encode(x=alt.X('chapter', title=\"Chapter\", type='ordinal', axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", - " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", - " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", - " color=alt.Color('value', legend=alt.Legend(title=\"Amount Quoted\")))\\\n", - ".properties(width=1000, height=300).configure_legend(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").save('Figure-5.png', ppi=300)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### *Middlemarch* top 5 most frequently quoted chapters, quotation counts by decade (normalized and weighted), " - ] - }, - { - "cell_type": "code", - "execution_count": 125, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -7451,23 +6517,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 125, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(diaMelted.loc[diaDFquoteOnlyMelted['chapter'].isin([0, 1, 15, 20, 87])], title=\"Middlemarch quotations per chapter, per decade (normalized by decade and weighted by word count)\")\\\n", - ".mark_line().encode(y=alt.Y('value:Q', title=\"Number of Quotations (normalized)\", axis=alt.Axis(labelAngle=0)), \n", - " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", - " strokeDash=\"chapter:O\",\n", - " color=alt.Color('chapter:O', scale=alt.Scale(scheme='category20'), legend=alt.Legend(title=\"Chapter\")))\\\n", - ".properties(width=500).configure_legend(\n", - "titleFontSize=11,\n", + "#Chart with raw quotations, transposed\n", + "alt.Chart(diaDFquoteOnlyMelted, title=\"Middlemarch quotations per chapter, per decade (not weighted or normalized)\")\\\n", + ".mark_rect().encode(x=alt.X('decade',title=\"Decade\", type='ordinal', sort='ascending', axis=alt.Axis(labelAngle=0, \n", + " labelExpr='datum.value + \"s\"')), \n", + " y=alt.Y('chapter', title=\"Chapter\", type='ordinal', sort='descending', axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", + " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", + ".properties(width=300, height=1000).configure_legend(\n", + "titleFontSize=14,\n", "labelFontSize=14\n", ").configure_axis(\n", "titleFontSize=14,\n", "labelFontSize=14\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6318681318681318" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get the normalized proportion of, say, Chapter 20 in 1950: \n", - "diachronicAnalysis(df)[20][1950]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# *Middlemarch* chapter-specific analysis\n", - "\n", - "- [Chapter 15](#Chapter-15)\n", - " - [Paragraph-level-analysis of Chapter 15](#Paragraph-level-analysis-of-Chapter-15)\n", - "- [Chapter 20](#Chapter-20)\n", - " - [Paragraph-level-analysis of Chapter 20](#Paragraph-level-analysis-of-Chapter-20)\n", - " - [#Which paragraphs in Chapter 20 are quoted most often?](#Which-paragraphs-in-Chapter-20-are-quoted-most-often?)" + ") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Chapter 15" + "### Number of quotations per chapter, per decade (normalized by decade and weighted by word count)" ] }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ - "# Try to find out why Ch. 15 was so big in the 80s and 90s. \n", - "chap15s = []\n", - "ids = []\n", - "for i, row in df.iterrows(): \n", - " locations = row['Locations in A']\n", - " starts = [item[0] for item in locations]\n", - " if row['Decade'] in [1980, 1990]: # Looking at the 1980s, 1990s\n", - " for start in starts: \n", - " if start > 290371 and start < 322052: # Does it cite Chapter XV? \n", - " if row.id not in ids: \n", - " chap15s.append(row)\n", - " ids.append(row.id)" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Titles of articles that quote Chapter 15:\n" - ] - }, - { - "data": { - "text/plain": [ - "['Woman of Maxims:',\n", - " 'Brava! And Farewell to Greatheart',\n", - " 'The Union of \"Miss Brooke\" and \"Middlemarch\": A Study of the Manuscript',\n", - " '\"Middlemarch\" and George Eliot\\'s Female (Re) Vision of Shakespeare',\n", - " 'Heroism and Organicism in the Case of Lydgate',\n", - " 'Professional Judgment and the Rationing of Medical Care',\n", - " 'SILENCE, GESTURE, AND MEANING IN \"MIDDLEMARCH\"',\n", - " 'Reflections on \"The Philosophical Bases of Feminist Literary Criticisms\"',\n", - " 'Strategies for Writing: Theories and Practices',\n", - " 'Review Article',\n", - " 'AN END TO CONVERTING PATIENTS\\' STOMACHS INTO DRUG-SHOPS: LYDGATE\\'S NEW METHOD OF CHARGING HIS PATIENTS IN \"MIDDLEMARCH\"',\n", - " 'Review Article',\n", - " 'Illuminating the Vision of Ordinary Life: A Tribute to \"Middlemarch\"',\n", - " 'Review Article',\n", - " \"PLEXUSES AND GANGLIA: ELIOTS AND LEWES'S THEORY OF NERVE-CONSCIOUSNESS\",\n", - " 'Review Article',\n", - " 'Middlemarch, Realism and the Birth of the Clinic',\n", - " 'ERZÄHLERISCHE OBJEKTIVITÄT, ,AUTHORIAL INTRUSIONS‘ UND ENGLISCHER REALISMUS',\n", - " 'Review Article',\n", - " 'The Aesthetics of Sympathy:',\n", - " 'NARRATIVE VOICE AND THE \"FEMININE\" NOVELIST: DINAH MULOCK AND GEORGE ELIOT',\n", - " 'Lamarque and Olsen on Literature and Truth',\n", - " 'Review Article',\n", - " 'Microscopy and Semiotic in Middlemarch',\n", - " \"George Eliot's Reflexive Text: Three Tonalities in the Narrative Voice of Middlemarch\",\n", - " 'Review Article',\n", - " 'George Eliot and the Eighteenth-Century Novel',\n", - " 'Versions of Narrative: Overt and Covert Narrators in Nineteenth Century Historiography',\n", - " 'LYDGATE\\'S RESEARCH PROJECT IN \"MIDDLEMARCH\"',\n", - " 'George Eliot\\'s Scrupulous Research: The Facts behind Eliot\\'s Use of the \"Keepsake in Middlemarch\"',\n", - " 'Eliot and Woolf as Historians of the Common Life',\n", - " 'The Language of Discovery: William Whewell and George Eliot',\n", - " \"George Eliot's Hypothesis of Reality\",\n", - " 'Re-Reading Character',\n", - " 'The Strange Case of Monomania: Patriarchy in Literature, Murder in Middlemarch, Drowning in Daniel Deronda',\n", - " '\"Wrinkled Deep in Time\": The Alexandria Quartet as Many-Layered Palimpsest',\n", - " 'THE DIALOGIC UNIVERSE OF \"MIDDLEMARCH\"',\n", - " 'MIXED AND ERRING HUMANITY: GEORGE ELIOT, G. H. LEWES AND GOETHE',\n", - " '1978 And All That',\n", - " \"The Turn of George Eliot's Realism\",\n", - " 'Dangerous Crossings: Dickens, Digression, and Montage',\n", - " 'In Defence of Research for Evidence-Based Teaching: A Rejoinder to Martyn Hammersley',\n", - " 'Review Article',\n", - " 'THE WONDROUS MARRIAGES OF \"DANIEL DERONDA:\" GENDER, WORK, AND LOVE',\n", - " \"The Victorian Discourse of Gambling: Speculations on Middlemarch and the Duke's Children\",\n", - " 'Struggling for Medical Reform in Middlemarch',\n", - " 'Steamboat Surfacing: Scott and the English Novelists']" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get the titles of those articles.\n", - "print('Titles of articles that quote Chapter 15:')\n", - "[item.title for item in chap15s]" + "# Weighted by wordcount (by the number of words in the quoatation) and normalized by decade(counts are scaled to the maximum value per decade)\n", + "diaDF = diachronicAnalysis(df, decades=(1960, 2020), bins=chapterLocations, useWordcounts=True, normalize=True).sort_index()\n", + "diaDF.columns.name = 'chapter'\n", + "diaDF.index.name = 'decade'" ] }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 111, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "47" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(chap15s)" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [], - "source": [ - "xvStart, xvEnd = chapterLocations[15:17]" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CHAPTER XV.\n", - "\n", - " \"Black eyes you have left, you say,\n", - " Blue eyes fail to draw you;\n", - " Yet you seem more rapt to-day,\n", - " Than of old we saw you.\n", - "\n", - " \"Oh, I track the fairest fair\n", - " Through new haunts of pleasure;\n", - " Footprints here and echoes there\n", - " Guide me to my treasure:\n", - "\n", - " \"Lo! she turns--immortal youth\n", - " Wrought to mortal stature,\n", - " Fresh as starlight's aged truth--\n", - " Many-named Nature!\"\n", - "\n", - "\n", - "A great historian, as he insisted on calling himself, who had the\n", - "happiness to be dead a hundred and twenty years ago, and so to take his\n", - "place among the colossi whose huge legs our living pettiness is\n", - "observed to walk under, glories in his copious remarks and digressions\n", - "as the least imitable part of his work, and especially in those initial\n", - "chapters to the successive books of his history, where he seems to\n", - "bring his armchair to the proscenium and chat with us in all the lusty\n", - "ease of his fine English. But Fielding lived when the days were longer\n", - "(for time, like mone\n" - ] - } - ], - "source": [ - "print(mm[xvStart:xvStart+1000]) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Paragraph-level analysis of Chapter 15" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [], - "source": [ - "# Try to find out which articles cite the first 2/3 of Chapter XV (with Lydgate's scientific research) \n", - "# vs the last 1/3 on the story of Laure\n", - "chap15p1s = []\n", - "ids = []\n", - "for i, row in df.iterrows(): \n", - " locations = row['Locations in A']\n", - " starts = [item[0] for item in locations]\n", - " if row['Decade'] in [1980, 1990]: \n", - " for start in starts: \n", - " if start > 290371 and start < 313892: # Does it cite the first 2/3 of Chapter XV? \n", - " if row.id not in ids: \n", - " chap15p1s.append(row)\n", - " ids.append(row.id)\n", - "chap15p2s = []\n", - "ids = []\n", - "for i, row in df.iterrows(): \n", - " locations = row['Locations in A']\n", - " starts = [item[0] for item in locations]\n", - " if row['Decade'] in [1980, 1990]: \n", - " for start in starts: \n", - " if start > 313892 and start < 322052: # Does it cite the last 1/3 of Chapter XV? \n", - " if row.id not in ids: \n", - " chap15p2s.append(row)\n", - " ids.append(row.id) \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Titles of articles that quote the first 2/3 of Chapter 15:\n" - ] - }, - { - "data": { - "text/plain": [ - "['Woman of Maxims:',\n", - " 'Brava! And Farewell to Greatheart',\n", - " 'The Union of \"Miss Brooke\" and \"Middlemarch\": A Study of the Manuscript',\n", - " 'Heroism and Organicism in the Case of Lydgate',\n", - " 'Professional Judgment and the Rationing of Medical Care',\n", - " 'SILENCE, GESTURE, AND MEANING IN \"MIDDLEMARCH\"',\n", - " 'Reflections on \"The Philosophical Bases of Feminist Literary Criticisms\"',\n", - " 'Strategies for Writing: Theories and Practices',\n", - " 'Review Article',\n", - " 'AN END TO CONVERTING PATIENTS\\' STOMACHS INTO DRUG-SHOPS: LYDGATE\\'S NEW METHOD OF CHARGING HIS PATIENTS IN \"MIDDLEMARCH\"',\n", - " 'Review Article',\n", - " 'Illuminating the Vision of Ordinary Life: A Tribute to \"Middlemarch\"',\n", - " 'Review Article',\n", - " \"PLEXUSES AND GANGLIA: ELIOTS AND LEWES'S THEORY OF NERVE-CONSCIOUSNESS\",\n", - " 'Review Article',\n", - " 'Middlemarch, Realism and the Birth of the Clinic',\n", - " 'ERZÄHLERISCHE OBJEKTIVITÄT, ,AUTHORIAL INTRUSIONS‘ UND ENGLISCHER REALISMUS',\n", - " 'Review Article',\n", - " 'The Aesthetics of Sympathy:',\n", - " 'NARRATIVE VOICE AND THE \"FEMININE\" NOVELIST: DINAH MULOCK AND GEORGE ELIOT',\n", - " 'Lamarque and Olsen on Literature and Truth',\n", - " 'Review Article',\n", - " 'Microscopy and Semiotic in Middlemarch',\n", - " \"George Eliot's Reflexive Text: Three Tonalities in the Narrative Voice of Middlemarch\",\n", - " 'Review Article',\n", - " 'George Eliot and the Eighteenth-Century Novel',\n", - " 'Versions of Narrative: Overt and Covert Narrators in Nineteenth Century Historiography',\n", - " 'LYDGATE\\'S RESEARCH PROJECT IN \"MIDDLEMARCH\"',\n", - " 'George Eliot\\'s Scrupulous Research: The Facts behind Eliot\\'s Use of the \"Keepsake in Middlemarch\"',\n", - " 'Eliot and Woolf as Historians of the Common Life',\n", - " 'The Language of Discovery: William Whewell and George Eliot',\n", - " \"George Eliot's Hypothesis of Reality\",\n", - " '\"Wrinkled Deep in Time\": The Alexandria Quartet as Many-Layered Palimpsest',\n", - " 'THE DIALOGIC UNIVERSE OF \"MIDDLEMARCH\"',\n", - " 'MIXED AND ERRING HUMANITY: GEORGE ELIOT, G. H. LEWES AND GOETHE',\n", - " '1978 And All That',\n", - " \"The Turn of George Eliot's Realism\",\n", - " 'Dangerous Crossings: Dickens, Digression, and Montage',\n", - " 'In Defence of Research for Evidence-Based Teaching: A Rejoinder to Martyn Hammersley',\n", - " 'Review Article',\n", - " 'THE WONDROUS MARRIAGES OF \"DANIEL DERONDA:\" GENDER, WORK, AND LOVE',\n", - " \"The Victorian Discourse of Gambling: Speculations on Middlemarch and the Duke's Children\",\n", - " 'Struggling for Medical Reform in Middlemarch',\n", - " 'Steamboat Surfacing: Scott and the English Novelists']" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get the titles of articles citing the first 2/3 \n", - "print('Titles of articles that quote the first 2/3 of Chapter 15:')\n", - "[item.title for item in chap15p1s]" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Titles of articles that quote the last 1/3 of Chapter 15:\n" - ] - }, - { - "data": { - "text/plain": [ - "['The Union of \"Miss Brooke\" and \"Middlemarch\": A Study of the Manuscript',\n", - " '\"Middlemarch\" and George Eliot\\'s Female (Re) Vision of Shakespeare',\n", - " 'Microscopy and Semiotic in Middlemarch',\n", - " \"George Eliot's Reflexive Text: Three Tonalities in the Narrative Voice of Middlemarch\",\n", - " 'Re-Reading Character',\n", - " 'The Strange Case of Monomania: Patriarchy in Literature, Murder in Middlemarch, Drowning in Daniel Deronda']" - ] - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get the titles of those articles.\n", - "print('Titles of articles that quote the last 1/3 of Chapter 15:')\n", - "[item.title for item in chap15p2s]" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "As to women, he had once already been drawn headlong by impetuous\n", - "folly, which he meant to be final, since marriage at some distant\n", - "period would of course not be impetuous. For those who want to be\n", - "acquainted with Lydgate it will be good to know what was that case of\n", - "impetuous folly, for it may stand as an example of the fitful swerving\n", - "of passion to which he was prone, together with the chivalrous kindness\n", - "which helped to make him morally lovable. The story can be told\n", - "without many words. It happened when he was studying in Paris, and\n", - "just at the time when, over and above his other work, he was occupied\n", - "with some galvanic experiments. One evening, tired with his\n", - "experimenting, and not being able to elicit the facts he needed, he\n", - "left his frogs and rabbits to some repose under their trying and\n", - "mysterious dispensation of unexplained shocks, and went to finish his\n", - "evening at the theatre of the Porte Saint Martin, where there was a\n", - "melodrama which he had already seen several times; attracted, not by\n", - "the ingenious work of the collaborating authors, but by an actress\n", - "whose part it was to stab her lover, mistaking him for the\n", - "evil-designing duke of the piece. Lydgate was in love with this\n", - "actress, as a man is in love with a woman whom he never expects to\n", - "speak to. She was a Provencale, with dark eyes, a Greek profile, and\n", - "rounded majestic form, having that sort of beauty which carries a sweet\n", - "matronliness even in youth, and her voice was a soft cooing. She had\n", - "but lately c\n" - ] - } - ], - "source": [ - "# Verify that we have the right location for the start of Laure's story in the last 1/3 of Chapter XV\n", - "print(mm[313892:313892+1500]) " - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CHAPTER XV.\n", - "\n", - " \"Black eyes you have left, you say,\n", - " Blue eyes fail to draw you;\n", - " Yet you seem more rapt to-day,\n", - " Than of old we saw you.\n", - "\n", - " \"Oh, I track the fairest fair\n", - " Through new haunts of pleasure;\n", - " Footprints here and echoes there\n", - " Guide me to my treasure:\n", - "\n", - " \"Lo! she turns--immortal youth\n", - " Wrought to mortal stature,\n", - " Fresh as starlight's aged truth--\n", - " Many-named Nature!\"\n", - "\n", - "\n", - "A great historian, as he insisted on calling himself, who had the\n", - "happiness to be dead a hundred and twenty years ago, and so to take his\n", - "place among the colossi whose huge legs our living pettiness is\n", - "observed to walk under, glories in his copious remarks and digressions\n", - "as the least imitable part of his work, and especially in those initial\n", - "chapters to the successive books of his history, where he seems to\n", - "bring his armchair to the proscenium and chat with us in all the lusty\n", - "ease of his fine English. But Fielding lived when the days were longer\n", - "(for time, like money, is measured by our needs), when summer\n", - "afternoons were spacious, and the clock ticked slowly in the winter\n", - "evenings. We belated historians must not linger after his example; and\n", - "if we did so, it is probable that our chat would be thin and eager, as\n", - "if delivered from a campstool in a parrot-house. I at least have so\n", - "much to do in unraveling certain human lots, and seeing how they were\n", - "woven and interwoven, that all the light I can command must be\n", - "concentrated on this particular web, and not dispersed over that\n", - "tempting range of relevancies called the universe.\n", - "\n" - ] - } - ], - "source": [ - "# Verify the location of the eipgraph and first paragraph\n", - "print(mm[290371:290371+1571]) " - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [], - "source": [ - "chap15para1s = []\n", - "ids = []\n", - "for i, row in df.iterrows(): \n", - " locations = row['Locations in A']\n", - " starts = [item[0] for item in locations]\n", - " if row['Decade'] in [1980, 1990]: \n", - " for start in starts: \n", - " if start > 290371 and start < 291943: # Does it cite the last 1/3 of Chapter XV? \n", - " if row.id not in ids: \n", - " chap15para1s.append(row)\n", - " ids.append(row.id) " - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Titles of articles that quote paragraph 1 of Chapter 15:\n" - ] - }, - { - "data": { - "text/plain": [ - "['Woman of Maxims:',\n", - " 'Brava! And Farewell to Greatheart',\n", - " 'The Union of \"Miss Brooke\" and \"Middlemarch\": A Study of the Manuscript',\n", - " 'Strategies for Writing: Theories and Practices',\n", - " 'Illuminating the Vision of Ordinary Life: A Tribute to \"Middlemarch\"',\n", - " 'Middlemarch, Realism and the Birth of the Clinic',\n", - " 'NARRATIVE VOICE AND THE \"FEMININE\" NOVELIST: DINAH MULOCK AND GEORGE ELIOT',\n", - " 'Review Article',\n", - " 'Microscopy and Semiotic in Middlemarch',\n", - " \"George Eliot's Reflexive Text: Three Tonalities in the Narrative Voice of Middlemarch\",\n", - " 'George Eliot and the Eighteenth-Century Novel',\n", - " 'Versions of Narrative: Overt and Covert Narrators in Nineteenth Century Historiography',\n", - " 'George Eliot\\'s Scrupulous Research: The Facts behind Eliot\\'s Use of the \"Keepsake in Middlemarch\"',\n", - " 'Eliot and Woolf as Historians of the Common Life',\n", - " \"George Eliot's Hypothesis of Reality\",\n", - " 'MIXED AND ERRING HUMANITY: GEORGE ELIOT, G. H. LEWES AND GOETHE',\n", - " 'Dangerous Crossings: Dickens, Digression, and Montage',\n", - " 'Steamboat Surfacing: Scott and the English Novelists']" - ] - }, - "execution_count": 138, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get the titles of articles that cite paragraph 1 of Chapter 15\n", - "print('Titles of articles that quote paragraph 1 of Chapter 15:')\n", - "[item.title for item in chap15para1s]" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Titles of scholarly writings that quote the first 2/3 of Chapter 15:\n" - ] - }, - { - "data": { - "text/plain": [ - "['The Union of \"Miss Brooke\" and \"Middlemarch\": A Study of the Manuscript',\n", - " 'Heroism and Organicism in the Case of Lydgate',\n", - " 'Professional Judgment and the Rationing of Medical Care',\n", - " 'SILENCE, GESTURE, AND MEANING IN \"MIDDLEMARCH\"',\n", - " 'Reflections on \"The Philosophical Bases of Feminist Literary Criticisms\"',\n", - " 'Review Article',\n", - " 'AN END TO CONVERTING PATIENTS\\' STOMACHS INTO DRUG-SHOPS: LYDGATE\\'S NEW METHOD OF CHARGING HIS PATIENTS IN \"MIDDLEMARCH\"',\n", - " 'Review Article',\n", - " 'Review Article',\n", - " \"PLEXUSES AND GANGLIA: ELIOTS AND LEWES'S THEORY OF NERVE-CONSCIOUSNESS\",\n", - " 'Review Article',\n", - " 'Middlemarch, Realism and the Birth of the Clinic',\n", - " 'ERZÄHLERISCHE OBJEKTIVITÄT, ,AUTHORIAL INTRUSIONS‘ UND ENGLISCHER REALISMUS',\n", - " 'Review Article',\n", - " 'The Aesthetics of Sympathy:',\n", - " 'Lamarque and Olsen on Literature and Truth',\n", - " 'Microscopy and Semiotic in Middlemarch',\n", - " 'Review Article',\n", - " 'LYDGATE\\'S RESEARCH PROJECT IN \"MIDDLEMARCH\"',\n", - " 'Eliot and Woolf as Historians of the Common Life',\n", - " 'The Language of Discovery: William Whewell and George Eliot',\n", - " '\"Wrinkled Deep in Time\": The Alexandria Quartet as Many-Layered Palimpsest',\n", - " 'THE DIALOGIC UNIVERSE OF \"MIDDLEMARCH\"',\n", - " '1978 And All That',\n", - " \"The Turn of George Eliot's Realism\",\n", - " 'Dangerous Crossings: Dickens, Digression, and Montage',\n", - " 'In Defence of Research for Evidence-Based Teaching: A Rejoinder to Martyn Hammersley',\n", - " 'Review Article',\n", - " 'THE WONDROUS MARRIAGES OF \"DANIEL DERONDA:\" GENDER, WORK, AND LOVE',\n", - " \"The Victorian Discourse of Gambling: Speculations on Middlemarch and the Duke's Children\",\n", - " 'Struggling for Medical Reform in Middlemarch']" - ] - }, - "execution_count": 139, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chap15Lydgates = []\n", - "ids = []\n", - "for i, row in df.iterrows(): \n", - " locations = row['Locations in A']\n", - " starts = [item[0] for item in locations]\n", - " if row['Decade'] in [1980, 1990]: \n", - " for start in starts: \n", - " if start > 291942 and start < 313892: # Does it cite the first 2/3 of Chapter XV?\n", - " if row.id not in ids: \n", - " chap15Lydgates.append(row)\n", - " ids.append(row.id)\n", - " \n", - "# Get the titles of articles that cite Lydgate section\n", - "print('Titles of scholarly writings that quote the first 2/3 of Chapter 15:')\n", - "[item.title for item in chap15Lydgates]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Chapter 20" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "# Try to find out what articles cited chapter 20 \n", - "chap20s = []\n", - "ids = []\n", - "for i, row in df.iterrows(): \n", - " locations = row['Locations in A']\n", - " starts = [item[0] for item in locations]\n", - " if row['Decade'] in [1870, 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010]: \n", - " for start in starts: \n", - " if start > 1236993 and start < 1278826: # Does it cite Chapter XX? \n", - " if row.id not in ids: \n", - " chap20s.append(row)\n", - " ids.append(row.id)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Titles of scholarly writings that quote Chapter 20:\n" - ] - }, - { - "data": { - "text/plain": [ - "['\"Radiant as a Diamond\": George Eliot, Jewelry and the Female Role',\n", - " \"The Hidden Abortion Plot in George Eliot's Middlemarch\",\n", - " 'George Eliot and the Feminine Gift',\n", - " 'The Rosamond Plots',\n", - " 'Afterword',\n", - " 'Near Confinement: Pregnant Women in the Nineteenth-Century British Novel',\n", - " 'Mencken, Cushing, and The Life of Sir William Osler',\n", - " 'Egoism, Desires, and Friendship',\n", - " 'Of Many Minds in Middlemarch',\n", - " 'Teaching Middlemarch with a Focus on Theory of Mind',\n", - " \"The Power of Women's Hair in the Victorian Imagination\",\n", - " 'The Traffic in Men: Female Kinship in Three Novels by George Eliot',\n", - " 'Transformations, Style, and the Writing Experience',\n", - " '\"Neutral Physiognomy\": The Unreadable Faces of \"Middlemarch\"',\n", - " 'The Web of Utterance: Middlemarch',\n", - " 'Dora Spenlow, Female Communities, and Female Narrative in Charles Dickens\\'s \"David Copperfield\" and George Eliot\\'s \"Middlemarch\"',\n", - " 'Ibsen and Some Current Superstitions',\n", - " 'Realism as a Practical and Cosmic Joke',\n", - " '\"The One Poor Word\" in \"Middlemarch\"',\n", - " 'The abuses of literacy',\n", - " 'Existentially Complete Abelian Lattice-Ordered Groups',\n", - " 'Narrative and History',\n", - " 'THE WRITER AND THE COMMISSARS',\n", - " 'George Eliot\\'s Scrupulous Research: The Facts behind Eliot\\'s Use of the \"Keepsake in Middlemarch\"',\n", - " 'Letter From England October 1995',\n", - " 'Middlemarch and History',\n", - " 'The Gendering of Habit in George Eliot\\'s \"Middlemarch\"',\n", - " 'ROSAMOND VINCY OF \"MIDDLEMARCH\"',\n", - " '[Discussion in Four Parts]',\n", - " 'F. R. Leavis Special Issue',\n", - " \"DISCERNING SYNTAX: GEORGE ELIOT'S RELATIVE CLAUSES\",\n", - " 'When George Eliot Reads Milton: The Muse in a Different Voice',\n", - " \"Character and Destiny in George Eliot's Fiction\"]" - ] - }, - "execution_count": 141, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get the titles of those articles.\n", - "print('Titles of scholarly writings that quote Chapter 20:')\n", - "[item.title for item in chap20s]" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "33" - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Number of articles that cite Chapter 20\n", - "len(chap20s)" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": {}, - "outputs": [], - "source": [ - "xxStart, xxEnd = chapterLocations[20:22] # Chapter 20 Boundaries" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CHAPTER XX.\n", - "\n", - " \"A child forsaken, waking suddenly,\n", - " Whose gaze afeard on all things round doth rove,\n", - " And seeth only that it cannot see\n", - " The meeting eyes of love.\"\n", - "\n", - "\n", - "Two hours later, Dorothea was seated in an inner room or boudoir of a\n", - "handsome apartment in the Via Sistina.\n", - "\n", - "I am sorry to add that she was sobbing bitterly, with such abandonment\n", - "to this relief of an oppressed heart as a woman habitually controlled\n", - "by pride on her own account and thoughtfulness for others will\n", - "sometimes allow herself when she feels securely alone. And Mr.\n", - "Casaubon was certain to remain away for some time at the Vatican.\n", - "\n", - "Yet Dorothea had no distinctly shapen grievance that she could state\n", - "even to herself; and in the midst of her confused thought and passion,\n", - "the mental act that was struggling forth into clearness was a\n", - "self-accusing cry that her feeling of desolation was the fault of her\n", - "own spiritual poverty. She had married the man of her choice, and with\n", - "the advantage over most girls t\n" - ] - } - ], - "source": [ - "print(mm[xxStart:xxStart+1000]) # Verify we have Ch. 20" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": {}, - "outputs": [], - "source": [ - "xx = mm[xxStart:xxEnd]" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": {}, - "outputs": [], - "source": [ - "xxParaLocations = [match.start() for match in re.finditer('\\n\\n+', mm)]\n", - "xxParaLocations = [x for x in xxParaLocations if (x > xxStart) and (x < xxEnd)] " - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\n\\nBut this stupendous fragmentariness heightened the dreamlike\\nstrangeness of her bridal life. Dorothea had now been five weeks in\\nRome, and in the kindly mornings when autumn and winter seemed to go\\nhand in hand like a happy aged couple one of whom would presently\\nsurvive in chiller loneliness, she had driven about at first with Mr.\\nCasaubon, but of late chiefly with Tantripp and their experienced\\ncourier. She had been led through the best galleries, had been taken\\nto the chief points of view, had been shown the grandest ruins and the\\nmost glorious churches, and she had ended by oftenest choosing to drive\\nout to the Campagna where she could feel alone with the earth and sky,\\naway-from the oppressive masquerade of ages, in which her own life too\\nseemed to become a masque with enigmatical costumes.'" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mm[xxParaLocations[4]:xxParaLocations[5]]" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[130022, 130046]]" - ] - }, - "execution_count": 148, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "articlesWithMatches['Locations in A'].loc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [], - "source": [ - "def inXX(matches): \n", - " \"\"\" Determine if the article has a match in Ch. 20\"\"\"\n", - " for match in matches: \n", - " if match[0] > xxStart and match[0] < xxEnd:\n", - " return True\n", - " return False" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 False\n", - "9 False\n", - "17 False\n", - "19 False\n", - "21 False\n", - "Name: Locations in A, dtype: bool" - ] - }, - "execution_count": 150, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "articlesWithMatches['Locations in A'].apply(inXX).head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Paragraph-level analysis of Chapter 20" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [], - "source": [ - "# Try to find out what articles cite paragraph 6 in Chapter 20\n", - "chap20par6s = []\n", - "ids = []\n", - "for i, row in df.iterrows(): \n", - " locations = row['Locations in A']\n", - " starts = [item[0] for item in locations]\n", - " if row['Decade'] in [1870, 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010]: \n", - " for start in starts: \n", - " if start > 411152 and start < 412177: # Does it cite Chapter XX, paragraph 6? \n", - " if row.id not in ids: \n", - " chap20par6s.append(row)\n", - " ids.append(row.id)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Titles of scholarly writings that quote paragraph 6 of Chapter 20:\n" - ] - }, - { - "data": { - "text/plain": [ - "['“I Mistook the Faint Shadow”',\n", - " 'Torpedoes, tapirs and tortoises: scientific discourse in \"Middlemarch\"',\n", - " 'Sympathy Time: Adam Smith, George Eliot, and the Realist Novel',\n", - " '“A True Prophet”? Speculation in Victorian Sensory Physiology and George Eliot’s “The Lifted Veil”',\n", - " 'One-Way Communication',\n", - " 'Review Article',\n", - " 'A Note on Middlemarch',\n", - " \"Ian McEwan's Saturday and the Aesthetics of Prose\",\n", - " 'Responsibility without Consciousness',\n", - " 'Proserpine and Pessimism: Goddesses of Death, Life, and Language from Swinburne to Wharton',\n", - " 'Rights, Communities, and Tradition',\n", - " 'Reading, Writing, and Eavesdropping: Some Thoughts on the Nature of Realistic Fiction',\n", - " 'The Sound and the Fury: A Logic of Tragedy',\n", - " 'Views from above and below: George Eliot and Fakir Mohan Senapati',\n", - " 'Review Article',\n", - " 'The Not-Quite Said',\n", - " 'Review Article',\n", - " 'Incarnation and Inwardness:',\n", - " '\"Be Ye Lukewarm!\": The Nineteenth-Century Novel and Social Action',\n", - " 'Development and the Learning Organisation: An Introduction',\n", - " 'Lost in Magnification: Nineteenth-Century Microscopy and The Lifted Veil',\n", - " 'As Sure as Shooting',\n", - " 'Review Article',\n", - " 'George Eliot and Wordsworth: The Power of Sound and the Power of Mind',\n", - " 'Came Glancing like an Arrow',\n", - " \"My Tears See More Than My Eyes MY SON'S DEPRESSION AND THE POWER OF ART\",\n", - " \"Incarnation, Inwardness, and Imagination: George Eliot's Early Fiction\",\n", - " \"ENGLAND AND ENGLISHNESS: FORD'S FIRST TRILOGY\",\n", - " 'COMMONPLACE BOOK: Secrets',\n", - " \"T. S. Eliot's Quartets: A New Reading\",\n", - " 'Eliot, Proust, Stein',\n", - " 'George Eliot and Greek Tragedy',\n", - " \"Woolf's Copernican Shift: Nonhuman Nature in Virginia Woolf's Short Fiction\",\n", - " 'The Abyss of Sympathy: the Conventions of Pathos in Eighteenth and Nineteenth Century British Novels',\n", - " 'THE ECONOMIC PROBLEM OF SYMPATHY: PARABASIS, INTEREST, AND REALIST FORM IN \"MIDDLEMARCH\"',\n", - " 'One-Way Communication',\n", - " 'Gwendolen Harleth - Character Creation or Character Analysis?',\n", - " \"The Tramp of a Fly's Footstep: or, The Shriek, Rattle, and Roar of a Victorian Sound Track\",\n", - " 'Breathless',\n", - " 'Dorothea and \"Miss Brooke\" in Middlemarch',\n", - " \"Louis Guilloux's Working Class Novels: Some Problems of Social Realism\",\n", - " '\"Myriad-Headed, Myriad-Handed\": Labor in \"Middlemarch\"',\n", - " \"The Squirrel's Heartbeat: Some Thoughts on the Later Style of Henry James\",\n", - " \"Tolstoj's Reading of George Eliot: Visions and Revisions\",\n", - " 'Shifting from Stories to Live by to Stories to Leave by: Early Career Teacher Attrition',\n", - " 'What Is Prosaics?',\n", - " 'Sound Object Lessons',\n", - " 'Exiling the Encyclopedia: The Individual in \"Janet\\'s Repentance\"',\n", - " 'Programs and Abstracts',\n", - " 'A SHIFT IN THE ETHICS OF HARDY’S FICTION',\n", - " 'The Divine Comedy of Language: Tennyson\\'s \"In Memoriam\"',\n", - " 'Review Article',\n", - " 'GEORGE ELIOT: THE SIBYL OF MERCIA',\n", - " '“The Continuity of Married Companionship”',\n", - " 'Sympathy Biography and Sympathy Margin',\n", - " 'In the Scene of Being',\n", - " 'Review Article',\n", - " 'Fiction as Vivisection: G. H. Lewes and George Eliot',\n", - " 'Against Detachment',\n", - " 'Review Article',\n", - " 'Why Read George Eliot? Her novels are just modern enough—and just old-fashioned enough, too',\n", - " 'The Language of Silence: A Citation',\n", - " 'Forecasting Falls: Icarus from Freud to Auden to 9/11',\n", - " '\"THE OTHER SIDE OF SILENCE\": KATHERINE ANNE PORTER\\'S \"HE\" AS TRAGEDY',\n", - " 'The Made Man and the “Minor” Novel: Erewhon, ANT, and Empire',\n", - " 'Charles Darwin and the Victorian Pre-History of Climate Denial']" - ] - }, - "execution_count": 152, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get the titles of those articles.\n", - "print('Titles of scholarly writings that quote paragraph 6 of Chapter 20:')\n", - "[item.title for item in chap20par6s]" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "66" - ] - }, - "execution_count": 153, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(chap20par6s) # The number of items citing paragraph 6 in chapter 20" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\n\\nNot that this inward amazement of Dorothea\\'s was anything very\\nexceptional: many souls in their young nudity are tumbled out among\\nincongruities and left to \"find their feet\" among them, while their\\nelders go about their business. Nor can I suppose that when Mrs.\\nCasaubon is discovered in a fit of weeping six weeks after her wedding,\\nthe situation will be regarded as tragic. Some discouragement, some\\nfaintness of heart at the new real future which replaces the imaginary,\\nis not unusual, and we do not expect people to be deeply moved by what\\nis not unusual. That element of tragedy which lies in the very fact of\\nfrequency, has not yet wrought itself into the coarse emotion of\\nmankind; and perhaps our frames could hardly bear much of it. If we\\nhad a keen vision and feeling of all ordinary human life, it would be\\nlike hearing the grass grow and the squirrel\\'s heart beat, and we\\nshould die of that roar which lies on the other side of silence. As it\\nis, the quickest of us walk about well wadded with stupidity'" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mm[411152:412177]" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [], - "source": [ - "def paraIndicesIn20(matches, paraLocations=xxParaLocations): \n", - " \"\"\" Determine paragraph number (index) for match in Ch. 20. \"\"\"\n", - " paraIndices = []\n", - " if inXX(matches): \n", - " paraBoundaries = list(zip(paraLocations, paraLocations[1:]))\n", - " for match in matches: \n", - " for i, paraBoundary in enumerate(paraBoundaries): \n", - " if set(range(match[0], match[1])) & set(range(paraBoundary[0], paraBoundary[1])): # find the set intersection of the ranges of pairs\n", - " paraIndices.append(i)\n", - " else: \n", - " paraIndices.append(None)\n", - " return paraIndices\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 156, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(set(range(8, 10)) & set(range(1, 9)))" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/hg/n067xqnn1nbbk0txk1mdhcq80000gn/T/ipykernel_95694/4864444.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " articlesWithMatches['paraIndicesIn20'] = articlesWithMatches['Locations in A'].apply(paraIndicesIn20)\n" - ] - } - ], - "source": [ - "articlesWithMatches['paraIndicesIn20'] = articlesWithMatches['Locations in A'].apply(paraIndicesIn20)" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": {}, - "outputs": [], - "source": [ - "counters = list(articlesWithMatches['paraIndicesIn20'].apply(Counter))" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": {}, - "outputs": [], - "source": [ - "grandTally = Counter()" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": {}, - "outputs": [], - "source": [ - "for counter in counters: \n", - " grandTally += counter" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "metadata": {}, - "outputs": [], - "source": [ - "del grandTally[None]" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{6: 69,\n", - " 5: 47,\n", - " 3: 7,\n", - " 15: 4,\n", - " 10: 20,\n", - " 29: 2,\n", - " 25: 3,\n", - " 4: 6,\n", - " 7: 9,\n", - " 12: 3,\n", - " 14: 3,\n", - " 33: 6,\n", - " 18: 3,\n", - " 26: 8,\n", - " 17: 7,\n", - " 16: 7,\n", - " 11: 8,\n", - " 22: 1,\n", - " 1: 1,\n", - " 2: 1,\n", - " 13: 1,\n", - " 8: 1,\n", - " 9: 2}" - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict(grandTally)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Which paragraphs in Chapter 20 are quoted most often?" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 163, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pd.Series(dict(grandTally)).sort_index().plot(kind='bar', title=\"Which paragraphs in Chapter 20 are quoted most often?\")" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "To those who have looked at Rome with the quickening power of a\n", - "knowledge which breathes a growing soul into all historic shapes, and\n", - "traces out the suppressed transitions which unite all contrasts, Rome\n", - "may still be the spiritual centre and interpreter of the world. But\n", - "let them conceive one more historical contrast: the gigantic broken\n", - "revelations of that Imperial and Papal city thrust abruptly on the\n", - "notions of a girl who had been brought up in English and Swiss\n", - "Puritanism, fed on meagre Protestant histories and on art chiefly of\n", - "the hand-screen sort; a girl whose ardent nature turned all her small\n", - "allowance of knowledge into principles, fusing her actions into their\n", - "mould, and whose quick emotions gave the most abstract things the\n", - "quality of a pleasure or a pain; a girl who had lately become a wife,\n", - "and from the enthusiastic acceptance of untried duty found herself\n", - "plunged in tumultuous preoccupation with her personal lot. The weight\n", - "of unintelligible Rome might lie easily on bright nymphs to whom it\n", - "formed a background for the brilliant picnic of Anglo-foreign society;\n", - "but Dorothea had no such defence against deep impressions. Ruins and\n", - "basilicas, palaces and colossi, set in the midst of a sordid present,\n", - "where all that was living and warm-blooded seemed sunk in the deep\n", - "degeneracy of a superstition divorced from reverence; the dimmer but\n", - "yet eager Titanic life gazing and struggling on walls and ceilings; the\n", - "long vistas of white forms whose marble eyes seemed to hold the\n", - "monotonous light of an alien world: all this vast wreck of ambitious\n", - "ideals, sensuous and spiritual, mixed confusedly with the signs of\n", - "breathing forgetfulness and degradation, at first jarred her as with an\n", - "electric shock, and then urged themselves on her with that ache\n", - "belonging to a glut of confused ideas which check the flow of emotion.\n", - "Forms both pale and glowing took possession of her young sense, and\n", - "fixed themselves in her memory even when she was not thinking of them,\n", - "preparing strange associations which remained through her after-years.\n", - "Our moods are apt to bring with them images which succeed each other\n", - "like the magic-lantern pictures of a doze; and in certain states of\n", - "dull forlornness Dorothea all her life continued to see the vastness of\n", - "St. Peter's, the huge bronze canopy, the excited intention in the\n", - "attitudes and garments of the prophets and evangelists in the mosaics\n", - "above, and the red drapery which was being hung for Christmas spreading\n", - "itself everywhere like a disease of the retina.\n", - "\n", - "Not that this inward amazement of Dorothea's was anything very\n", - "exceptional: many souls in their young nudity are tumbled out among\n", - "incongruities and left to \"find their feet\" among them, while their\n", - "elders go about their business. Nor can I suppose that when Mrs.\n", - "Casaubon is discovered in a fit of weeping six weeks after her wedding,\n", - "the situation will be regarded as tragic. Some discouragement, some\n", - "faintness of heart at the new real future which replaces the imaginary,\n", - "is not unusual, and we do not expect people to be deeply moved by what\n", - "is not unusual. That element of tragedy which lies in the very fact of\n", - "frequency, has not yet wrought itself into the coarse emotion of\n", - "mankind; and perhaps our frames could hardly bear much of it. If we\n", - "had a keen vision and feeling of all ordinary human life, it would be\n", - "like hearing the grass grow and the squirrel's heart beat, and we\n", - "should die of that roar which lies on the other side of silence. As it\n", - "is, the quickest of us walk about well wadded with stupidity.\n" - ] - } - ], - "source": [ - "print(mm[xxParaLocations[5]:xxParaLocations[7]]) # What are paragraphs #5 and #6? " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# *Middlemarch* Quotations, by Journal\n", - "\n", - "- [Descriptive-statistics-on-journals-in-JSTOR-dataset](#Descriptive-statistics-on-journals-in-JSTOR-dataset)\n", - "- [GE-GHLS, NLH, and ELH)](#GE-GHLS,-NLH,-and-ELH)\n", - " - [#George-Eliot---George-Henry-Lewes-Studies-(GE-GHLS)](#George-Eliot---George-Henry-Lewes-Studies-(GE-GHLS))\n", - " - [#George-Eliot---George-Henry-Lewes-Studies-articles-where-journal-title-is-\"George-Eliot---George-Henry-Lewes-Studies\"](#George-Eliot---George-Henry-Lewes-Studies-articles-where-journal-title-is-\"George-Eliot---George-Henry-Lewes-Studies\")\n", - " - [#George-Eliot---George-Henry-Lewes-Studies-articles-where-journal-code-is-\"georelioghlstud\"](#George-Eliot---George-Henry-Lewes-Studies-articles-where-journal-code-is-\"georelioghlstud\")\n", - " - [#Compare-the-specialist-journal,-George-Eliot---George-Henry-Lewes-Studies,-with-all-other-journals](#Compare-the-specialist-journal,-George-Eliot---George-Henry-Lewes-Studies,-with-all-other-journals)\n", - "- [NLH](#NLH)\n", - " - [#NLH-articles-where-journal-title-is-\"New-Literary-History\"](#NLH-articles-where-journal-title-is-\"New-Literary-History\")\n", - " - [NLH-articles-where-journal-code-is-\"newlitehist\"](#NLH-articles-where-journal-code-is-\"newlitehist\")\n", - "-[ELH](#ELH)\n", - " - [#ELH-articles-where-journal-title-is-\"ELH\"](#ELH-articles-where-journal-title-is-\"ELH\")\n", - " - [#ELH-articles-where-journal-code-is-\"elh\"](#ELH-articles-where-journal-code-is-\"elh\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Descriptive statistics on journals in JSTOR dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Journal | Number of articles in our dataset\n" - ] - }, - { - "data": { - "text/plain": [ - "Victorian Studies 459\n", - "George Eliot - George Henry Lewes Studies 231\n", - "The Modern Language Review 192\n", - "Nineteenth-Century Fiction 192\n", - "The Review of English Studies 190\n", - "PMLA 154\n", - "NOVEL: A Forum on Fiction 148\n", - "Nineteenth-Century Literature 139\n", - "Studies in the Novel 124\n", - "ELH 102\n", - "Name: journal, dtype: int64" - ] - }, - "execution_count": 165, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Top 10 journals with most articles in our dataset\n", - "# OLD\n", - "df['journal'] = df['isPartOf']\n", - "journalStats = df['journal'].value_counts()\n", - "print(\"Journal | Number of articles in our dataset\")\n", - "journalStats[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": {}, - "outputs": [], - "source": [ - "journalList = journalStats.index" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": {}, - "outputs": [], - "source": [ - "journals = pd.DataFrame({title: synchronicAnalysis(df.loc[df['journal'] == title]) for title in journalList }).T" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/hg/n067xqnn1nbbk0txk1mdhcq80000gn/T/ipykernel_95694/1477629281.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " topJournals.loc['Other'] = otherJournals.sum()\n" - ] - } - ], - "source": [ - "cutoff = 1500\n", - "topJournals = journals.loc[journals.sum(axis=1) > cutoff]\n", - "otherJournals = journals.loc[journals.sum(axis=1) < cutoff]\n", - "topJournals.loc['Other'] = otherJournals.sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 169, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "topJournals.T.plot(kind='bar', stacked=True, colormap='nipy_spectral')" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "FigureCanvasAgg.print_png() got an unexpected keyword argument 'bboxinches'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[170], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m ax \u001b[38;5;241m=\u001b[39m topJournals\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbar\u001b[39m\u001b[38;5;124m'\u001b[39m, stacked\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, colormap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnipy_spectral\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 2\u001b[0m fig \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mget_figure()\n\u001b[0;32m----> 3\u001b[0m fig\u001b[38;5;241m.\u001b[39msavefig(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msynchronic-journals.png\u001b[39m\u001b[38;5;124m'\u001b[39m, bboxinches\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m'\u001b[39m, dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/matplotlib/figure.py:3343\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m 3339\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[1;32m 3340\u001b[0m stack\u001b[38;5;241m.\u001b[39menter_context(\n\u001b[1;32m 3341\u001b[0m ax\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39m_cm_set(facecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m, edgecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m-> 3343\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(fname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/matplotlib/backend_bases.py:2366\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2362\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2363\u001b[0m \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[1;32m 2364\u001b[0m \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[1;32m 2365\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2366\u001b[0m result \u001b[38;5;241m=\u001b[39m print_method(\n\u001b[1;32m 2367\u001b[0m filename,\n\u001b[1;32m 2368\u001b[0m facecolor\u001b[38;5;241m=\u001b[39mfacecolor,\n\u001b[1;32m 2369\u001b[0m edgecolor\u001b[38;5;241m=\u001b[39medgecolor,\n\u001b[1;32m 2370\u001b[0m orientation\u001b[38;5;241m=\u001b[39morientation,\n\u001b[1;32m 2371\u001b[0m bbox_inches_restore\u001b[38;5;241m=\u001b[39m_bbox_inches_restore,\n\u001b[1;32m 2372\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 2373\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 2374\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n", - "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/matplotlib/backend_bases.py:2232\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method..\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2228\u001b[0m optional_kws \u001b[38;5;241m=\u001b[39m { \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[1;32m 2229\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 2230\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m 2231\u001b[0m skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[0;32m-> 2232\u001b[0m print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: meth(\n\u001b[1;32m 2233\u001b[0m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m skip}))\n\u001b[1;32m 2234\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[1;32m 2235\u001b[0m print_method \u001b[38;5;241m=\u001b[39m meth\n", - "\u001b[0;31mTypeError\u001b[0m: FigureCanvasAgg.print_png() got an unexpected keyword argument 'bboxinches'" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = topJournals.T.plot(kind='bar', stacked=True, colormap='nipy_spectral')\n", - "fig = ax.get_figure()\n", - "fig.savefig('synchronic-journals.png', bboxinches='tight', dpi=300)" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "789" - ] - }, - "execution_count": 171, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Print the total number of journals\n", - "len(journalStats)" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "metadata": {}, - "outputs": [], - "source": [ - "list_of_VS_journals = ['Victorian Studies', 'George Eliot - George Henry Lewes Studies', 'Nineteenth-Century Fiction', 'Nineteenth-Century Literature', 'Dickens Studies Annual', 'Victorian Literature and Culture', 'Victorian Review', 'The George Eliot, George Henry Lewes Newsletter', 'Victorian Periodicals Review', 'Dickens Quarterly', 'Victorian Poetry', 'The Thomas Hardy Journal', 'The Gaskell Society Journal', 'The Gaskell Journal', 'Newsletter of the Victorian Studies Association of Western Canada', 'Dickens Studies Newsletter', 'Browning Institute Studies', 'Victorian Periodicals Newsletter', 'Carlyle Studies Annual', 'Conradiana', 'Tennyson Research Bulletin', 'The Conradian', 'The Hardy Society Journal', 'The Hardy Review', 'Studies in Browning and His Circle', 'Nineteenth-Century French Studies', 'The Wilkie Collins Journal', 'Carlyle Newsletter', 'The Wildean', 'Dickens Studies', 'Carlyle Annual', '19th-Century Music', 'The Trollopian', 'Conrad Studies']" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "34" - ] - }, - "execution_count": 173, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(list_of_VS_journals)" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": {}, - "outputs": [], - "source": [ - "just_VS_journals_quotes = articlesWithMatches[articlesWithMatches['isPartOf'].isin(list_of_VS_journals)]" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "429" - ] - }, - "execution_count": 175, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(just_VS_journals_quotes)" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "George Eliot - George Henry Lewes Studies 106\n", - "Victorian Studies 78\n", - "Nineteenth-Century Fiction 68\n", - "Nineteenth-Century Literature 37\n", - "Victorian Literature and Culture 37\n", - "Dickens Studies Annual 19\n", - "Victorian Review 13\n", - "Victorian Poetry 12\n", - "The George Eliot, George Henry Lewes Newsletter 11\n", - "Victorian Periodicals Review 8\n", - "Dickens Quarterly 5\n", - "The Thomas Hardy Journal 5\n", - "The Gaskell Society Journal 4\n", - "Browning Institute Studies 4\n", - "Tennyson Research Bulletin 4\n", - "Carlyle Studies Annual 3\n", - "The Gaskell Journal 2\n", - "Conradiana 2\n", - "Dickens Studies Newsletter 2\n", - "19th-Century Music 1\n", - "Newsletter of the Victorian Studies Association of Western Canada 1\n", - "Conrad Studies 1\n", - "Nineteenth-Century French Studies 1\n", - "The Wilkie Collins Journal 1\n", - "Carlyle Annual 1\n", - "The Hardy Review 1\n", - "Victorian Periodicals Newsletter 1\n", - "The Wildean 1\n", - "Name: isPartOf, dtype: int64" - ] - }, - "execution_count": 176, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "just_VS_journals_quotes['isPartOf'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "George Eliot - George Henry Lewes Studies 106\n", - "Victorian Studies 78\n", - "Nineteenth-Century Fiction 68\n", - "PMLA 47\n", - "ELH 42\n", - " ... \n", - "Science 1\n", - "Transformation of Rage 1\n", - "Anglican and Episcopal History 1\n", - "The Journal of Ethics 1\n", - "Sociological Forum 1\n", - "Name: isPartOf, Length: 403, dtype: int64" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "articlesWithMatches['isPartOf'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1258" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "just_VS_journals_quotes['numMatches'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "150" - ] - }, - "execution_count": 179, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "just_VS_journals_quotes[just_VS_journals_quotes['isPartOf']== \"Victorian Studies\"].numMatches.sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3800" - ] - }, - "execution_count": 180, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "articlesWithMatches['numMatches'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3310526315789474" - ] - }, - "execution_count": 181, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# What proportion of matches come from Victorian studies journals?\n", - "just_VS_journals_quotes['numMatches'].sum() / articlesWithMatches['numMatches'].sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# *GE-GHLS*, *NLH*, and *ELH* " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## *George Eliot - George Henry Lewes Studies* (*GE-GHLS*)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### George Eliot - George Henry Lewes Studies articles where journal title is \"George Eliot - George Henry Lewes Studies\"" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": {}, - "outputs": [], - "source": [ - "geJournals = df.loc[df['journal'] == 'George Eliot - George Henry Lewes Studies']" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_columns', 207)" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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creatordatePublisheddocSubTypedocTypeididentifierisPartOfissueNumberlanguageoutputFormatpageCountpageEndpageStartpaginationproviderpublicationYearpublishersourceCategorytdmCategorytitleurlvolumeNumberwordCountnumMatchesLocations in ALocations in BabstractkeyphrasesubTitleyearDecadeQuoted WordsLocations in A with WordcountsWordcountsjournal
37[ELIZABETH WINSTON]1995-09-01book-reviewarticlehttp://www.jstor.org/stable/43595523[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies28/29[eng][unigram, bigram, trigram]6.0106101pp. 101-106jstor1995Penn State University Press[Language & Literature, Humanities][Arts - Literature]Review Articlehttp://www.jstor.org/stable/43595523None19810[][]NoneNoneNone199519900[][]George Eliot - George Henry Lewes Studies
76[Katherine Newey]2011-09-01research-articlearticlehttp://www.jstor.org/stable/42827892[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies60/61[eng][unigram, bigram, trigram]16.0141126pp. 126-141jstor2011Penn State University Press[Language & Literature, Humanities][Arts - Literature]The \"British Matron\" and the Poetic Drama: The...http://www.jstor.org/stable/42827892None70381[[502448, 502471]][[18540, 18563]]NoneNoneNone201120104[([502448, 502471], 4)][4]George Eliot - George Henry Lewes Studies
101None2015-11-01otherarticlehttp://www.jstor.org/stable/10.5325/georeliogh...[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies2[eng][unigram, bigram, trigram]2.0iiipp. i-iijstor2015Penn State University Press[Language & Literature, Humanities]NoneFront Matterhttp://www.jstor.org/stable/10.5325/georeliogh...674380[][]NoneNoneNone201520100[][]George Eliot - George Henry Lewes Studies
107[AVROM FLEISHMAN]2008-09-01research-articlearticlehttp://www.jstor.org/stable/42827960[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies54/55[eng][unigram, bigram, trigram]79.0761pp. 1-76jstor2008Penn State University Press[Language & Literature, Humanities][Arts - Performing arts]GEORGE ELIOT'S READING: A CHRONOLOGICAL LISThttp://www.jstor.org/stable/42827960None227291[[1138948, 1138968]][[73073, 73093]]NoneNoneNone200820004[([1138948, 1138968], 4)][4]George Eliot - George Henry Lewes Studies
108[Judith Adler]2018-10-01research-articlearticlehttp://www.jstor.org/stable/10.5325/georeliogh...[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies2[eng][unigram, bigram, trigram]29.0171143pp. 143-171jstor2018Penn State University Press[Language & Literature, Humanities][Arts - Literature]Hidden Allusion in the Finale of <em>Middlemar...http://www.jstor.org/stable/10.5325/georeliogh...7092581[[1792915, 1793447]][[350, 876]]This article argues that the famous concluding...NoneNone2018201097[([1792915, 1793447], 97)][97]George Eliot - George Henry Lewes Studies
............................................................................................................
5798[TERENCE R. WRIGHT]1995-09-01book-reviewarticlehttp://www.jstor.org/stable/43595524[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies28/29[eng][unigram, bigram, trigram]3.0109107pp. 107-109jstor1995Penn State University Press[Language & Literature, Humanities]NoneReview Articlehttp://www.jstor.org/stable/43595524None8620[][]NoneNoneNone199519900[][]George Eliot - George Henry Lewes Studies
5835[SALEEL NURBHAI]1997-09-01research-articlearticlehttp://www.jstor.org/stable/42827636[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies32/33[eng][unigram, bigram, trigram]18.0181pp. 1-18jstor1997Penn State University Press[Language & Literature, Humanities][Arts - Literature]JEWISH MYTH IN GEORGE ELIOT'S FICTIONhttp://www.jstor.org/stable/42827636None67555[[190333, 190518], [939772, 940069], [940403, ...[[30280, 30465], [30822, 31114], [31125, 31327...NoneNoneNone19971990161[([190333, 190518], 36), ([939772, 940069], 59...[36, 59, 39, 9, 18]George Eliot - George Henry Lewes Studies
5853[DONALD HAWES]2001-09-01research-articlearticlehttp://www.jstor.org/stable/42827734[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies40/41[eng][unigram, bigram, trigram]8.07568pp. 68-75jstor2001Penn State University Press[Language & Literature, Humanities][Arts - Literature]GEORGE ELIOT AND GEORGE HENRY LEWES: SELECTED ...http://www.jstor.org/stable/42827734None29021[[1316376, 1316406]][[3676, 3706]]NoneNoneNone200120005[([1316376, 1316406], 5)][5]George Eliot - George Henry Lewes Studies
5865None1995-09-01miscarticlehttp://www.jstor.org/stable/43595525[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies28/29[eng][unigram, bigram, trigram]5.0NoneNoneNonejstor1995Penn State University Press[Language & Literature, Humanities][Arts - Literature]Back Matterhttp://www.jstor.org/stable/43595525None11470[][]NoneNoneNone199519900[][]George Eliot - George Henry Lewes Studies
5876[BUFF LINDAU]2013-10-01research-articlearticlehttp://www.jstor.org/stable/42827928[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies64/65[eng][unigram, bigram, trigram]1.0109109p. 109jstor2013Penn State University Press[Language & Literature, Humanities]NoneA GEORGE ELIOT NOTEhttp://www.jstor.org/stable/42827928None1290[][]NoneNoneNone201320100[][]George Eliot - George Henry Lewes Studies
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[36, 59, 39, 9, 18] \n", - "5853 [([1316376, 1316406], 5)] [5] \n", - "5865 [] [] \n", - "5876 [] [] \n", - "\n", - " journal \n", - "37 George Eliot - George Henry Lewes Studies \n", - "76 George Eliot - George Henry Lewes Studies \n", - "101 George Eliot - George Henry Lewes Studies \n", - "107 George Eliot - George Henry Lewes Studies \n", - "108 George Eliot - George Henry Lewes Studies \n", - "... ... \n", - "5798 George Eliot - George Henry Lewes Studies \n", - "5835 George Eliot - George Henry Lewes Studies \n", - "5853 George Eliot - George Henry Lewes Studies \n", - "5865 George Eliot - George Henry Lewes Studies \n", - "5876 George Eliot - George Henry Lewes Studies \n", - "\n", - "[231 rows x 35 columns]" - ] - }, - "execution_count": 184, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geJournals " - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "37 Review Article\n", - "76 The \"British Matron\" and the Poetic Drama: The...\n", - "101 Front Matter\n", - "107 GEORGE ELIOT'S READING: A CHRONOLOGICAL LIST\n", - "108 Hidden Allusion in the Finale of Middlemar...\n", - " ... \n", - "5798 Review Article\n", - "5835 JEWISH MYTH IN GEORGE ELIOT'S FICTION\n", - "5853 GEORGE ELIOT AND GEORGE HENRY LEWES: SELECTED ...\n", - "5865 Back Matter\n", - "5876 A GEORGE ELIOT NOTE\n", - "Name: title, Length: 231, dtype: object\n" - ] - } - ], - "source": [ - "print(geJournals.title)" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of George ELiot - George Henry Lewes Studies articles where journal title is 'George ELiot - George Henry Lewes Studies':\n" - ] - }, - { - "data": { - "text/plain": [ - "231" - ] - }, - "execution_count": 186, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(\"Number of George ELiot - George Henry Lewes Studies articles where journal title is 'George ELiot - George Henry Lewes Studies':\")\n", - "len(geJournals)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Diachronic Analysis of *GE-GHLS* Quotations" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GE-GHLS Quotations per book, per decade (weighted by length of quotation and normalized by decade):\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " 0 1 2 3 4 5 6 \\\n", - "1990 0.0 1.000000 0.658422 0.133021 0.121658 0.098930 0.116310 \n", - "2000 0.0 0.832359 0.805068 0.736842 1.000000 0.508772 0.666667 \n", - "2010 0.0 1.000000 0.613139 0.811436 0.364964 0.004866 0.074209 \n", - "\n", - " 7 8 \n", - "1990 0.097594 0.231283 \n", - "2000 0.249513 0.586745 \n", - "2010 0.209246 0.542579 " - ] - }, - "execution_count": 187, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Weighted by wordcount (by the number of words in the quotation) and normalized by decade(counts are scaled to the maximum value per decade)\n", - "GEGHLSbooksDiaDF = diachronicAnalysis(geJournals, decades=(1990, 2020), bins=bookLocations, useWordcounts=True, normalize=True).sort_index()\n", - "print('GE-GHLS Quotations per book, per decade (weighted by length of quotation and normalized by decade):')\n", - "GEGHLSbooksDiaDF" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [], - "source": [ - "GEGHLSbooksDiaDF['decade'] = GEGHLSbooksDiaDF.index" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "metadata": {}, - "outputs": [], - "source": [ - "GEGHLSbooksMelted = GEGHLSbooksDiaDF.melt(id_vars='decade', var_name='book')" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "metadata": {}, - "outputs": [], - "source": [ - "# cut out erroneous \"book 0\" material (ie title page)\n", - "GEGHLSbooksMelted = GEGHLSbooksMelted[GEGHLSbooksMelted.book != 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### *GE-GHLS* : Middlemarch quotations per book, per decade (normalized and weighted), table bubble plots" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of quotations per book, per decade in GE-GHLS\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " 0 1 2 3 4 5 6 7 8\n", - "1990 0 60 22 10 4 6 5 3 12\n", - "2000 0 16 19 11 11 9 12 3 9\n", - "2010 0 34 21 17 7 1 4 4 16" - ] - }, - "execution_count": 191, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Raw quotation counts (not weighted by the number of words in the quoatation or normalized by decade)\n", - "# Turning on useWordcounts makes it so that it's weighted by wordcount. Turning it off uses raw numbers of quotations.\n", - "GEGHLSbooksNotNormalizedNotWeightedDiaDF = diachronicAnalysis(geJournals, decades=(1960, 2020), bins=bookLocations,\\\n", - " useWordcounts=False, normalize=False).sort_index()\n", - "print('Number of quotations per book, per decade in GE-GHLS')\n", - "GEGHLSbooksNotNormalizedNotWeightedDiaDF" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [], - "source": [ - "GEGHLSbooksNotNormalizedNotWeightedDiaDF['decade'] = GEGHLSbooksNotNormalizedNotWeightedDiaDF.index" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": {}, - "outputs": [], - "source": [ - "GEGHLSbooksNotNormalizedNotWeightedDiaDFMelted = GEGHLSbooksNotNormalizedNotWeightedDiaDF.melt(id_vars='decade', var_name='book')" - ] - }, - { - "cell_type": "code", - "execution_count": 194, - "metadata": {}, - "outputs": [], - "source": [ - "# cut out erroneous \"book 0\" material (ie title page)\n", - "GEGHLSbooksNotNormalizedNotWeightedDiaDFMelted = GEGHLSbooksNotNormalizedNotWeightedDiaDFMelted[GEGHLSbooksNotNormalizedNotWeightedDiaDFMelted.book != 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### *GE-GHLS* : Middlemarch quotations per book, per decade (not normalized or weighted), table bubble plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### *GE-GHLS*: Number of quotations per chapter, per decade (not normalized or weighted)" - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": {}, - "outputs": [], - "source": [ - "# Raw quotation counts (not weighted by the number of words in the quoatation or normalized by decade)\n", - "# Turning on useWordcounts makes it so that it's weighted by wordcount. Turning it off uses raw numbers of quotations.\n", - "GEGHLSdiaDFquoteOnly = diachronicAnalysis(geJournals, decades=(1960, 2020), bins=chapterLocations, useWordcounts=False, normalize=False).sort_index()\n", - "GEGHLSdiaDFquoteOnly.columns.name ='chapter'\n", - "GEGHLSdiaDFquoteOnly.index.name = 'decade'" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "chapter 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 \\\n", - "decade \n", - "1990 1 5 2 6 1 5 7 3 1 2 10 10 7 2 1 6 0 \n", - "2000 2 1 2 3 1 0 0 0 0 3 4 0 0 0 1 9 3 \n", - "2010 8 6 3 1 0 1 4 1 1 4 2 0 3 0 0 4 0 \n", - "\n", - "chapter 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 \\\n", - "decade \n", - "1990 0 0 2 8 2 1 0 1 2 0 2 1 1 0 0 0 3 \n", - "2000 0 1 2 3 0 0 0 2 2 0 1 1 0 5 0 0 0 \n", - "2010 1 0 9 3 2 2 4 9 1 0 0 0 3 0 0 0 0 \n", - "\n", - "chapter 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 \\\n", - "decade \n", - "1990 0 1 0 3 0 0 0 0 0 1 0 4 1 0 0 0 0 \n", - "2000 0 0 0 2 1 1 1 0 6 1 1 1 0 0 2 3 1 \n", - "2010 0 2 1 0 0 1 1 0 2 0 0 0 0 0 0 0 0 \n", - "\n", - "chapter 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 \\\n", - "decade \n", - "1990 0 0 0 0 0 2 1 0 0 0 2 0 0 0 0 0 0 \n", - "2000 0 0 0 3 0 1 3 0 0 0 3 2 0 0 0 0 0 \n", - "2010 0 0 1 0 1 1 0 1 0 1 0 0 4 0 0 0 0 \n", - "\n", - "chapter 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 \\\n", - "decade \n", - "1990 0 0 0 3 0 0 1 0 1 0 0 2 0 0 0 2 0 \n", - "2000 1 1 0 1 0 0 0 1 0 0 0 0 0 2 0 2 0 \n", - "2010 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 \n", - "\n", - "chapter 85 86 87 \n", - "decade \n", - "1990 0 2 4 \n", - "2000 0 0 4 \n", - "2010 0 0 13 " - ] - }, - "execution_count": 196, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "GEGHLSdiaDFquoteOnly" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "metadata": {}, - "outputs": [], - "source": [ - "GEGHLSdiaDFquoteOnly['decade'] = GEGHLSdiaDFquoteOnly.index" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "metadata": {}, - "outputs": [], - "source": [ - "GEGHLSdiaDFquoteOnlyMelted = GEGHLSdiaDFquoteOnly.melt(id_vars='decade')" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 199, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alt.Chart(GEGHLSdiaDFquoteOnlyMelted, title=\"GE-GHLS Middlemarch quotations per chapter, per decade (not weighted or normalized)\").mark_circle().encode(x='chapter:O', \n", - " y=alt.Y('decade:O'), size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", - ".properties(width=1000, height=300).configure_legend(\n", - "titleFontSize=10,\n", - "labelFontSize=10\n", - ") " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### *GE-GHLS*: Number of quotations per chapter, per decade (normalized by decade and weighted by word count)" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": {}, - "outputs": [], - "source": [ - "# Weighted by wordcount (by the number of words in the quoatation) and normalized by decade(counts are scaled to the maximum value per decade)\n", - "GEGHLSnormalizeddiaDF = diachronicAnalysis(geJournals, decades=(1960, 2020), bins=chapterLocations, useWordcounts=True, normalize=True).sort_index()\n", - "GEGHLSnormalizeddiaDF.columns.name = 'chapter'\n", - "GEGHLSnormalizeddiaDF.index.name = 'decade'" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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chapter0123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687
decade
19900.0176990.3539820.0420350.2588500.0154870.3296460.3207960.1969030.0707960.1592920.3584070.4823010.7035400.0508850.0575220.5995580.0000000.0000000.0000000.3871681.0000000.0663720.0176990.0000000.1084070.0575220.00.0508850.0110620.0265490.0000000.00.00.1858410.00.0398230.0000000.3628320.0000000.0000000.0000000.00.0000000.0376110.0000000.2765490.0132740.00.0000000.0000000.0000000.00.00.0000000.0000000.0000000.0818580.0088500.0000000.00.0000000.2942480.0000000.0000000.00.00.00.00.0000000.0000000.00.3230090.0000000.00.0663720.0000000.0110620.00.00.2278760.0000000.0000000.00.1283190.00.00.0420350.289823
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" - ], - "text/plain": [ - "chapter 0 1 2 3 4 5 6 \\\n", - "decade \n", - "1990 0.017699 0.353982 0.042035 0.258850 0.015487 0.329646 0.320796 \n", - "2000 0.091575 0.161172 0.139194 0.238095 0.032967 0.000000 0.000000 \n", - "2010 0.288939 0.349887 0.273138 0.045147 0.000000 0.018059 0.275395 \n", - "\n", - "chapter 7 8 9 10 11 12 13 \\\n", - "decade \n", - "1990 0.196903 0.070796 0.159292 0.358407 0.482301 0.703540 0.050885 \n", - "2000 0.000000 0.000000 0.457875 0.443223 0.000000 0.000000 0.000000 \n", - "2010 0.079007 0.049661 0.108352 0.139955 0.000000 0.227991 0.000000 \n", - "\n", - "chapter 14 15 16 17 18 19 20 \\\n", - "decade \n", - "1990 0.057522 0.599558 0.000000 0.000000 0.000000 0.387168 1.000000 \n", - "2000 0.043956 1.000000 0.194139 0.000000 0.018315 0.080586 0.175824 \n", - "2010 0.000000 0.142212 0.000000 0.011287 0.000000 0.564334 0.158014 \n", - "\n", - "chapter 21 22 23 24 25 26 27 \\\n", - "decade \n", - "1990 0.066372 0.017699 0.000000 0.108407 0.057522 0.0 0.050885 \n", - "2000 0.000000 0.000000 0.000000 0.076923 0.395604 0.0 0.018315 \n", - "2010 0.160271 0.101580 0.081264 1.000000 0.009029 0.0 0.000000 \n", - "\n", - "chapter 28 29 30 31 32 33 34 35 \\\n", - "decade \n", - "1990 0.011062 0.026549 0.000000 0.0 0.0 0.185841 0.0 0.039823 \n", - "2000 0.468864 0.000000 0.424908 0.0 0.0 0.000000 0.0 0.000000 \n", - "2010 0.000000 0.415350 0.000000 0.0 0.0 0.000000 0.0 0.178330 \n", - "\n", - "chapter 36 37 38 39 40 41 42 \\\n", - "decade \n", - "1990 0.000000 0.362832 0.000000 0.000000 0.000000 0.0 0.000000 \n", - "2000 0.000000 0.703297 0.106227 0.146520 0.018315 0.0 0.904762 \n", - "2010 0.045147 0.000000 0.000000 0.173815 0.085779 0.0 0.194131 \n", - "\n", - "chapter 43 44 45 46 47 48 49 \\\n", - "decade \n", - "1990 0.037611 0.000000 0.276549 0.013274 0.0 0.000000 0.000000 \n", - "2000 0.223443 0.014652 0.219780 0.000000 0.0 0.131868 0.311355 \n", - "2010 0.000000 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 \n", - "\n", - "chapter 50 51 52 53 54 55 56 57 \\\n", - "decade \n", - "1990 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.081858 0.008850 \n", - "2000 0.054945 0.0 0.0 0.000000 0.164835 0.000000 0.201465 0.238095 \n", - "2010 0.000000 0.0 0.0 0.009029 0.000000 0.058691 0.054176 0.000000 \n", - "\n", - "chapter 58 59 60 61 62 63 64 65 66 \\\n", - "decade \n", - "1990 0.000000 0.0 0.000000 0.294248 0.000000 0.000000 0.0 0.0 0.0 \n", - "2000 0.000000 0.0 0.000000 0.402930 0.245421 0.000000 0.0 0.0 0.0 \n", - "2010 0.013544 0.0 0.011287 0.000000 0.000000 0.388262 0.0 0.0 0.0 \n", - "\n", - "chapter 67 68 69 70 71 72 73 74 \\\n", - "decade \n", - "1990 0.0 0.000000 0.000000 0.0 0.323009 0.000000 0.0 0.066372 \n", - "2000 0.0 0.413919 0.029304 0.0 0.025641 0.000000 0.0 0.000000 \n", - "2010 0.0 0.000000 0.000000 0.0 0.000000 0.038375 0.0 0.000000 \n", - "\n", - "chapter 75 76 77 78 79 80 81 82 \\\n", - "decade \n", - "1990 0.000000 0.011062 0.0 0.0 0.227876 0.000000 0.000000 0.0 \n", - "2000 0.205128 0.000000 0.0 0.0 0.000000 0.000000 0.179487 0.0 \n", - "2010 0.000000 0.000000 0.0 0.0 0.000000 0.108352 0.000000 0.0 \n", - "\n", - "chapter 83 84 85 86 87 \n", - "decade \n", - "1990 0.128319 0.0 0.0 0.042035 0.289823 \n", - "2000 0.245421 0.0 0.0 0.000000 0.472527 \n", - "2010 0.018059 0.0 0.0 0.000000 0.841986 " - ] - }, - "execution_count": 201, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "GEGHLSnormalizeddiaDF" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "metadata": {}, - "outputs": [], - "source": [ - "GEGHLSnormalizeddiaDF['decade'] = GEGHLSnormalizeddiaDF.index" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "metadata": {}, - "outputs": [], - "source": [ - "GEGHLSnormalizeddiaMelted = GEGHLSnormalizeddiaDF.melt(id_vars='decade')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### *GE-GHLS*: *Middlemarch* quotations per chapter, per decade (normalized and weighted), table bubble plots" - ] - }, - { - "cell_type": "code", - "execution_count": 204, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 204, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Redo Chart to rotate tick marks\n", - "alt.Chart(GEGHLSnormalizeddiaMelted, title=\"GE-GHLS Middlemarch quotations per chapter, per decade (normalized by decade and weighted by word count)\").mark_circle().encode(\n", - " x=alt.X('chapter:Q', axis=alt.Axis(tickMinStep=5,\n", - " labelOverlap=False,\n", - " labelAngle=0)), \n", - " y=alt.Y('decade:O'), \n", - " size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations (normalized)\"), \n", - " scale=alt.Scale(type = 'threshold', domain = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1], range =[0, 20, 60, 100, 150, 250, 350, 500, 750, 1000, 1500, 2000,]))).properties(width=1000, height=300).configure_legend(\n", - "titleFontSize=10,\n", - "labelFontSize=10\n", - ") " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compare the specialist journal, *George Eliot - George Henry Lewes Studies*, with all other journals" - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "metadata": {}, - "outputs": [], - "source": [ - "geJournals = df.loc[df['journal'] == 'George Eliot - George Henry Lewes Studies']\n", - "otherJournals = df.loc[df['journal'] != 'George Eliot - George Henry Lewes Studies']" - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "metadata": {}, - "outputs": [], - "source": [ - "# Normalize\n", - "geDF = synchronicAnalysis(geJournals)\n", - "otherDF = synchronicAnalysis(otherJournals)\n", - "normGE = geDF.div(geDF.max())\n", - "normOther = otherDF.div(otherDF.max())" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "FigureCanvasAgg.print_png() got an unexpected keyword argument 'bboxinches'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[207], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSpecialization Index\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Save a big version for publication. \u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m fig\u001b[38;5;241m.\u001b[39msavefig(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspecialization.png\u001b[39m\u001b[38;5;124m'\u001b[39m, bboxinches\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m'\u001b[39m, dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/matplotlib/figure.py:3343\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m 3339\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[1;32m 3340\u001b[0m stack\u001b[38;5;241m.\u001b[39menter_context(\n\u001b[1;32m 3341\u001b[0m ax\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39m_cm_set(facecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m, edgecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m-> 3343\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(fname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/matplotlib/backend_bases.py:2366\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2362\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2363\u001b[0m \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[1;32m 2364\u001b[0m \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[1;32m 2365\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2366\u001b[0m result \u001b[38;5;241m=\u001b[39m print_method(\n\u001b[1;32m 2367\u001b[0m filename,\n\u001b[1;32m 2368\u001b[0m facecolor\u001b[38;5;241m=\u001b[39mfacecolor,\n\u001b[1;32m 2369\u001b[0m edgecolor\u001b[38;5;241m=\u001b[39medgecolor,\n\u001b[1;32m 2370\u001b[0m orientation\u001b[38;5;241m=\u001b[39morientation,\n\u001b[1;32m 2371\u001b[0m bbox_inches_restore\u001b[38;5;241m=\u001b[39m_bbox_inches_restore,\n\u001b[1;32m 2372\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 2373\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 2374\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n", - "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/matplotlib/backend_bases.py:2232\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method..\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2228\u001b[0m optional_kws \u001b[38;5;241m=\u001b[39m { \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[1;32m 2229\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 2230\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m 2231\u001b[0m skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[0;32m-> 2232\u001b[0m print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: meth(\n\u001b[1;32m 2233\u001b[0m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m skip}))\n\u001b[1;32m 2234\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[1;32m 2235\u001b[0m print_method \u001b[38;5;241m=\u001b[39m meth\n", - "\u001b[0;31mTypeError\u001b[0m: FigureCanvasAgg.print_png() got an unexpected keyword argument 'bboxinches'" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "ax = (normGE - normOther).plot(kind='bar')\n", - "fig.add_subplot(ax)\n", - "ax.set_xlabel('Chapter')\n", - "ax.set_ylabel('Specialization Index')\n", - "# Save a big version for publication. \n", - "fig.savefig('specialization.png', bboxinches='tight', dpi=300)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## *NLH*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### NLH articles where journal title is \"New Literary History\"" - ] - }, - { - "cell_type": "code", - "execution_count": 208, - "metadata": {}, - "outputs": [], - "source": [ - "nlhJournals = df.loc[df['journal'] == 'New Literary History']" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_rows', 300)" - ] - }, - { - "cell_type": "code", - "execution_count": 210, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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creatordatePublisheddocSubTypedocTypeididentifierisPartOfissueNumberlanguageoutputFormatpageCountpageEndpageStartpaginationproviderpublicationYearpublishersourceCategorytdmCategorytitleurlvolumeNumberwordCountnumMatchesLocations in ALocations in BabstractkeyphrasesubTitleyearDecadeQuoted WordsLocations in A with WordcountsWordcountsjournal
122[Patrick Fessenbecker]2013-01-01research-articlearticlehttp://www.jstor.org/stable/24542541[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History1[eng][unigram, bigram, trigram]23.0139117pp. 117-139jstor2013The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]In Defense of Paraphrasehttp://www.jstor.org/stable/2454254144112354[[866220, 866260], [866343, 867046], [867327, ...[[26530, 26570], [26597, 27290], [27364, 27671...NoneNoneNone20132010286[([866220, 866260], 9), ([866343, 867046], 142...[9, 142, 59, 76]New Literary History
274[Martin Price]1973-01-01research-articlearticlehttp://www.jstor.org/stable/468483[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History2[eng][unigram, bigram, trigram]7.0387381pp. 381-387jstor1973Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Form and Discontenthttp://www.jstor.org/stable/468483430740[][]NoneNoneNone197319700[][]New Literary History
503[Joan E. Hartman]1987-10-01research-articlearticlehttp://www.jstor.org/stable/469303[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History1[eng][unigram, bigram, trigram]12.0116105pp. 105-116jstor1987Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Reflections on \"The Philosophical Bases of Fem...http://www.jstor.org/stable/4693031952421[[313385, 313629]][[16250, 16495]]NoneNoneNone1987198039[([313385, 313629], 39)][39]New Literary History
511[Nathan A. Scott, Jr.]1983-10-01research-articlearticlehttp://www.jstor.org/stable/468995[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History1[eng][unigram, bigram, trigram]26.011893pp. 93-118jstor1983Johns Hopkins University Press[Language & Literature, Humanities][History - Historical methodology]Pater's Imperative, to Dwell Poeticallyhttp://www.jstor.org/stable/46899515128020[][]NoneNoneNone198319800[][]New Literary History
787[Murray Baumgarten]1975-01-01research-articlearticlehttp://www.jstor.org/stable/468428[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History2[eng][unigram, bigram, trigram]13.0427415pp. 415-427jstor1975Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Metaphysics]From Realism to Expressionism: Toward a Histor...http://www.jstor.org/stable/468428657500[][]NoneNoneNone197519700[][]New Literary History
805[Robert Coles]1980-10-01research-articlearticlehttp://www.jstor.org/stable/468816[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History1[eng][unigram, bigram, trigram]5.0211207pp. 207-211jstor1980Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Commentary on \"Psychology and Literature\"http://www.jstor.org/stable/4688161220210[][]NoneNoneNone198019800[][]New Literary History
1131[Gary Saul Morson]2003-07-01research-articlearticlehttp://www.jstor.org/stable/20057791[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History3[eng][unigram, bigram, trigram]21.0429409pp. 409-429jstor2003Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Metaphysics, Philosophy - Episte...The Aphorism: Fragments from the Breakdown of ...http://www.jstor.org/stable/200577913489780[][]NoneNoneNone200320000[][]New Literary History
1323[Harold Fisch]1990-04-01research-articlearticlehttp://www.jstor.org/stable/469129[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History3[eng][unigram, bigram, trigram]14.0606593pp. 593-606jstor1990Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Character as Linguistic Signhttp://www.jstor.org/stable/4691292160880[][]NoneNoneNone199019900[][]New Literary History
1667[Thomas Pavel]1998-10-01research-articlearticlehttp://www.jstor.org/stable/20057501[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History4[eng][unigram, bigram, trigram]20.0598579pp. 579-598jstor1998Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Freedom, from Romance to the Novel: Three Anti...http://www.jstor.org/stable/200575012985780[][]NoneNoneNone199819900[][]New Literary History
1902[Susan Rubin Suleiman]1981-04-01research-articlearticlehttp://www.jstor.org/stable/469032[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History3[eng][unigram, bigram, trigram]13.0583571pp. 571-583jstor1981Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]The Place of Linguistics in Contemporary Liter...http://www.jstor.org/stable/4690321254750[][]NoneNoneNone198119800[][]New Literary History
2689[John M. Picker]2006-04-01research-articlearticlehttp://www.jstor.org/stable/20057949[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History2[eng][unigram, bigram, trigram]28.0388361pp. 361-388jstor2006Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]George Eliot and the Sequel Questionhttp://www.jstor.org/stable/2005794937136691[[1292109, 1292133]][[17409, 17433]]NoneNoneNone200620005[([1292109, 1292133], 5)][5]New Literary History
2816None1970-01-01miscarticlehttp://www.jstor.org/stable/468637[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History2[eng][unigram, bigram, trigram]8.0NoneNoneNonejstor1970Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Back Matterhttp://www.jstor.org/stable/468637122230[][]NoneNoneNone197019700[][]New Literary History
2896[Sara Danius]2008-10-01research-articlearticlehttp://www.jstor.org/stable/20533126[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History4[eng][unigram, bigram, trigram]28.01016989pp. 989-1016jstor2008Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Joyce's Scissors: Modernism and the Dissolutio...http://www.jstor.org/stable/2053312639135310[][]NoneNoneNone200820000[][]New Literary History
2988[Gary Saul Morson]2009-10-01research-articlearticlehttp://www.jstor.org/stable/40666450[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History4[eng][unigram, bigram, trigram]23.0865843pp. 843-865jstor2009The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Return to Process: The Unfolding of ...http://www.jstor.org/stable/4066645040102150[][]NoneNoneNone200920000[][]New Literary History
3444[Hilary Putnam]1983-10-01research-articlearticlehttp://www.jstor.org/stable/469002[{'name': 'issn', 'value': '00286087'}, {'name...New Literary History1[eng][unigram, bigram, trigram]8.0200193pp. 193-200jstor1983Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Epistemology, Social sciences - ...Taking Rules Seriously: A Response to Martha N...http://www.jstor.org/stable/4690021533631[[1327580, 1327746]][[6581, 6860]]NoneNoneNone1983198026[([1327580, 1327746], 26)][26]New Literary History
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[46, 111, 50] \n", - "5443 [] [] \n", - "5463 [([449859, 450244], 64)] [64] \n", - "5789 [] [] \n", - "5808 [] [] \n", - "\n", - " journal \n", - "122 New Literary History \n", - "274 New Literary History \n", - "503 New Literary History \n", - "511 New Literary History \n", - "787 New Literary History \n", - "805 New Literary History \n", - "1131 New Literary History \n", - "1323 New Literary History \n", - "1667 New Literary History \n", - "1902 New Literary History \n", - "2689 New Literary History \n", - "2816 New Literary History \n", - "2896 New Literary History \n", - "2988 New Literary History \n", - "3444 New Literary History \n", - "3459 New Literary History \n", - "3668 New Literary History \n", - "3697 New Literary History \n", - "3973 New Literary History \n", - "4113 New Literary History \n", - "4337 New Literary History \n", - "4611 New Literary History \n", - "5300 New Literary History \n", - "5332 New Literary History \n", - "5348 New Literary History \n", - "5443 New Literary History \n", - "5463 New Literary History \n", - "5789 New Literary History \n", - "5808 New Literary History " - ] - }, - "execution_count": 210, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nlhJournals " - ] - }, - { - "cell_type": "code", - "execution_count": 211, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of NLH articles where journal title is 'New Literary History':\n" - ] - }, - { - "data": { - "text/plain": [ - "29" - ] - }, - "execution_count": 211, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(\"Number of NLH articles where journal title is 'New Literary History':\")\n", - "len(nlhJournals)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## *ELH*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ELH articles where journal title is \"ELH\"" - ] - }, - { - "cell_type": "code", - "execution_count": 212, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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24[ANDREW H. MILLER]2012-10-01research-articlearticlehttp://www.jstor.org/stable/23256775[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]24.0796773pp. 773-796jstor2012The Johns Hopkins University Press[Humanities, Language & Literature][Arts - Literature]\"A CASE OF METAPHYSICS\": COUNTERFACTUALS, REAL...http://www.jstor.org/stable/2325677579106363[[8757, 8787], [1047312, 1047351], [1793006, 1...[[25399, 25429], [25765, 25804], [26424, 26549]]NoneNoneNone2012201035[([8757, 8787], 6), ([1047312, 1047351], 6), (...[6, 6, 23]ELH
214[Brian Swann]1972-06-01research-articlearticlehttp://www.jstor.org/stable/2872247[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]30.0308279pp. 279-308jstor1972Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Middlemarch: Realism and Symbolic Formhttp://www.jstor.org/stable/287224739130133[[698733, 699017], [1368201, 1368353], [169094...[[15264, 15597], [29017, 29180], [40262, 40962]]NoneNoneNone19721970204[([698733, 699017], 48), ([1368201, 1368353], ...[48, 26, 130]ELH
452[John M. Picker]1998-10-01research-articlearticlehttp://www.jstor.org/stable/30030197[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]16.0652637pp. 637-652jstor1998Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Disturbing Surfaces: Representations of the Fr...http://www.jstor.org/stable/300301976568101[[452081, 452143]][[34791, 34853]]NoneNoneNone199819908[([452081, 452143], 8)][8]ELH
457[William J. Overton]1978-07-01research-articlearticlehttp://www.jstor.org/stable/2872517[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]21.0302285pp. 285-302jstor1978Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy]Self and Society in Trollopehttp://www.jstor.org/stable/28725174588061[[379883, 379939]][[29261, 29317]]NoneNoneNone197819707[([379883, 379939], 7)][7]ELH
557[RUTH ABBOTT]2015-12-01research-articlearticlehttp://www.jstor.org/stable/24735517[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]33.012111179pp. 1179-1211jstor2015The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]GEORGE ELIOT, METER, AND THE MATTER OF IDEAS: ...http://www.jstor.org/stable/2473551782140281[[476590, 477092]][[73432, 73930]]NoneNoneNone2015201092[([476590, 477092], 92)][92]ELH
573[Jesse M. Molesworth]2007-07-01research-articlearticlehttp://www.jstor.org/stable/30029566[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]16.0508493pp. 493-508jstor2007Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]\"A Dreadful Course of Calamities\": Roxana's En...http://www.jstor.org/stable/300295667469530[][]NoneNoneNone200720000[][]ELH
652[Claude T. Bissell]1951-09-01research-articlearticlehttp://www.jstor.org/stable/2871810[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]19.0239221pp. 221-239jstor1951Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Social Analysis in the Novels of George Eliothttp://www.jstor.org/stable/28718101878341[[992656, 992778]][[42918, 43038]]NoneNoneNone1951195026[([992656, 992778], 26)][26]ELH
680None2013-10-01miscarticlehttp://www.jstor.org/stable/24475536[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]9.0NoneNoneNonejstor2013The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Front Matterhttp://www.jstor.org/stable/244755368011910[][]NoneNoneNone201320100[][]ELH
769None2007-01-01miscarticlehttp://www.jstor.org/stable/30029595[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]4.0NoneNoneNonejstor2007Johns Hopkins University Press[Language & Literature, Humanities]NoneVolume Informationhttp://www.jstor.org/stable/30029595745370[][]NoneNoneNone200720000[][]ELH
862[Joseph M. Duffy, Jr.]1968-09-01research-articlearticlehttp://www.jstor.org/stable/2872284[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]19.0421403pp. 403-421jstor1968Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature, Philosophy - Applied philo...Another Version of Pastoral: Oliver Twisthttp://www.jstor.org/stable/28722843581410[][]NoneNoneNone196819600[][]ELH
1035[Rosemary Clark-Beattie]1986-12-01research-articlearticlehttp://www.jstor.org/stable/2873176[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]27.0847821pp. 821-847jstor1986Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy, Arts - Liter...Fables of Rebellion: Anti-Catholicism and the ...http://www.jstor.org/stable/287317653114650[][]NoneNoneNone198619800[][]ELH
1074[Hilda Hollis]2001-04-01research-articlearticlehttp://www.jstor.org/stable/30031962[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]23.0177155pp. 155-177jstor2001Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature, Philosophy - Applied philo...Felix Holt: Independent Spokesman or Eliot's M...http://www.jstor.org/stable/3003196268106820[][]NoneNoneNone200120000[][]ELH
1140[J. M. Rignall]1984-10-01research-articlearticlehttp://www.jstor.org/stable/2872938[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]13.0587575pp. 575-587jstor1984Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Dickens and the Catastrophic Continuum of Hist...http://www.jstor.org/stable/28729385158030[][]NoneNoneNone198419800[][]ELH
1142[Michael Peled Ginsburg]1980-10-01research-articlearticlehttp://www.jstor.org/stable/2872795[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]17.0558542pp. 542-558jstor1980Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Pseudonym, Epigraphs, and Narrative Voice: Mid...http://www.jstor.org/stable/287279547743012[[11700, 11779], [40138, 40450], [48998, 49711...[[5441, 5520], [6026, 6337], [20063, 20772], [...NoneNoneNone19801980569[([11700, 11779], 14), ([40138, 40450], 46), (...[14, 46, 130, 21, 14, 25, 28, 111, 42, 93, 8, 37]ELH
1197[FRANCES FERGUSON]2013-07-01research-articlearticlehttp://www.jstor.org/stable/24475509[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]19.0341323pp. 323-341jstor2013The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]PHILOLOGY, LITERATURE, STYLEhttp://www.jstor.org/stable/244755098084280[][]NoneNoneNone201320100[][]ELH
1245[Sarah Gilead]1986-04-01research-articlearticlehttp://www.jstor.org/stable/2873153[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]15.0197183pp. 183-197jstor1986Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy]Liminality, Anti-Liminality, and the Victorian...http://www.jstor.org/stable/28731535359750[][]NoneNoneNone198619800[][]ELH
1365[Rebecca F. Stern]1998-07-01research-articlearticlehttp://www.jstor.org/stable/30030186[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]27.0449423pp. 423-449jstor1998Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Epistemology, Philosophy - Appli...Moving Parts and Speaking Parts: Situating Vic...http://www.jstor.org/stable/3003018665120210[][]NoneNoneNone199819900[][]ELH
1451[Thomas Albrecht]2006-07-01research-articlearticlehttp://www.jstor.org/stable/30030019[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]27.0463437pp. 437-463jstor2006Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Metaphysics, Philosophy - Episte...Sympathy and Telepathy: The Problem of Ethics ...http://www.jstor.org/stable/3003001973128472[[173657, 173756], [292143, 292406]][[14718, 14816], [64553, 64816]]NoneNoneNone2006200065[([173657, 173756], 18), ([292143, 292406], 47)][18, 47]ELH
1483[RACHEL ABLOW]2013-12-01research-articlearticlehttp://www.jstor.org/stable/24475530[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]27.011711145pp. 1145-1171jstor2013The Johns Hopkins University Press[Language & Literature, Humanities][Social sciences - Behavioral sciences]TORTURED SYMPATHIES: VICTORIAN LITERATURE AND ...http://www.jstor.org/stable/2447553080126416[[449670, 449764], [1689183, 1689217], [169033...[[23394, 23526], [42759, 42793], [42821, 42914...NoneNoneNone20132010178[([449670, 449764], 19), ([1689183, 1689217], ...[19, 6, 16, 6, 9, 122]ELH
1532[MARK ALLISON]2011-10-01research-articlearticlehttp://www.jstor.org/stable/41236564[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]25.0739715pp. 715-739jstor2011The Johns Hopkins University Press[Humanities, Language & Literature][Philosophy - Applied philosophy]UTOPIAN SOCIALISM, WOMEN'S EMANCIPATION, AND T...http://www.jstor.org/stable/4123656478104019[[13154, 13237], [13340, 13383], [176340, 1764...[[5637, 5717], [5724, 5767], [8798, 8898], [15...NoneNoneNone20112010205[([13154, 13237], 15), ([13340, 13383], 8), ([...[15, 8, 18, 66, 9, 43, 9, 29, 8]ELH
1672[Dorothy M. Mermin]1976-04-01research-articlearticlehttp://www.jstor.org/stable/2872464[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]20.0119100pp. 100-119jstor1976Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Poetry as Fiction: Meredith's Modern Lovehttp://www.jstor.org/stable/28724644380890[][]NoneNoneNone197619700[][]ELH
1698[Jeremy Tambling]1990-12-01research-articlearticlehttp://www.jstor.org/stable/2873091[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]22.0960939pp. 939-960jstor1990Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature, Philosophy - Metaphysics]Middlemarch, Realism and the Birth of the Clinichttp://www.jstor.org/stable/287309157936914[[291679, 291940], [297602, 297771], [298305, ...[[1777, 2037], [4485, 4686], [4796, 4849], [52...NoneNoneNone19901990440[([291679, 291940], 45), ([297602, 297771], 29...[45, 29, 9, 13, 10, 38, 26, 46, 13, 22, 62, 69...ELH
1740None1990-12-01miscarticlehttp://www.jstor.org/stable/2873082[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]5.01003999pp. 999-1003jstor1990Johns Hopkins University Press[Language & Literature, Humanities]NoneVolume Informationhttp://www.jstor.org/stable/2873082576120[][]NoneNoneNone199019900[][]ELH
1758[STEPHEN ARATA]2014-10-01research-articlearticlehttp://www.jstor.org/stable/24475614[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]21.010271007pp. 1007-1027jstor2014The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]DECADENT FORMhttp://www.jstor.org/stable/244756148198130[][]NoneNoneNone201420100[][]ELH
1857None2010-12-01miscarticlehttp://www.jstor.org/stable/40963121[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]7.0NoneNoneNonejstor2010The Johns Hopkins University Press[Language & Literature, Humanities]NoneBack Matterhttp://www.jstor.org/stable/40963121779860[][]NoneNoneNone201020100[][]ELH
1862None2011-10-01miscarticlehttp://www.jstor.org/stable/41236555[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]4.0NoneNoneNonejstor2011The Johns Hopkins University Press[Humanities, Language & Literature]NoneFront Matterhttp://www.jstor.org/stable/41236555785400[][]NoneNoneNone201120100[][]ELH
1914[Deanna K. Kreisel]2003-07-01research-articlearticlehttp://www.jstor.org/stable/30029887[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]34.0574541pp. 541-574jstor2003Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Incognito, Intervention, and Dismemberment in ...http://www.jstor.org/stable/3002988770150180[][]NoneNoneNone200320000[][]ELH
1915[Robert Preyer]1965-03-01research-articlearticlehttp://www.jstor.org/stable/2872372[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]23.08462pp. 62-84jstor1965Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Two Styles in the Verse of Robert Browninghttp://www.jstor.org/stable/28723723280970[][]NoneNoneNone196519600[][]ELH
1929[Bernard J. Paris]1962-12-01research-articlearticlehttp://www.jstor.org/stable/2871945[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]26.0443418pp. 418-443jstor1962Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy]George Eliot's Religion of Humanityhttp://www.jstor.org/stable/287194529106691[[1735890, 1736157]][[61502, 61765]]NoneNoneNone1962196052[([1735890, 1736157], 52)][52]ELH
1932[Sarah Gates]2001-10-01research-articlearticlehttp://www.jstor.org/stable/30031989[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]26.0724699pp. 699-724jstor2001Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]\"A Difference of Native Language\": Gender, Gen...http://www.jstor.org/stable/3003198968119141[[242610, 242688]][[33272, 33349]]NoneNoneNone2001200016[([242610, 242688], 16)][16]ELH
1953None1994-12-01miscarticlehttp://www.jstor.org/stable/2873355[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]9.0922782pp. 782-922jstor1994Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature, Social sciences - Communic...Front Matterhttp://www.jstor.org/stable/28733556121880[][]NoneNoneNone199419900[][]ELH
2005[George Levine]1963-09-01research-articlearticlehttp://www.jstor.org/stable/2872038[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]14.0257244pp. 244-257jstor1963Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Isabel, Gwendolen, and Dorotheahttp://www.jstor.org/stable/28720383054354[[5420, 5488], [95746, 95797], [127877, 127994...[[9943, 10026], [12449, 12500], [16587, 16704]...NoneNoneNone19631960132[([5420, 5488], 11), ([95746, 95797], 10), ([1...[11, 10, 20, 91]ELH
2010[ELSIE MICHIE]2013-10-01research-articlearticlehttp://www.jstor.org/stable/24475546[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]20.0916897pp. 897-916jstor2013The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]HISTORY AFTER WATERLOO: MARGARET OLIPHANT READ...http://www.jstor.org/stable/244755468092250[][]NoneNoneNone201320100[][]ELH
2125[Elizabeth Duquette]2005-10-01research-articlearticlehttp://www.jstor.org/stable/30030070[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]29.0745717pp. 717-745jstor2005Johns Hopkins University Press[Language & Literature, Humanities][Arts - Art history]\"A New Claim for the Family Renown\": Alice Jam...http://www.jstor.org/stable/3003007072129581[[839129, 839214]][[80058, 80143]]NoneNoneNone2005200012[([839129, 839214], 12)][12]ELH
2351None2002-04-01miscarticlehttp://www.jstor.org/stable/30032007[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]4.0NoneNoneNonejstor2002Johns Hopkins University Press[Language & Literature, Humanities]NoneVolume Informationhttp://www.jstor.org/stable/30032007695390[][]NoneNoneNone200220000[][]ELH
2452None2002-04-01miscarticlehttp://www.jstor.org/stable/30032008[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]10.0NoneNoneNonejstor2002Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Front Matterhttp://www.jstor.org/stable/300320086916790[][]NoneNoneNone200220000[][]ELH
2496[Lauren M. E. Goodlad]2000-04-01research-articlearticlehttp://www.jstor.org/stable/30031909[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]36.0178143pp. 143-178jstor2000Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy]\"A Middle Class Cut into Two\": Historiography ...http://www.jstor.org/stable/3003190967152071[[1174261, 1174303]][[50667, 50709]]NoneNoneNone200020008[([1174261, 1174303], 8)][8]ELH
2509[Melissa J. Ganz]2008-10-01research-articlearticlehttp://www.jstor.org/stable/27654626[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]38.0602565pp. 565-602jstor2008Johns Hopkins University Press[Language & Literature, Humanities][Law - Civil law]Binding the Will: George Eliot and the Practic...http://www.jstor.org/stable/27654626751802512[[36628, 36671], [1019006, 1019209], [1019478,...[[37159, 37202], [37699, 37918], [37994, 38254...NoneNoneNone20082000386[([36628, 36671], 7), ([1019006, 1019209], 36)...[7, 36, 47, 13, 30, 34, 37, 119, 8, 9, 6, 40]ELH
2538[CLAIRE JARVIS]2014-12-01research-articlearticlehttp://www.jstor.org/stable/24477777[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]21.012731253pp. 1253-1273jstor2014The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]PIP'S LIFEhttp://www.jstor.org/stable/244777778197730[][]NoneNoneNone201420100[][]ELH
2613[Anna Neill]2008-12-01research-articlearticlehttp://www.jstor.org/stable/27654643[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]24.0962939pp. 939-962jstor2008Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy]The Primitive Mind of \"Silas Marner\"http://www.jstor.org/stable/2765464375105570[][]NoneNoneNone200820000[][]ELH
2706[JAMES BUZARD]2014-12-01research-articlearticlehttp://www.jstor.org/stable/24477776[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]27.012511225pp. 1225-1251jstor2014The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]ITEM OF MORTALITY: LIVES LED AND UNLED IN \"OLI...http://www.jstor.org/stable/2447777681124180[][]NoneNoneNone201420100[][]ELH
2722[DANIEL COTTOM]2012-10-01research-articlearticlehttp://www.jstor.org/stable/23256766[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]31.0567537pp. 537-567jstor2012The Johns Hopkins University Press[Humanities, Language & Literature][Arts - Art history, Arts - Literature]SHERLOCK HOLMES MEETS DRACULAhttp://www.jstor.org/stable/2325676679141160[][]NoneNoneNone201220100[][]ELH
2778[Peter Allen]1988-07-01research-articlearticlehttp://www.jstor.org/stable/2873214[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]17.0503487pp. 487-503jstor1988Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Sir Edmund Gosse and his Modern Readers: The C...http://www.jstor.org/stable/28732145573000[][]NoneNoneNone198819800[][]ELH
2920[Christopher Lane]2002-04-01research-articlearticlehttp://www.jstor.org/stable/30032016[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]24.0222199pp. 199-222jstor2002Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy]Charlotte Brontë on the Pleasure of Hatinghttp://www.jstor.org/stable/3003201669104140[][]NoneNoneNone200220000[][]ELH
2933[Jessie Givner]2002-04-01research-articlearticlehttp://www.jstor.org/stable/30032017[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]21.0243223pp. 223-243jstor2002Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Industrial History, Preindustrial Literature: ...http://www.jstor.org/stable/3003201769940310[[761927, 761995], [888787, 888912], [1175936,...[[347, 415], [1790, 1915], [15106, 15538], [22...NoneNoneNone20022000307[([761927, 761995], 11), ([888787, 888912], 19...[11, 19, 74, 10, 18, 110, 30, 9, 11, 15]ELH
3140[Jonathan Arac]1979-12-01research-articlearticlehttp://www.jstor.org/stable/2872484[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]20.0692673pp. 673-692jstor1979Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Rhetoric and Realism in Nineteenth-Century Fic...http://www.jstor.org/stable/28724844686430[][]NoneNoneNone197919700[][]ELH
3220[Jay Clayton]1979-12-01research-articlearticlehttp://www.jstor.org/stable/2872483[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]28.0672645pp. 645-672jstor1979Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Visionary Power and Narrative Form: Wordsworth...http://www.jstor.org/stable/287248346122420[][]NoneNoneNone197919700[][]ELH
3287None1988-12-01miscarticlehttp://www.jstor.org/stable/2873132[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]4.0NoneNoneNonejstor1988Johns Hopkins University Press[Language & Literature, Humanities]NoneVolume Informationhttp://www.jstor.org/stable/2873132554730[][]NoneNoneNone198819800[][]ELH
3308[Bonnie Zimmerman]1979-10-01research-articlearticlehttp://www.jstor.org/stable/2872689[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]20.0451432pp. 432-451jstor1979Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Felix Holt and the True Power of Womanhoodhttp://www.jstor.org/stable/28726894688400[][]NoneNoneNone197919700[][]ELH
3324[Walter E. Houghton]1946-03-01research-articlearticlehttp://www.jstor.org/stable/2871500[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]15.07864pp. 64-78jstor1946Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]The Meaning of Keats's Eve of St. Markhttp://www.jstor.org/stable/28715001361665[[82, 119], [207, 802], [952, 1011], [1213, 16...[[27377, 27414], [27426, 28615], [28623, 28688...NoneNoneNone19461940195[([82, 119], 8), ([207, 802], 98), ([952, 1011...[8, 98, 8, 70, 11]ELH
3338[Nina Auerbach]1975-10-01research-articlearticlehttp://www.jstor.org/stable/2872711[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]25.0419395pp. 395-419jstor1975Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Incarnations of the Orphanhttp://www.jstor.org/stable/287271142113590[][]NoneNoneNone197519700[][]ELH
3380[ANNA KORNBLUH]2010-12-01research-articlearticlehttp://www.jstor.org/stable/40963115[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]27.0967941pp. 941-967jstor2010The Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy]THE ECONOMIC PROBLEM OF SYMPATHY: PARABASIS, I...http://www.jstor.org/stable/40963115771195411[[169761, 169925], [171205, 171814], [410171, ...[[24312, 24732], [25621, 26230], [30117, 30156...NoneNoneNone20102010582[([169761, 169925], 28), ([171205, 171814], 11...[28, 111, 7, 129, 73, 11, 100, 29, 31, 25, 38]ELH
3433[Jeff Nunokawa]2002-12-01research-articlearticlehttp://www.jstor.org/stable/30032047[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]26.0860835pp. 835-860jstor2002Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Eros and Isolation: The Antisocial George Eliothttp://www.jstor.org/stable/3003204769120425[[82, 813], [827, 1083], [1106, 1205], [2860, ...[[14538, 15266], [15279, 15534], [15556, 15658...NoneNoneNone20022000245[([82, 813], 122), ([827, 1083], 40), ([1106, ...[122, 40, 17, 30, 36]ELH
3463[Ernest Tuveson]1966-06-01research-articlearticlehttp://www.jstor.org/stable/2872392[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]24.0270247pp. 247-270jstor1966Johns Hopkins University Press[Language & Literature, Humanities][Religion - Spiritual belief systems, Religion...The Creed of the Confidence-Manhttp://www.jstor.org/stable/28723923396160[][]NoneNoneNone196619600[][]ELH
3520None1986-07-01miscarticlehttp://www.jstor.org/stable/2873265[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]3.0NoneNoneNonejstor1986Johns Hopkins University Press[Language & Literature, Humanities]NoneBack Matterhttp://www.jstor.org/stable/2873265537230[][]NoneNoneNone198619800[][]ELH
3522None1988-12-01miscarticlehttp://www.jstor.org/stable/2873133[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]8.0916754pp. 754-916jstor1988Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Front Matterhttp://www.jstor.org/stable/28731335515500[][]NoneNoneNone198819800[][]ELH
3529[GEORGE LEVINE]2016-04-01research-articlearticlehttp://www.jstor.org/stable/24735474[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]26.0258233pp. 233-258jstor2016The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]TAKING OLIPHANT SERIOUSLY: A COUNTRY GENTLEMAN...http://www.jstor.org/stable/2473547483115491[[1691186, 1691245]][[13597, 13656]]NoneNoneNone2016201013[([1691186, 1691245], 13)][13]ELH
3585[MARY JEAN CORBETT]2014-04-01research-articlearticlehttp://www.jstor.org/stable/24475596[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]25.0323299pp. 299-323jstor2014The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]NO SECOND FRIEND?: PERPETUAL MAIDENHOOD AND SE...http://www.jstor.org/stable/2447559681108341[[804029, 804078]][[39747, 39771]]NoneNoneNone201420108[([804029, 804078], 8)][8]ELH
3595[John H. Hagan, Jr.]1954-03-01research-articlearticlehttp://www.jstor.org/stable/2871933[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]13.06654pp. 54-66jstor1954Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Structural Patterns in Dickens's Great Expecta...http://www.jstor.org/stable/28719332154640[][]NoneNoneNone195419500[][]ELH
3609[EMILY COIT]2015-12-01research-articlearticlehttp://www.jstor.org/stable/24735518[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]26.012381213pp. 1213-1238jstor2015The Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy]MARY AUGUSTA WARD'S \"PERFECT ECONOMIST\" AND TH...http://www.jstor.org/stable/2473551882115241[[1726073, 1726326]][[9154, 9405]]NoneNoneNone2015201048[([1726073, 1726326], 48)][48]ELH
3693None1972-06-01miscarticlehttp://www.jstor.org/stable/2872240[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]2.0NoneNoneNonejstor1972Johns Hopkins University Press[Language & Literature, Humanities]NoneFront Matterhttp://www.jstor.org/stable/2872240392840[][]NoneNoneNone197219700[][]ELH
3701[J. Hillis Miller]1974-10-01research-articlearticlehttp://www.jstor.org/stable/2872596[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]19.0473455pp. 455-473jstor1974Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Narrative and Historyhttp://www.jstor.org/stable/28725964185708[[82, 260], [196063, 196541], [408633, 409407]...[[22354, 22532], [23981, 24459], [24501, 25274...NoneNoneNone19741970335[([82, 260], 32), ([196063, 196541], 71), ([40...[32, 71, 130, 28, 7, 22, 28, 17]ELH
3896[Dennis Taylor]1975-07-01research-articlearticlehttp://www.jstor.org/stable/2872628[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]18.0275258pp. 258-275jstor1975Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature, Arts - Art history]The Patterns in Hardy's Poetryhttp://www.jstor.org/stable/28726284274080[][]NoneNoneNone197519700[][]ELH
3898[J. Jeffrey Franklin]2005-01-01research-articlearticlehttp://www.jstor.org/stable/30029996[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]34.0974941pp. 941-974jstor2005Johns Hopkins University Press[Language & Literature, Humanities][Religion - Theology]The Life of the Buddha in Victorian Englandhttp://www.jstor.org/stable/3002999672148881[[425745, 425769]][[834, 858]]NoneNoneNone200520004[([425745, 425769], 4)][4]ELH
3960None2013-12-01miscarticlehttp://www.jstor.org/stable/24475534[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]7.0NoneNoneNonejstor2013The Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy]Back Matterhttp://www.jstor.org/stable/244755348011740[][]NoneNoneNone201320100[][]ELH
4009[EMILY STEINLIGHT]2012-07-01research-articlearticlehttp://www.jstor.org/stable/23256763[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]35.0535501pp. 501-535jstor2012The Johns Hopkins University Press[Humanities, Language & Literature][Arts - Literature]WHY NOVELS ARE REDUNDANT: SENSATION FICTION AN...http://www.jstor.org/stable/2325676379160060[][]NoneNoneNone201220100[][]ELH
4112None1994-12-01miscarticlehttp://www.jstor.org/stable/2873354[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]6.0NoneNoneNonejstor1994Johns Hopkins University Press[Language & Literature, Humanities]NoneVolume Informationhttp://www.jstor.org/stable/2873354616390[][]NoneNoneNone199419900[][]ELH
4114[John P. Farrell]1989-04-01research-articlearticlehttp://www.jstor.org/stable/2873128[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]36.0208173pp. 173-208jstor1989Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Reading the Text of Community in Wuthering Hei...http://www.jstor.org/stable/287312856161430[][]NoneNoneNone198919800[][]ELH
4119None2007-10-01miscarticlehttp://www.jstor.org/stable/30029569[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]5.0NoneNoneNonejstor2007Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature, Education - Educational re...Front Matterhttp://www.jstor.org/stable/300295697411260[][]NoneNoneNone200720000[][]ELH
4203[Helena Michie]1989-07-01research-articlearticlehttp://www.jstor.org/stable/2873065[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]21.0421401pp. 401-421jstor1989Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]\"There is no Friend Like a Sister\": Sisterhood...http://www.jstor.org/stable/28730655681240[][]NoneNoneNone198919800[][]ELH
4289[JENNIFER ESMAIL]2011-12-01research-articlearticlehttp://www.jstor.org/stable/41337562[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]30.01020991pp. 991-1020jstor2011The Johns Hopkins University Press[Humanities, Language & Literature][Arts - Literature]\"I LISTENED WITH MY EYES\": WRITING SPEECH AND ...http://www.jstor.org/stable/4133756278129810[][]NoneNoneNone201120100[][]ELH
4303None2011-12-01miscarticlehttp://www.jstor.org/stable/41337563[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]9.0NoneNoneNonejstor2011The Johns Hopkins University Press[Humanities, Language & Literature][Arts - Literature]Back Matterhttp://www.jstor.org/stable/413375637813800[][]NoneNoneNone201120100[][]ELH
4324[Walter L. Reed]1971-09-01research-articlearticlehttp://www.jstor.org/stable/2872227[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]21.0431411pp. 411-431jstor1971Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]The Pattern of Conversion in Sartor Resartushttp://www.jstor.org/stable/28722273886351[[173657, 173756]][[49225, 49339]]NoneNoneNone1971197018[([173657, 173756], 18)][18]ELH
4408[John S. Diekhoff]1936-09-01research-articlearticlehttp://www.jstor.org/stable/2871573[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]7.0227221pp. 221-227jstor1936Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]The Happy Ending of Adam Bedehttp://www.jstor.org/stable/2871573329080[][]NoneNoneNone193619300[][]ELH
4596None1996-04-01miscarticlehttp://www.jstor.org/stable/30030281[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]9.0NoneNoneNonejstor1996Johns Hopkins University Press[Language & Literature, Humanities][Social sciences - Communications, Education -...Back Matterhttp://www.jstor.org/stable/300302816316990[][]NoneNoneNone199619900[][]ELH
4698[Amit Yahav-Brown]2006-01-01research-articlearticlehttp://www.jstor.org/stable/30030039[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]26.0830805pp. 805-830jstor2006Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy]Reasonableness and Domestic Fictionhttp://www.jstor.org/stable/3003003973122660[][]NoneNoneNone200620000[][]ELH
4715[JORDAN BROWER]2016-04-01research-articlearticlehttp://www.jstor.org/stable/24735473[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]22.0232211pp. 211-232jstor2016The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]\"THE MILL ON THE FLOSS\", RIPARIAN LAW, AND THE...http://www.jstor.org/stable/247354738395751[[75484, 75511]][[55852, 55883]]NoneNoneNone201620105[([75484, 75511], 5)][5]ELH
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4766[Karen B. Mann]1981-04-01research-articlearticlehttp://www.jstor.org/stable/2873017[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]27.0216190pp. 190-216jstor1981Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]George Eliot's Language of Nature: Production ...http://www.jstor.org/stable/287301748116703[[22883, 22932], [26729, 26764], [34203, 34232]][[42105, 42155], [42595, 42633], [42985, 43014]]NoneNoneNone1981198021[([22883, 22932], 9), ([26729, 26764], 7), ([3...[9, 7, 5]ELHdecade
4788[David Kurnick]2007-10-01research-articlearticlehttp://www.jstor.org/stable/30029573[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]26.0608583pp. 583-608jstor2007Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]An Erotics of Detachment: \"Middlemarch\" and No...http://www.jstor.org/stable/30029573741158911[[746, 892], [1106, 1149], [2000, 2160], [2541...[[16443, 16601], [17083, 17126], [17466, 17627...NoneNoneNone20072000383[([746, 892], 22), ([1106, 1149], 6), ([2000, ...[22, 6, 27, 24, 36, 135, 61, 8, 8, 20, 36]ELH19600.1195140.1195140.2661060.0681610.0121380.0224090.0000000.1045750.0084030.2679740.2306260.0364150.0746970.0000000.0000000.6218490.2726420.0205420.0000000.0056021.0000000.3725490.0532210.0196080.0130720.0056020.0046690.1839400.2987860.0000000.0065360.0065360.0000000.0000000.0000000.1064430.0485530.1568630.0000000.0308120.0457520.3202610.1232490.0242760.0000000.0000000.3267970.0476190.0261440.0000000.2287580.0074700.0532210.4145660.0028010.0000000.1027080.0000000.0158730.0000000.0140060.0242760.0000000.0205420.0224090.0000000.0746970.0000000.0074700.0000000.0000000.2651730.0000000.0046690.0000000.0149390.1307190.0177400.1615310.0392160.1830070.2259570.0037350.0653590.0000000.00.0224090.228758
4826[U. C. Knoepflmacher]1967-12-01research-articlearticlehttp://www.jstor.org/stable/2872183[{'name': 'issn', 'value': '00138304'}, {'name...ELH4[eng][unigram, bigram, trigram]23.0540518pp. 518-540jstor1967Johns Hopkins University Press[Language & Literature, Humanities][Religion - Spiritual belief systems]The Post-Romantic Imagination: Adam Bede, Word...http://www.jstor.org/stable/28721833495790[][]NoneNoneNone196719600[][]ELH19700.2240550.5752580.5841920.3938140.1079040.0618560.3608250.1154640.1580760.0879730.0797250.2268040.0639180.0041240.0027490.6274910.3408930.0048110.0123710.2192441.0000000.1340210.2048110.0000000.0611680.0020620.0096220.3814430.2295530.0948450.0034360.0419240.0000000.0000000.0329900.0000000.0625430.1883160.0075600.0584190.0185570.1072160.3292100.1979380.0041240.0316150.0749140.0130580.1518900.0000000.0714780.0041240.0185570.0591070.0872850.0659790.1030930.0123710.1216490.0034360.0096220.0460480.0034360.0178690.2268040.0048110.0000000.0000000.0707900.0213060.0096220.0178690.0515460.0233680.5230240.0027490.1319590.0350520.0103090.0130580.3271480.1113400.0000000.0371130.0048110.00.0082470.408935
4841[Janet K. Gezari]1978-04-01research-articlearticlehttp://www.jstor.org/stable/2872453[{'name': 'issn', 'value': '00138304'}, {'name...ELH1[eng][unigram, bigram, trigram]14.010693pp. 93-106jstor1978Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]The Metaphorical Imagination of George Eliothttp://www.jstor.org/stable/28724534557312[[415187, 415972], [1691326, 1691647]][[8469, 9251], [18464, 18785]]NoneNoneNone19781970188[([415187, 415972], 129), ([1691326, 1691647],...[129, 59]ELH19800.4370370.7723910.1643100.3777780.0329970.2498320.1063970.1757580.0000000.1050510.2121210.1602690.0841750.0922560.0619530.8013470.3771040.0356900.0996630.4006730.9185190.2424240.0734010.2356900.0107740.0000000.0060610.1515150.0848480.2969700.0471380.0222220.0262630.0000000.0646460.0417510.0424240.2538720.0000000.4154880.0000000.0000000.1259260.1272730.0000000.0080810.0040400.1919190.0060610.0000000.0989900.0760940.0000000.0000000.1515150.1521890.0195290.0000000.2040400.0282830.0323230.1919190.0417510.0127950.1292930.0552190.0067340.0033670.0000000.0309760.0000000.0882150.1441080.2457910.0585860.0242420.2929290.1037040.0646460.0000000.3602690.3030300.0101010.0855220.0000001.00.0154880.571717
4845[THOMAS ALBRECHT]2012-07-01research-articlearticlehttp://www.jstor.org/stable/23256759[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]28.0416389pp. 389-416jstor2012The Johns Hopkins University Press[Humanities, Language & Literature][Social sciences - Behavioral sciences, Arts -...\"THE BALANCE OF SEPARATENESS AND COMMUNICATION...http://www.jstor.org/stable/2325675979128271[[350157, 350274]][[51005, 51122]]NoneNoneNone2012201020[([350157, 350274], 20)][20]ELH19900.5140660.4360610.1500430.2719520.0464620.1355500.1965050.0682010.0136400.0750210.2493610.2647060.2271950.0728900.0336741.0000000.2374250.1091220.1138110.5115090.7774940.2907080.0937770.0085250.0733160.0289860.0601020.2591650.0848250.0647910.0017050.0345270.0021310.0571180.1930950.2011940.1858480.1768970.0034100.0780050.0000000.1628300.3554990.0144930.0021310.1513210.0260020.0575450.1645350.0000000.1219100.0161980.0046890.0144930.0179030.0341010.0183290.0017050.0822680.0383630.0272800.0924980.0336740.0200340.0059680.0579710.0025580.0046890.0298380.0000000.0694800.0647910.0652170.0225920.0605290.0541350.1104010.0481670.1265980.0439050.2983800.2557540.0225920.2178180.0132140.00.0098040.364024
4900[ADELA PINCH]2016-10-01research-articlearticlehttp://www.jstor.org/stable/26173879[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]17.0837821pp. 821-837jstor2016The Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]REALITY SENSING IN ELIZABETH GASKELL: OR, HALF...http://www.jstor.org/stable/261738798377270[][]NoneNoneNone201620100[][]ELH20000.4520620.7396890.1767540.3711840.0524910.1799680.1510440.0690950.0096410.3026250.4001070.2694160.0353510.0289230.0064270.4708090.3058380.0208890.1017680.5399041.0000000.1462240.4761650.2126410.2174610.0610610.0069630.1119440.2522760.1949650.0621320.0551690.0899840.4311730.0000000.0273170.0230320.2581680.0182110.1039100.0889130.0449920.2860200.0840920.0021420.1215850.0085700.0021420.1676490.0455280.1183720.0037490.0637390.0996250.1339050.1526510.1515800.0465990.0653450.0160690.0230320.2570970.0824850.0428490.0000000.0000000.0251740.0000000.0733800.0417780.0471340.0262450.0465990.0058920.0000000.0594540.3336900.0696300.0653450.0267810.2469200.0423140.0000000.0519550.0219600.00.0358860.479914
4962[SUMMER J. STAR]2013-10-01research-articlearticlehttp://www.jstor.org/stable/24475544[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]31.0869839pp. 839-869jstor2013The Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Metaphysics]FEELING REAL IN \"MIDDLEMARCH\"http://www.jstor.org/stable/24475544801408910[[412189, 412751], [580473, 580871], [581340, ...[[540, 1102], [18982, 19380], [19448, 20069], ...NoneNoneNone20132010656[([412189, 412751], 105), ([580473, 580871], 6...[105, 66, 105, 64, 43, 29, 14, 28, 35, 167]ELH20100.2125490.9847370.1984170.1582820.0491800.0446580.1362350.0384400.0169590.2391180.2956470.0774450.1000570.0220460.0107410.6139060.2730360.0395700.1594120.3499150.7337481.0000000.2074620.0333520.6625210.0237420.0000000.3487850.4465800.1181460.0028260.1051440.0288300.0000000.0339170.0446580.1865460.1639340.0090450.3566990.1831540.1967210.1023180.0667040.0000000.0542680.0655740.0000000.0559640.0000000.1085360.1096660.0000000.0576600.0039570.0791410.1079710.0000000.2345960.0000000.1486720.0537030.0220460.1147540.0576600.0000000.0056530.0260030.0299600.0101750.2074620.1927640.0412660.0220460.2091580.0180890.1074050.0282650.0000000.0000000.2877330.1927640.0446580.0797060.0000000.00.0045220.805540
5058[Robert E. Lougy]2002-07-01research-articlearticlehttp://www.jstor.org/stable/30032028[{'name': 'issn', 'value': '00138304'}, {'name...ELH2[eng][unigram, bigram, trigram]28.0500473pp. 473-500jstor2002Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]Filth, Liminality, and Abjection in Charles Di...http://www.jstor.org/stable/3003202869124681[[925380, 925568]][[26680, 26874]]NoneNoneNone2002200032[([925380, 925568], 32)][32]ELH
\n", + "
" + ], + "text/plain": [ + "chapter 0 1 2 3 4 5 6 \\\n", + "decade \n", + "1960 0.119514 0.119514 0.266106 0.068161 0.012138 0.022409 0.000000 \n", + "1970 0.224055 0.575258 0.584192 0.393814 0.107904 0.061856 0.360825 \n", + "1980 0.437037 0.772391 0.164310 0.377778 0.032997 0.249832 0.106397 \n", + "1990 0.514066 0.436061 0.150043 0.271952 0.046462 0.135550 0.196505 \n", + "2000 0.452062 0.739689 0.176754 0.371184 0.052491 0.179968 0.151044 \n", + "2010 0.212549 0.984737 0.198417 0.158282 0.049180 0.044658 0.136235 \n", + "\n", + "chapter 7 8 9 10 11 12 13 \\\n", + "decade \n", + "1960 0.104575 0.008403 0.267974 0.230626 0.036415 0.074697 0.000000 \n", + "1970 0.115464 0.158076 0.087973 0.079725 0.226804 0.063918 0.004124 \n", + "1980 0.175758 0.000000 0.105051 0.212121 0.160269 0.084175 0.092256 \n", + "1990 0.068201 0.013640 0.075021 0.249361 0.264706 0.227195 0.072890 \n", + "2000 0.069095 0.009641 0.302625 0.400107 0.269416 0.035351 0.028923 \n", + "2010 0.038440 0.016959 0.239118 0.295647 0.077445 0.100057 0.022046 \n", + "\n", + "chapter 14 15 16 17 18 19 20 \\\n", + "decade \n", + "1960 0.000000 0.621849 0.272642 0.020542 0.000000 0.005602 1.000000 \n", + "1970 0.002749 0.627491 0.340893 0.004811 0.012371 0.219244 1.000000 \n", + "1980 0.061953 0.801347 0.377104 0.035690 0.099663 0.400673 0.918519 \n", + "1990 0.033674 1.000000 0.237425 0.109122 0.113811 0.511509 0.777494 \n", + "2000 0.006427 0.470809 0.305838 0.020889 0.101768 0.539904 1.000000 \n", + "2010 0.010741 0.613906 0.273036 0.039570 0.159412 0.349915 0.733748 \n", + "\n", + "chapter 21 22 23 24 25 26 27 \\\n", + "decade \n", + "1960 0.372549 0.053221 0.019608 0.013072 0.005602 0.004669 0.183940 \n", + "1970 0.134021 0.204811 0.000000 0.061168 0.002062 0.009622 0.381443 \n", + "1980 0.242424 0.073401 0.235690 0.010774 0.000000 0.006061 0.151515 \n", + "1990 0.290708 0.093777 0.008525 0.073316 0.028986 0.060102 0.259165 \n", + "2000 0.146224 0.476165 0.212641 0.217461 0.061061 0.006963 0.111944 \n", + "2010 1.000000 0.207462 0.033352 0.662521 0.023742 0.000000 0.348785 \n", + "\n", + "chapter 28 29 30 31 32 33 34 \\\n", + "decade \n", + "1960 0.298786 0.000000 0.006536 0.006536 0.000000 0.000000 0.000000 \n", + "1970 0.229553 0.094845 0.003436 0.041924 0.000000 0.000000 0.032990 \n", + "1980 0.084848 0.296970 0.047138 0.022222 0.026263 0.000000 0.064646 \n", + "1990 0.084825 0.064791 0.001705 0.034527 0.002131 0.057118 0.193095 \n", + "2000 0.252276 0.194965 0.062132 0.055169 0.089984 0.431173 0.000000 \n", + "2010 0.446580 0.118146 0.002826 0.105144 0.028830 0.000000 0.033917 \n", + "\n", + "chapter 35 36 37 38 39 40 41 \\\n", + "decade \n", + "1960 0.106443 0.048553 0.156863 0.000000 0.030812 0.045752 0.320261 \n", + "1970 0.000000 0.062543 0.188316 0.007560 0.058419 0.018557 0.107216 \n", + "1980 0.041751 0.042424 0.253872 0.000000 0.415488 0.000000 0.000000 \n", + "1990 0.201194 0.185848 0.176897 0.003410 0.078005 0.000000 0.162830 \n", + "2000 0.027317 0.023032 0.258168 0.018211 0.103910 0.088913 0.044992 \n", + "2010 0.044658 0.186546 0.163934 0.009045 0.356699 0.183154 0.196721 \n", + "\n", + "chapter 42 43 44 45 46 47 48 \\\n", + "decade \n", + "1960 0.123249 0.024276 0.000000 0.000000 0.326797 0.047619 0.026144 \n", + "1970 0.329210 0.197938 0.004124 0.031615 0.074914 0.013058 0.151890 \n", + "1980 0.125926 0.127273 0.000000 0.008081 0.004040 0.191919 0.006061 \n", + "1990 0.355499 0.014493 0.002131 0.151321 0.026002 0.057545 0.164535 \n", + "2000 0.286020 0.084092 0.002142 0.121585 0.008570 0.002142 0.167649 \n", + "2010 0.102318 0.066704 0.000000 0.054268 0.065574 0.000000 0.055964 \n", + "\n", + "chapter 49 50 51 52 53 54 55 \\\n", + "decade \n", + "1960 0.000000 0.228758 0.007470 0.053221 0.414566 0.002801 0.000000 \n", + "1970 0.000000 0.071478 0.004124 0.018557 0.059107 0.087285 0.065979 \n", + "1980 0.000000 0.098990 0.076094 0.000000 0.000000 0.151515 0.152189 \n", + "1990 0.000000 0.121910 0.016198 0.004689 0.014493 0.017903 0.034101 \n", + "2000 0.045528 0.118372 0.003749 0.063739 0.099625 0.133905 0.152651 \n", + "2010 0.000000 0.108536 0.109666 0.000000 0.057660 0.003957 0.079141 \n", + "\n", + "chapter 56 57 58 59 60 61 62 \\\n", + "decade \n", + "1960 0.102708 0.000000 0.015873 0.000000 0.014006 0.024276 0.000000 \n", + "1970 0.103093 0.012371 0.121649 0.003436 0.009622 0.046048 0.003436 \n", + "1980 0.019529 0.000000 0.204040 0.028283 0.032323 0.191919 0.041751 \n", + "1990 0.018329 0.001705 0.082268 0.038363 0.027280 0.092498 0.033674 \n", + "2000 0.151580 0.046599 0.065345 0.016069 0.023032 0.257097 0.082485 \n", + "2010 0.107971 0.000000 0.234596 0.000000 0.148672 0.053703 0.022046 \n", + "\n", + "chapter 63 64 65 66 67 68 69 \\\n", + "decade \n", + "1960 0.020542 0.022409 0.000000 0.074697 0.000000 0.007470 0.000000 \n", + "1970 0.017869 0.226804 0.004811 0.000000 0.000000 0.070790 0.021306 \n", + "1980 0.012795 0.129293 0.055219 0.006734 0.003367 0.000000 0.030976 \n", + "1990 0.020034 0.005968 0.057971 0.002558 0.004689 0.029838 0.000000 \n", + "2000 0.042849 0.000000 0.000000 0.025174 0.000000 0.073380 0.041778 \n", + "2010 0.114754 0.057660 0.000000 0.005653 0.026003 0.029960 0.010175 \n", + "\n", + "chapter 70 71 72 73 74 75 76 \\\n", + "decade \n", + "1960 0.000000 0.265173 0.000000 0.004669 0.000000 0.014939 0.130719 \n", + "1970 0.009622 0.017869 0.051546 0.023368 0.523024 0.002749 0.131959 \n", + "1980 0.000000 0.088215 0.144108 0.245791 0.058586 0.024242 0.292929 \n", + "1990 0.069480 0.064791 0.065217 0.022592 0.060529 0.054135 0.110401 \n", + "2000 0.047134 0.026245 0.046599 0.005892 0.000000 0.059454 0.333690 \n", + "2010 0.207462 0.192764 0.041266 0.022046 0.209158 0.018089 0.107405 \n", + "\n", + "chapter 77 78 79 80 81 82 83 \\\n", + "decade \n", + "1960 0.017740 0.161531 0.039216 0.183007 0.225957 0.003735 0.065359 \n", + "1970 0.035052 0.010309 0.013058 0.327148 0.111340 0.000000 0.037113 \n", + "1980 0.103704 0.064646 0.000000 0.360269 0.303030 0.010101 0.085522 \n", + "1990 0.048167 0.126598 0.043905 0.298380 0.255754 0.022592 0.217818 \n", + "2000 0.069630 0.065345 0.026781 0.246920 0.042314 0.000000 0.051955 \n", + "2010 0.028265 0.000000 0.000000 0.287733 0.192764 0.044658 0.079706 \n", + "\n", + "chapter 84 85 86 87 \n", + "decade \n", + "1960 0.000000 0.0 0.022409 0.228758 \n", + "1970 0.004811 0.0 0.008247 0.408935 \n", + "1980 0.000000 1.0 0.015488 0.571717 \n", + "1990 0.013214 0.0 0.009804 0.364024 \n", + "2000 0.021960 0.0 0.035886 0.479914 \n", + "2010 0.000000 0.0 0.004522 0.805540 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with pd.option_context(\"display.min_rows\", 6, \"display.max_rows\", 100, \\\n", + " \"display.max_columns\", 90, 'display.max_colwidth', 150):\n", + " display(diaDF)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "diaDF['decade'] = diaDF.index" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "diaMelted = diaDF.melt(id_vars='decade')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Middlemarch* quotations per chapter, per decade (normalized and weighted), heat map\n", + "\n", + "Because our corpus contains [varying numbers of JSTOR texsts per decade](#How-many-articles-do-we-have-published-in-each-year?), we've decided to also weigh by length of quotation and normalize per decade." + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diachronic_chap = alt.Chart(diaMelted, title=\"Middlemarch quotations per chapter, per decade (normalized by decade and weighted by word count)\")\\\n", + ".mark_rect().encode(x=alt.X('chapter', title=\"Chapter\", type='ordinal', axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", + " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", + " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", + " color=alt.Color('value', legend=alt.Legend(title=\"Amount Quoted\")))\\\n", + ".properties(width=1000, height=300).configure_legend(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ")\n", + "diachronic_chap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alt.Chart(diaMelted, )\\\n", + ".mark_rect().encode(x=alt.X('chapter', title=\"Chapter\", type='ordinal', axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", + " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", + " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", + " color=alt.Color('value', legend=alt.Legend(title=\"Amount Quoted\")))\\\n", + ".properties(width=1000, height=300).configure_legend(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").save('Figure-5.png', ppi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "top5 = diaDFquoteOnlyMelted[\"chapter\"].where(diaDFquoteOnlyMelted[\"chapter\"].isin([0, 1, 15, 20, 87]), other=\"Other\")" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "diaDFquoteOnlyMelted['top5'] = top5" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "color = alt.condition(alt.datum.top5 == 'top5',\n", + " alt.Color('chapter:O', legend=None),\n", + " alt.value('gainsboro')\n", + " )\n", + "\n", + "line = alt.Chart(diaDFquoteOnlyMelted.loc[diaDFquoteOnlyMelted['chapter'].isin([0, 1, 15, 20, 87])], title=\"Middlemarch top 5 most quoted chapters, by decade (not weighted or normalized)\")\\\n", + ".mark_line().encode(\n", + " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", + " y=alt.Y('value:Q', title=\"Number of Quotations\", axis=alt.Axis(labelAngle=0)),\n", + " color=alt.Color('chapter:O', scale=alt.Scale(scheme='greys'), legend=None,),\n", + " \n", + ")\n", + "\n", + "points = line.mark_point(filled=True).encode(\n", + " color=alt.Color('chapter:O', scale=alt.Scale(scheme='greys')),\n", + " shape=alt.Shape('chapter:O', legend=alt.Legend(title=\"Chapter\"), scale=alt.Scale(range=['square', 'circle', 'cross','triangle-right', 'diamond'])),\n", + " size=alt.Size('chapter', legend=None, scale=alt.Scale(range=[200,200],domain=['0', '1', '15', '20', '87']))\n", + ")\n", + "\n", + "greyed = alt.Chart(diaDFquoteOnlyMelted.loc[~diaDFquoteOnlyMelted['chapter'].isin([0, 1, 15, 20, 87])])\\\n", + ".mark_line().encode(\n", + " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", + " y=alt.Y('value:Q', title=\"Number of Quotations\", axis=alt.Axis(labelAngle=0)),\n", + " color=color, \n", + ")\n", + "\n", + "top5_chart = alt.layer(\n", + " greyed,\n", + " line,\n", + " points,\n", + ").resolve_scale(\n", + " color='independent',\n", + " shape='independent'\n", + ").properties(width=400).configure_legend(\n", + "titleFontSize=11,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ")\n", + "#top5_chart.save('Figure-6.png', ppi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Middlemarch* top 5 most frequently quoted chapters, line chart" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top5_chart#.save('Figure-6.png', ppi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "color = alt.condition(alt.datum.top5 == 'top5',\n", + " alt.Color('chapter:O', legend=None),\n", + " alt.value('gainsboro')\n", + " )\n", + "\n", + "line = alt.Chart(diaDFquoteOnlyMelted.loc[diaDFquoteOnlyMelted['chapter'].isin([0, 1, 15, 20, 87])], title=\"Middlemarch top 5 most quoted chapters, by decade (not weighted or normalized)\")\\\n", + ".mark_line().encode(\n", + " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", + " y=alt.Y('value:Q', title=\"Number of Quotations\", axis=alt.Axis(labelAngle=0)),\n", + " color=alt.Color('chapter:O', scale=alt.Scale(scheme='category20'), legend=None,),\n", + " \n", + ")\n", + "\n", + "points = line.mark_point(filled=True).encode(\n", + " color=alt.Color('chapter:O', scale=alt.Scale(scheme='category20')),\n", + " shape=alt.Shape('chapter:O', legend=alt.Legend(title=\"Chapter\"), scale=alt.Scale(range=['square', 'circle', 'cross','triangle-right', 'diamond'])),\n", + " size=alt.Size('chapter', legend=None, scale=alt.Scale(range=[200,200],domain=['0', '1', '15', '20', '87']))\n", + ")\n", + "\n", + "greyed = alt.Chart(diaDFquoteOnlyMelted.loc[~diaDFquoteOnlyMelted['chapter'].isin([0, 1, 15, 20, 87])])\\\n", + ".mark_line().encode(\n", + " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", + " y=alt.Y('value:Q', title=\"Number of Quotations\", axis=alt.Axis(labelAngle=0)),\n", + " color=color, \n", + ")\n", + "\n", + "top5_chart_color = alt.layer(\n", + " greyed,\n", + " line,\n", + " points,\n", + ").resolve_scale(\n", + " color='independent',\n", + " shape='independent'\n", + ").properties(width=400).configure_legend(\n", + "titleFontSize=11,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Middlemarch* top 5 most frequently quoted chapters, line chart (color)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top5_chart_color" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "color = alt.condition(alt.datum.top5 == 'top5',\n", + " alt.Color('chapter:O', legend=None),\n", + " alt.value('gainsboro')\n", + " )\n", + "\n", + "line = alt.Chart(diaMelted.loc[diaMelted['chapter'].isin([0, 1, 15, 20, 87])], title=\"Middlemarch top 5 most quoted chapters, by decade (normalized and weighted)\")\\\n", + ".mark_line().encode(\n", + " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", + " y=alt.Y('value:Q', title=\"Amount Quoted\", axis=alt.Axis(labelAngle=0)),\n", + " color=alt.Color('chapter:O', scale=alt.Scale(scheme='greys'), legend=None,),\n", + " \n", + ")\n", + "\n", + "points = line.mark_point(filled=True).encode(\n", + " color=alt.Color('chapter:O', scale=alt.Scale(scheme='greys')),\n", + " shape=alt.Shape('chapter:O', legend=alt.Legend(title=\"Chapter\"), scale=alt.Scale(range=['square', 'circle', 'cross','triangle-right', 'diamond'])),\n", + " size=alt.Size('chapter', legend=None, scale=alt.Scale(range=[200,200],domain=['0', '1', '15', '20', '87']))\n", + ")\n", + "\n", + "greyed = alt.Chart(diaMelted.loc[~diaMelted['chapter'].isin([0, 1, 15, 20, 87])])\\\n", + ".mark_line().encode(\n", + " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", + " y=alt.Y('value:Q', title=\"Amount Quoted\", axis=alt.Axis(labelAngle=0)),\n", + " color=color, \n", + ")\n", + "\n", + "top5_chart_normalized = alt.layer(\n", + " greyed,\n", + " line,\n", + " points,\n", + ").resolve_scale(\n", + " color='independent',\n", + " shape='independent'\n", + ").properties(width=400).configure_legend(\n", + "titleFontSize=11,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Middlemarch* top 5 most frequently quoted chapters (normalized and weighted), line chart" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top5_chart_normalized" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "color = alt.condition(alt.datum.top5 == 'top5',\n", + " alt.Color('chapter:O', legend=None),\n", + " alt.value('gainsboro')\n", + " )\n", + "\n", + "line = alt.Chart(diaMelted.loc[diaMelted['chapter'].isin([0, 1, 15, 20, 87])], title=\"Middlemarch top 5 most quoted chapters, by decade (normalized and weighted)\")\\\n", + ".mark_line().encode(\n", + " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", + " y=alt.Y('value:Q', title=\"Amount Quoted\", axis=alt.Axis(labelAngle=0)),\n", + " color=alt.Color('chapter:O', scale=alt.Scale(scheme='category20'), legend=None,),\n", + " \n", + ")\n", + "\n", + "points = line.mark_point(filled=True).encode(\n", + " color=alt.Color('chapter:O', scale=alt.Scale(scheme='category20')),\n", + " shape=alt.Shape('chapter:O', legend=alt.Legend(title=\"Chapter\"), scale=alt.Scale(range=['square', 'circle', 'cross','triangle-right', 'diamond'])),\n", + " size=alt.Size('chapter', legend=None, scale=alt.Scale(range=[200,200],domain=['0', '1', '15', '20', '87']))\n", + ")\n", + "\n", + "greyed = alt.Chart(diaMelted.loc[~diaMelted['chapter'].isin([0, 1, 15, 20, 87])])\\\n", + ".mark_line().encode(\n", + " x=alt.X('decade', title=\"Decade\",type='ordinal', sort='ascending', \n", + " axis=alt.Axis(labelAngle=0, labelExpr='datum.value + \"s\"')), \n", + " y=alt.Y('value:Q', title=\"Amount Quoted\", axis=alt.Axis(labelAngle=0)),\n", + " color=color, \n", + ")\n", + "\n", + "top5_chart_normalized_color = alt.layer(\n", + " greyed,\n", + " line,\n", + " points,\n", + ").resolve_scale(\n", + " color='independent',\n", + " shape='independent'\n", + ").properties(width=400).configure_legend(\n", + "titleFontSize=11,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Middlemarch* top 5 most frequently quoted chapters (normalized and weighted), line chart (color)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top5_chart_normalized_color" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6318681318681318" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the normalized proportion of, say, Chapter 20 in 1950: \n", + "diachronicAnalysis(df)[20][1950]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# *Middlemarch* chapter-specific analysis\n", + "\n", + "- [Chapter 15](#Chapter-15)\n", + " - [Paragraph-level-analysis of Chapter 15](#Paragraph-level-analysis-of-Chapter-15)\n", + "- [Chapter 20](#Chapter-20)\n", + " - [Paragraph-level-analysis of Chapter 20](#Paragraph-level-analysis-of-Chapter-20)\n", + " - [Which paragraphs in Chapter 20 are quoted most often?](#Which-paragraphs-in-Chapter-20-are-quoted-most-often?)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chapter 15" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "# Try to find out why Ch. 15 was so big in the 80s and 90s. \n", + "chap15s = []\n", + "ids = []\n", + "for i, row in df.iterrows(): \n", + " locations = row['Locations in A']\n", + " starts = [item[0] for item in locations]\n", + " if row['Decade'] in [1980, 1990]: # Looking at the 1980s, 1990s\n", + " for start in starts: \n", + " if start > 290371 and start < 322052: # Does it cite Chapter XV? \n", + " if row.id not in ids: \n", + " chap15s.append(row)\n", + " ids.append(row.id)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Titles of articles that quote Chapter 15:\n" + ] + }, + { + "data": { + "text/plain": [ + "['Woman of Maxims:',\n", + " 'Brava! And Farewell to Greatheart',\n", + " 'The Union of \"Miss Brooke\" and \"Middlemarch\": A Study of the Manuscript',\n", + " '\"Middlemarch\" and George Eliot\\'s Female (Re) Vision of Shakespeare',\n", + " 'Heroism and Organicism in the Case of Lydgate',\n", + " 'Professional Judgment and the Rationing of Medical Care',\n", + " 'SILENCE, GESTURE, AND MEANING IN \"MIDDLEMARCH\"',\n", + " 'Reflections on \"The Philosophical Bases of Feminist Literary Criticisms\"',\n", + " 'Strategies for Writing: Theories and Practices',\n", + " 'Review Article',\n", + " 'AN END TO CONVERTING PATIENTS\\' STOMACHS INTO DRUG-SHOPS: LYDGATE\\'S NEW METHOD OF CHARGING HIS PATIENTS IN \"MIDDLEMARCH\"',\n", + " 'Review Article',\n", + " 'Illuminating the Vision of Ordinary Life: A Tribute to \"Middlemarch\"',\n", + " 'Review Article',\n", + " \"PLEXUSES AND GANGLIA: ELIOTS AND LEWES'S THEORY OF NERVE-CONSCIOUSNESS\",\n", + " 'Review Article',\n", + " 'Middlemarch, Realism and the Birth of the Clinic',\n", + " 'ERZÄHLERISCHE OBJEKTIVITÄT, ,AUTHORIAL INTRUSIONS‘ UND ENGLISCHER REALISMUS',\n", + " 'Review Article',\n", + " 'The Aesthetics of Sympathy:',\n", + " 'NARRATIVE VOICE AND THE \"FEMININE\" NOVELIST: DINAH MULOCK AND GEORGE ELIOT',\n", + " 'Lamarque and Olsen on Literature and Truth',\n", + " 'Review Article',\n", + " 'Microscopy and Semiotic in Middlemarch',\n", + " \"George Eliot's Reflexive Text: Three Tonalities in the Narrative Voice of Middlemarch\",\n", + " 'Review Article',\n", + " 'George Eliot and the Eighteenth-Century Novel',\n", + " 'Versions of Narrative: Overt and Covert Narrators in Nineteenth Century Historiography',\n", + " 'LYDGATE\\'S RESEARCH PROJECT IN \"MIDDLEMARCH\"',\n", + " 'George Eliot\\'s Scrupulous Research: The Facts behind Eliot\\'s Use of the \"Keepsake in Middlemarch\"',\n", + " 'Eliot and Woolf as Historians of the Common Life',\n", + " 'The Language of Discovery: William Whewell and George Eliot',\n", + " \"George Eliot's Hypothesis of Reality\",\n", + " 'Re-Reading Character',\n", + " 'The Strange Case of Monomania: Patriarchy in Literature, Murder in Middlemarch, Drowning in Daniel Deronda',\n", + " '\"Wrinkled Deep in Time\": The Alexandria Quartet as Many-Layered Palimpsest',\n", + " 'THE DIALOGIC UNIVERSE OF \"MIDDLEMARCH\"',\n", + " 'MIXED AND ERRING HUMANITY: GEORGE ELIOT, G. H. LEWES AND GOETHE',\n", + " '1978 And All That',\n", + " \"The Turn of George Eliot's Realism\",\n", + " 'Dangerous Crossings: Dickens, Digression, and Montage',\n", + " 'In Defence of Research for Evidence-Based Teaching: A Rejoinder to Martyn Hammersley',\n", + " 'Review Article',\n", + " 'THE WONDROUS MARRIAGES OF \"DANIEL DERONDA:\" GENDER, WORK, AND LOVE',\n", + " \"The Victorian Discourse of Gambling: Speculations on Middlemarch and the Duke's Children\",\n", + " 'Struggling for Medical Reform in Middlemarch',\n", + " 'Steamboat Surfacing: Scott and the English Novelists']" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the titles of those articles.\n", + "print('Titles of articles that quote Chapter 15:')\n", + "[item.title for item in chap15s]" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "47" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(chap15s)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "xvStart, xvEnd = chapterLocations[15:17]" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CHAPTER XV.\n", + "\n", + " \"Black eyes you have left, you say,\n", + " Blue eyes fail to draw you;\n", + " Yet you seem more rapt to-day,\n", + " Than of old we saw you.\n", + "\n", + " \"Oh, I track the fairest fair\n", + " Through new haunts of pleasure;\n", + " Footprints here and echoes there\n", + " Guide me to my treasure:\n", + "\n", + " \"Lo! she turns--immortal youth\n", + " Wrought to mortal stature,\n", + " Fresh as starlight's aged truth--\n", + " Many-named Nature!\"\n", + "\n", + "\n", + "A great historian, as he insisted on calling himself, who had the\n", + "happiness to be dead a hundred and twenty years ago, and so to take his\n", + "place among the colossi whose huge legs our living pettiness is\n", + "observed to walk under, glories in his copious remarks and digressions\n", + "as the least imitable part of his work, and especially in those initial\n", + "chapters to the successive books of his history, where he seems to\n", + "bring his armchair to the proscenium and chat with us in all the lusty\n", + "ease of his fine English. But Fielding lived when the days were longer\n", + "(for time, like mone\n" + ] + } + ], + "source": [ + "print(mm[xvStart:xvStart+1000]) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Paragraph-level analysis of Chapter 15" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "# Try to find out which articles cite the first 2/3 of Chapter XV (with Lydgate's scientific research) \n", + "# vs the last 1/3 on the story of Laure\n", + "chap15p1s = []\n", + "ids = []\n", + "for i, row in df.iterrows(): \n", + " locations = row['Locations in A']\n", + " starts = [item[0] for item in locations]\n", + " if row['Decade'] in [1980, 1990]: \n", + " for start in starts: \n", + " if start > 290371 and start < 313892: # Does it cite the first 2/3 of Chapter XV? \n", + " if row.id not in ids: \n", + " chap15p1s.append(row)\n", + " ids.append(row.id)\n", + "chap15p2s = []\n", + "ids = []\n", + "for i, row in df.iterrows(): \n", + " locations = row['Locations in A']\n", + " starts = [item[0] for item in locations]\n", + " if row['Decade'] in [1980, 1990]: \n", + " for start in starts: \n", + " if start > 313892 and start < 322052: # Does it cite the last 1/3 of Chapter XV? \n", + " if row.id not in ids: \n", + " chap15p2s.append(row)\n", + " ids.append(row.id) \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Titles of articles that quote the first 2/3 of Chapter 15:\n" + ] + }, + { + "data": { + "text/plain": [ + "['Woman of Maxims:',\n", + " 'Brava! And Farewell to Greatheart',\n", + " 'The Union of \"Miss Brooke\" and \"Middlemarch\": A Study of the Manuscript',\n", + " 'Heroism and Organicism in the Case of Lydgate',\n", + " 'Professional Judgment and the Rationing of Medical Care',\n", + " 'SILENCE, GESTURE, AND MEANING IN \"MIDDLEMARCH\"',\n", + " 'Reflections on \"The Philosophical Bases of Feminist Literary Criticisms\"',\n", + " 'Strategies for Writing: Theories and Practices',\n", + " 'Review Article',\n", + " 'AN END TO CONVERTING PATIENTS\\' STOMACHS INTO DRUG-SHOPS: LYDGATE\\'S NEW METHOD OF CHARGING HIS PATIENTS IN \"MIDDLEMARCH\"',\n", + " 'Review Article',\n", + " 'Illuminating the Vision of Ordinary Life: A Tribute to \"Middlemarch\"',\n", + " 'Review Article',\n", + " \"PLEXUSES AND GANGLIA: ELIOTS AND LEWES'S THEORY OF NERVE-CONSCIOUSNESS\",\n", + " 'Review Article',\n", + " 'Middlemarch, Realism and the Birth of the Clinic',\n", + " 'ERZÄHLERISCHE OBJEKTIVITÄT, ,AUTHORIAL INTRUSIONS‘ UND ENGLISCHER REALISMUS',\n", + " 'Review Article',\n", + " 'The Aesthetics of Sympathy:',\n", + " 'NARRATIVE VOICE AND THE \"FEMININE\" NOVELIST: DINAH MULOCK AND GEORGE ELIOT',\n", + " 'Lamarque and Olsen on Literature and Truth',\n", + " 'Review Article',\n", + " 'Microscopy and Semiotic in Middlemarch',\n", + " \"George Eliot's Reflexive Text: Three Tonalities in the Narrative Voice of Middlemarch\",\n", + " 'Review Article',\n", + " 'George Eliot and the Eighteenth-Century Novel',\n", + " 'Versions of Narrative: Overt and Covert Narrators in Nineteenth Century Historiography',\n", + " 'LYDGATE\\'S RESEARCH PROJECT IN \"MIDDLEMARCH\"',\n", + " 'George Eliot\\'s Scrupulous Research: The Facts behind Eliot\\'s Use of the \"Keepsake in Middlemarch\"',\n", + " 'Eliot and Woolf as Historians of the Common Life',\n", + " 'The Language of Discovery: William Whewell and George Eliot',\n", + " \"George Eliot's Hypothesis of Reality\",\n", + " '\"Wrinkled Deep in Time\": The Alexandria Quartet as Many-Layered Palimpsest',\n", + " 'THE DIALOGIC UNIVERSE OF \"MIDDLEMARCH\"',\n", + " 'MIXED AND ERRING HUMANITY: GEORGE ELIOT, G. H. LEWES AND GOETHE',\n", + " '1978 And All That',\n", + " \"The Turn of George Eliot's Realism\",\n", + " 'Dangerous Crossings: Dickens, Digression, and Montage',\n", + " 'In Defence of Research for Evidence-Based Teaching: A Rejoinder to Martyn Hammersley',\n", + " 'Review Article',\n", + " 'THE WONDROUS MARRIAGES OF \"DANIEL DERONDA:\" GENDER, WORK, AND LOVE',\n", + " \"The Victorian Discourse of Gambling: Speculations on Middlemarch and the Duke's Children\",\n", + " 'Struggling for Medical Reform in Middlemarch',\n", + " 'Steamboat Surfacing: Scott and the English Novelists']" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the titles of articles citing the first 2/3 \n", + "print('Titles of articles that quote the first 2/3 of Chapter 15:')\n", + "[item.title for item in chap15p1s]" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Titles of articles that quote the last 1/3 of Chapter 15:\n" + ] + }, + { + "data": { + "text/plain": [ + "['The Union of \"Miss Brooke\" and \"Middlemarch\": A Study of the Manuscript',\n", + " '\"Middlemarch\" and George Eliot\\'s Female (Re) Vision of Shakespeare',\n", + " 'Microscopy and Semiotic in Middlemarch',\n", + " \"George Eliot's Reflexive Text: Three Tonalities in the Narrative Voice of Middlemarch\",\n", + " 'Re-Reading Character',\n", + " 'The Strange Case of Monomania: Patriarchy in Literature, Murder in Middlemarch, Drowning in Daniel Deronda']" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the titles of those articles.\n", + "print('Titles of articles that quote the last 1/3 of Chapter 15:')\n", + "[item.title for item in chap15p2s]" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "As to women, he had once already been drawn headlong by impetuous\n", + "folly, which he meant to be final, since marriage at some distant\n", + "period would of course not be impetuous. For those who want to be\n", + "acquainted with Lydgate it will be good to know what was that case of\n", + "impetuous folly, for it may stand as an example of the fitful swerving\n", + "of passion to which he was prone, together with the chivalrous kindness\n", + "which helped to make him morally lovable. The story can be told\n", + "without many words. It happened when he was studying in Paris, and\n", + "just at the time when, over and above his other work, he was occupied\n", + "with some galvanic experiments. One evening, tired with his\n", + "experimenting, and not being able to elicit the facts he needed, he\n", + "left his frogs and rabbits to some repose under their trying and\n", + "mysterious dispensation of unexplained shocks, and went to finish his\n", + "evening at the theatre of the Porte Saint Martin, where there was a\n", + "melodrama which he had already seen several times; attracted, not by\n", + "the ingenious work of the collaborating authors, but by an actress\n", + "whose part it was to stab her lover, mistaking him for the\n", + "evil-designing duke of the piece. Lydgate was in love with this\n", + "actress, as a man is in love with a woman whom he never expects to\n", + "speak to. She was a Provencale, with dark eyes, a Greek profile, and\n", + "rounded majestic form, having that sort of beauty which carries a sweet\n", + "matronliness even in youth, and her voice was a soft cooing. She had\n", + "but lately c\n" + ] + } + ], + "source": [ + "# Verify that we have the right location for the start of Laure's story in the last 1/3 of Chapter XV\n", + "print(mm[313892:313892+1500]) " + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CHAPTER XV.\n", + "\n", + " \"Black eyes you have left, you say,\n", + " Blue eyes fail to draw you;\n", + " Yet you seem more rapt to-day,\n", + " Than of old we saw you.\n", + "\n", + " \"Oh, I track the fairest fair\n", + " Through new haunts of pleasure;\n", + " Footprints here and echoes there\n", + " Guide me to my treasure:\n", + "\n", + " \"Lo! she turns--immortal youth\n", + " Wrought to mortal stature,\n", + " Fresh as starlight's aged truth--\n", + " Many-named Nature!\"\n", + "\n", + "\n", + "A great historian, as he insisted on calling himself, who had the\n", + "happiness to be dead a hundred and twenty years ago, and so to take his\n", + "place among the colossi whose huge legs our living pettiness is\n", + "observed to walk under, glories in his copious remarks and digressions\n", + "as the least imitable part of his work, and especially in those initial\n", + "chapters to the successive books of his history, where he seems to\n", + "bring his armchair to the proscenium and chat with us in all the lusty\n", + "ease of his fine English. But Fielding lived when the days were longer\n", + "(for time, like money, is measured by our needs), when summer\n", + "afternoons were spacious, and the clock ticked slowly in the winter\n", + "evenings. We belated historians must not linger after his example; and\n", + "if we did so, it is probable that our chat would be thin and eager, as\n", + "if delivered from a campstool in a parrot-house. I at least have so\n", + "much to do in unraveling certain human lots, and seeing how they were\n", + "woven and interwoven, that all the light I can command must be\n", + "concentrated on this particular web, and not dispersed over that\n", + "tempting range of relevancies called the universe.\n", + "\n" + ] + } + ], + "source": [ + "# Verify the location of the eipgraph and first paragraph\n", + "print(mm[290371:290371+1571]) " + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "chap15para1s = []\n", + "ids = []\n", + "for i, row in df.iterrows(): \n", + " locations = row['Locations in A']\n", + " starts = [item[0] for item in locations]\n", + " if row['Decade'] in [1980, 1990]: \n", + " for start in starts: \n", + " if start > 290371 and start < 291943: # Does it cite the last 1/3 of Chapter XV? \n", + " if row.id not in ids: \n", + " chap15para1s.append(row)\n", + " ids.append(row.id) " + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Titles of articles that quote paragraph 1 of Chapter 15:\n" + ] + }, + { + "data": { + "text/plain": [ + "['Woman of Maxims:',\n", + " 'Brava! And Farewell to Greatheart',\n", + " 'The Union of \"Miss Brooke\" and \"Middlemarch\": A Study of the Manuscript',\n", + " 'Strategies for Writing: Theories and Practices',\n", + " 'Illuminating the Vision of Ordinary Life: A Tribute to \"Middlemarch\"',\n", + " 'Middlemarch, Realism and the Birth of the Clinic',\n", + " 'NARRATIVE VOICE AND THE \"FEMININE\" NOVELIST: DINAH MULOCK AND GEORGE ELIOT',\n", + " 'Review Article',\n", + " 'Microscopy and Semiotic in Middlemarch',\n", + " \"George Eliot's Reflexive Text: Three Tonalities in the Narrative Voice of Middlemarch\",\n", + " 'George Eliot and the Eighteenth-Century Novel',\n", + " 'Versions of Narrative: Overt and Covert Narrators in Nineteenth Century Historiography',\n", + " 'George Eliot\\'s Scrupulous Research: The Facts behind Eliot\\'s Use of the \"Keepsake in Middlemarch\"',\n", + " 'Eliot and Woolf as Historians of the Common Life',\n", + " \"George Eliot's Hypothesis of Reality\",\n", + " 'MIXED AND ERRING HUMANITY: GEORGE ELIOT, G. H. LEWES AND GOETHE',\n", + " 'Dangerous Crossings: Dickens, Digression, and Montage',\n", + " 'Steamboat Surfacing: Scott and the English Novelists']" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the titles of articles that cite paragraph 1 of Chapter 15\n", + "print('Titles of articles that quote paragraph 1 of Chapter 15:')\n", + "[item.title for item in chap15para1s]" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Titles of scholarly writings that quote the first 2/3 of Chapter 15:\n" + ] + }, + { + "data": { + "text/plain": [ + "['The Union of \"Miss Brooke\" and \"Middlemarch\": A Study of the Manuscript',\n", + " 'Heroism and Organicism in the Case of Lydgate',\n", + " 'Professional Judgment and the Rationing of Medical Care',\n", + " 'SILENCE, GESTURE, AND MEANING IN \"MIDDLEMARCH\"',\n", + " 'Reflections on \"The Philosophical Bases of Feminist Literary Criticisms\"',\n", + " 'Review Article',\n", + " 'AN END TO CONVERTING PATIENTS\\' STOMACHS INTO DRUG-SHOPS: LYDGATE\\'S NEW METHOD OF CHARGING HIS PATIENTS IN \"MIDDLEMARCH\"',\n", + " 'Review Article',\n", + " 'Review Article',\n", + " \"PLEXUSES AND GANGLIA: ELIOTS AND LEWES'S THEORY OF NERVE-CONSCIOUSNESS\",\n", + " 'Review Article',\n", + " 'Middlemarch, Realism and the Birth of the Clinic',\n", + " 'ERZÄHLERISCHE OBJEKTIVITÄT, ,AUTHORIAL INTRUSIONS‘ UND ENGLISCHER REALISMUS',\n", + " 'Review Article',\n", + " 'The Aesthetics of Sympathy:',\n", + " 'Lamarque and Olsen on Literature and Truth',\n", + " 'Microscopy and Semiotic in Middlemarch',\n", + " 'Review Article',\n", + " 'LYDGATE\\'S RESEARCH PROJECT IN \"MIDDLEMARCH\"',\n", + " 'Eliot and Woolf as Historians of the Common Life',\n", + " 'The Language of Discovery: William Whewell and George Eliot',\n", + " '\"Wrinkled Deep in Time\": The Alexandria Quartet as Many-Layered Palimpsest',\n", + " 'THE DIALOGIC UNIVERSE OF \"MIDDLEMARCH\"',\n", + " '1978 And All That',\n", + " \"The Turn of George Eliot's Realism\",\n", + " 'Dangerous Crossings: Dickens, Digression, and Montage',\n", + " 'In Defence of Research for Evidence-Based Teaching: A Rejoinder to Martyn Hammersley',\n", + " 'Review Article',\n", + " 'THE WONDROUS MARRIAGES OF \"DANIEL DERONDA:\" GENDER, WORK, AND LOVE',\n", + " \"The Victorian Discourse of Gambling: Speculations on Middlemarch and the Duke's Children\",\n", + " 'Struggling for Medical Reform in Middlemarch']" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chap15Lydgates = []\n", + "ids = []\n", + "for i, row in df.iterrows(): \n", + " locations = row['Locations in A']\n", + " starts = [item[0] for item in locations]\n", + " if row['Decade'] in [1980, 1990]: \n", + " for start in starts: \n", + " if start > 291942 and start < 313892: # Does it cite the first 2/3 of Chapter XV?\n", + " if row.id not in ids: \n", + " chap15Lydgates.append(row)\n", + " ids.append(row.id)\n", + " \n", + "# Get the titles of articles that cite Lydgate section\n", + "print('Titles of scholarly writings that quote the first 2/3 of Chapter 15:')\n", + "[item.title for item in chap15Lydgates]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chapter 20" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "# Try to find out what articles cited chapter 20 \n", + "chap20s = []\n", + "ids = []\n", + "for i, row in df.iterrows(): \n", + " locations = row['Locations in A']\n", + " starts = [item[0] for item in locations]\n", + " if row['Decade'] in [1870, 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010]: \n", + " for start in starts: \n", + " if start > 1236993 and start < 1278826: # Does it cite Chapter XX? \n", + " if row.id not in ids: \n", + " chap20s.append(row)\n", + " ids.append(row.id)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Titles of scholarly writings that quote Chapter 20:\n" + ] + }, + { + "data": { + "text/plain": [ + "['\"Radiant as a Diamond\": George Eliot, Jewelry and the Female Role',\n", + " \"The Hidden Abortion Plot in George Eliot's Middlemarch\",\n", + " 'George Eliot and the Feminine Gift',\n", + " 'The Rosamond Plots',\n", + " 'Afterword',\n", + " 'Near Confinement: Pregnant Women in the Nineteenth-Century British Novel',\n", + " 'Mencken, Cushing, and The Life of Sir William Osler',\n", + " 'Egoism, Desires, and Friendship',\n", + " 'Of Many Minds in Middlemarch',\n", + " 'Teaching Middlemarch with a Focus on Theory of Mind',\n", + " \"The Power of Women's Hair in the Victorian Imagination\",\n", + " 'The Traffic in Men: Female Kinship in Three Novels by George Eliot',\n", + " 'Transformations, Style, and the Writing Experience',\n", + " '\"Neutral Physiognomy\": The Unreadable Faces of \"Middlemarch\"',\n", + " 'The Web of Utterance: Middlemarch',\n", + " 'Dora Spenlow, Female Communities, and Female Narrative in Charles Dickens\\'s \"David Copperfield\" and George Eliot\\'s \"Middlemarch\"',\n", + " 'Ibsen and Some Current Superstitions',\n", + " 'Realism as a Practical and Cosmic Joke',\n", + " '\"The One Poor Word\" in \"Middlemarch\"',\n", + " 'The abuses of literacy',\n", + " 'Existentially Complete Abelian Lattice-Ordered Groups',\n", + " 'Narrative and History',\n", + " 'THE WRITER AND THE COMMISSARS',\n", + " 'George Eliot\\'s Scrupulous Research: The Facts behind Eliot\\'s Use of the \"Keepsake in Middlemarch\"',\n", + " 'Letter From England October 1995',\n", + " 'Middlemarch and History',\n", + " 'The Gendering of Habit in George Eliot\\'s \"Middlemarch\"',\n", + " 'ROSAMOND VINCY OF \"MIDDLEMARCH\"',\n", + " '[Discussion in Four Parts]',\n", + " 'F. R. Leavis Special Issue',\n", + " \"DISCERNING SYNTAX: GEORGE ELIOT'S RELATIVE CLAUSES\",\n", + " 'When George Eliot Reads Milton: The Muse in a Different Voice',\n", + " \"Character and Destiny in George Eliot's Fiction\"]" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the titles of those articles.\n", + "print('Titles of scholarly writings that quote Chapter 20:')\n", + "[item.title for item in chap20s]" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Number of articles that cite Chapter 20\n", + "len(chap20s)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "xxStart, xxEnd = chapterLocations[20:22] # Chapter 20 Boundaries" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CHAPTER XX.\n", + "\n", + " \"A child forsaken, waking suddenly,\n", + " Whose gaze afeard on all things round doth rove,\n", + " And seeth only that it cannot see\n", + " The meeting eyes of love.\"\n", + "\n", + "\n", + "Two hours later, Dorothea was seated in an inner room or boudoir of a\n", + "handsome apartment in the Via Sistina.\n", + "\n", + "I am sorry to add that she was sobbing bitterly, with such abandonment\n", + "to this relief of an oppressed heart as a woman habitually controlled\n", + "by pride on her own account and thoughtfulness for others will\n", + "sometimes allow herself when she feels securely alone. And Mr.\n", + "Casaubon was certain to remain away for some time at the Vatican.\n", + "\n", + "Yet Dorothea had no distinctly shapen grievance that she could state\n", + "even to herself; and in the midst of her confused thought and passion,\n", + "the mental act that was struggling forth into clearness was a\n", + "self-accusing cry that her feeling of desolation was the fault of her\n", + "own spiritual poverty. She had married the man of her choice, and with\n", + "the advantage over most girls t\n" + ] + } + ], + "source": [ + "print(mm[xxStart:xxStart+1000]) # Verify we have Ch. 20" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "xx = mm[xxStart:xxEnd]" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "xxParaLocations = [match.start() for match in re.finditer('\\n\\n+', mm)]\n", + "xxParaLocations = [x for x in xxParaLocations if (x > xxStart) and (x < xxEnd)] " + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n\\nBut this stupendous fragmentariness heightened the dreamlike\\nstrangeness of her bridal life. Dorothea had now been five weeks in\\nRome, and in the kindly mornings when autumn and winter seemed to go\\nhand in hand like a happy aged couple one of whom would presently\\nsurvive in chiller loneliness, she had driven about at first with Mr.\\nCasaubon, but of late chiefly with Tantripp and their experienced\\ncourier. She had been led through the best galleries, had been taken\\nto the chief points of view, had been shown the grandest ruins and the\\nmost glorious churches, and she had ended by oftenest choosing to drive\\nout to the Campagna where she could feel alone with the earth and sky,\\naway-from the oppressive masquerade of ages, in which her own life too\\nseemed to become a masque with enigmatical costumes.'" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mm[xxParaLocations[4]:xxParaLocations[5]]" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[130022, 130046]]" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "articlesWithMatches['Locations in A'].loc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [], + "source": [ + "def inXX(matches): \n", + " \"\"\" Determine if the article has a match in Ch. 20\"\"\"\n", + " for match in matches: \n", + " if match[0] > xxStart and match[0] < xxEnd:\n", + " return True\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "9 False\n", + "17 False\n", + "19 False\n", + "21 False\n", + "Name: Locations in A, dtype: bool" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "articlesWithMatches['Locations in A'].apply(inXX).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Paragraph-level analysis of Chapter 20" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "# Try to find out what articles cite paragraph 6 in Chapter 20\n", + "chap20par6s = []\n", + "ids = []\n", + "for i, row in df.iterrows(): \n", + " locations = row['Locations in A']\n", + " starts = [item[0] for item in locations]\n", + " if row['Decade'] in [1870, 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010]: \n", + " for start in starts: \n", + " if start > 411152 and start < 412177: # Does it cite Chapter XX, paragraph 6? \n", + " if row.id not in ids: \n", + " chap20par6s.append(row)\n", + " ids.append(row.id)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Titles of scholarly writings that quote paragraph 6 of Chapter 20:\n" + ] + }, + { + "data": { + "text/plain": [ + "['“I Mistook the Faint Shadow”',\n", + " 'Torpedoes, tapirs and tortoises: scientific discourse in \"Middlemarch\"',\n", + " 'Sympathy Time: Adam Smith, George Eliot, and the Realist Novel',\n", + " '“A True Prophet”? Speculation in Victorian Sensory Physiology and George Eliot’s “The Lifted Veil”',\n", + " 'One-Way Communication',\n", + " 'Review Article',\n", + " 'A Note on Middlemarch',\n", + " \"Ian McEwan's Saturday and the Aesthetics of Prose\",\n", + " 'Responsibility without Consciousness',\n", + " 'Proserpine and Pessimism: Goddesses of Death, Life, and Language from Swinburne to Wharton',\n", + " 'Rights, Communities, and Tradition',\n", + " 'Reading, Writing, and Eavesdropping: Some Thoughts on the Nature of Realistic Fiction',\n", + " 'The Sound and the Fury: A Logic of Tragedy',\n", + " 'Views from above and below: George Eliot and Fakir Mohan Senapati',\n", + " 'Review Article',\n", + " 'The Not-Quite Said',\n", + " 'Review Article',\n", + " 'Incarnation and Inwardness:',\n", + " '\"Be Ye Lukewarm!\": The Nineteenth-Century Novel and Social Action',\n", + " 'Development and the Learning Organisation: An Introduction',\n", + " 'Lost in Magnification: Nineteenth-Century Microscopy and The Lifted Veil',\n", + " 'As Sure as Shooting',\n", + " 'Review Article',\n", + " 'George Eliot and Wordsworth: The Power of Sound and the Power of Mind',\n", + " 'Came Glancing like an Arrow',\n", + " \"My Tears See More Than My Eyes MY SON'S DEPRESSION AND THE POWER OF ART\",\n", + " \"Incarnation, Inwardness, and Imagination: George Eliot's Early Fiction\",\n", + " \"ENGLAND AND ENGLISHNESS: FORD'S FIRST TRILOGY\",\n", + " 'COMMONPLACE BOOK: Secrets',\n", + " \"T. S. Eliot's Quartets: A New Reading\",\n", + " 'Eliot, Proust, Stein',\n", + " 'George Eliot and Greek Tragedy',\n", + " \"Woolf's Copernican Shift: Nonhuman Nature in Virginia Woolf's Short Fiction\",\n", + " 'The Abyss of Sympathy: the Conventions of Pathos in Eighteenth and Nineteenth Century British Novels',\n", + " 'THE ECONOMIC PROBLEM OF SYMPATHY: PARABASIS, INTEREST, AND REALIST FORM IN \"MIDDLEMARCH\"',\n", + " 'One-Way Communication',\n", + " 'Gwendolen Harleth - Character Creation or Character Analysis?',\n", + " \"The Tramp of a Fly's Footstep: or, The Shriek, Rattle, and Roar of a Victorian Sound Track\",\n", + " 'Breathless',\n", + " 'Dorothea and \"Miss Brooke\" in Middlemarch',\n", + " \"Louis Guilloux's Working Class Novels: Some Problems of Social Realism\",\n", + " '\"Myriad-Headed, Myriad-Handed\": Labor in \"Middlemarch\"',\n", + " \"The Squirrel's Heartbeat: Some Thoughts on the Later Style of Henry James\",\n", + " \"Tolstoj's Reading of George Eliot: Visions and Revisions\",\n", + " 'Shifting from Stories to Live by to Stories to Leave by: Early Career Teacher Attrition',\n", + " 'What Is Prosaics?',\n", + " 'Sound Object Lessons',\n", + " 'Exiling the Encyclopedia: The Individual in \"Janet\\'s Repentance\"',\n", + " 'Programs and Abstracts',\n", + " 'A SHIFT IN THE ETHICS OF HARDY’S FICTION',\n", + " 'The Divine Comedy of Language: Tennyson\\'s \"In Memoriam\"',\n", + " 'Review Article',\n", + " 'GEORGE ELIOT: THE SIBYL OF MERCIA',\n", + " '“The Continuity of Married Companionship”',\n", + " 'Sympathy Biography and Sympathy Margin',\n", + " 'In the Scene of Being',\n", + " 'Review Article',\n", + " 'Fiction as Vivisection: G. H. Lewes and George Eliot',\n", + " 'Against Detachment',\n", + " 'Review Article',\n", + " 'Why Read George Eliot? Her novels are just modern enough—and just old-fashioned enough, too',\n", + " 'The Language of Silence: A Citation',\n", + " 'Forecasting Falls: Icarus from Freud to Auden to 9/11',\n", + " '\"THE OTHER SIDE OF SILENCE\": KATHERINE ANNE PORTER\\'S \"HE\" AS TRAGEDY',\n", + " 'The Made Man and the “Minor” Novel: Erewhon, ANT, and Empire',\n", + " 'Charles Darwin and the Victorian Pre-History of Climate Denial']" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the titles of those articles.\n", + "print('Titles of scholarly writings that quote paragraph 6 of Chapter 20:')\n", + "[item.title for item in chap20par6s]" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "66" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(chap20par6s) # The number of items citing paragraph 6 in chapter 20" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n\\nNot that this inward amazement of Dorothea\\'s was anything very\\nexceptional: many souls in their young nudity are tumbled out among\\nincongruities and left to \"find their feet\" among them, while their\\nelders go about their business. Nor can I suppose that when Mrs.\\nCasaubon is discovered in a fit of weeping six weeks after her wedding,\\nthe situation will be regarded as tragic. Some discouragement, some\\nfaintness of heart at the new real future which replaces the imaginary,\\nis not unusual, and we do not expect people to be deeply moved by what\\nis not unusual. That element of tragedy which lies in the very fact of\\nfrequency, has not yet wrought itself into the coarse emotion of\\nmankind; and perhaps our frames could hardly bear much of it. If we\\nhad a keen vision and feeling of all ordinary human life, it would be\\nlike hearing the grass grow and the squirrel\\'s heart beat, and we\\nshould die of that roar which lies on the other side of silence. As it\\nis, the quickest of us walk about well wadded with stupidity'" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mm[411152:412177]" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [], + "source": [ + "def paraIndicesIn20(matches, paraLocations=xxParaLocations): \n", + " \"\"\" Determine paragraph number (index) for match in Ch. 20. \"\"\"\n", + " paraIndices = []\n", + " if inXX(matches): \n", + " paraBoundaries = list(zip(paraLocations, paraLocations[1:]))\n", + " for match in matches: \n", + " for i, paraBoundary in enumerate(paraBoundaries): \n", + " if set(range(match[0], match[1])) & set(range(paraBoundary[0], paraBoundary[1])): # find the set intersection of the ranges of pairs\n", + " paraIndices.append(i)\n", + " else: \n", + " paraIndices.append(None)\n", + " return paraIndices\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(set(range(8, 10)) & set(range(1, 9)))" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/hg/n067xqnn1nbbk0txk1mdhcq80000gn/T/ipykernel_49489/4864444.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " articlesWithMatches['paraIndicesIn20'] = articlesWithMatches['Locations in A'].apply(paraIndicesIn20)\n" + ] + } + ], + "source": [ + "articlesWithMatches['paraIndicesIn20'] = articlesWithMatches['Locations in A'].apply(paraIndicesIn20)" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [], + "source": [ + "counters = list(articlesWithMatches['paraIndicesIn20'].apply(Counter))" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "grandTally = Counter()" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "for counter in counters: \n", + " grandTally += counter" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [], + "source": [ + "del grandTally[None]" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{6: 69,\n", + " 5: 47,\n", + " 3: 7,\n", + " 15: 4,\n", + " 10: 20,\n", + " 29: 2,\n", + " 25: 3,\n", + " 4: 6,\n", + " 7: 9,\n", + " 12: 3,\n", + " 14: 3,\n", + " 33: 6,\n", + " 18: 3,\n", + " 26: 8,\n", + " 17: 7,\n", + " 16: 7,\n", + " 11: 8,\n", + " 22: 1,\n", + " 1: 1,\n", + " 2: 1,\n", + " 13: 1,\n", + " 8: 1,\n", + " 9: 2}" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict(grandTally)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Which paragraphs in Chapter 20 are quoted most often?" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series(dict(grandTally)).sort_index().plot(kind='bar', title=\"Which paragraphs in Chapter 20 are quoted most often?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "To those who have looked at Rome with the quickening power of a\n", + "knowledge which breathes a growing soul into all historic shapes, and\n", + "traces out the suppressed transitions which unite all contrasts, Rome\n", + "may still be the spiritual centre and interpreter of the world. But\n", + "let them conceive one more historical contrast: the gigantic broken\n", + "revelations of that Imperial and Papal city thrust abruptly on the\n", + "notions of a girl who had been brought up in English and Swiss\n", + "Puritanism, fed on meagre Protestant histories and on art chiefly of\n", + "the hand-screen sort; a girl whose ardent nature turned all her small\n", + "allowance of knowledge into principles, fusing her actions into their\n", + "mould, and whose quick emotions gave the most abstract things the\n", + "quality of a pleasure or a pain; a girl who had lately become a wife,\n", + "and from the enthusiastic acceptance of untried duty found herself\n", + "plunged in tumultuous preoccupation with her personal lot. The weight\n", + "of unintelligible Rome might lie easily on bright nymphs to whom it\n", + "formed a background for the brilliant picnic of Anglo-foreign society;\n", + "but Dorothea had no such defence against deep impressions. Ruins and\n", + "basilicas, palaces and colossi, set in the midst of a sordid present,\n", + "where all that was living and warm-blooded seemed sunk in the deep\n", + "degeneracy of a superstition divorced from reverence; the dimmer but\n", + "yet eager Titanic life gazing and struggling on walls and ceilings; the\n", + "long vistas of white forms whose marble eyes seemed to hold the\n", + "monotonous light of an alien world: all this vast wreck of ambitious\n", + "ideals, sensuous and spiritual, mixed confusedly with the signs of\n", + "breathing forgetfulness and degradation, at first jarred her as with an\n", + "electric shock, and then urged themselves on her with that ache\n", + "belonging to a glut of confused ideas which check the flow of emotion.\n", + "Forms both pale and glowing took possession of her young sense, and\n", + "fixed themselves in her memory even when she was not thinking of them,\n", + "preparing strange associations which remained through her after-years.\n", + "Our moods are apt to bring with them images which succeed each other\n", + "like the magic-lantern pictures of a doze; and in certain states of\n", + "dull forlornness Dorothea all her life continued to see the vastness of\n", + "St. Peter's, the huge bronze canopy, the excited intention in the\n", + "attitudes and garments of the prophets and evangelists in the mosaics\n", + "above, and the red drapery which was being hung for Christmas spreading\n", + "itself everywhere like a disease of the retina.\n", + "\n", + "Not that this inward amazement of Dorothea's was anything very\n", + "exceptional: many souls in their young nudity are tumbled out among\n", + "incongruities and left to \"find their feet\" among them, while their\n", + "elders go about their business. Nor can I suppose that when Mrs.\n", + "Casaubon is discovered in a fit of weeping six weeks after her wedding,\n", + "the situation will be regarded as tragic. Some discouragement, some\n", + "faintness of heart at the new real future which replaces the imaginary,\n", + "is not unusual, and we do not expect people to be deeply moved by what\n", + "is not unusual. That element of tragedy which lies in the very fact of\n", + "frequency, has not yet wrought itself into the coarse emotion of\n", + "mankind; and perhaps our frames could hardly bear much of it. If we\n", + "had a keen vision and feeling of all ordinary human life, it would be\n", + "like hearing the grass grow and the squirrel's heart beat, and we\n", + "should die of that roar which lies on the other side of silence. As it\n", + "is, the quickest of us walk about well wadded with stupidity.\n" + ] + } + ], + "source": [ + "print(mm[xxParaLocations[5]:xxParaLocations[7]]) # What are paragraphs #5 and #6? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# *Middlemarch* quotations, by journal\n", + "\n", + "- [Descriptive statistics on journals in JSTOR dataset](#Descriptive-statistics-on-journals-in-JSTOR-dataset)\n", + "\t- [*Middlemarch* quotations per chapter, by journal, stacked bar chart](#Middlemarch-quotations-per-chapter,-by-journal,-stacked-bar-chart)\n", + "\t- [*George Eliot - George Henry Lewes Studies* (*GE-GHLS*)](#George-Eliot---George-Henry-Lewes-Studies-(GE-GHLS))\n", + "\t\t- [*GE-GHLS*: *Middlemarch* quotations per chapter](#GE-GHLS:-Middlemarch-quotations-per-chapter)\n", + " - [Diachronic Analysis of *GE-GHLS* Quotations](#Diachronic-Analysis-of-GE-GHLS-Quotations)\n", + "\t\t- [*GE-GHLS*: *Middlemarch* quotations per chapter, per decade (not normalized or weighted)](#GE-GHLS:-Middlemarch-quotations-per-chapter,-per-decade-(not-normalized-or-weighted))\n", + "\t\t- [*GE-GHLS*: *Middlemarch* quotations per chapter, per decade (not normalized or weighted), heat map](#GE-GHLS:-Middlemarch-quotations-per-chapter,-per-decade-(not-normalized-or-weighted),-heat-map)\n", + "\t\t- [*GE-GHLS*: *Middlemarch* quotations per chapter, per decade (normalized by decade and weighted by word count)](#GE-GHLS:-Middlemarch-quotations-per-chapter,-per-decade-(normalized-by-decade-and-weighted-by-word-count))\n", + "\t\t- [*GE-GHLS*: *Middlemarch* quotations per chapter, per decade (normalized and weighted), heat map](#GE-GHLS:-Middlemarch-quotations-per-chapter,-per-decade-(normalized-and-weighted),-heat-map)\n", + "\t\t- [Compare the specialist journal, *George Eliot - George Henry Lewes Studies*, with all other journals](#Compare-the-specialist-journal,-George-Eliot---George-Henry-Lewes-Studies,-with-all-other-journals)\n", + "\t- [*Victorian Studies*](#Victorian-Studies)\n", + "\t\t- [*Victorian Studies*: *Middlemarch* quotations per chapter](#Victorian-Studies:-Middlemarch-quotations-per-chapter)\n", + "\t\t- [Diachronic Analysis of *Victorian Studies* Quotations](#Diachronic-Analysis-of-Victorian-Studies-Quotations)\n", + "\t\t- [*Victorian Studies*: *Middlemarch* quotations per book, per decade (normalized and weighted), heat map](#Victorian-Studies:-Middlemarch-quotations-per-book,-per-decade-(normalized-and-weighted),-heat-map)\n", + "\t\t- [*Victorian Studies*: *Middlemarch* quotations per book, per decade (not normalized or weighted)](#Victorian-Studies:-Middlemarch-quotations-per-book,-per-decade-(not-normalized-or-weighted))\n", + "\t\t- [*Victorian Studies*: *Middlemarch* quotations per book, per decade (not normalized or weighted), heat map](#Victorian-Studies:-Middlemarch-quotations-per-book,-per-decade-(not-normalized-or-weighted),-heat-map)\n", + "\t\t- [*Victorian Studies*: *Middlemarch* quotations per chapter, per decade (not normalized or weighted)](#Victorian-Studies:-Middlemarch-quotations-per-chapter,-per-decade-(not-normalized-or-weighted))\n", + " - [*Victorian Studies*: *Middlemarch* quotations per chapter, per decade (not normalized or weighted), heat map](#Victorian-Studies:-Middlemarch-quotations-per-chapter,-per-decade-(not-normalized-or-weighted),-heat-map)\n", + "\t\t- [*Victorian Studies*: *Middlemarch* quotations per chapter, per decade (normalized by decade and weighted by word count)](#Victorian-Studies:-Middlemarch-quotations-per-chapter,-per-decade-(normalized-by-decade-and-weighted-by-word-count))\n", + "\t\t- [*Victorian Studies*: *Middlemarch* quotations per chapter, per decade (normalized and weighted), heat map](#Victorian-Studies:-Middlemarch-quotations-per-chapter,-per-decade-(normalized-and-weighted),-heat-map)\n", + "\t\t- [*Victorian Studies* Chapter 15](#Victorian-Studies-Chapter-15)\n", + "\t- [All Victorianist journals](#All-Victorianist-journals)\n", + "\t\t- [Victorianist journals: *Middlemarch* quotations per chapter](#Victorianist-journals:-Middlemarch-quotations-per-chapter)\n", + "\t\t- [Diachronic Analysis of Victorianist Journals Quotations](#Diachronic-Analysis-of-Victorianist-Journals-Quotations)\n", + "\t\t- [Victorianist journals: *Middlemarch* quotations per book, per decade (normalized and weighted), heat map](#Victorianist-journals:-Middlemarch-quotations-per-book,-per-decade-(normalized-and-weighted),-heat-map)\n", + "\t\t- [Victorianist journals: *Middlemarch* quotations per book, per decade (not normalized or weighted)](#Victorianist-journals:-Middlemarch-quotations-per-book,-per-decade-(not-normalized-or-weighted))\n", + "\t\t- [Victorianist journals: *Middlemarch* quotations per book, per decade (not normalized or weighted), heat map](#Victorianist-journals:-Middlemarch-quotations-per-book,-per-decade-(not-normalized-or-weighted),-heat-map)\n", + "\t\t- [Victorianist journals: *Middlemarch* quotations per chapter, per decade (not normalized or weighted)](#Victorianist-journals:-Middlemarch-quotations-per-chapter,-per-decade-(not-normalized-or-weighted))\n", + "\t\t- [Victorianist journals: *Middlemarch* quotations per chapter, per decade (not normalized or weighted), heat map](#Victorianist-journals:-Middlemarch-quotations-per-chapter,-per-decade-(not-normalized-or-weighted),-heat-map)\n", + "\t\t- [Victorianist journals: *Middlemarch* quotations per chapter, per decade (normalized by decade and weighted by word count)](#Victorianist-journals:-Middlemarch-quotations-per-chapter,-per-decade-(normalized-by-decade-and-weighted-by-word-count))\n", + "\t\t- [Victorianist journals: *Middlemarch* quotations per chapter, per decade (normalized and weighted), heat map](#Victorianist-journals:-Middlemarch-quotations-per-chapter,-per-decade-(normalized-and-weighted),-heat-map)\n", + "\t\t- [Most Distinctive Words: Victorianist Journals vs. Non-Victorianist Journals](#Most-Distinctive-Words:-Victorianist-Journals-vs.-Non-Victorianist-Journals)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Descriptive statistics on journals in JSTOR dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Journal | Number of articles in our dataset\n" + ] + }, + { + "data": { + "text/plain": [ + "Victorian Studies 459\n", + "George Eliot - George Henry Lewes Studies 231\n", + "The Modern Language Review 192\n", + "Nineteenth-Century Fiction 192\n", + "The Review of English Studies 190\n", + "PMLA 154\n", + "NOVEL: A Forum on Fiction 148\n", + "Nineteenth-Century Literature 139\n", + "Studies in the Novel 124\n", + "ELH 102\n", + "Name: journal, dtype: int64" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Top 10 journals with most articles in our dataset\n", + "df['journal'] = df['isPartOf']\n", + "journalStats = df['journal'].value_counts()\n", + "print(\"Journal | Number of articles in our dataset\")\n", + "journalStats[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [], + "source": [ + "journalList = journalStats.index" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [], + "source": [ + "journals = pd.DataFrame({title: synchronicAnalysis(df.loc[df['journal'] == title]) for title in journalList }).T" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/hg/n067xqnn1nbbk0txk1mdhcq80000gn/T/ipykernel_49489/1477629281.py:4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " topJournals.loc['Other'] = otherJournals.sum()\n" + ] + } + ], + "source": [ + "cutoff = 1500\n", + "topJournals = journals.loc[journals.sum(axis=1) > cutoff]\n", + "otherJournals = journals.loc[journals.sum(axis=1) < cutoff]\n", + "topJournals.loc['Other'] = otherJournals.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Middlemarch* quotations per chapter, by journal, stacked bar chart" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "topJournals.T.plot(kind='bar', title=\"Middlemarch quotations per chapter, by journal\", stacked=True, colormap='nipy_spectral')" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [], + "source": [ + "#ax = topJournals.T.plot(kind='bar', stacked=True, colormap='nipy_spectral')\n", + "#fig = ax.get_figure()\n", + "#fig.savefig('synchronic-journals.png', bboxinches='tight', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "789" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print the total number of journals\n", + "len(journalStats)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statistics on Victorianist journals in the dataset\n", + "\n", + "For full list of journals classified as \"Victorianist,\" see below.\n", + "\n", + "Downloadable link: [CSV with complete list of Victorianist-classified journals](https://github.com/lit-mod-viz/middlemarch-critical-histories/blob/master/data/list-of-Victorianist-journals.csv)" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [], + "source": [ + "list_of_VS_journals = ['Victorian Studies', 'George Eliot - George Henry Lewes Studies', 'Nineteenth-Century Fiction', 'Nineteenth-Century Literature', 'Dickens Studies Annual', 'Victorian Literature and Culture', 'Victorian Review', 'The George Eliot, George Henry Lewes Newsletter', 'Victorian Periodicals Review', 'Dickens Quarterly', 'Victorian Poetry', 'The Thomas Hardy Journal', 'The Gaskell Society Journal', 'The Gaskell Journal', 'Newsletter of the Victorian Studies Association of Western Canada', 'Dickens Studies Newsletter', 'Browning Institute Studies', 'Victorian Periodicals Newsletter', 'Carlyle Studies Annual', 'Conradiana', 'Tennyson Research Bulletin', 'The Conradian', 'The Hardy Society Journal', 'The Hardy Review', 'Studies in Browning and His Circle', 'Nineteenth-Century French Studies', 'The Wilkie Collins Journal', 'Carlyle Newsletter', 'The Wildean', 'Dickens Studies', 'Carlyle Annual', '19th-Century Music', 'The Trollopian', 'Conrad Studies']" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LIST OF VICTORIANIST JOURNALS \n", + "\n", + "Victorian Studies\n", + "George Eliot - George Henry Lewes Studies\n", + "Nineteenth-Century Fiction\n", + "Nineteenth-Century Literature\n", + "Dickens Studies Annual\n", + "Victorian Literature and Culture\n", + "Victorian Review\n", + "The George Eliot, George Henry Lewes Newsletter\n", + "Victorian Periodicals Review\n", + "Dickens Quarterly\n", + "Victorian Poetry\n", + "The Thomas Hardy Journal\n", + "The Gaskell Society Journal\n", + "The Gaskell Journal\n", + "Newsletter of the Victorian Studies Association of Western Canada\n", + "Dickens Studies Newsletter\n", + "Browning Institute Studies\n", + "Victorian Periodicals Newsletter\n", + "Carlyle Studies Annual\n", + "Conradiana\n", + "Tennyson Research Bulletin\n", + "The Conradian\n", + "The Hardy Society Journal\n", + "The Hardy Review\n", + "Studies in Browning and His Circle\n", + "Nineteenth-Century French Studies\n", + "The Wilkie Collins Journal\n", + "Carlyle Newsletter\n", + "The Wildean\n", + "Dickens Studies\n", + "Carlyle Annual\n", + "19th-Century Music\n", + "The Trollopian\n", + "Conrad Studies\n" + ] + } + ], + "source": [ + "print(\"LIST OF VICTORIANIST JOURNALS \\n\")\n", + "for item in list_of_VS_journals:\n", + " print(item)" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "34" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(list_of_VS_journals)" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [], + "source": [ + "just_VS_journals_quotes = articlesWithMatches[articlesWithMatches['isPartOf'].isin(list_of_VS_journals)]" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "429" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Number of Victorianist articles containing matches\n", + "len(just_VS_journals_quotes)" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "George Eliot - George Henry Lewes Studies 106\n", + "Victorian Studies 78\n", + "Nineteenth-Century Fiction 68\n", + "Nineteenth-Century Literature 37\n", + "Victorian Literature and Culture 37\n", + "Dickens Studies Annual 19\n", + "Victorian Review 13\n", + "Victorian Poetry 12\n", + "The George Eliot, George Henry Lewes Newsletter 11\n", + "Victorian Periodicals Review 8\n", + "Dickens Quarterly 5\n", + "The Thomas Hardy Journal 5\n", + "The Gaskell Society Journal 4\n", + "Browning Institute Studies 4\n", + "Tennyson Research Bulletin 4\n", + "Carlyle Studies Annual 3\n", + "The Gaskell Journal 2\n", + "Conradiana 2\n", + "Dickens Studies Newsletter 2\n", + "19th-Century Music 1\n", + "Newsletter of the Victorian Studies Association of Western Canada 1\n", + "Conrad Studies 1\n", + "Nineteenth-Century French Studies 1\n", + "The Wilkie Collins Journal 1\n", + "Carlyle Annual 1\n", + "The Hardy Review 1\n", + "Victorian Periodicals Newsletter 1\n", + "The Wildean 1\n", + "Name: isPartOf, dtype: int64" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "just_VS_journals_quotes['isPartOf'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "George Eliot - George Henry Lewes Studies 106\n", + "Victorian Studies 78\n", + "Nineteenth-Century Fiction 68\n", + "PMLA 47\n", + "ELH 42\n", + " ... \n", + "Science 1\n", + "Transformation of Rage 1\n", + "Anglican and Episcopal History 1\n", + "The Journal of Ethics 1\n", + "Sociological Forum 1\n", + "Name: isPartOf, Length: 403, dtype: int64" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "articlesWithMatches['isPartOf'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1258" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Number of matches from Victorianist journals\n", + "just_VS_journals_quotes['numMatches'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "150" + ] + }, + "execution_count": 179, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Number of matches from the journal *Victorian Studies*\n", + "just_VS_journals_quotes[just_VS_journals_quotes['isPartOf']== \"Victorian Studies\"].numMatches.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3800" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Number of matches, overall\n", + "articlesWithMatches['numMatches'].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Proportion of matches from Victorianist journals" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.3310526315789474" + ] + }, + "execution_count": 181, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# What proportion of matches come from Victorianist journals?\n", + "just_VS_journals_quotes['numMatches'].sum() / articlesWithMatches['numMatches'].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## *George Eliot - George Henry Lewes Studies* (*GE-GHLS*)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Articles where journal title is *George Eliot - George Henry Lewes Studies*" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [], + "source": [ + "geJournals = df.loc[df['journal'] == 'George Eliot - George Henry Lewes Studies']" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_columns', 207)" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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creatordatePublisheddocSubTypedocTypeididentifierisPartOfissueNumberlanguageoutputFormatpageCountpageEndpageStartpaginationproviderpublicationYearpublishersourceCategorytdmCategorytitleurlvolumeNumberwordCountnumMatchesLocations in ALocations in BabstractkeyphrasesubTitleyearDecadeQuoted WordsLocations in A with WordcountsWordcountsjournal
5070[Tim Dolin]1995-04-01research-article37[ELIZABETH WINSTON]1995-09-01book-reviewarticlehttp://www.jstor.org/stable/30030266[{'name': 'issn', 'value': '00138304'}, {'name...ELH1http://www.jstor.org/stable/43595523[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies28/29[eng][unigram, bigram, trigram]19.0215197pp. 197-2156.0106101pp. 101-106jstor1995Johns Hopkins University PressPenn State University Press[Language & Literature, Humanities][Arts - Literature]Fictional Territory and a Woman's Place: Regio...http://www.jstor.org/stable/300302666279540[][]NoneNoneNone199519900[][]ELH
5114[JESSE ROSENTHAL]2010-10-01research-articlearticlehttp://www.jstor.org/stable/40963186[{'name': 'issn', 'value': '00138304'}, {'name...ELH3[eng][unigram, bigram, trigram]35.0811777pp. 777-811jstor2010Johns Hopkins University Press[Language & Literature, Humanities][Philosophy - Applied philosophy, Philosophy -...THE LARGE NOVEL AND THE LAW OF LARGE NUMBERS; ...http://www.jstor.org/stable/4096318677165533[[68597, 68675], [1787728, 1787883], [1792137,...[[9115, 9193], [10928, 11083], [11250, 11353]]NoneNoneNone2010201058[([68597, 68675], 12), ([1787728, 1787883], 30...[12, 30, 16]ELH
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5422[Diana Postlethwaite]1990-04-01research-articlearticlehttp://www.jstor.org/stable/2873251[{'name': 'issn', 'value': '00138304'}, {'name...ELH70381[eng][unigram, bigram, trigram]25.0221197pp. 197-221jstor1990Johns Hopkins University Press[Language & Literature, Humanities][Arts - Literature]When George Eliot Reads Milton: The Muse in a ...http://www.jstor.org/stable/2873251571039126[[1884, 1991], [560018, 560102], [560366, 5607...[[1440, 1547], [7011, 7091], [8710, 9128], [91...[[502448, 502471]][[18540, 18563]]NoneNoneNone19901990704[([1884, 1991], 17), ([560018, 560102], 16), (...[17, 16, 67, 14, 24, 21, 25, 83, 55, 12, 13, 3...ELH
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5534None1972-12-01misc108[Judith Adler]2018-10-01research-articlearticlehttp://www.jstor.org/stable/2872695[{'name': 'issn', 'value': '00138304'}, {'name...ELH4http://www.jstor.org/stable/10.5325/georeliogh...[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies2[eng][unigram, bigram, trigram]4.0None29.0171143pp. 143-171jstor2018Penn State University Press[Language & Literature, Humanities][Arts - Literature]Hidden Allusion in the Finale of <em>Middlemar...http://www.jstor.org/stable/10.5325/georeliogh...7092581[[1792915, 1793447]][[350, 876]]This article argues that the famous concluding...NoneNone2018201097[([1792915, 1793447], 97)][97]George Eliot - George Henry Lewes Studies
............................................................................................................
5798[TERENCE R. WRIGHT]1995-09-01book-reviewarticlehttp://www.jstor.org/stable/43595524[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies28/29[eng][unigram, bigram, trigram]3.0109107pp. 107-109jstor1972Johns Hopkins University Press1995Penn State University Press[Language & Literature, Humanities]NoneVolume Informationhttp://www.jstor.org/stable/287269539478Review Articlehttp://www.jstor.org/stable/43595524None8620[][]NoneNoneNone19721970199519900[][]ELHGeorge Eliot - George Henry Lewes Studies
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5751[KRISTIN EWINS]2015-04-01research-article5865None1995-09-01miscarticlehttp://www.jstor.org/stable/24477815[{'name': 'issn', 'value': '00138304'}, {'name...ELH1http://www.jstor.org/stable/43595525[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies28/29[eng][unigram, bigram, trigram]29.0279251pp. 251-2795.0NoneNoneNonejstor2015The Johns Hopkins University Press1995Penn State University Press[Language & Literature, Humanities][Arts - Art history]\"REVOLUTIONIZING A MODE OF LIFE\": LEFTIST MIDD...http://www.jstor.org/stable/244778158212601[Arts - Literature]Back Matterhttp://www.jstor.org/stable/43595525None11470[][]NoneNoneNone20152010199519900[][]ELHGeorge Eliot - George Henry Lewes Studies
5817[ANNA E. CLARK]2014-04-015876[BUFF LINDAU]2013-10-01research-articlearticlehttp://www.jstor.org/stable/24475594[{'name': 'issn', 'value': '00138304'}, {'name...ELH1http://www.jstor.org/stable/42827928[{'name': 'issn', 'value': '23721901'}, {'name...George Eliot - George Henry Lewes Studies64/65[eng][unigram, bigram, trigram]24.0268245pp. 245-2681.0109109p. 109jstor2014The Johns Hopkins University Press2013Penn State University Press[Language & Literature, Humanities][Arts - Literature]\"FRANKENSTEIN\"; OR, THE MODERN PROTAGONISThttp://www.jstor.org/stable/244755948110135NoneA GEORGE ELIOT NOTEhttp://www.jstor.org/stable/42827928None1290[][]NoneNoneNone2014201320100[][]ELHGeorge Eliot - George Henry Lewes Studies
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Houghton] 1946-03-01 research-article article \n", - "3338 [Nina Auerbach] 1975-10-01 research-article article \n", - "3380 [ANNA KORNBLUH] 2010-12-01 research-article article \n", - "3433 [Jeff Nunokawa] 2002-12-01 research-article article \n", - "3463 [Ernest Tuveson] 1966-06-01 research-article article \n", - "3520 None 1986-07-01 misc article \n", - "3522 None 1988-12-01 misc article \n", - "3529 [GEORGE LEVINE] 2016-04-01 research-article article \n", - "3585 [MARY JEAN CORBETT] 2014-04-01 research-article article \n", - "3595 [John H. Hagan, Jr.] 1954-03-01 research-article article \n", - "3609 [EMILY COIT] 2015-12-01 research-article article \n", - "3693 None 1972-06-01 misc article \n", - "3701 [J. Hillis Miller] 1974-10-01 research-article article \n", - "3896 [Dennis Taylor] 1975-07-01 research-article article \n", - "3898 [J. 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"3701 http://www.jstor.org/stable/2872596 \n", - "3896 http://www.jstor.org/stable/2872628 \n", - "3898 http://www.jstor.org/stable/30029996 \n", - "3960 http://www.jstor.org/stable/24475534 \n", - "4009 http://www.jstor.org/stable/23256763 \n", - "4112 http://www.jstor.org/stable/2873354 \n", - "4114 http://www.jstor.org/stable/2873128 \n", - "4119 http://www.jstor.org/stable/30029569 \n", - "4203 http://www.jstor.org/stable/2873065 \n", - "4289 http://www.jstor.org/stable/41337562 \n", - "4303 http://www.jstor.org/stable/41337563 \n", - "4324 http://www.jstor.org/stable/2872227 \n", - "4408 http://www.jstor.org/stable/2871573 \n", - "4596 http://www.jstor.org/stable/30030281 \n", - "4698 http://www.jstor.org/stable/30030039 \n", - "4715 http://www.jstor.org/stable/24735473 \n", - "4766 http://www.jstor.org/stable/2873017 \n", - "4788 http://www.jstor.org/stable/30029573 \n", - "4826 http://www.jstor.org/stable/2872183 \n", - "4841 http://www.jstor.org/stable/2872453 \n", - "4845 http://www.jstor.org/stable/23256759 \n", - "4900 http://www.jstor.org/stable/26173879 \n", - "4962 http://www.jstor.org/stable/24475544 \n", - "5058 http://www.jstor.org/stable/30032028 \n", - "5070 http://www.jstor.org/stable/30030266 \n", - "5114 http://www.jstor.org/stable/40963186 \n", - "5154 http://www.jstor.org/stable/2872788 \n", - "5160 http://www.jstor.org/stable/2873451 \n", - "5324 http://www.jstor.org/stable/2873083 \n", - "5355 http://www.jstor.org/stable/30031927 \n", - "5376 http://www.jstor.org/stable/24477778 \n", - "5422 http://www.jstor.org/stable/2873251 \n", - "5426 http://www.jstor.org/stable/2873139 \n", - "5446 http://www.jstor.org/stable/2873079 \n", - "5487 http://www.jstor.org/stable/2872849 \n", - "5509 http://www.jstor.org/stable/40963110 \n", - "5534 http://www.jstor.org/stable/2872695 \n", - "5546 http://www.jstor.org/stable/2873363 \n", - "5586 http://www.jstor.org/stable/30030146 \n", - "5751 http://www.jstor.org/stable/24477815 \n", - "5817 http://www.jstor.org/stable/24475594 \n", - "\n", - " identifier isPartOf issueNumber \\\n", - "24 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "214 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "452 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "457 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "557 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "573 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "652 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "680 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "769 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "862 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "1035 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "1074 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "1140 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "1142 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "1197 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "1245 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "1365 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "1451 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "1483 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "1532 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "1672 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "1698 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "1740 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "1758 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "1857 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "1862 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "1914 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "1915 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "1929 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "1932 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "1953 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "2005 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "2010 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "2125 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "2351 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "2452 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "2496 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "2509 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "2538 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "2613 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "2706 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "2722 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "2778 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "2920 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "2933 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "3140 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "3220 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "3287 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "3308 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "3324 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "3338 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "3380 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "3433 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "3463 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "3520 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "3522 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "3529 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "3585 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "3595 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "3609 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "3693 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "3701 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "3896 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "3898 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "3960 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "4009 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "4112 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "4114 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "4119 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "4203 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "4289 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "4303 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "4324 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "4408 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "4596 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "4698 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "4715 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "4766 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "4788 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "4826 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "4841 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "4845 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "4900 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "4962 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "5058 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "5070 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "5114 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "5154 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "5160 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 3 \n", - "5324 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "5355 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "5376 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "5422 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "5426 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "5446 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "5487 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "5509 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "5534 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "5546 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 4 \n", - "5586 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 2 \n", - "5751 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "5817 [{'name': 'issn', 'value': '00138304'}, {'name... ELH 1 \n", - "\n", - " language outputFormat pageCount pageEnd pageStart \\\n", - "24 [eng] [unigram, bigram, trigram] 24.0 796 773 \n", - "214 [eng] [unigram, bigram, trigram] 30.0 308 279 \n", - "452 [eng] [unigram, bigram, trigram] 16.0 652 637 \n", - "457 [eng] [unigram, bigram, trigram] 21.0 302 285 \n", - "557 [eng] [unigram, bigram, trigram] 33.0 1211 1179 \n", - "573 [eng] [unigram, bigram, trigram] 16.0 508 493 \n", - "652 [eng] [unigram, bigram, trigram] 19.0 239 221 \n", - "680 [eng] [unigram, bigram, trigram] 9.0 None None \n", - "769 [eng] [unigram, bigram, trigram] 4.0 None None \n", - "862 [eng] [unigram, bigram, trigram] 19.0 421 403 \n", - "1035 [eng] [unigram, bigram, trigram] 27.0 847 821 \n", - "1074 [eng] [unigram, bigram, trigram] 23.0 177 155 \n", - "1140 [eng] [unigram, bigram, trigram] 13.0 587 575 \n", - "1142 [eng] [unigram, bigram, trigram] 17.0 558 542 \n", - "1197 [eng] [unigram, bigram, trigram] 19.0 341 323 \n", - "1245 [eng] [unigram, bigram, trigram] 15.0 197 183 \n", - "1365 [eng] [unigram, bigram, trigram] 27.0 449 423 \n", - "1451 [eng] [unigram, bigram, trigram] 27.0 463 437 \n", - "1483 [eng] [unigram, bigram, trigram] 27.0 1171 1145 \n", - "1532 [eng] [unigram, bigram, trigram] 25.0 739 715 \n", - "1672 [eng] [unigram, bigram, trigram] 20.0 119 100 \n", - "1698 [eng] [unigram, bigram, trigram] 22.0 960 939 \n", - "1740 [eng] [unigram, bigram, trigram] 5.0 1003 999 \n", - "1758 [eng] [unigram, bigram, trigram] 21.0 1027 1007 \n", - "1857 [eng] [unigram, bigram, trigram] 7.0 None None \n", - "1862 [eng] [unigram, bigram, trigram] 4.0 None None \n", - "1914 [eng] [unigram, bigram, trigram] 34.0 574 541 \n", - "1915 [eng] [unigram, bigram, trigram] 23.0 84 62 \n", - "1929 [eng] [unigram, bigram, trigram] 26.0 443 418 \n", - "1932 [eng] [unigram, bigram, trigram] 26.0 724 699 \n", - "1953 [eng] [unigram, bigram, trigram] 9.0 922 782 \n", - "2005 [eng] [unigram, bigram, trigram] 14.0 257 244 \n", - "2010 [eng] [unigram, bigram, trigram] 20.0 916 897 \n", - "2125 [eng] [unigram, bigram, trigram] 29.0 745 717 \n", - "2351 [eng] [unigram, bigram, trigram] 4.0 None None \n", - "2452 [eng] [unigram, bigram, trigram] 10.0 None None \n", - "2496 [eng] [unigram, bigram, trigram] 36.0 178 143 \n", - "2509 [eng] [unigram, bigram, trigram] 38.0 602 565 \n", - "2538 [eng] [unigram, bigram, trigram] 21.0 1273 1253 \n", - "2613 [eng] [unigram, bigram, trigram] 24.0 962 939 \n", - "2706 [eng] [unigram, bigram, trigram] 27.0 1251 1225 \n", - "2722 [eng] [unigram, bigram, trigram] 31.0 567 537 \n", - "2778 [eng] [unigram, bigram, trigram] 17.0 503 487 \n", - "2920 [eng] [unigram, bigram, trigram] 24.0 222 199 \n", - "2933 [eng] [unigram, bigram, trigram] 21.0 243 223 \n", - "3140 [eng] [unigram, bigram, trigram] 20.0 692 673 \n", - "3220 [eng] [unigram, bigram, trigram] 28.0 672 645 \n", - "3287 [eng] [unigram, bigram, trigram] 4.0 None None \n", - "3308 [eng] [unigram, bigram, trigram] 20.0 451 432 \n", - "3324 [eng] [unigram, bigram, trigram] 15.0 78 64 \n", - "3338 [eng] [unigram, bigram, trigram] 25.0 419 395 \n", - "3380 [eng] [unigram, bigram, trigram] 27.0 967 941 \n", - "3433 [eng] [unigram, bigram, trigram] 26.0 860 835 \n", - "3463 [eng] [unigram, bigram, trigram] 24.0 270 247 \n", - "3520 [eng] [unigram, bigram, trigram] 3.0 None None \n", - "3522 [eng] [unigram, bigram, trigram] 8.0 916 754 \n", - "3529 [eng] [unigram, bigram, trigram] 26.0 258 233 \n", - "3585 [eng] [unigram, bigram, trigram] 25.0 323 299 \n", - "3595 [eng] [unigram, bigram, trigram] 13.0 66 54 \n", - "3609 [eng] [unigram, bigram, trigram] 26.0 1238 1213 \n", - "3693 [eng] [unigram, bigram, trigram] 2.0 None None \n", - "3701 [eng] [unigram, bigram, trigram] 19.0 473 455 \n", - "3896 [eng] [unigram, bigram, trigram] 18.0 275 258 \n", - "3898 [eng] [unigram, bigram, trigram] 34.0 974 941 \n", - "3960 [eng] [unigram, bigram, trigram] 7.0 None None \n", - "4009 [eng] [unigram, bigram, trigram] 35.0 535 501 \n", - "4112 [eng] [unigram, bigram, trigram] 6.0 None None \n", - "4114 [eng] [unigram, bigram, trigram] 36.0 208 173 \n", - "4119 [eng] [unigram, bigram, trigram] 5.0 None None \n", - "4203 [eng] [unigram, bigram, trigram] 21.0 421 401 \n", - "4289 [eng] [unigram, bigram, trigram] 30.0 1020 991 \n", - "4303 [eng] [unigram, bigram, trigram] 9.0 None None \n", - "4324 [eng] [unigram, bigram, trigram] 21.0 431 411 \n", - "4408 [eng] [unigram, bigram, trigram] 7.0 227 221 \n", - "4596 [eng] [unigram, bigram, trigram] 9.0 None None \n", - "4698 [eng] [unigram, bigram, trigram] 26.0 830 805 \n", - "4715 [eng] [unigram, bigram, trigram] 22.0 232 211 \n", - "4766 [eng] [unigram, bigram, trigram] 27.0 216 190 \n", - "4788 [eng] [unigram, bigram, trigram] 26.0 608 583 \n", - "4826 [eng] [unigram, bigram, trigram] 23.0 540 518 \n", - "4841 [eng] [unigram, bigram, trigram] 14.0 106 93 \n", - "4845 [eng] [unigram, bigram, trigram] 28.0 416 389 \n", - "4900 [eng] [unigram, bigram, trigram] 17.0 837 821 \n", - "4962 [eng] [unigram, bigram, trigram] 31.0 869 839 \n", - 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None None \n", - "5058 [[26680, 26874]] None None \n", - "5070 [] None None \n", - "5114 [[9115, 9193], [10928, 11083], [11250, 11353]] None None \n", - "5154 [] None None \n", - "5160 [] None None \n", - "5324 [] None None \n", - "5355 [[39110, 39162], [39596, 39673], [41962, 42455... None None \n", - "5376 [[69, 155], [4474, 4744], [10438, 10667], [148... None None \n", - "5422 [[1440, 1547], [7011, 7091], [8710, 9128], [91... None None \n", - "5426 [[9299, 9639], [10539, 10627], [13434, 13477],... None None \n", - "5446 [[17475, 17502], [17523, 18004], [18036, 18663]] None None \n", - "5487 [] None None \n", - "5509 [] None None \n", - "5534 [] None None \n", - "5546 [[21578, 21733], [29548, 29905], [43000, 43317... None None \n", - "5586 [[87407, 87434]] None None \n", - "5751 [] None None \n", - "5817 [] None None \n", - "\n", - " subTitle year Decade Quoted Words \\\n", - "24 None 2012 2010 35 \n", - "214 None 1972 1970 204 \n", - "452 None 1998 1990 8 \n", - "457 None 1978 1970 7 \n", - "557 None 2015 2010 92 \n", - "573 None 2007 2000 0 \n", - "652 None 1951 1950 26 \n", - "680 None 2013 2010 0 \n", - "769 None 2007 2000 0 \n", - "862 None 1968 1960 0 \n", - "1035 None 1986 1980 0 \n", - "1074 None 2001 2000 0 \n", - "1140 None 1984 1980 0 \n", - "1142 None 1980 1980 569 \n", - "1197 None 2013 2010 0 \n", - "1245 None 1986 1980 0 \n", - "1365 None 1998 1990 0 \n", - "1451 None 2006 2000 65 \n", - "1483 None 2013 2010 178 \n", - "1532 None 2011 2010 205 \n", - "1672 None 1976 1970 0 \n", - "1698 None 1990 1990 440 \n", - "1740 None 1990 1990 0 \n", - "1758 None 2014 2010 0 \n", - "1857 None 2010 2010 0 \n", - "1862 None 2011 2010 0 \n", - "1914 None 2003 2000 0 \n", - "1915 None 1965 1960 0 \n", - "1929 None 1962 1960 52 \n", - "1932 None 2001 2000 16 \n", - "1953 None 1994 1990 0 \n", - "2005 None 1963 1960 132 \n", - "2010 None 2013 2010 0 \n", - "2125 None 2005 2000 12 \n", - "2351 None 2002 2000 0 \n", - "2452 None 2002 2000 0 \n", - "2496 None 2000 2000 8 \n", - "2509 None 2008 2000 386 \n", - "2538 None 2014 2010 0 \n", - "2613 None 2008 2000 0 \n", - "2706 None 2014 2010 0 \n", - "2722 None 2012 2010 0 \n", - "2778 None 1988 1980 0 \n", - "2920 None 2002 2000 0 \n", - "2933 None 2002 2000 307 \n", - "3140 None 1979 1970 0 \n", - "3220 None 1979 1970 0 \n", - "3287 None 1988 1980 0 \n", - "3308 None 1979 1970 0 \n", - "3324 None 1946 1940 195 \n", - "3338 None 1975 1970 0 \n", - "3380 None 2010 2010 582 \n", - "3433 None 2002 2000 245 \n", - "3463 None 1966 1960 0 \n", - "3520 None 1986 1980 0 \n", - "3522 None 1988 1980 0 \n", - "3529 None 2016 2010 13 \n", - "3585 None 2014 2010 8 \n", - "3595 None 1954 1950 0 \n", - "3609 None 2015 2010 48 \n", - "3693 None 1972 1970 0 \n", - "3701 None 1974 1970 335 \n", - "3896 None 1975 1970 0 \n", - "3898 None 2005 2000 4 \n", - "3960 None 2013 2010 0 \n", - "4009 None 2012 2010 0 \n", - "4112 None 1994 1990 0 \n", - "4114 None 1989 1980 0 \n", - "4119 None 2007 2000 0 \n", - "4203 None 1989 1980 0 \n", - "4289 None 2011 2010 0 \n", - "4303 None 2011 2010 0 \n", - "4324 None 1971 1970 18 \n", - "4408 None 1936 1930 0 \n", - "4596 None 1996 1990 0 \n", - "4698 None 2006 2000 0 \n", - "4715 None 2016 2010 5 \n", - "4766 None 1981 1980 21 \n", - "4788 None 2007 2000 383 \n", - "4826 None 1967 1960 0 \n", - "4841 None 1978 1970 188 \n", - "4845 None 2012 2010 20 \n", - "4900 None 2016 2010 0 \n", - "4962 None 2013 2010 656 \n", - "5058 None 2002 2000 32 \n", - "5070 None 1995 1990 0 \n", - "5114 None 2010 2010 58 \n", - "5154 None 1980 1980 0 \n", - "5160 None 1992 1990 0 \n", - "5324 None 1990 1990 0 \n", - "5355 None 2000 2000 423 \n", - "5376 None 2014 2010 456 \n", - "5422 None 1990 1990 704 \n", - "5426 None 1988 1980 513 \n", - 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ELH \n", - "5376 [17, 45, 35, 98, 10, 34, 44, 40, 62, 71] ELH \n", - "5422 [17, 16, 67, 14, 24, 21, 25, 83, 55, 12, 13, 3... ELH \n", - "5426 [61, 16, 7, 31, 22, 37, 28, 11, 6, 16, 11, 23,... ELH \n", - "5446 [4, 88, 107] ELH \n", - "5487 [] ELH \n", - "5509 [] ELH \n", - "5534 [] ELH \n", - "5546 [25, 65, 59, 33, 7] ELH \n", - "5586 [5] ELH \n", - "5751 [] ELH \n", - "5817 [] ELH " + " creator datePublished docSubType docType \\\n", + "37 [ELIZABETH WINSTON] 1995-09-01 book-review article \n", + "76 [Katherine Newey] 2011-09-01 research-article article \n", + "101 None 2015-11-01 other article \n", + "107 [AVROM FLEISHMAN] 2008-09-01 research-article article \n", + "108 [Judith Adler] 2018-10-01 research-article article \n", + "... ... ... ... ... \n", + "5798 [TERENCE R. WRIGHT] 1995-09-01 book-review article \n", + "5835 [SALEEL NURBHAI] 1997-09-01 research-article article \n", + "5853 [DONALD HAWES] 2001-09-01 research-article article \n", + "5865 None 1995-09-01 misc article \n", + "5876 [BUFF LINDAU] 2013-10-01 research-article article \n", + "\n", + " id \\\n", + "37 http://www.jstor.org/stable/43595523 \n", + "76 http://www.jstor.org/stable/42827892 \n", + "101 http://www.jstor.org/stable/10.5325/georeliogh... \n", + "107 http://www.jstor.org/stable/42827960 \n", + "108 http://www.jstor.org/stable/10.5325/georeliogh... \n", + "... ... \n", + "5798 http://www.jstor.org/stable/43595524 \n", + "5835 http://www.jstor.org/stable/42827636 \n", + "5853 http://www.jstor.org/stable/42827734 \n", + "5865 http://www.jstor.org/stable/43595525 \n", + "5876 http://www.jstor.org/stable/42827928 \n", + "\n", + " identifier \\\n", + "37 [{'name': 'issn', 'value': '23721901'}, {'name... \n", + "76 [{'name': 'issn', 'value': '23721901'}, {'name... \n", + "101 [{'name': 'issn', 'value': '23721901'}, {'name... \n", + "107 [{'name': 'issn', 'value': '23721901'}, {'name... \n", + "108 [{'name': 'issn', 'value': '23721901'}, {'name... \n", + "... ... \n", + "5798 [{'name': 'issn', 'value': '23721901'}, {'name... \n", + "5835 [{'name': 'issn', 'value': '23721901'}, {'name... \n", + "5853 [{'name': 'issn', 'value': '23721901'}, {'name... \n", + "5865 [{'name': 'issn', 'value': '23721901'}, {'name... \n", + "5876 [{'name': 'issn', 'value': '23721901'}, {'name... \n", + "\n", + " isPartOf issueNumber language \\\n", + "37 George Eliot - George Henry Lewes Studies 28/29 [eng] \n", + "76 George Eliot - George Henry Lewes Studies 60/61 [eng] \n", + "101 George Eliot - George Henry Lewes Studies 2 [eng] \n", + "107 George Eliot - George Henry Lewes Studies 54/55 [eng] \n", + "108 George Eliot - George Henry Lewes Studies 2 [eng] \n", + "... ... ... ... \n", + "5798 George Eliot - George Henry Lewes Studies 28/29 [eng] \n", + "5835 George Eliot - George Henry Lewes Studies 32/33 [eng] \n", + "5853 George Eliot - George Henry Lewes Studies 40/41 [eng] \n", + "5865 George Eliot - George Henry Lewes Studies 28/29 [eng] \n", + "5876 George Eliot - George Henry Lewes Studies 64/65 [eng] \n", + "\n", + " outputFormat pageCount pageEnd pageStart pagination \\\n", + "37 [unigram, bigram, trigram] 6.0 106 101 pp. 101-106 \n", + "76 [unigram, bigram, trigram] 16.0 141 126 pp. 126-141 \n", + "101 [unigram, bigram, trigram] 2.0 ii i pp. i-ii \n", + "107 [unigram, bigram, trigram] 79.0 76 1 pp. 1-76 \n", + "108 [unigram, bigram, trigram] 29.0 171 143 pp. 143-171 \n", + "... ... ... ... ... ... \n", + "5798 [unigram, bigram, trigram] 3.0 109 107 pp. 107-109 \n", + "5835 [unigram, bigram, trigram] 18.0 18 1 pp. 1-18 \n", + "5853 [unigram, bigram, trigram] 8.0 75 68 pp. 68-75 \n", + "5865 [unigram, bigram, trigram] 5.0 None None None \n", + "5876 [unigram, bigram, trigram] 1.0 109 109 p. 109 \n", + "\n", + " provider publicationYear publisher \\\n", + "37 jstor 1995 Penn State University Press \n", + "76 jstor 2011 Penn State University Press \n", + "101 jstor 2015 Penn State University Press \n", + "107 jstor 2008 Penn State University Press \n", + "108 jstor 2018 Penn State University Press \n", + "... ... ... ... \n", + "5798 jstor 1995 Penn State University Press \n", + "5835 jstor 1997 Penn State University Press \n", + "5853 jstor 2001 Penn State University Press \n", + "5865 jstor 1995 Penn State University Press \n", + "5876 jstor 2013 Penn State University Press \n", + "\n", + " sourceCategory tdmCategory \\\n", + "37 [Language & Literature, Humanities] [Arts - 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None None \n", + "... ... ... ... \n", + "5798 None None None \n", + "5835 None None None \n", + "5853 None None None \n", + "5865 None None None \n", + "5876 None None None \n", + "\n", + " year Decade Quoted Words \\\n", + "37 1995 1990 0 \n", + "76 2011 2010 4 \n", + "101 2015 2010 0 \n", + "107 2008 2000 4 \n", + "108 2018 2010 97 \n", + "... ... ... ... \n", + "5798 1995 1990 0 \n", + "5835 1997 1990 161 \n", + "5853 2001 2000 5 \n", + "5865 1995 1990 0 \n", + "5876 2013 2010 0 \n", + "\n", + " Locations in A with Wordcounts Wordcounts \\\n", + "37 [] [] \n", + "76 [([502448, 502471], 4)] [4] \n", + "101 [] [] \n", + "107 [([1138948, 1138968], 4)] [4] \n", + "108 [([1792915, 1793447], 97)] [97] \n", + "... ... ... \n", + "5798 [] [] \n", + "5835 [([190333, 190518], 36), ([939772, 940069], 59... [36, 59, 39, 9, 18] \n", + "5853 [([1316376, 1316406], 5)] [5] \n", + "5865 [] [] \n", + "5876 [] [] \n", + "\n", + " journal \n", + "37 George Eliot - George Henry Lewes Studies \n", + "76 George Eliot - George Henry Lewes Studies \n", + "101 George Eliot - George Henry Lewes Studies \n", + "107 George Eliot - George Henry Lewes Studies \n", + "108 George Eliot - George Henry Lewes Studies \n", + "... ... \n", + "5798 George Eliot - George Henry Lewes Studies \n", + "5835 George Eliot - George Henry Lewes Studies \n", + "5853 George Eliot - George Henry Lewes Studies \n", + "5865 George Eliot - George Henry Lewes Studies \n", + "5876 George Eliot - George Henry Lewes Studies \n", + "\n", + "[231 rows x 35 columns]" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geJournals " + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "37 Review Article\n", + "76 The \"British Matron\" and the Poetic Drama: The...\n", + "101 Front Matter\n", + "107 GEORGE ELIOT'S READING: A CHRONOLOGICAL LIST\n", + "108 Hidden Allusion in the Finale of Middlemar...\n", + " ... \n", + "5798 Review Article\n", + "5835 JEWISH MYTH IN GEORGE ELIOT'S FICTION\n", + "5853 GEORGE ELIOT AND GEORGE HENRY LEWES: SELECTED ...\n", + "5865 Back Matter\n", + "5876 A GEORGE ELIOT NOTE\n", + "Name: title, Length: 231, dtype: object\n" + ] + } + ], + "source": [ + "print(geJournals.title)" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of articles where journal title is 'George ELiot - George Henry Lewes Studies':\n" + ] + }, + { + "data": { + "text/plain": [ + "231" ] }, - "execution_count": 212, + "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "elhJournals = df.loc[df['journal'] == 'ELH']\n", - "elhJournals" + "print(\"Number of articles where journal title is 'George ELiot - George Henry Lewes Studies':\")\n", + "len(geJournals)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *GE-GHLS*: *Middlemarch* quotations per chapter" ] }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 187, "metadata": {}, "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "102" + "
" ] }, - "execution_count": 213, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "len(elhJournals)" + "plotSynchronicAnalysis(synchronicAnalysis(geJournals, useWordcounts=False), useWordcounts=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Diachronic Analysis of *ELH* Quotations" + "### Diachronic Analysis of *GE-GHLS* Quotations" ] }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 188, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "ELH Quotations per book, per decade (weighted by length of quotation and normalized by decade):\n" + "GE-GHLS Quotations per book, per decade (weighted by length of quotation and normalized by decade):\n" ] }, { @@ -18931,76 +10886,40 @@ " \n", " \n", " \n", - " 1960\n", - " 0.0\n", - " 0.450549\n", - " 1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.571429\n", - " \n", - " \n", - " 1970\n", - " 0.0\n", - " 0.374613\n", - " 1.000000\n", - " 0.086687\n", - " 0.148607\n", - " 0.000000\n", - " 0.052632\n", - " 0.080495\n", - " 0.585139\n", - " \n", - " \n", - " 1980\n", - " 0.0\n", - " 1.000000\n", - " 0.342857\n", - " 0.184416\n", - " 0.184416\n", - " 0.361039\n", - " 0.350649\n", - " 0.000000\n", - " 0.441558\n", - " \n", - " \n", " 1990\n", " 0.0\n", - " 0.504098\n", " 1.000000\n", - " 0.247951\n", - " 0.899590\n", - " 0.321721\n", - " 0.178279\n", - " 0.014344\n", - " 0.000000\n", + " 0.658422\n", + " 0.133021\n", + " 0.121658\n", + " 0.098930\n", + " 0.116310\n", + " 0.097594\n", + " 0.231283\n", " \n", " \n", " 2000\n", " 0.0\n", - " 0.484935\n", + " 0.832359\n", + " 0.805068\n", + " 0.736842\n", " 1.000000\n", - " 0.000000\n", - " 0.060258\n", - " 0.522238\n", - " 0.450502\n", - " 0.000000\n", - " 0.180775\n", + " 0.508772\n", + " 0.666667\n", + " 0.249513\n", + " 0.586745\n", " \n", " \n", " 2010\n", " 0.0\n", - " 0.285344\n", " 1.000000\n", - " 0.498054\n", - " 0.242542\n", - " 0.121920\n", - " 0.132296\n", - " 0.298314\n", - " 0.477302\n", + " 0.613139\n", + " 0.811436\n", + " 0.364964\n", + " 0.004866\n", + " 0.074209\n", + " 0.209246\n", + " 0.542579\n", " \n", " \n", "\n", @@ -19008,170 +10927,66 @@ ], "text/plain": [ " 0 1 2 3 4 5 6 \\\n", - "1960 0.0 0.450549 1.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1970 0.0 0.374613 1.000000 0.086687 0.148607 0.000000 0.052632 \n", - "1980 0.0 1.000000 0.342857 0.184416 0.184416 0.361039 0.350649 \n", - "1990 0.0 0.504098 1.000000 0.247951 0.899590 0.321721 0.178279 \n", - "2000 0.0 0.484935 1.000000 0.000000 0.060258 0.522238 0.450502 \n", - "2010 0.0 0.285344 1.000000 0.498054 0.242542 0.121920 0.132296 \n", + "1990 0.0 1.000000 0.658422 0.133021 0.121658 0.098930 0.116310 \n", + "2000 0.0 0.832359 0.805068 0.736842 1.000000 0.508772 0.666667 \n", + "2010 0.0 1.000000 0.613139 0.811436 0.364964 0.004866 0.074209 \n", "\n", " 7 8 \n", - "1960 0.000000 0.571429 \n", - "1970 0.080495 0.585139 \n", - "1980 0.000000 0.441558 \n", - "1990 0.014344 0.000000 \n", - "2000 0.000000 0.180775 \n", - "2010 0.298314 0.477302 " + "1990 0.097594 0.231283 \n", + "2000 0.249513 0.586745 \n", + "2010 0.209246 0.542579 " ] }, - "execution_count": 214, + "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Weighted by wordcount (by the number of words in the quotation) and normalized by decade(counts are scaled to the maximum value per decade)\n", - "ELHbooksDiaDF = diachronicAnalysis(elhJournals, decades=(1960, 2020), bins=bookLocations, useWordcounts=True, normalize=True).sort_index()\n", - "print('ELH Quotations per book, per decade (weighted by length of quotation and normalized by decade):')\n", - "ELHbooksDiaDF" + "GEGHLSbooksDiaDF = diachronicAnalysis(geJournals, decades=(1990, 2020), bins=bookLocations, useWordcounts=True, normalize=True).sort_index()\n", + "print('GE-GHLS Quotations per book, per decade (weighted by length of quotation and normalized by decade):')\n", + "GEGHLSbooksDiaDF" ] }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 189, "metadata": {}, "outputs": [], "source": [ - "ELHbooksDiaDF['decade'] = ELHbooksDiaDF.index" + "GEGHLSbooksDiaDF['decade'] = GEGHLSbooksDiaDF.index" ] }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 190, "metadata": {}, "outputs": [], "source": [ - "ELHbooksMelted = ELHbooksDiaDF.melt(id_vars='decade', var_name='book')" + "GEGHLSbooksMelted = GEGHLSbooksDiaDF.melt(id_vars='decade', var_name='book')" ] }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 191, "metadata": {}, "outputs": [], "source": [ "# cut out erroneous \"book 0\" material (ie title page)\n", - "ELHbooksMelted = ELHbooksMelted[ELHbooksMelted.book != 0]" + "GEGHLSbooksMelted = GEGHLSbooksMelted[GEGHLSbooksMelted.book != 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### *ELH* : Middlemarch quotations per book, per decade (normalized and weighted), table bubble plots" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 218, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Change scale of the circle markers to a threshold scale (and resize to make the steps in the scale more visible)\n", - "alt.Chart(ELHbooksMelted, title=\"ELH Middlemarch quotations per book, per decade (weighted by length of quotation and normalized by decade)\")\\\n", - ".mark_circle().encode(\n", - " x=alt.X('book:O', axis=alt.Axis(labelOverlap=True,\n", - " labelAngle=0)), \n", - " y=alt.Y('decade:O'), \n", - " size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations (normalized)\"), \n", - " scale=alt.Scale(type = 'threshold', domain = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1], range =[0, 20, 60, 100, 150, 250, 350, 500, 750, 1000, 1500, 2000,]))).properties(width=500, height=300).configure_legend(\n", - "titleFontSize=10,\n", - "labelFontSize=10\n", - ") " + "### *GE-GHLS*: *Middlemarch* quotations per book, per decade (normalized and weighted)" ] }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 192, "metadata": { "scrolled": false }, @@ -19217,92 +11032,53 @@ " \n", " \n", " \n", - " 1960\n", - " 0\n", - " 3\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " \n", - " \n", - " 1970\n", - " 0\n", - " 3\n", - " 6\n", - " 1\n", - " 1\n", - " 0\n", - " 1\n", - " 1\n", - " 2\n", - " \n", - " \n", - " 1980\n", - " 0\n", - " 12\n", - " 8\n", - " 4\n", - " 2\n", - " 2\n", - " 2\n", - " 0\n", - " 6\n", - " \n", - " \n", " 1990\n", " 0\n", - " 6\n", - " 15\n", + " 60\n", + " 22\n", + " 10\n", " 4\n", - " 16\n", + " 6\n", " 5\n", " 3\n", - " 1\n", - " 0\n", + " 12\n", " \n", " \n", " 2000\n", " 0\n", + " 16\n", + " 19\n", + " 11\n", + " 11\n", + " 9\n", " 12\n", - " 23\n", - " 0\n", " 3\n", - " 10\n", " 9\n", - " 0\n", - " 7\n", " \n", " \n", " 2010\n", " 0\n", - " 9\n", - " 15\n", - " 8\n", - " 5\n", + " 34\n", + " 21\n", + " 17\n", + " 7\n", + " 1\n", " 4\n", - " 2\n", - " 3\n", - " 12\n", + " 4\n", + " 16\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8\n", - "1960 0 3 1 0 0 0 0 0 1\n", - "1970 0 3 6 1 1 0 1 1 2\n", - "1980 0 12 8 4 2 2 2 0 6\n", - "1990 0 6 15 4 16 5 3 1 0\n", - "2000 0 12 23 0 3 10 9 0 7\n", - "2010 0 9 15 8 5 4 2 3 12" + " 0 1 2 3 4 5 6 7 8\n", + "1990 0 60 22 10 4 6 5 3 12\n", + "2000 0 16 19 11 11 9 12 3 9\n", + "2010 0 34 21 17 7 1 4 4 16" ] }, - "execution_count": 219, + "execution_count": 192, "metadata": {}, "output_type": "execute_result" } @@ -19310,165 +11086,63 @@ "source": [ "# Raw quotation counts (not weighted by the number of words in the quoatation or normalized by decade)\n", "# Turning on useWordcounts makes it so that it's weighted by wordcount. Turning it off uses raw numbers of quotations.\n", - "ELHbooksNotNormalizedNotWeightedDiaDF = diachronicAnalysis(elhJournals, decades=(1960, 2020), bins=bookLocations,\\\n", + "GEGHLSbooksNotNormalizedNotWeightedDiaDF = diachronicAnalysis(geJournals, decades=(1960, 2020), bins=bookLocations,\\\n", " useWordcounts=False, normalize=False).sort_index()\n", "print('Number of quotations per book, per decade in GE-GHLS')\n", - "ELHbooksNotNormalizedNotWeightedDiaDF" + "GEGHLSbooksNotNormalizedNotWeightedDiaDF" ] }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 193, "metadata": {}, "outputs": [], "source": [ - "ELHbooksNotNormalizedNotWeightedDiaDF['decade'] = ELHbooksNotNormalizedNotWeightedDiaDF.index" + "GEGHLSbooksNotNormalizedNotWeightedDiaDF['decade'] = GEGHLSbooksNotNormalizedNotWeightedDiaDF.index" ] }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 194, "metadata": {}, "outputs": [], "source": [ - "ELHbooksNotNormalizedNotWeightedDiaDFMelted = ELHbooksNotNormalizedNotWeightedDiaDF.melt(id_vars='decade', var_name='book')" + "GEGHLSbooksNotNormalizedNotWeightedDiaDFMelted = GEGHLSbooksNotNormalizedNotWeightedDiaDF.melt(id_vars='decade', var_name='book')" ] }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 195, "metadata": {}, "outputs": [], "source": [ "# cut out erroneous \"book 0\" material (ie title page)\n", - "ELHbooksNotNormalizedNotWeightedDiaDFMelted = ELHbooksNotNormalizedNotWeightedDiaDFMelted[ELHbooksNotNormalizedNotWeightedDiaDFMelted.book != 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### *ELH* : Middlemarch quotations per book, per decade (not normalized or weighted), table bubble plots" - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 223, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alt.Chart(ELHbooksNotNormalizedNotWeightedDiaDFMelted, title=\"ELH Middlemarch quotations per book, per decade (not weighted or normalized by decade)\").mark_circle().encode(\n", - " x=alt.X('book:O', axis=alt.Axis(labelOverlap=True,\n", - " labelAngle=0)), \n", - " y=alt.Y('decade:O'), size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", - ".properties(width=1000, height=300).configure_legend(\n", - "titleFontSize=10,\n", - "labelFontSize=10\n", - ") " + "GEGHLSbooksNotNormalizedNotWeightedDiaDFMelted = GEGHLSbooksNotNormalizedNotWeightedDiaDFMelted[GEGHLSbooksNotNormalizedNotWeightedDiaDFMelted.book != 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### *ELH*: Number of quotations per chapter, per decade (not normalized or weighted)" + "### *GE-GHLS*: *Middlemarch* quotations per chapter, per decade (not normalized or weighted)" ] }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 196, "metadata": {}, "outputs": [], "source": [ "# Raw quotation counts (not weighted by the number of words in the quoatation or normalized by decade)\n", "# Turning on useWordcounts makes it so that it's weighted by wordcount. Turning it off uses raw numbers of quotations.\n", - "ELHdiaDFquoteOnly = diachronicAnalysis(elhJournals, decades=(1960, 2020), bins=chapterLocations, useWordcounts=False, normalize=False).sort_index()\n", - "ELHdiaDFquoteOnly.columns.name ='chapter'\n", - "ELHdiaDFquoteOnly.index.name = 'decade'" + "GEGHLSdiaDFquoteOnly = diachronicAnalysis(geJournals, decades=(1960, 2020), bins=chapterLocations, useWordcounts=False, normalize=False).sort_index()\n", + "GEGHLSdiaDFquoteOnly.columns.name ='chapter'\n", + "GEGHLSdiaDFquoteOnly.index.name = 'decade'" ] }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 197, "metadata": { "scrolled": true }, @@ -19673,204 +11347,49 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " 1960\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " \n", - " \n", - " 1970\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " \n", + " \n", + " \n", + " \n", + " 1990\n", " 1\n", - " 0\n", + " 5\n", " 2\n", - " 0\n", + " 6\n", + " 1\n", + " 5\n", + " 7\n", " 3\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 1\n", - " 0\n", - " 0\n", + " 2\n", + " 10\n", + " 10\n", + " 7\n", + " 2\n", " 1\n", + " 6\n", " 0\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 2\n", + " 8\n", + " 2\n", " 1\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 2\n", " 0\n", + " 2\n", + " 1\n", + " 1\n", " 0\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " \n", - " \n", - " 1980\n", - " 0\n", " 3\n", - " 5\n", - " 4\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", + " 1\n", " 0\n", + " 3\n", " 0\n", " 0\n", " 0\n", @@ -19878,39 +11397,19 @@ " 0\n", " 1\n", " 0\n", - " 2\n", - " 0\n", " 4\n", - " 0\n", " 1\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 4\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 2\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 2\n", + " 1\n", " 0\n", " 0\n", " 0\n", @@ -19924,128 +11423,57 @@ " 0\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 3\n", " 0\n", " 0\n", " 1\n", - " 1\n", " 0\n", + " 1\n", " 0\n", - " 3\n", " 0\n", + " 2\n", " 0\n", " 0\n", " 0\n", + " 2\n", " 0\n", " 0\n", - " 1\n", + " 2\n", + " 4\n", " \n", " \n", - " 1990\n", + " 2000\n", + " 2\n", " 1\n", - " 4\n", - " 0\n", - " 0\n", - " 0\n", + " 2\n", + " 3\n", " 1\n", " 0\n", " 0\n", " 0\n", " 0\n", + " 3\n", + " 4\n", " 0\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 12\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 9\n", " 3\n", " 0\n", - " 0\n", - " 0\n", " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 2\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 13\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 3\n", " 0\n", - " 2\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 1\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", " 0\n", " 0\n", + " 2\n", + " 2\n", " 0\n", " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " \n", - " \n", - " 2000\n", - " 8\n", - " 0\n", " 1\n", " 0\n", + " 5\n", " 0\n", " 0\n", " 0\n", @@ -20053,146 +11481,123 @@ " 0\n", " 0\n", " 2\n", - " 0\n", " 1\n", - " 0\n", - " 0\n", - " 7\n", - " 11\n", - " 0\n", - " 0\n", - " 0\n", - " 5\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 1\n", - " 0\n", " 1\n", " 0\n", + " 6\n", + " 1\n", " 1\n", - " 0\n", " 1\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 6\n", - " 0\n", " 2\n", - " 0\n", + " 3\n", " 1\n", " 0\n", - " 2\n", " 0\n", - " 6\n", " 0\n", + " 3\n", " 0\n", " 1\n", + " 3\n", " 0\n", " 0\n", " 0\n", + " 3\n", + " 2\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", + " 1\n", + " 1\n", " 0\n", + " 1\n", " 0\n", " 0\n", " 0\n", - " 2\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 1\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", + " 2\n", " 0\n", + " 2\n", " 0\n", " 0\n", " 0\n", - " 3\n", - " 2\n", + " 4\n", " \n", " \n", " 2010\n", + " 8\n", + " 6\n", + " 3\n", + " 1\n", " 0\n", + " 1\n", + " 4\n", + " 1\n", + " 1\n", " 4\n", - " 0\n", - " 0\n", " 2\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " 3\n", " 0\n", " 0\n", + " 4\n", " 0\n", - " 0\n", - " 2\n", - " 2\n", " 1\n", " 0\n", - " 0\n", - " 4\n", + " 9\n", + " 3\n", + " 2\n", " 2\n", " 4\n", - " 0\n", - " 1\n", + " 9\n", " 1\n", " 0\n", " 0\n", - " 6\n", - " 0\n", " 0\n", + " 3\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 2\n", - " 3\n", - " 0\n", + " 1\n", " 0\n", " 0\n", + " 1\n", + " 1\n", " 0\n", + " 2\n", " 0\n", - " 3\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 1\n", " 0\n", " 0\n", " 0\n", + " 1\n", " 0\n", + " 1\n", + " 1\n", " 0\n", + " 1\n", " 0\n", + " 1\n", " 0\n", - " 2\n", " 0\n", + " 4\n", " 0\n", " 0\n", " 0\n", @@ -20204,23 +11609,19 @@ " 1\n", " 0\n", " 0\n", - " 2\n", - " 0\n", " 0\n", " 0\n", " 0\n", - " 1\n", " 0\n", " 0\n", + " 1\n", " 0\n", - " 4\n", - " 3\n", " 0\n", " 1\n", " 0\n", " 0\n", " 0\n", - " 3\n", + " 13\n", " \n", " \n", "\n", @@ -20229,89 +11630,78 @@ "text/plain": [ "chapter 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 \\\n", "decade \n", - "1960 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 \n", - "1970 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 \n", - "1980 0 3 5 4 0 0 0 0 0 0 0 0 0 0 0 0 1 \n", - "1990 1 4 0 0 0 1 0 0 0 0 0 0 0 0 0 12 1 \n", - "2000 8 0 1 0 0 0 0 0 0 0 2 0 1 0 0 7 11 \n", - "2010 0 4 0 0 2 0 0 0 0 0 3 0 0 0 0 2 2 \n", + "1990 1 5 2 6 1 5 7 3 1 2 10 10 7 2 1 6 0 \n", + "2000 2 1 2 3 1 0 0 0 0 3 4 0 0 0 1 9 3 \n", + "2010 8 6 3 1 0 1 4 1 1 4 2 0 3 0 0 4 0 \n", "\n", "chapter 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 \\\n", "decade \n", - "1960 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 \n", - "1970 0 1 0 2 0 3 0 0 0 0 0 0 0 0 1 0 0 \n", - "1980 0 2 0 4 0 1 0 0 0 0 0 4 0 0 0 0 0 \n", - "1990 0 0 0 1 0 1 0 0 0 0 3 0 0 0 1 0 0 \n", - "2000 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 \n", - "2010 1 0 0 4 2 4 0 1 1 0 0 6 0 0 0 0 0 \n", + "1990 0 0 2 8 2 1 0 1 2 0 2 1 1 0 0 0 3 \n", + "2000 0 1 2 3 0 0 0 2 2 0 1 1 0 5 0 0 0 \n", + "2010 1 0 9 3 2 2 4 9 1 0 0 0 3 0 0 0 0 \n", "\n", "chapter 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 \\\n", "decade \n", - "1960 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \n", - "1970 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \n", - "1980 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2 \n", - "1990 0 0 0 2 0 1 0 0 13 0 0 0 0 0 3 0 2 \n", - "2000 0 0 0 1 0 1 0 1 0 1 0 0 0 0 6 0 2 \n", - "2010 0 0 2 3 0 0 0 0 0 3 0 0 0 0 0 0 1 \n", + "1990 0 1 0 3 0 0 0 0 0 1 0 4 1 0 0 0 0 \n", + "2000 0 0 0 2 1 1 1 0 6 1 1 1 0 0 2 3 1 \n", + "2010 0 2 1 0 0 1 1 0 2 0 0 0 0 0 0 0 0 \n", "\n", "chapter 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 \\\n", "decade \n", - "1960 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \n", - "1970 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 \n", - "1980 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 \n", - "1990 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 \n", - "2000 0 1 0 2 0 6 0 0 1 0 0 0 0 0 0 0 0 \n", - "2010 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 \n", + "1990 0 0 0 0 0 2 1 0 0 0 2 0 0 0 0 0 0 \n", + "2000 0 0 0 3 0 1 3 0 0 0 3 2 0 0 0 0 0 \n", + "2010 0 0 1 0 1 1 0 1 0 1 0 0 4 0 0 0 0 \n", "\n", "chapter 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 \\\n", "decade \n", - "1960 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 \n", - "1970 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 \n", - "1980 0 0 0 0 0 0 0 0 1 1 0 0 3 0 0 0 0 \n", - "1990 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \n", - "2000 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 \n", - "2010 1 0 0 2 0 0 0 0 1 0 0 0 4 3 0 1 0 \n", + "1990 0 0 0 3 0 0 1 0 1 0 0 2 0 0 0 2 0 \n", + "2000 1 1 0 1 0 0 0 1 0 0 0 0 0 2 0 2 0 \n", + "2010 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 \n", "\n", "chapter 85 86 87 \n", "decade \n", - "1960 0 0 0 \n", - "1970 0 0 0 \n", - "1980 0 0 1 \n", - "1990 0 0 0 \n", - "2000 0 3 2 \n", - "2010 0 0 3 " + "1990 0 2 4 \n", + "2000 0 0 4 \n", + "2010 0 0 13 " ] }, - "execution_count": 225, + "execution_count": 197, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ELHdiaDFquoteOnly" + "GEGHLSdiaDFquoteOnly" ] }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 198, "metadata": {}, "outputs": [], "source": [ - "ELHdiaDFquoteOnly['decade'] = ELHdiaDFquoteOnly.index" + "GEGHLSdiaDFquoteOnly['decade'] = GEGHLSdiaDFquoteOnly.index" ] }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 199, "metadata": {}, "outputs": [], "source": [ - "ELHdiaDFquoteOnlyMelted = ELHdiaDFquoteOnly.melt(id_vars='decade')" + "GEGHLSdiaDFquoteOnlyMelted = GEGHLSdiaDFquoteOnly.melt(id_vars='decade')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *GE-GHLS*: *Middlemarch* quotations per chapter, per decade (not normalized or weighted), heat map" ] }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 200, "metadata": {}, "outputs": [ { @@ -20319,23 +11709,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 228, + "execution_count": 200, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(ELHdiaDFquoteOnlyMelted, title=\"ELH Middlemarch quotations per chapter, per decade (not weighted or normalized)\").mark_circle().encode(x='chapter:O', \n", - " y=alt.Y('decade:O'), size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", + "alt.Chart(GEGHLSdiaDFquoteOnlyMelted, title=\"GE-GHLS Middlemarch quotations per chapter, per decade (not weighted or normalized)\")\\\n", + ".mark_rect().encode(x=alt.X('chapter', title=\"Chapter\", type='ordinal', axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", + " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", + " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", + " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", ".properties(width=1000, height=300).configure_legend(\n", - "titleFontSize=10,\n", - "labelFontSize=10\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", ") " ] }, @@ -20406,24 +11802,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### *ELH*: Number of quotations per chapter, per decade (normalized by decade and weighted by word count)" + "### *GE-GHLS*: *Middlemarch* quotations per chapter, per decade (normalized by decade and weighted by word count)" ] }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 201, "metadata": {}, "outputs": [], "source": [ "# Weighted by wordcount (by the number of words in the quoatation) and normalized by decade(counts are scaled to the maximum value per decade)\n", - "ELHnormalizeddiaDF = diachronicAnalysis(df, decades=(1960, 2020), bins=chapterLocations, useWordcounts=True, normalize=True).sort_index()\n", - "ELHnormalizeddiaDF.columns.name = 'chapter'\n", - "ELHnormalizeddiaDF.index.name = 'decade'" + "GEGHLSnormalizeddiaDF = diachronicAnalysis(geJournals, decades=(1960, 2020), bins=chapterLocations, useWordcounts=True, normalize=True).sort_index()\n", + "GEGHLSnormalizeddiaDF.columns.name = 'chapter'\n", + "GEGHLSnormalizeddiaDF.index.name = 'decade'" ] }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 202, "metadata": {}, "outputs": [ { @@ -20630,550 +12026,277 @@ " \n", " \n", " \n", - " 1960\n", - " 0.119514\n", - " 0.119514\n", - " 0.266106\n", - " 0.068161\n", - " 0.012138\n", - " 0.022409\n", - " 0.000000\n", - " 0.104575\n", - " 0.008403\n", - " 0.267974\n", - " 0.230626\n", - " 0.036415\n", - " 0.074697\n", - " 0.000000\n", - " 0.000000\n", - " 0.621849\n", - " 0.272642\n", - " 0.020542\n", - " 0.000000\n", - " 0.005602\n", - " 1.000000\n", - " 0.372549\n", - " 0.053221\n", - " 0.019608\n", - " 0.013072\n", - " 0.005602\n", - " 0.004669\n", - " 0.183940\n", - " 0.298786\n", - " 0.000000\n", - " 0.006536\n", - " 0.006536\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.106443\n", - " 0.048553\n", - " 0.156863\n", - " 0.000000\n", - " 0.030812\n", - " 0.045752\n", - " 0.320261\n", - " 0.123249\n", - " 0.024276\n", - " 0.000000\n", - " 0.000000\n", - " 0.326797\n", - " 0.047619\n", - " 0.026144\n", - " 0.000000\n", - " 0.228758\n", - " 0.007470\n", - " 0.053221\n", - " 0.414566\n", - " 0.002801\n", - " 0.000000\n", - " 0.102708\n", - " 0.000000\n", - " 0.015873\n", - " 0.000000\n", - " 0.014006\n", - " 0.024276\n", - " 0.000000\n", - " 0.020542\n", - " 0.022409\n", - " 0.000000\n", - " 0.074697\n", - " 0.000000\n", - " 0.007470\n", - " 0.000000\n", - " 0.000000\n", - " 0.265173\n", - " 0.000000\n", - " 0.004669\n", - " 0.000000\n", - " 0.014939\n", - " 0.130719\n", - " 0.017740\n", - " 0.161531\n", - " 0.039216\n", - " 0.183007\n", - " 0.225957\n", - " 0.003735\n", - " 0.065359\n", - " 0.000000\n", - " 0.0\n", - " 0.022409\n", - " 0.228758\n", - " \n", - " \n", - " 1970\n", - " 0.224055\n", - " 0.575258\n", - " 0.584192\n", - " 0.393814\n", - " 0.107904\n", - " 0.061856\n", - " 0.360825\n", - " 0.115464\n", - " 0.158076\n", - " 0.087973\n", - " 0.079725\n", - " 0.226804\n", - " 0.063918\n", - " 0.004124\n", - " 0.002749\n", - " 0.627491\n", - " 0.340893\n", - " 0.004811\n", - " 0.012371\n", - " 0.219244\n", - " 1.000000\n", - " 0.134021\n", - " 0.204811\n", - " 0.000000\n", - " 0.061168\n", - " 0.002062\n", - " 0.009622\n", - " 0.381443\n", - " 0.229553\n", - " 0.094845\n", - " 0.003436\n", - " 0.041924\n", + " 1990\n", + " 0.017699\n", + " 0.353982\n", + " 0.042035\n", + " 0.258850\n", + " 0.015487\n", + " 0.329646\n", + " 0.320796\n", + " 0.196903\n", + " 0.070796\n", + " 0.159292\n", + " 0.358407\n", + " 0.482301\n", + " 0.703540\n", + " 0.050885\n", + " 0.057522\n", + " 0.599558\n", " 0.000000\n", " 0.000000\n", - " 0.032990\n", " 0.000000\n", - " 0.062543\n", - " 0.188316\n", - " 0.007560\n", - " 0.058419\n", - " 0.018557\n", - " 0.107216\n", - " 0.329210\n", - " 0.197938\n", - " 0.004124\n", - " 0.031615\n", - " 0.074914\n", - " 0.013058\n", - " 0.151890\n", + " 0.387168\n", + " 1.000000\n", + " 0.066372\n", + " 0.017699\n", " 0.000000\n", - " 0.071478\n", - " 0.004124\n", - " 0.018557\n", - " 0.059107\n", - " 0.087285\n", - " 0.065979\n", - " 0.103093\n", - " 0.012371\n", - " 0.121649\n", - " 0.003436\n", - " 0.009622\n", - " 0.046048\n", - " 0.003436\n", - " 0.017869\n", - " 0.226804\n", - " 0.004811\n", + " 0.108407\n", + " 0.057522\n", + " 0.0\n", + " 0.050885\n", + " 0.011062\n", + " 0.026549\n", " 0.000000\n", + " 0.0\n", + " 0.0\n", + " 0.185841\n", + " 0.0\n", + " 0.039823\n", " 0.000000\n", - " 0.070790\n", - " 0.021306\n", - " 0.009622\n", - " 0.017869\n", - " 0.051546\n", - " 0.023368\n", - " 0.523024\n", - " 0.002749\n", - " 0.131959\n", - " 0.035052\n", - " 0.010309\n", - " 0.013058\n", - " 0.327148\n", - " 0.111340\n", + " 0.362832\n", " 0.000000\n", - " 0.037113\n", - " 0.004811\n", - " 0.0\n", - " 0.008247\n", - " 0.408935\n", - " \n", - " \n", - " 1980\n", - " 0.437037\n", - " 0.772391\n", - " 0.164310\n", - " 0.377778\n", - " 0.032997\n", - " 0.249832\n", - " 0.106397\n", - " 0.175758\n", " 0.000000\n", - " 0.105051\n", - " 0.212121\n", - " 0.160269\n", - " 0.084175\n", - " 0.092256\n", - " 0.061953\n", - " 0.801347\n", - " 0.377104\n", - " 0.035690\n", - " 0.099663\n", - " 0.400673\n", - " 0.918519\n", - " 0.242424\n", - " 0.073401\n", - " 0.235690\n", - " 0.010774\n", " 0.000000\n", - " 0.006061\n", - " 0.151515\n", - " 0.084848\n", - " 0.296970\n", - " 0.047138\n", - " 0.022222\n", - " 0.026263\n", + " 0.0\n", " 0.000000\n", - " 0.064646\n", - " 0.041751\n", - " 0.042424\n", - " 0.253872\n", + " 0.037611\n", " 0.000000\n", - " 0.415488\n", + " 0.276549\n", + " 0.013274\n", + " 0.0\n", " 0.000000\n", " 0.000000\n", - " 0.125926\n", - " 0.127273\n", " 0.000000\n", - " 0.008081\n", - " 0.004040\n", - " 0.191919\n", - " 0.006061\n", + " 0.0\n", + " 0.0\n", " 0.000000\n", - " 0.098990\n", - " 0.076094\n", " 0.000000\n", " 0.000000\n", - " 0.151515\n", - " 0.152189\n", - " 0.019529\n", + " 0.081858\n", + " 0.008850\n", " 0.000000\n", - " 0.204040\n", - " 0.028283\n", - " 0.032323\n", - " 0.191919\n", - " 0.041751\n", - " 0.012795\n", - " 0.129293\n", - " 0.055219\n", - " 0.006734\n", - " 0.003367\n", + " 0.0\n", " 0.000000\n", - " 0.030976\n", + " 0.294248\n", " 0.000000\n", - 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"2010 0.212549 0.984737 0.198417 0.158282 0.049180 0.044658 0.136235 \n", + "1990 0.017699 0.353982 0.042035 0.258850 0.015487 0.329646 0.320796 \n", + "2000 0.091575 0.161172 0.139194 0.238095 0.032967 0.000000 0.000000 \n", + "2010 0.288939 0.349887 0.273138 0.045147 0.000000 0.018059 0.275395 \n", "\n", "chapter 7 8 9 10 11 12 13 \\\n", "decade \n", - "1960 0.104575 0.008403 0.267974 0.230626 0.036415 0.074697 0.000000 \n", - "1970 0.115464 0.158076 0.087973 0.079725 0.226804 0.063918 0.004124 \n", - "1980 0.175758 0.000000 0.105051 0.212121 0.160269 0.084175 0.092256 \n", - "1990 0.068201 0.013640 0.075021 0.249361 0.264706 0.227195 0.072890 \n", - "2000 0.069095 0.009641 0.302625 0.400107 0.269416 0.035351 0.028923 \n", - "2010 0.038440 0.016959 0.239118 0.295647 0.077445 0.100057 0.022046 \n", + "1990 0.196903 0.070796 0.159292 0.358407 0.482301 0.703540 0.050885 \n", + "2000 0.000000 0.000000 0.457875 0.443223 0.000000 0.000000 0.000000 \n", + "2010 0.079007 0.049661 0.108352 0.139955 0.000000 0.227991 0.000000 \n", "\n", "chapter 14 15 16 17 18 19 20 \\\n", "decade \n", - "1960 0.000000 0.621849 0.272642 0.020542 0.000000 0.005602 1.000000 \n", - "1970 0.002749 0.627491 0.340893 0.004811 0.012371 0.219244 1.000000 \n", - "1980 0.061953 0.801347 0.377104 0.035690 0.099663 0.400673 0.918519 \n", - "1990 0.033674 1.000000 0.237425 0.109122 0.113811 0.511509 0.777494 \n", - "2000 0.006427 0.470809 0.305838 0.020889 0.101768 0.539904 1.000000 \n", - "2010 0.010741 0.613906 0.273036 0.039570 0.159412 0.349915 0.733748 \n", - "\n", - "chapter 21 22 23 24 25 26 27 \\\n", - "decade \n", - "1960 0.372549 0.053221 0.019608 0.013072 0.005602 0.004669 0.183940 \n", - "1970 0.134021 0.204811 0.000000 0.061168 0.002062 0.009622 0.381443 \n", - "1980 0.242424 0.073401 0.235690 0.010774 0.000000 0.006061 0.151515 \n", - "1990 0.290708 0.093777 0.008525 0.073316 0.028986 0.060102 0.259165 \n", - "2000 0.146224 0.476165 0.212641 0.217461 0.061061 0.006963 0.111944 \n", - "2010 1.000000 0.207462 0.033352 0.662521 0.023742 0.000000 0.348785 \n", + "1990 0.057522 0.599558 0.000000 0.000000 0.000000 0.387168 1.000000 \n", + "2000 0.043956 1.000000 0.194139 0.000000 0.018315 0.080586 0.175824 \n", + "2010 0.000000 0.142212 0.000000 0.011287 0.000000 0.564334 0.158014 \n", "\n", - "chapter 28 29 30 31 32 33 34 \\\n", - "decade \n", - "1960 0.298786 0.000000 0.006536 0.006536 0.000000 0.000000 0.000000 \n", - "1970 0.229553 0.094845 0.003436 0.041924 0.000000 0.000000 0.032990 \n", - "1980 0.084848 0.296970 0.047138 0.022222 0.026263 0.000000 0.064646 \n", - "1990 0.084825 0.064791 0.001705 0.034527 0.002131 0.057118 0.193095 \n", - "2000 0.252276 0.194965 0.062132 0.055169 0.089984 0.431173 0.000000 \n", - "2010 0.446580 0.118146 0.002826 0.105144 0.028830 0.000000 0.033917 \n", + "chapter 21 22 23 24 25 26 27 \\\n", + "decade \n", + "1990 0.066372 0.017699 0.000000 0.108407 0.057522 0.0 0.050885 \n", + "2000 0.000000 0.000000 0.000000 0.076923 0.395604 0.0 0.018315 \n", + "2010 0.160271 0.101580 0.081264 1.000000 0.009029 0.0 0.000000 \n", "\n", - "chapter 35 36 37 38 39 40 41 \\\n", - "decade \n", - "1960 0.106443 0.048553 0.156863 0.000000 0.030812 0.045752 0.320261 \n", - "1970 0.000000 0.062543 0.188316 0.007560 0.058419 0.018557 0.107216 \n", - "1980 0.041751 0.042424 0.253872 0.000000 0.415488 0.000000 0.000000 \n", - "1990 0.201194 0.185848 0.176897 0.003410 0.078005 0.000000 0.162830 \n", - "2000 0.027317 0.023032 0.258168 0.018211 0.103910 0.088913 0.044992 \n", - "2010 0.044658 0.186546 0.163934 0.009045 0.356699 0.183154 0.196721 \n", + "chapter 28 29 30 31 32 33 34 35 \\\n", + "decade \n", + "1990 0.011062 0.026549 0.000000 0.0 0.0 0.185841 0.0 0.039823 \n", + "2000 0.468864 0.000000 0.424908 0.0 0.0 0.000000 0.0 0.000000 \n", + "2010 0.000000 0.415350 0.000000 0.0 0.0 0.000000 0.0 0.178330 \n", "\n", - "chapter 42 43 44 45 46 47 48 \\\n", - "decade \n", - "1960 0.123249 0.024276 0.000000 0.000000 0.326797 0.047619 0.026144 \n", - "1970 0.329210 0.197938 0.004124 0.031615 0.074914 0.013058 0.151890 \n", - "1980 0.125926 0.127273 0.000000 0.008081 0.004040 0.191919 0.006061 \n", - "1990 0.355499 0.014493 0.002131 0.151321 0.026002 0.057545 0.164535 \n", - "2000 0.286020 0.084092 0.002142 0.121585 0.008570 0.002142 0.167649 \n", - "2010 0.102318 0.066704 0.000000 0.054268 0.065574 0.000000 0.055964 \n", + "chapter 36 37 38 39 40 41 42 \\\n", + "decade \n", + "1990 0.000000 0.362832 0.000000 0.000000 0.000000 0.0 0.000000 \n", + "2000 0.000000 0.703297 0.106227 0.146520 0.018315 0.0 0.904762 \n", + "2010 0.045147 0.000000 0.000000 0.173815 0.085779 0.0 0.194131 \n", "\n", - "chapter 49 50 51 52 53 54 55 \\\n", - "decade \n", - "1960 0.000000 0.228758 0.007470 0.053221 0.414566 0.002801 0.000000 \n", - "1970 0.000000 0.071478 0.004124 0.018557 0.059107 0.087285 0.065979 \n", - "1980 0.000000 0.098990 0.076094 0.000000 0.000000 0.151515 0.152189 \n", - "1990 0.000000 0.121910 0.016198 0.004689 0.014493 0.017903 0.034101 \n", - "2000 0.045528 0.118372 0.003749 0.063739 0.099625 0.133905 0.152651 \n", - "2010 0.000000 0.108536 0.109666 0.000000 0.057660 0.003957 0.079141 \n", + "chapter 43 44 45 46 47 48 49 \\\n", + "decade \n", + "1990 0.037611 0.000000 0.276549 0.013274 0.0 0.000000 0.000000 \n", + "2000 0.223443 0.014652 0.219780 0.000000 0.0 0.131868 0.311355 \n", + "2010 0.000000 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 \n", "\n", - "chapter 56 57 58 59 60 61 62 \\\n", + "chapter 50 51 52 53 54 55 56 57 \\\n", "decade \n", - "1960 0.102708 0.000000 0.015873 0.000000 0.014006 0.024276 0.000000 \n", - "1970 0.103093 0.012371 0.121649 0.003436 0.009622 0.046048 0.003436 \n", - "1980 0.019529 0.000000 0.204040 0.028283 0.032323 0.191919 0.041751 \n", - "1990 0.018329 0.001705 0.082268 0.038363 0.027280 0.092498 0.033674 \n", - "2000 0.151580 0.046599 0.065345 0.016069 0.023032 0.257097 0.082485 \n", - "2010 0.107971 0.000000 0.234596 0.000000 0.148672 0.053703 0.022046 \n", + "1990 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.081858 0.008850 \n", + "2000 0.054945 0.0 0.0 0.000000 0.164835 0.000000 0.201465 0.238095 \n", + "2010 0.000000 0.0 0.0 0.009029 0.000000 0.058691 0.054176 0.000000 \n", "\n", - "chapter 63 64 65 66 67 68 69 \\\n", + "chapter 58 59 60 61 62 63 64 65 66 \\\n", "decade \n", - "1960 0.020542 0.022409 0.000000 0.074697 0.000000 0.007470 0.000000 \n", - "1970 0.017869 0.226804 0.004811 0.000000 0.000000 0.070790 0.021306 \n", - "1980 0.012795 0.129293 0.055219 0.006734 0.003367 0.000000 0.030976 \n", - "1990 0.020034 0.005968 0.057971 0.002558 0.004689 0.029838 0.000000 \n", - "2000 0.042849 0.000000 0.000000 0.025174 0.000000 0.073380 0.041778 \n", - "2010 0.114754 0.057660 0.000000 0.005653 0.026003 0.029960 0.010175 \n", + "1990 0.000000 0.0 0.000000 0.294248 0.000000 0.000000 0.0 0.0 0.0 \n", + "2000 0.000000 0.0 0.000000 0.402930 0.245421 0.000000 0.0 0.0 0.0 \n", + "2010 0.013544 0.0 0.011287 0.000000 0.000000 0.388262 0.0 0.0 0.0 \n", "\n", - "chapter 70 71 72 73 74 75 76 \\\n", - "decade \n", - "1960 0.000000 0.265173 0.000000 0.004669 0.000000 0.014939 0.130719 \n", - "1970 0.009622 0.017869 0.051546 0.023368 0.523024 0.002749 0.131959 \n", - "1980 0.000000 0.088215 0.144108 0.245791 0.058586 0.024242 0.292929 \n", - "1990 0.069480 0.064791 0.065217 0.022592 0.060529 0.054135 0.110401 \n", - "2000 0.047134 0.026245 0.046599 0.005892 0.000000 0.059454 0.333690 \n", - "2010 0.207462 0.192764 0.041266 0.022046 0.209158 0.018089 0.107405 \n", + "chapter 67 68 69 70 71 72 73 74 \\\n", + "decade \n", + "1990 0.0 0.000000 0.000000 0.0 0.323009 0.000000 0.0 0.066372 \n", + "2000 0.0 0.413919 0.029304 0.0 0.025641 0.000000 0.0 0.000000 \n", + "2010 0.0 0.000000 0.000000 0.0 0.000000 0.038375 0.0 0.000000 \n", "\n", - "chapter 77 78 79 80 81 82 83 \\\n", - "decade \n", - "1960 0.017740 0.161531 0.039216 0.183007 0.225957 0.003735 0.065359 \n", - "1970 0.035052 0.010309 0.013058 0.327148 0.111340 0.000000 0.037113 \n", - "1980 0.103704 0.064646 0.000000 0.360269 0.303030 0.010101 0.085522 \n", - "1990 0.048167 0.126598 0.043905 0.298380 0.255754 0.022592 0.217818 \n", - "2000 0.069630 0.065345 0.026781 0.246920 0.042314 0.000000 0.051955 \n", - "2010 0.028265 0.000000 0.000000 0.287733 0.192764 0.044658 0.079706 \n", + "chapter 75 76 77 78 79 80 81 82 \\\n", + "decade \n", + "1990 0.000000 0.011062 0.0 0.0 0.227876 0.000000 0.000000 0.0 \n", + "2000 0.205128 0.000000 0.0 0.0 0.000000 0.000000 0.179487 0.0 \n", + "2010 0.000000 0.000000 0.0 0.0 0.000000 0.108352 0.000000 0.0 \n", "\n", - "chapter 84 85 86 87 \n", - "decade \n", - "1960 0.000000 0.0 0.022409 0.228758 \n", - "1970 0.004811 0.0 0.008247 0.408935 \n", - "1980 0.000000 1.0 0.015488 0.571717 \n", - "1990 0.013214 0.0 0.009804 0.364024 \n", - "2000 0.021960 0.0 0.035886 0.479914 \n", - "2010 0.000000 0.0 0.004522 0.805540 " + "chapter 83 84 85 86 87 \n", + "decade \n", + "1990 0.128319 0.0 0.0 0.042035 0.289823 \n", + "2000 0.245421 0.0 0.0 0.000000 0.472527 \n", + "2010 0.018059 0.0 0.0 0.000000 0.841986 " ] }, - "execution_count": 230, + "execution_count": 202, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ELHnormalizeddiaDF" + "GEGHLSnormalizeddiaDF" ] }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 203, "metadata": {}, "outputs": [], "source": [ - "ELHnormalizeddiaDF['decade'] = ELHnormalizeddiaDF.index" + "GEGHLSnormalizeddiaDF['decade'] = GEGHLSnormalizeddiaDF.index" ] }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 204, "metadata": {}, "outputs": [], "source": [ - "ELHnormalizeddiaMelted = diaDF.melt(id_vars='decade')" + "GEGHLSnormalizeddiaMelted = GEGHLSnormalizeddiaDF.melt(id_vars='decade')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### ELH *Middlemarch* quotations per chapter, per decade (normalized and weighted), table bubble plots" + "### *GE-GHLS*: *Middlemarch* quotations per chapter, per decade (normalized and weighted), heat map\n", + "Because our corpus contains [varying numbers of JSTOR texsts per decade](#How-many-articles-do-we-have-published-in-each-year?), we've decided to also weigh by length of quotation and normalize per decade." ] }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 205, "metadata": {}, "outputs": [ { @@ -21342,23 +12421,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ - "alt.Chart(...)" + "alt.Chart(...)" + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(GEGHLSnormalizeddiaMelted, title=\"GE-GHLS Middlemarch quotations per chapter, per decade (normalized by decade and weighted by word count)\")\\\n", + ".mark_rect().encode(x=alt.X('chapter', title=\"Chapter\", type='ordinal', axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", + " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", + " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", + " color=alt.Color('value', legend=alt.Legend(title=\"Amount Quoted\")))\\\n", + ".properties(width=1000, height=300).configure_legend(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare the specialist journal, *George Eliot - George Henry Lewes Studies*, with all other journals" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [], + "source": [ + "geJournals = df.loc[df['journal'] == 'George Eliot - George Henry Lewes Studies']\n", + "otherJournals = df.loc[df['journal'] != 'George Eliot - George Henry Lewes Studies']" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [], + "source": [ + "# Normalize\n", + "geDF = synchronicAnalysis(geJournals)\n", + "otherDF = synchronicAnalysis(otherJournals)\n", + "normGE = geDF.div(geDF.max())\n", + "normOther = otherDF.div(otherDF.max())" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Specialization Index')" ] }, - "execution_count": 233, + "execution_count": 208, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "#Redo Chart to rotate tick marks\n", - "alt.Chart(ELHnormalizeddiaMelted, title=\"ELH Middlemarch quotations per chapter, per decade (normalized by decade and weighted by word count)\").mark_circle().encode(\n", - " x=alt.X('chapter:Q', axis=alt.Axis(tickMinStep=5,\n", - " labelOverlap=False,\n", - " labelAngle=0)), \n", - " y=alt.Y('decade:O'), \n", - " size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations (normalized)\"), \n", - " scale=alt.Scale(type = 'threshold', domain = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1], range =[0, 20, 60, 100, 150, 250, 350, 500, 750, 1000, 1500, 2000,]))).properties(width=1000, height=300).configure_legend(\n", - "titleFontSize=10,\n", - "labelFontSize=10\n", - ") " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# *Victorian Studies* and Victorianist Journals" + "fig = plt.figure()\n", + "ax = (normGE - normOther).plot(kind='bar')\n", + "fig.add_subplot(ax)\n", + "ax.set_xlabel('Chapter')\n", + "ax.set_ylabel('Specialization Index')\n", + "# Save a big version for publication. \n", + "#fig.savefig('specialization.png', bboxinches='tight', dpi=300)" ] }, { @@ -21448,12 +12589,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Articles where journal title is \"Victorian Studies\"" + "### Articles where journal title is *Victorian Studies*" ] }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 209, "metadata": {}, "outputs": [], "source": [ @@ -21462,7 +12603,7 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 210, "metadata": {}, "outputs": [], "source": [ @@ -21471,7 +12612,7 @@ }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 211, "metadata": {}, "outputs": [ { @@ -22129,7 +13270,7 @@ "[459 rows x 35 columns]" ] }, - "execution_count": 236, + "execution_count": 211, "metadata": {}, "output_type": "execute_result" } @@ -22140,7 +13281,7 @@ }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 212, "metadata": {}, "outputs": [ { @@ -22156,7 +13297,7 @@ "459" ] }, - "execution_count": 237, + "execution_count": 212, "metadata": {}, "output_type": "execute_result" } @@ -22166,6 +13307,33 @@ "len(vsJournals)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Victorian Studies*: *Middlemarch* quotations per chapter" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotSynchronicAnalysis(synchronicAnalysis(vsJournals, useWordcounts=False), useWordcounts=False)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -22175,7 +13343,7 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 214, "metadata": {}, "outputs": [ { @@ -22312,7 +13480,7 @@ "2010 0.016216 0.589189 " ] }, - "execution_count": 238, + "execution_count": 214, "metadata": {}, "output_type": "execute_result" } @@ -22326,7 +13494,7 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 215, "metadata": {}, "outputs": [], "source": [ @@ -22335,7 +13503,7 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 216, "metadata": {}, "outputs": [], "source": [ @@ -22344,7 +13512,7 @@ }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 217, "metadata": {}, "outputs": [], "source": [ @@ -22356,12 +13524,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### *Victorian Studies* : Middlemarch quotations per book, per decade (normalized and weighted), table bubble plots" + "### *Victorian Studies*: *Middlemarch* quotations per book, per decade (normalized and weighted), heat map" ] }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 218, "metadata": {}, "outputs": [ { @@ -22369,23 +13537,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 242, + "execution_count": 218, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(VSbooksMelted, title=\"Victorian Studies Middlemarch quotations per book, per decade (normalized by decade and weighted by word count)\")\\\n", + "alt.Chart(VSbooksMelted, title=\"*Victorian Studies* Middlemarch quotations per book, per decade (normalized by decade and weighted by word count)\")\\\n", ".mark_rect().encode(x=alt.X('book', title=\"Book\", type='ordinal', axis=alt.Axis(labelAngle=0)), \n", " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", - " color=alt.Color('value', legend=alt.Legend(title=\"Quotation Count\")))\\\n", - ".properties(width=1000, height=300).configure_legend(\n", + " color=alt.Color('value', legend=alt.Legend(title=\"Amount Quoted\")))\\\n", + ".properties(width=500, height=300).configure_legend(\n", "titleFontSize=14,\n", "labelFontSize=14\n", ").configure_axis(\n", @@ -22459,106 +13627,15 @@ ] }, { - "cell_type": "code", - "execution_count": 243, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 243, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "#Change scale of the circle markers to a threshold scale (and resize to make the steps in the scale more visible)\n", - "alt.Chart(VSbooksMelted, title=\"Victorian Studies Middlemarch quotations per book, per decade (weighted by length of quotation and normalized by decade)\")\\\n", - ".mark_circle().encode(\n", - " x=alt.X('book:O', axis=alt.Axis(labelOverlap=True,\n", - " labelAngle=0)), \n", - " y=alt.Y('decade:O'), \n", - " size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations (normalized)\"), \n", - " scale=alt.Scale(type = 'threshold', domain = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1], range =[0, 20, 60, 100, 150, 250, 350, 500, 750, 1000, 1500, 2000,]))).properties(width=500, height=300).configure_legend(\n", - "titleFontSize=10,\n", - "labelFontSize=10\n", - ") " + "### *Victorian Studies*: *Middlemarch* quotations per book, per decade (not normalized or weighted)" ] }, { "cell_type": "code", - "execution_count": 244, + "execution_count": 219, "metadata": { "scrolled": false }, @@ -22689,7 +13766,7 @@ "2010 0 9 12 3 3 2 6 1 12" ] }, - "execution_count": 244, + "execution_count": 219, "metadata": {}, "output_type": "execute_result" } @@ -22705,7 +13782,7 @@ }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 220, "metadata": {}, "outputs": [], "source": [ @@ -22714,7 +13791,7 @@ }, { "cell_type": "code", - "execution_count": 246, + "execution_count": 221, "metadata": {}, "outputs": [], "source": [ @@ -22723,7 +13800,7 @@ }, { "cell_type": "code", - "execution_count": 247, + "execution_count": 222, "metadata": {}, "outputs": [], "source": [ @@ -22735,12 +13812,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### *Victorian Studies* : Middlemarch quotations per book, per decade (not normalized or weighted), table bubble plots" + "### *Victorian Studies*: *Middlemarch* quotations per book, per decade (not normalized or weighted), heat map" ] }, { "cell_type": "code", - "execution_count": 248, + "execution_count": 223, "metadata": {}, "outputs": [ { @@ -22748,23 +13825,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 248, + "execution_count": 223, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(VSbooksNotNormalizedNotWeightedDiaDFMelted, title=\"Victorian Studies Middlemarch quotations per book, per decade (not weighted or normalized by decade)\").mark_circle().encode(\n", - " x=alt.X('book:O', axis=alt.Axis(labelOverlap=True,\n", - " labelAngle=0)), \n", - " y=alt.Y('decade:O'), size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", - ".properties(width=1000, height=300).configure_legend(\n", - "titleFontSize=10,\n", - "labelFontSize=10\n", - ") " + "alt.Chart(VSbooksNotNormalizedNotWeightedDiaDFMelted, title=\"Victorian Studies Middlemarch quotations per book, per decade (not weighted or normalized by decade)\")\\\n", + ".mark_rect().encode(x=alt.X('book', title=\"Book\", type='ordinal', axis=alt.Axis(labelAngle=0)), \n", + " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", + " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", + " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", + ".properties(width=500, height=300).configure_legend(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### *Victorian Studies*: Number of quotations per chapter, per decade (not normalized or weighted)" + "### *Victorian Studies*: *Middlemarch* quotations per chapter, per decade (not normalized or weighted)" ] }, { "cell_type": "code", - "execution_count": 249, + "execution_count": 224, "metadata": {}, "outputs": [], "source": [ @@ -22855,7 +13936,7 @@ }, { "cell_type": "code", - "execution_count": 250, + "execution_count": 225, "metadata": { "scrolled": true }, @@ -23669,7 +14750,7 @@ "2010 0 0 3 " ] }, - "execution_count": 250, + "execution_count": 225, "metadata": {}, "output_type": "execute_result" } @@ -23680,7 +14761,7 @@ }, { "cell_type": "code", - "execution_count": 251, + "execution_count": 226, "metadata": {}, "outputs": [], "source": [ @@ -23689,16 +14770,23 @@ }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 227, "metadata": {}, "outputs": [], "source": [ "VSdiaDFquoteOnlyMelted = VSdiaDFquoteOnly.melt(id_vars='decade')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### *Victorian Studies*: *Middlemarch* quotations per chapter, per decade (not normalized or weighted), heat map" + ] + }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 228, "metadata": {}, "outputs": [ { @@ -23706,23 +14794,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 253, + "execution_count": 228, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(VSdiaDFquoteOnlyMelted, title=\"Victorian Studies Middlemarch quotations per chapter, per decade (not weighted or normalized)\")\\\n", + "alt.Chart(VSdiaDFquoteOnlyMelted, title=\"*Victorian Studies* Middlemarch quotations per chapter, per decade (not weighted or normalized)\")\\\n", ".mark_rect().encode(x=alt.X('chapter', title=\"Chapter\", type='ordinal', axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", - " color=alt.Color('value', legend=alt.Legend(title=\"Quotations Count\")))\\\n", + " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", ".properties(width=1000, height=300).configure_legend(\n", "titleFontSize=14,\n", "labelFontSize=14\n", @@ -23799,12 +14887,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### *Victorian Studies*: Number of quotations per chapter, per decade (normalized by decade and weighted by word count)" + "### *Victorian Studies*: *Middlemarch* quotations per chapter, per decade (normalized by decade and weighted by word count)" ] }, { "cell_type": "code", - "execution_count": 254, + "execution_count": 229, "metadata": {}, "outputs": [], "source": [ @@ -23816,7 +14904,7 @@ }, { "cell_type": "code", - "execution_count": 255, + "execution_count": 230, "metadata": {}, "outputs": [ { @@ -24664,7 +15752,7 @@ "2010 0.0 0.360947 " ] }, - "execution_count": 255, + "execution_count": 230, "metadata": {}, "output_type": "execute_result" } @@ -24675,7 +15763,7 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 231, "metadata": {}, "outputs": [], "source": [ @@ -24684,7 +15772,7 @@ }, { "cell_type": "code", - "execution_count": 257, + "execution_count": 232, "metadata": {}, "outputs": [], "source": [ @@ -24695,12 +15783,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### *Victorian Studies* *Middlemarch* quotations per chapter, per decade (normalized and weighted), heatmap" + "### *Victorian Studies*: *Middlemarch* quotations per chapter, per decade (normalized and weighted), heat map\n", + "Because our corpus contains [varying numbers of JSTOR texsts per decade](#How-many-articles-do-we-have-published-in-each-year?), we've decided to also weigh by length of quotation and normalize per decade." ] }, { "cell_type": "code", - "execution_count": 258, + "execution_count": 233, "metadata": { "scrolled": true }, @@ -24710,23 +15799,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 258, + "execution_count": 233, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(VSnormalizeddiaMelted, title=\"Victorian Studies Middlemarch quotations per chapter, per decade (normalized by decade and weighted by word count)\")\\\n", + "alt.Chart(VSnormalizeddiaMelted, title=\"*Victorian Studies* Middlemarch quotations per chapter, per decade (normalized by decade and weighted by word count)\")\\\n", ".mark_rect().encode(x=alt.X('chapter', title=\"Chapter\", type='ordinal', axis=alt.Axis(labelAngle=0, values=list(range(0, 87, 5)))), \n", " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", - " color=alt.Color('value', legend=alt.Legend(title=\"Quotations Count\")))\\\n", + " color=alt.Color('value', legend=alt.Legend(title=\"Amount Quoted\")))\\\n", ".properties(width=1000, height=300).configure_legend(\n", "titleFontSize=14,\n", "labelFontSize=14\n", @@ -24799,26 +15888,6 @@ ") " ] }, - { - "cell_type": "code", - "execution_count": 259, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plotSynchronicAnalysis(synchronicAnalysis(vsJournals, useWordcounts=False), useWordcounts=False)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -24828,7 +15897,7 @@ }, { "cell_type": "code", - "execution_count": 260, + "execution_count": 234, "metadata": {}, "outputs": [], "source": [ @@ -24849,7 +15918,7 @@ }, { "cell_type": "code", - "execution_count": 261, + "execution_count": 235, "metadata": {}, "outputs": [ { @@ -24869,7 +15938,7 @@ " 'Review Article']" ] }, - "execution_count": 261, + "execution_count": 235, "metadata": {}, "output_type": "execute_result" } @@ -24881,7 +15950,7 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": 236, "metadata": {}, "outputs": [ { @@ -24901,7 +15970,7 @@ " 'http://www.jstor.org/stable/4618985']" ] }, - "execution_count": 262, + "execution_count": 236, "metadata": {}, "output_type": "execute_result" } @@ -24914,7 +15983,7 @@ }, { "cell_type": "code", - "execution_count": 263, + "execution_count": 237, "metadata": {}, "outputs": [ { @@ -24923,7 +15992,7 @@ "'CHAPTER LXXX.\\n\\n \"Stern lawgiver! yet thou dost wear\\n The Godhead\\'s most benignant grace;\\n Nor know we anything so fair\\n As is the smile upon thy face;\\n Flowers laugh before thee on their beds,\\n And fragrance in thy footing treads;\\n Thou dost preserve the Stars from wrong;\\n And the most ancient Heavens, through thee, are fresh and strong.\\n --WORDSWORTH: Ode to Duty.\\n\\n\\nWhen Dorothea had seen Mr. Farebrother in the morning, she had promised\\nto go and dine at the parsonage on her return from Freshitt. There was\\na frequent interchange of visits between her and the Farebrother\\nfamily, which enabled her to say that she was not at all lonely at the\\nManor, and to resist for the present the severe prescription of a lady\\ncompanion. When she reached home and remembered her engagement, she\\nwas glad of it; and finding that she had still an hour before she could\\ndress for dinner, she walked straight to the schoolhouse and entered\\ninto a conversation with the master and mistress about the new bell,\\ngiving eager attention to their small details and repetitions, and\\ngetting up a dramatic sense that her life was very busy. She paused on\\nher way back to talk to old Master Bunney who was putting in some\\ngarden-seeds, and discoursed wisely with that rural sage about the\\ncrops that would make the most return on a perch of ground, and the\\nresult of sixty years\\' experience as to soils--namely, that if your\\nsoil was pretty mellow it would do, but if there came wet, wet, wet to\\nmake it all of a mummy, why then--\\n\\nFinding that the social spirit had beguiled her into being rather late,\\nshe dressed hastily and went over to the parsonage rather earlier than\\nwas necessary. That house was never dull, Mr. Farebrother, like\\nanother White of Selborne, having continually something new to tell of\\nhis inarticulate guests and proteges, whom he was teaching the boys not\\nto torment; and he had just set up a pair of beautiful goats to be pets\\nof the village in general, and to walk at large as sacred animals. The\\nevening went by cheerfully till after tea, Dorothea talking more than\\nusual and dilating with Mr. Farebrother on the possible histories of\\ncreatures that converse compendiously with their antennae, and for\\naught we know may hold reformed parliaments; when suddenly some\\ninarticulate little sounds were heard which called everybody\\'s\\nattention.\\n\\n\"Henrietta Noble,\" said Mrs. Farebrother, seeing her small sister\\nmoving about the furniture-legs distressfully, \"what is the matter?\"\\n\\n\"I have lost my tortoise-shell lozenge-box. I fear the kitten has\\nrolled it away,\" said the tiny old lady, involuntarily continuing her\\nbeaver-like notes.\\n\\n\"Is it a great treasure, aunt?\" said Mr. Farebrother, putting up his\\nglasses and looking at the carpet.\\n\\n\"Mr. Ladislaw gave it me,\" said Miss Noble. \"A German box--very\\npretty, but if it falls it always spins away as far as it can.\"\\n\\n\"Oh, if it is Ladislaw\\'s present,\" said Mr. Farebrother, in a deep tone\\nof comprehension, getting up and hunting. The box was found at last\\nunder a chiffonier, and Miss Noble grasped it with delight, saying, \"it\\nwas under a fender the last time.\"\\n\\n\"That is an affair of the heart with my aunt,\" said Mr. Farebrother,\\nsmiling at Dorothea, as he reseated himself.\\n\\n\"If Henrietta Noble forms an attachment to any one, Mrs. Casaubon,\"\\nsaid his mother, emphatically,--\"she is like a dog--she would take\\ntheir shoes for a pillow and sleep the better.\"\\n\\n\"Mr. Ladislaw\\'s shoes, I would,\" said Henrietta Noble.\\n\\nDorothea made an attempt at smiling in return. She was surprised and\\nannoyed to find that her heart was palpitating violently, and that it\\nwas quite useless to try after a recovery of her former animation.\\nAlarmed at herself--fearing some further betrayal of a change so marked\\nin its occasion, she rose and said in a low voice with undisguised\\nanxiety, \"I must go; I have overtired myself.\"\\n\\nMr. Farebrother, quick in perception, rose and said, \"It is true; you\\nmust have half-exhausted yourself in talking about Lydgate. That sort\\nof work tells upon one after the excitement is over.\"\\n\\nHe gave her his arm back to the Manor, but Dorothea did not attempt to\\nspeak, even when he said good-night.\\n\\nThe limit of resistance was reached, and she had sunk back helpless\\nwithin the clutch of inescapable anguish. Dismissing Tantripp with a\\nfew faint words, she locked her door, and turning away from it towards\\nthe vacant room she pressed her hands hard on the top of her head, and\\nmoaned out--\\n\\n\"Oh, I did love him!\"\\n\\nThen came the hour in which the waves of suffering shook her too\\nthoroughly to leave any power of thought. She could only cry in loud\\nwhispers, between her sobs, after her lost belief which she had planted\\nand kept alive from a very little seed since the days in Rome--after\\nher lost joy of clinging with silent love and faith to one who,\\nmisprized by others, was worthy in her thought--after her lost woman\\'s\\npride of reigning in his memory--after her sweet dim perspective of\\nhope, that along some pathway they should meet with unchanged\\nrecognition and take up the backward years as a yesterday.\\n\\nIn that hour she repeated what the merciful eyes of solitude have\\nlooked on for ages in the spiritual struggles of man--she besought\\nhardness and coldness and aching weariness to bring her relief from the\\nmysterious incorporeal might of her anguish: she lay on the bare floor\\nand let the night grow cold around her; while her grand woman\\'s frame\\nwas shaken by sobs as if she had been a despairing child.\\n\\nThere were two images--two living forms that tore her heart in two, as\\nif it had been the heart of a mother who seems to see her child divided\\nby the sword, and presses one bleeding half to her breast while her\\ngaze goes forth in agony towards the half which is carried away by the\\nlying woman that has never known the mother\\'s pang.\\n\\nHere, with the nearness of an answering smile, here within the\\nvibrating bond of mutual speech, was the bright creature whom she had\\ntrusted--who had come to her like the spirit of morning visiting the\\ndim vault where she sat as the bride of a worn-out life; and now, with\\na full consciousness which had never awakened before, she stretched out\\nher arms towards him and cried with bitter cries that their nearness\\nwas a parting vision: she discovered her passion to herself in the\\nunshrinking utterance of despair.\\n\\nAnd there, aloof, yet persistently with her, moving wherever she moved,\\nwas the Will Ladislaw who was a changed belief exhausted of hope, a\\ndetected illusion--no, a living man towards whom there could not yet\\nstruggle any wail of regretful pity, from the midst of scorn and\\nindignation and jealous offended pride. The fire of Dorothea\\'s anger\\nwas not easily spent, and it flamed out in fitful returns of spurning\\nreproach. Why had he come obtruding his life into hers, hers that\\nmight have been whole enough without him? Why had he brought his cheap\\nregard and his lip-born words to her who had nothing paltry to give in\\nexchange? He knew that he was deluding her--wished, in the very moment\\nof farewell, to make her believe that he gave her the whole price of\\nher heart, and knew that he had spent it half before. Why had he not\\nstayed among the crowd of whom she asked nothing--but only prayed that\\nthey might be less contemptible?\\n\\nBut she lost energy at last even for her loud-whispered cries and\\nmoans: she subsided into helpless sobs, and on the cold floor she\\nsobbed herself to sleep.\\n\\nIn the chill hours of the morning twilight, when all was dim around\\nher, she awoke--not with any amazed wondering where she was or what had\\nhappened, but with the clearest consciousness that she was looking into\\nthe eyes of sorrow. She rose, and wrapped warm things around her, and\\nseated herself in a great chair where she had often watched before.\\nShe was vigorous enough to have borne that hard night without feeling\\nill in body, beyond some aching and fatigue; but she had waked to a new\\ncondition: she felt as if her soul had been liberated from its terrible\\nconflict; she was no longer wrestling with her grief, but could sit\\ndown with it as a lasting companion and make it a sharer in her\\nthoughts. For now the thoughts came thickly. It was not in Dorothea\\'s\\nnature, for longer than the duration of a paroxysm, to sit in the\\nnarrow cell of her calamity, in the besotted misery of a consciousness\\nthat only sees another\\'s lot as an accident of its own.\\n\\nShe began now to live through that yesterday morning deliberately\\nagain, forcing herself to dwell on every detail and its possible\\nmeaning. Was she alone in that scene? Was it her event only? She\\nforced herself to think of it as bound up with another woman\\'s life--a\\nwoman towards whom she had set out with a longing to carry some\\nclearness and comfort into her beclouded youth. In her first outleap\\nof jealous indignation and disgust, when quitting the hateful room, she\\nhad flung away all the mercy with which she had undertaken that visit.\\nShe had enveloped both Will and Rosamond in her burning scorn, and it\\nseemed to her as if Rosamond were burned out of her sight forever. But\\nthat base prompting which makes a women more cruel to a rival than to a\\nfaithless lover, could have no strength of recurrence in Dorothea when\\nthe dominant spirit of justice within her had once overcome the tumult\\nand had once shown her the truer measure of things. All the active\\nthought with which she had before been representing to herself the\\ntrials of Lydgate\\'s lot, and this young marriage union which, like her\\nown, seemed to have its hidden as well as evident troubles--all this\\nvivid sympathetic experience returned to her now as a power: it\\nasserted itself as acquired knowledge asserts itself and will not let\\nus see as we saw in the day of our ignorance. She said to her own\\nirremediable grief, that it should make her more helpful, instead of\\ndriving her back from effort.\\n\\nAnd what sort of crisis might not this be in three lives whose contact\\nwith hers laid an obligation on her as if they had been suppliants\\nbearing the sacred branch? The objects of her rescue were not to be\\nsought out by her fancy: they were chosen for her. She yearned towards\\nthe perfect Right, that it might make a throne within her, and rule her\\nerrant will. \"What should I do--how should I act now, this very day,\\nif I could clutch my own pain, and compel it to silence, and think of\\nthose three?\"\\n\\nIt had taken long for her to come to that question, and there was light\\npiercing into the room. She opened her curtains, and looked out\\ntowards the bit of road that lay in view, with fields beyond outside\\nthe entrance-gates. On the road there was a man with a bundle on his\\nback and a woman carrying her baby; in the field she could see figures\\nmoving--perhaps the shepherd with his dog. Far off in the bending sky\\nwas the pearly light; and she felt the largeness of the world and the\\nmanifold wakings of men to labor and endurance. She was a part of that\\ninvoluntary, palpitating life, and could neither look out on it from\\nher luxurious shelter as a mere spectator, nor hide her eyes in selfish\\ncomplaining.\\n\\nWhat she would resolve to do that day did not yet seem quite clear, but\\nsomething that she could achieve stirred her as with an approaching\\nmurmur which would soon gather distinctness. She took off the clothes\\nwhich seemed to have some of the weariness of a hard watching in them,\\nand began to make her toilet. Presently she rang for Tantripp, who\\ncame in her dressing-gown.\\n\\n\"Why, madam, you\\'ve never been in bed this blessed night,\" burst out\\nTantripp, looking first at the bed and then at Dorothea\\'s face, which\\nin spite of bathing had the pale cheeks and pink eyelids of a mater\\ndolorosa. \"You\\'ll kill yourself, you _will_. Anybody might think now\\nyou had a right to give yourself a little comfort.\"\\n\\n\"Don\\'t be alarmed, Tantripp,\" said Dorothea, smiling. \"I have slept; I\\nam not ill. I shall be glad of a cup of coffee as soon as possible.\\nAnd I want you to bring me my new dress; and most likely I shall want\\nmy new bonnet to-day.\"\\n\\n\"They\\'ve lain there a month and more ready for you, madam, and most\\nthankful I shall be to see you with a couple o\\' pounds\\' worth less of\\ncrape,\" said Tantripp, stooping to light the fire. \"There\\'s a reason\\nin mourning, as I\\'ve always said; and three folds at the bottom of your\\nskirt and a plain quilling in your bonnet--and if ever anybody looked\\nlike an angel, it\\'s you in a net quilling--is what\\'s consistent for a\\nsecond year. At least, that\\'s _my_ thinking,\" ended Tantripp, looking\\nanxiously at the fire; \"and if anybody was to marry me flattering\\nhimself I should wear those hijeous weepers two years for him, he\\'d be\\ndeceived by his own vanity, that\\'s all.\"\\n\\n\"The fire will do, my good Tan,\" said Dorothea, speaking as she used to\\ndo in the old Lausanne days, only with a very low voice; \"get me the\\ncoffee.\"\\n\\nShe folded herself in the large chair, and leaned her head against it\\nin fatigued quiescence, while Tantripp went away wondering at this\\nstrange contrariness in her young mistress--that just the morning when\\nshe had more of a widow\\'s face than ever, she should have asked for her\\nlighter mourning which she had waived before. Tantripp would never\\nhave found the clew to this mystery. Dorothea wished to acknowledge\\nthat she had not the less an active life before her because she had\\nburied a private joy; and the tradition that fresh garments belonged to\\nall initiation, haunting her mind, made her grasp after even that\\nslight outward help towards calm resolve. For the resolve was not easy.\\n\\nNevertheless at eleven o\\'clock she was walking towards Middlemarch,\\nhaving made up her mind that she would make as quietly and unnoticeably\\nas possible her second attempt to see and save Rosamond.\\n\\n\\n\\n'" ] }, - "execution_count": 263, + "execution_count": 237, "metadata": {}, "output_type": "execute_result" } @@ -24935,7 +16004,7 @@ }, { "cell_type": "code", - "execution_count": 264, + "execution_count": 238, "metadata": {}, "outputs": [], "source": [ @@ -24956,7 +16025,7 @@ }, { "cell_type": "code", - "execution_count": 265, + "execution_count": 239, "metadata": {}, "outputs": [ { @@ -24977,7 +16046,7 @@ " 'Victorian Bibliography for 1974']" ] }, - "execution_count": 265, + "execution_count": 239, "metadata": {}, "output_type": "execute_result" } @@ -24989,7 +16058,7 @@ }, { "cell_type": "code", - "execution_count": 266, + "execution_count": 240, "metadata": {}, "outputs": [], "source": [ @@ -24998,7 +16067,7 @@ }, { "cell_type": "code", - "execution_count": 267, + "execution_count": 241, "metadata": {}, "outputs": [ { @@ -25375,7 +16444,7 @@ "5014 [7] Victorian Studies " ] }, - "execution_count": 267, + "execution_count": 241, "metadata": {}, "output_type": "execute_result" } @@ -25387,23 +16456,27 @@ }, { "cell_type": "code", - "execution_count": 268, + "execution_count": 242, "metadata": {}, "outputs": [], "source": [ - "df_filtered.to_csv('../../../Middlematch/victorian_studies_chap_15.csv', encoding='utf-8')" + "#df_filtered.to_csv('../../../Middlematch/victorian_studies_chap_15.csv', encoding='utf-8')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## All Victorianist Journals" + "## All Victorianist journals\n", + "\n", + "For full list of journals classified as \"Victorianist,\" see [Statistics on Victorianist journals in the dataset](#Statistics-on-Victorianist-journals-in-the-dataset).\n", + "\n", + "Downloadable link: [CSV with complete list of Victorianist-classified journals](https://github.com/lit-mod-viz/middlemarch-critical-histories/blob/master/data/list-of-Victorianist-journals.csv)." ] }, { "cell_type": "code", - "execution_count": 269, + "execution_count": 243, "metadata": {}, "outputs": [], "source": [ @@ -25412,7 +16485,7 @@ }, { "cell_type": "code", - "execution_count": 270, + "execution_count": 244, "metadata": {}, "outputs": [], "source": [ @@ -25421,7 +16494,7 @@ }, { "cell_type": "code", - "execution_count": 271, + "execution_count": 245, "metadata": {}, "outputs": [ { @@ -25557,576 +16630,281 @@ " 0\n", " []\n", " []\n", - " The Gaskell Society Journal\n", - " \n", - " \n", - " 10\n", - " [Cheryl Cassidy]\n", - " 1992-12-01\n", - " misc\n", - " article\n", - " http://www.jstor.org/stable/20082630\n", - " [{'name': 'issn', 'value': '07094698'}, {'name...\n", - " Victorian Periodicals Review\n", - " 4\n", - " [eng]\n", - " [unigram, bigram, trigram]\n", - " 23.0\n", - " 205\n", - " 183\n", - " pp. 183-205\n", - " jstor\n", - " 1992\n", - " Research Society for Victorian Periodicals\n", - " [Language & Literature, British Studies, Area ...\n", - " [Arts - Literature]\n", - " Victorian Periodicals 1991: An Annotated Bibli...\n", - " http://www.jstor.org/stable/20082630\n", - " 25\n", - " 15737\n", - " 0\n", - " []\n", - " []\n", - " None\n", - " None\n", - " None\n", - " 1992\n", - " 1990\n", - " 0\n", - " []\n", - " []\n", - " Victorian Periodicals Review\n", - " \n", - " \n", - " 14\n", - " [Catherine Gallagher]\n", - " 2006-10-01\n", - " book-review\n", - " article\n", - " http://www.jstor.org/stable/4618956\n", - " [{'name': 'issn', 'value': '00425222'}, {'name...\n", - " Victorian Studies\n", - " 1\n", - " [eng]\n", - " [unigram, bigram, trigram]\n", - " 3.0\n", - " 111\n", - " 109\n", - " pp. 109-111\n", - " jstor\n", - " 2006\n", - " Indiana University Press\n", - " [Language & Literature, History, British Studi...\n", - " [Arts - Literature]\n", - " Review Article\n", - " http://www.jstor.org/stable/4618956\n", - " 49\n", - " 1378\n", - " 0\n", - " []\n", - " []\n", - " None\n", - " None\n", - " None\n", - " 2006\n", - " 2000\n", - " 0\n", - " []\n", - " []\n", - " Victorian Studies\n", - " \n", - " \n", - " 16\n", - " [Angelique Richardson]\n", - " 2006-07-01\n", - " book-review\n", - " article\n", - " http://www.jstor.org/stable/4618943\n", - " [{'name': 'issn', 'value': '00425222'}, {'name...\n", - " Victorian Studies\n", - " 4\n", - " [eng]\n", - " [unigram, bigram, trigram]\n", - " 4.0\n", - " 769\n", - " 766\n", - " pp. 766-769\n", - " jstor\n", - " 2006\n", - " Indiana University Press\n", - " [Language & Literature, History, British Studi...\n", - " [Arts - Literature]\n", - " Review Article\n", - " http://www.jstor.org/stable/4618943\n", - " 48\n", - " 1482\n", - " 0\n", - " []\n", - " []\n", - " None\n", - " None\n", - " None\n", - " 2006\n", - " 2000\n", - " 0\n", - " []\n", - " []\n", - " Victorian Studies\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 5875\n", - " None\n", - " 2011-10-01\n", - " misc\n", - " article\n", - " http://www.jstor.org/stable/10.2979/victorians...\n", - " [{'name': 'issn', 'value': '00425222'}, {'name...\n", - " Victorian Studies\n", - " 1\n", - " [eng]\n", - " [unigram, bigram, trigram]\n", - " 7.0\n", - " 190\n", - " 185\n", - " pp. 185-190\n", - " jstor\n", - " 2011\n", - " Indiana University Press\n", - " [Language & Literature, History, British Studi...\n", - " [Arts - Literature]\n", - " Contributors\n", - " http://www.jstor.org/stable/10.2979/victorians...\n", - " 54\n", - " 2413\n", - " 0\n", - " []\n", - " []\n", - " None\n", - " None\n", - " None\n", - " 2011\n", - " 2010\n", - " 0\n", - " []\n", - " []\n", - " Victorian Studies\n", - " \n", - " \n", - " 5876\n", - " [BUFF LINDAU]\n", - " 2013-10-01\n", - " research-article\n", - " article\n", - " http://www.jstor.org/stable/42827928\n", - " [{'name': 'issn', 'value': '23721901'}, {'name...\n", - " George Eliot - George Henry Lewes Studies\n", - " 64/65\n", - " [eng]\n", - " [unigram, bigram, trigram]\n", - " 1.0\n", - " 109\n", - " 109\n", - " p. 109\n", - " jstor\n", - " 2013\n", - " Penn State University Press\n", - " [Language & Literature, Humanities]\n", - " None\n", - " A GEORGE ELIOT NOTE\n", - " http://www.jstor.org/stable/42827928\n", - " None\n", - " 129\n", - " 0\n", - " []\n", - " []\n", - " None\n", - " None\n", - " None\n", - " 2013\n", - " 2010\n", - " 0\n", - " []\n", - " []\n", - " George Eliot - George Henry Lewes Studies\n", + " The Gaskell Society Journal\n", " \n", " \n", - " 5881\n", - " [Linda M. 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Literature] \n", + "6 [Arts - Literature] \n", + "10 [Arts - Literature] \n", + "14 [Arts - Literature] \n", + "16 [Arts - Literature] \n", + "\n", + " title \\\n", + "5 Index \n", + "6 EDUCATION THROUGH EXPERIENCE IN \"NORTH AND SOUTH\" \n", + "10 Victorian Periodicals 1991: An Annotated Bibli... \n", + "14 Review Article \n", + "16 Review Article \n", + "\n", + " url volumeNumber wordCount numMatches \\\n", + "5 http://www.jstor.org/stable/44371392 44 2235 0 \n", + "6 http://www.jstor.org/stable/45185621 10 5672 0 \n", + "10 http://www.jstor.org/stable/20082630 25 15737 0 \n", + "14 http://www.jstor.org/stable/4618956 49 1378 0 \n", + "16 http://www.jstor.org/stable/4618943 48 1482 0 \n", + "\n", + " Locations in A Locations in B abstract keyphrase subTitle year Decade \\\n", + "5 [] [] None None None 2013 2010 \n", + "6 [] [] None None None 1996 1990 \n", + "10 [] [] None None None 1992 1990 \n", + "14 [] [] None None None 2006 2000 \n", + "16 [] [] None None None 2006 2000 \n", + "\n", + " Quoted Words Locations in A with Wordcounts Wordcounts \\\n", + "5 0 [] [] \n", + "6 0 [] [] \n", + "10 0 [] [] \n", + "14 0 [] [] \n", + "16 0 [] [] \n", + "\n", + " journal \n", + "5 Dickens Studies Annual \n", + "6 The Gaskell Society Journal \n", + "10 Victorian Periodicals Review \n", + "14 Victorian Studies \n", + "16 Victorian Studies " ] }, - "execution_count": 271, + "execution_count": 245, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "victorian_studies_df" + "victorian_studies_df.head(5)" ] }, { "cell_type": "code", - "execution_count": 272, + "execution_count": 246, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Number of articles where journal title is 'Victorian Studies':\n" + "Number of articles in Victorianist journals:\n" ] }, { "data": { "text/plain": [ - "459" + "1632" ] }, - "execution_count": 272, + "execution_count": 246, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"Number of articles where journal title is 'Victorian Studies':\")\n", - "len(vsJournals)" + "print(\"Number of articles in Victorianist journals:\")\n", + "len(victorian_studies_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Victorianist journals: *Middlemarch* quotations per chapter" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotSynchronicAnalysis(synchronicAnalysis(victorian_studies_df, useWordcounts=False), useWordcounts=False)" ] }, { @@ -26138,7 +16916,7 @@ }, { "cell_type": "code", - "execution_count": 273, + "execution_count": 248, "metadata": {}, "outputs": [ { @@ -26275,7 +17053,7 @@ "2010 0.225428 0.900244 " ] }, - "execution_count": 273, + "execution_count": 248, "metadata": {}, "output_type": "execute_result" } @@ -26289,7 +17067,7 @@ }, { "cell_type": "code", - "execution_count": 274, + "execution_count": 249, "metadata": {}, "outputs": [], "source": [ @@ -26298,7 +17076,7 @@ }, { "cell_type": "code", - "execution_count": 275, + "execution_count": 250, "metadata": {}, "outputs": [], "source": [ @@ -26307,7 +17085,7 @@ }, { "cell_type": "code", - "execution_count": 276, + "execution_count": 251, "metadata": {}, "outputs": [], "source": [ @@ -26319,12 +17097,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Victorianist journals : Middlemarch quotations per book, per decade (normalized and weighted), table bubble plots" + "### Victorianist journals: *Middlemarch* quotations per book, per decade (normalized and weighted), heat map\n", + "Because our corpus contains [varying numbers of JSTOR texsts per decade](#How-many-articles-do-we-have-published-in-each-year?), we've decided to also weigh by length of quotation and normalize per decade." ] }, { "cell_type": "code", - "execution_count": 277, + "execution_count": 252, "metadata": {}, "outputs": [ { @@ -26332,23 +17111,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 277, + "execution_count": 252, "metadata": {}, "output_type": "execute_result" } @@ -26409,20 +17188,29 @@ "source": [ "#Change scale of the circle markers to a threshold scale (and resize to make the steps in the scale more visible)\n", "alt.Chart(VictorianStudiesbooksMelted, title=\"Victorianist Middlemarch quotations per book, per decade (weighted by length of quotation and normalized by decade)\")\\\n", - ".mark_circle().encode(\n", - " x=alt.X('book:O', axis=alt.Axis(labelOverlap=True,\n", - " labelAngle=0)), \n", - " y=alt.Y('decade:O'), \n", - " size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations (normalized)\"), \n", - " scale=alt.Scale(type = 'threshold', domain = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1], range =[0, 20, 60, 100, 150, 250, 350, 500, 750, 1000, 1500, 2000,]))).properties(width=500, height=300).configure_legend(\n", - "titleFontSize=10,\n", - "labelFontSize=10\n", + ".mark_rect().encode(x=alt.X('book', title=\"Book\", type='ordinal', axis=alt.Axis(labelAngle=0)), \n", + " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", + " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", + " color=alt.Color('value', legend=alt.Legend(title=\"Amount Quoted\")))\\\n", + ".properties(width=500, height=300).configure_legend(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", ") " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Victorianist journals: *Middlemarch* quotations per book, per decade (not normalized or weighted)" + ] + }, { "cell_type": "code", - "execution_count": 278, + "execution_count": 253, "metadata": { "scrolled": false }, @@ -26553,7 +17341,7 @@ "2010 0 67 66 30 28 17 26 21 69" ] }, - "execution_count": 278, + "execution_count": 253, "metadata": {}, "output_type": "execute_result" } @@ -26569,7 +17357,7 @@ }, { "cell_type": "code", - "execution_count": 279, + "execution_count": 254, "metadata": {}, "outputs": [], "source": [ @@ -26578,7 +17366,7 @@ }, { "cell_type": "code", - "execution_count": 280, + "execution_count": 255, "metadata": {}, "outputs": [], "source": [ @@ -26587,7 +17375,7 @@ }, { "cell_type": "code", - "execution_count": 281, + "execution_count": 256, "metadata": {}, "outputs": [], "source": [ @@ -26599,12 +17387,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Victorianist journals : Middlemarch quotations per book, per decade (not normalized or weighted), table bubble plots" + "### Victorianist journals: *Middlemarch* quotations per book, per decade (not normalized or weighted), heat map" ] }, { "cell_type": "code", - "execution_count": 282, + "execution_count": 257, "metadata": {}, "outputs": [ { @@ -26612,23 +17400,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 282, + "execution_count": 257, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(VictorianStudiesbooksNotNormalizedNotWeightedDiaDFMelted, title=\"Victorianist Middlemarch quotations per book, per decade (not weighted or normalized by decade)\").mark_circle().encode(\n", - " x=alt.X('book:O', axis=alt.Axis(labelOverlap=True,\n", - " labelAngle=0)), \n", - " y=alt.Y('decade:O'), size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", - ".properties(width=1000, height=300).configure_legend(\n", - "titleFontSize=10,\n", - "labelFontSize=10\n", + "alt.Chart(VictorianStudiesbooksNotNormalizedNotWeightedDiaDFMelted, title=\"Victorianist Middlemarch quotations per book, per decade (not weighted or normalized by decade)\")\\\n", + ".mark_rect().encode(x=alt.X('book', title=\"Book\", type='ordinal', axis=alt.Axis(labelAngle=0)), \n", + " y=alt.Y('decade', title=\"Decade\",type='ordinal', sort='descending', \n", + " axis=alt.Axis(labelExpr='datum.value + \"s\"')), \n", + " color=alt.Color('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", + ".properties(width=500, height=300).configure_legend(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", + ").configure_axis(\n", + "titleFontSize=14,\n", + "labelFontSize=14\n", ") " ] }, @@ -26701,12 +17493,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Victorianist journals: Number of quotations per chapter, per decade (not normalized or weighted)" + "### Victorianist journals: *Middlemarch* quotations per chapter, per decade (not normalized or weighted)" ] }, { "cell_type": "code", - "execution_count": 283, + "execution_count": 258, "metadata": {}, "outputs": [], "source": [ @@ -26719,7 +17511,7 @@ }, { "cell_type": "code", - "execution_count": 284, + "execution_count": 259, "metadata": { "scrolled": true }, @@ -27533,7 +18325,7 @@ "2010 0 1 33 " ] }, - "execution_count": 284, + "execution_count": 259, "metadata": {}, "output_type": "execute_result" } @@ -27544,7 +18336,7 @@ }, { "cell_type": "code", - "execution_count": 285, + "execution_count": 260, "metadata": {}, "outputs": [], "source": [ @@ -27553,16 +18345,23 @@ }, { "cell_type": "code", - "execution_count": 286, + "execution_count": 261, "metadata": {}, "outputs": [], "source": [ "VictorianStudiesdiaDFquoteOnlyMelted = VictorianStudiesdiaDFquoteOnly.melt(id_vars='decade')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Victorianist journals: *Middlemarch* quotations per chapter, per decade (not normalized or weighted), heat map" + ] + }, { "cell_type": "code", - "execution_count": 287, + "execution_count": 262, "metadata": {}, "outputs": [ { @@ -27570,23 +18369,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 288, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alt.Chart(VictorianStudiesdiaDFquoteOnlyMelted, title=\"Victorianist Journals Middlemarch quotations per chapter, per decade (not weighted or normalized)\").mark_circle().encode(x='chapter:O', \n", - " y=alt.Y('decade:O'), size=alt.Size('value', legend=alt.Legend(title=\"Number of Quotations\")))\\\n", - ".properties(width=1000, height=300).configure_legend(\n", - "titleFontSize=10,\n", - "labelFontSize=10\n", - ") " - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### *Victorian Studies*: Number of quotations per chapter, per decade (normalized by decade and weighted by word count)" + "### Victorianist journals: *Middlemarch* quotations per chapter, per decade (normalized by decade and weighted by word count)" ] }, { "cell_type": "code", - "execution_count": 289, + "execution_count": 263, "metadata": {}, "outputs": [], "source": [ @@ -27774,7 +18480,7 @@ }, { "cell_type": "code", - "execution_count": 290, + "execution_count": 264, "metadata": {}, "outputs": [ { @@ -28649,7 +19355,7 @@ "2010 0.000000 0.0 0.004188 1.000000 " ] }, - "execution_count": 290, + "execution_count": 264, "metadata": {}, "output_type": "execute_result" } @@ -28660,7 +19366,7 @@ }, { "cell_type": "code", - "execution_count": 291, + "execution_count": 265, "metadata": {}, "outputs": [], "source": [ @@ -28669,7 +19375,7 @@ }, { "cell_type": "code", - "execution_count": 292, + "execution_count": 266, "metadata": {}, "outputs": [], "source": [ @@ -28680,12 +19386,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### *Victorian Studies* *Middlemarch* quotations per chapter, per decade (normalized and weighted), heatmaps" + "### Victorianist journals: *Middlemarch* quotations per chapter, per decade (normalized and weighted), heat map\n", + "\n", + "Because our corpus contains [varying numbers of JSTOR texsts per decade](#How-many-articles-do-we-have-published-in-each-year?), we've decided to also weigh by length of quotation and normalize per decade." ] }, { "cell_type": "code", - "execution_count": 293, + "execution_count": 267, "metadata": { "scrolled": true }, @@ -28695,23 +19403,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ - " Author Decade Value\n", - "0 Bronte 1960s 4.238259\n", - "1 Dickens 1960s 25.429553\n", - "2 Eliot 1960s 21.534937\n", - "3 Hardy 1960s 15.349370\n", - "4 Bronte 1970s 10.292524\n", - "5 Dickens 1970s 29.902492\n", - "6 Eliot 1970s 23.618635\n", - "7 Hardy 1970s 23.185265\n", - "8 Bronte 1980s 11.409396\n", - "9 Dickens 1980s 30.285235\n", - "10 Eliot 1980s 29.194631\n", - "11 Hardy 1980s 20.805369\n", - "12 Bronte 1990s 11.659514\n", - "13 Dickens 1990s 25.894134\n", - "14 Eliot 1990s 22.031474\n", - "15 Hardy 1990s 15.021459\n", - "16 Bronte 2000s 8.901252\n", - "17 Dickens 2000s 25.173853\n", - "18 Eliot 2000s 23.922114\n", - "19 Hardy 2000s 9.805285\n", - "20 Bronte 2010s 0.122175\n", - "21 Dickens 2010s 25.656689\n", - "22 Eliot 2010s 22.052535\n", - "23 Hardy 2010s 11.484423" + "alt.LayerChart(...)" ] }, - "execution_count": 299, + "execution_count": 268, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "vs_authors_df" + "auth_chart" ] }, { - "cell_type": "code", - "execution_count": 300, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "vs_authors_df['Value'] = vs_authors_df['Value']* 0.01" + "### Figure 2\n", + "#### Frequency of novel title references in *Victorian Studies*\n", + "See link to [Figure 2 in notebook](#Most-frequent-title-references-in-Victorian-Studies,-line-chart) for more context. [Color version](#Most-frequent-title-references-in-Victorian-Studies,-line-chart-(color))" ] }, { "cell_type": "code", - "execution_count": 301, + "execution_count": 269, "metadata": {}, "outputs": [ { @@ -29184,23 +19745,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ - "alt.Chart(...)" + "alt.LayerChart(...)" ] }, - "execution_count": 301, + "execution_count": 269, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(vs_authors_df, title=\"Frequency of author references in *Victorian Studies*\")\\\n", - ".mark_line().encode(y=alt.Y('Value:Q', title=\"Percent of Documents\", axis=alt.Axis(labelAngle=0, format=\"%\")), \n", - " x=alt.X('Decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value')), \n", - " strokeDash=\"Author:O\",\n", - " color=alt.Color('Author:O', scale=alt.Scale(scheme='category20'),legend=alt.Legend(title=\"Author\")),\n", - " shape=alt.Shape('Author:O', scale=alt.Scale(range=['cross', 'circle', 'square', 'triangle-right', 'diamond']), legend=None)\n", - ")\\\n", - ".properties(width=300).configure_legend(\n", - "titleFontSize=11,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ")" + "title_chart" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Figure 3\n", + "#### *Middlemarch* quotations by chapter\n", + "\n", + "See link to [Figure 3 in notebook](#Number-of-quotations,-by-chapter-in-Middlemarch,-bar-chart) for more context" ] }, { "cell_type": "code", - "execution_count": 302, + "execution_count": 270, "metadata": {}, "outputs": [ { @@ -29286,23 +19843,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ - "alt.LayerChart(...)" + "alt.Chart(...)" ] }, - "execution_count": 302, + "execution_count": 270, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "line = alt.Chart(vs_authors_df, title=\"Frequency of author references in *Victorian Studies*\").mark_line().encode(\n", - " x=alt.X('Decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value')), \n", - " y=alt.Y('Value:Q', title=\"Percent of Documents\", axis=alt.Axis(labelAngle=0, format=\"%\")),\n", - " color=alt.Color('Author:O', scale=alt.Scale(scheme='greys'),legend=None),\n", - ")\n", - "\n", - "points = line.mark_point(filled=True).encode(\n", - " color=alt.Color('Author:O', scale=alt.Scale(scheme='greys')),\n", - " shape=alt.Shape('Author:O', scale=alt.Scale(range=[ 'circle', 'cross', 'square', 'triangle-right', 'diamond'])),\n", - " size=alt.Size('Author:O', legend=None, scale=alt.Scale(range=[200,200],domain=['Eliot', 'Dickens', 'Bronte', 'Hardy']))\n", - ")\n", - "\n", - "alt.layer(\n", - " line,\n", - " points\n", - ").resolve_scale(\n", - " color='independent',\n", - " shape='independent'\n", - ").properties(width=400).configure_legend(\n", - "titleFontSize=11,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 303, - "metadata": {}, - "outputs": [], - "source": [ - "line = alt.Chart(vs_authors_df, ).mark_line().encode(\n", - " x=alt.X('Decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value')), \n", - " y=alt.Y('Value:Q', title=\"Percent of Documents\", axis=alt.Axis(labelAngle=0, format=\"%\")),\n", - " color=alt.Color('Author:O', scale=alt.Scale(scheme='greys'),legend=None),\n", - ")\n", - "\n", - "points = line.mark_point(filled=True).encode(\n", - " color=alt.Color('Author:O', scale=alt.Scale(scheme='greys')),\n", - " shape=alt.Shape('Author:O', scale=alt.Scale(range=[ 'circle', 'cross', 'square', 'triangle-right', 'diamond'])),\n", - " size=alt.Size('Author:O', legend=None, scale=alt.Scale(range=[200,200],domain=['Eliot', 'Dickens', 'Bronte', 'Hardy']))\n", - ")\n", - "\n", - "alt.layer(\n", - " line,\n", - " points\n", - ").resolve_scale(\n", - " color='independent',\n", - " shape='independent'\n", - ").properties(width=400).configure_legend(\n", - "titleFontSize=11,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").save('Figure-1.png', ppi=300)" + "quotes_per_chap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Titles" - ] - }, - { - "cell_type": "code", - "execution_count": 304, - "metadata": {}, - "outputs": [], - "source": [ - "vs_titles_df = pd.read_csv(\"../../../Middlematch/VS-and-Victorian-studies/VS-author-title-frequencies/VS-title-term_frequencies.csv\")" + "### Figure 4\n", + "#### Rank-frequency distribution of quotations from *Middlemarch* by chapter.\n", + "\n", + "The ten most frequently quoted chapters, starting from the far left of the graph, are 20, 15, 1, 87 [Finale], 0 [Prelude], 3, 19, 10, 81, and 2. See link to [Figure 4 in notebook](#Number-of-quotations,-by-chapter-in-Middlemarch,-bar-chart-(ranked-by-frequency)) for more context" ] }, { "cell_type": "code", - "execution_count": 305, + "execution_count": 271, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
\n", - "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" ] }, - "execution_count": 305, + "execution_count": 271, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "vs_titles_df" - ] - }, - { - "cell_type": "code", - "execution_count": 306, - "metadata": {}, - "outputs": [], - "source": [ - "vs_titles_df = vs_titles_df.melt(id_vars=[\"Title\"], \n", - " var_name = \"Decade\",\n", - " value_name=\"Value\")" + "ranked_freq_chap" ] }, { - "cell_type": "code", - "execution_count": 307, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "vs_titles_df['Value'] = vs_titles_df['Value']* 0.01" + "### Figure 5\n", + "#### *Middlemarch* quotations by chapter (normalized), diachronic heatmap\n", + "See link to [Figure 5 in notebook](#Middlemarch-quotations-per-chapter,-per-decade-(normalized-and-weighted),-heat-map) for more context" ] }, { "cell_type": "code", - "execution_count": 308, + "execution_count": 272, "metadata": {}, "outputs": [ { @@ -29572,23 +20038,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 308, + "execution_count": 272, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alt.Chart(vs_titles_df, title=\"Frequency of title references in *Victorian Studies*\")\\\n", - ".mark_line().encode(y=alt.Y('Value:Q', title=\"Percent of Documents\", axis=alt.Axis(labelAngle=0)), \n", - " x=alt.X('Decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value')), \n", - " strokeDash=\"Title:O\",\n", - " color=alt.Color('Title:O', scale=alt.Scale(scheme='category20'),legend=alt.Legend(title=\"Title\")))\\\n", - ".properties(width=300).configure_legend(\n", - "titleFontSize=11,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ")" + "diachronic_chap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Figure 6\n", + "#### Number of quotations per *Middlemarch* chapter, by decade\n", + "\n", + "Note: Chapter 0 and 87 refer to the Prelude and Finale, respectively. Chapters not among the top five are represented in light-gray.\n", + "\n", + "See link to [Figure 6 in notebook](#Middlemarch-top-5-most-frequently-quoted-chapters,-line-chart) for more context. [Color version](#Middlemarch-top-5-most-frequently-quoted-chapters,-line-chart-(color))." ] }, { "cell_type": "code", - "execution_count": 309, + "execution_count": 273, "metadata": {}, "outputs": [ { @@ -29672,23 +20138,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.LayerChart(...)" ] }, - "execution_count": 309, + "execution_count": 273, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "line = alt.Chart(vs_titles_df, title=\"Frequency of title references in *Victorian Studies*\").mark_line().encode(\n", - " x=alt.X('Decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value')), \n", - " y=alt.Y('Value:Q', title=\"Percent of Documents\", axis=alt.Axis(labelAngle=0, format=\"%\")),\n", - " color=alt.Color('Title:O', scale=alt.Scale(scheme='greys'),legend=None),\n", - ")\n", - "\n", - "points = line.mark_point(filled=True).encode(\n", - " color=alt.Color('Title:O', scale=alt.Scale(scheme='greys')),\n", - " shape=alt.Shape('Title:O', scale=alt.Scale(range=[ 'circle', 'cross', 'triangle-right', 'square','diamond'])),\n", - " size=alt.Size('Title:O', legend=None, scale=alt.Scale(range=[200,200],domain=['Bleak House', 'David Copperfield', 'Middlemarch','Great Expectations', ]))\n", - ")\n", - "\n", - "alt.layer(\n", - " line,\n", - " points\n", - ").resolve_scale(\n", - " color='independent',\n", - " shape='independent'\n", - ").properties(width=400).configure_legend(\n", - "titleFontSize=11,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 310, - "metadata": {}, - "outputs": [], - "source": [ - "line = alt.Chart(vs_titles_df).mark_line().encode(\n", - " x=alt.X('Decade', title=\"Decade\",type='ordinal', sort='ascending', \n", - " axis=alt.Axis(labelAngle=0, labelExpr='datum.value')), \n", - " y=alt.Y('Value:Q', title=\"Percent of Documents\", axis=alt.Axis(labelAngle=0, format=\"%\")),\n", - " color=alt.Color('Title:O', scale=alt.Scale(scheme='greys'),legend=None),\n", - ")\n", - "\n", - "points = line.mark_point(filled=True).encode(\n", - " color=alt.Color('Title:O', scale=alt.Scale(scheme='greys')),\n", - " shape=alt.Shape('Title:O', scale=alt.Scale(range=[ 'circle', 'cross', 'triangle-right', 'square','diamond'])),\n", - " size=alt.Size('Title:O', legend=None, scale=alt.Scale(range=[200,200],domain=['Bleak House', 'David Copperfield', 'Middlemarch','Great Expectations', ]))\n", - ")\n", - "\n", - "alt.layer(\n", - " line,\n", - " points\n", - ").resolve_scale(\n", - " color='independent',\n", - " shape='independent'\n", - ").properties(width=400).configure_legend(\n", - "titleFontSize=11,\n", - "labelFontSize=14\n", - ").configure_axis(\n", - "titleFontSize=14,\n", - "labelFontSize=14\n", - ").save('Figure-2.png', ppi=300)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Evaluation" + "top5_chart" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Generating samples of dataset for evaluating the precision and recall of text matcher\n", - "First, we're going to generate a smaller sample dataset, which we'll then perform bootstrapping on.\n", + "### Figure 7\n", + "#### *Middlemarch* quotations by chapter(normalized), Victorianist journals only, diachronic heatmap\n", "\n", - "First, let's stratify our dataset by year, and then take a random sample in that year." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "articlesWithMatches1960_2015 = articlesWithMatches[articlesWithMatches['Decade'] >= 1960]\n", - "len(articlesWithMatches1960_2015)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(articlesWithMatches1960_2015['year'].value_counts())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate random sample" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sampleData = articlesWithMatches1960_2015.sample(n=56, random_state=56)\n", - "sampleData['journal'].value_counts(sort=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sampleData.to_csv('../data/sample_dataset.csv', encoding='utf-8')" + "For a complete list of journals classified as \"Victorianist\", see: [Statistics on Victorianist journals in the dataset](#Statistics-on-Victorianist-journals-in-the-dataset).\n", + "\n", + "See link to [Figure 7 in notebook](#Victorianist-journals:-Middlemarch-quotations-per-chapter,-per-decade-(normalized-and-weighted),-heat-map) for more context.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to loop over each row, extracting locations in A and metadata, then output that to a new text file\n", - "def extractSampleDataMatches(sampleData):\n", - " for i, row in sampleData.iterrows():\n", - " title = row['title']\n", - " year = row['year']\n", - " # Print a break between each article\n", - " with open('../data/sample-data-matches.txt', \"a\") as f:\n", - " print(\"---------------------------------------\\n\", file=f)\n", - " print(title, file=f)\n", - " print(year, file=f)\n", - " # For each pair of locations in the \"Locations in A\" column, iterate over, printing the location indexes\n", - " # Followed by the text of the match\n", - " for pair in row['Locations in A']:\n", - " print(f\"Location in A: {pair}\", file=f)\n", - " print(mm[pair[0]:pair[1]]+\"\\n\", file=f)\n", - " \n", - "extractSampleDataMatches(sampleData)" - ] - }, - { - "cell_type": "markdown", + "execution_count": 274, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 274, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### Evaluation metrics\n", - "\n", - "Terminology\n", - "TP (True Positives):\n", - "TN (True Negatives): \n", - "FP (False Posiives): \n", - "FN (False Negatives): \n", - "\n", - "**Classification accuracy:** percentage of correctly identified quotes and non-quotes, or overall, how often is the matcher correct? classification_accuracy = (TP + TN) / float(TP + TN + FP + FN)))\n", - "\n", - " **Recall (or \"sensitivity\")**: When the actual match is correc, how often is the prediction correct? recall = TP / float(FN + TP)\n", - "\n", - "\n", - "**Precision:** When a match is detected, how often is that match correct? precision = TP / float(TP + FP)\n" + "diachronic_chap_victorianist" ] } ], diff --git a/notebooks/specialization.png b/notebooks/specialization.png deleted file mode 100644 index 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