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"text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/jupyter-lab-files/README.md b/jupyter-lab-files/README.md new file mode 100644 index 0000000000..84d89cdbf1 --- /dev/null +++ b/jupyter-lab-files/README.md @@ -0,0 +1,13 @@ +# Using JupyterLab + +This folder contains completed notebooks and other files used in the Real Python tutorial on [Using JupyterLab](https://realpython.com/using-jupyterlab/). + +None of the files are mandatory to complete the tutorial, howevre you may find them of use to continue your learning by experimenting more with them. + +## Setup + +The easiest way to incorporate these files into JupyterLab is to copy them into their own samples folder as instructed in the tutorial. They can then be accessed from its File Browser when needed. + +## Usage + +After creating and activating your virtual environment, and installing the dependencies, you can load any file into JupyterLab by double-clicking on it. diff --git a/jupyter-lab-files/Ten_Bond_Movies.csv b/jupyter-lab-files/Ten_Bond_Movies.csv new file mode 100644 index 0000000000..1d50e08580 --- /dev/null +++ b/jupyter-lab-files/Ten_Bond_Movies.csv @@ -0,0 +1,11 @@ +Movie,Bond,Director,Year +Dr. No,Sean Connery,Terence Young,1962 +From Russia with Love,Sean Connery,Terence Young,1963 +Goldfinger,Sean Connery,Guy Hamilton,1964 +Thunderball,Sean Connery,Terence Young,1965 +You Only Live Twice,Sean Connery,Lewis Gilbert,1967 +On Her Majesty's Secret Service,George Lazenby,Peter R. 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deletions(-) diff --git a/jupyter-lab-files/Population Changes.ipynb b/jupyter-lab-files/Population Changes.ipynb index 14dae7d03b..5985607366 100644 --- a/jupyter-lab-files/Population Changes.ipynb +++ b/jupyter-lab-files/Population Changes.ipynb @@ -14,45 +14,35 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "4db0c4f2-ba67-4854-9f6b-2e2ca724971f", "metadata": {}, "outputs": [], "source": [ "def calculate_differences(data_set):\n", - " differences = [0, ]\n", + " differences = [\n", + " 0,\n", + " ]\n", " for index in range(1, len(data_set)):\n", - " differences.append(round(data_set[index] - data_set[index-1], 1))\n", + " differences.append(round(data_set[index] - data_set[index - 1], 1))\n", " return differences\n", "\n", + "\n", "population_change = calculate_differences(population)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "0822c8a4-829a-4806-b965-b2a88cf8798c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Decade Population(Bn) Change\n", - "0 1980 4.4 0.0\n", - "1 1990 5.3 0.9\n", - "2 2000 6.1 0.8\n", - "3 2010 7.0 0.9\n", - "4 2020 7.8 0.8\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", "zipped = list(zip(decades, population, population_change))\n", "\n", - "population_df = pd.DataFrame(zipped, columns=('Decade', 'Population(Bn)', 'Change'))\n", + "population_df = pd.DataFrame(zipped, columns=(\"Decade\", \"Population(Bn)\", \"Change\"))\n", "\n", "print(population_df)" ] diff --git a/jupyter-lab-files/Population Data.ipynb b/jupyter-lab-files/Population Data.ipynb index a09df05970..babb96c75f 100644 --- a/jupyter-lab-files/Population Data.ipynb +++ b/jupyter-lab-files/Population Data.ipynb @@ -24,15 +24,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "50d0f480-a0fd-4586-8a22-03949a2518f0", "metadata": {}, "outputs": [], "source": [ - "#decades = [1960, 1970, 1980, 1990, 2000, 2010, 2020]\n", + "# decades = [1960, 1970, 1980, 1990, 2000, 2010, 2020]\n", "\n", "# World population(billions)\n", - "#population = [3, 3.7, 4.4, 5.3, 6.1, 7.0, 7.8]\n", + "# population = [3, 3.7, 4.4, 5.3, 6.1, 7.0, 7.8]\n", "\n", "decades = [1980, 1990, 2000, 2010, 2020]\n", "population = [4.4, 5.3, 6.1, 7.0, 7.8]" @@ -40,21 +40,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "0d399a86-4d1b-48fb-928d-f2860621c90b", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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zmYz=1I;~j6fZ_AYUC-C+$2uqemBVA@ug`DlTp1b37_h7RgE{cEI6f8^t^56ua(4Q| z_WDPEcU~XT%3$NNNjh-@0$l4eMIZK;x~eKxd7qi*DknQ--aT$EE#u4R9slx)1n-W$ z7Y4T)iXy8@Zr@+3p!L_lU4o&tH{BM3*e_dN-XNbn=>|iRNIh30AXlE2(O~6gi$i}z zVs`M}nV@+%qYi?TyY*ief-Y)cf&~Ko0!Dc?5PHrN3T5%XU=v*%>HnwD$HvO_e?{i} zpGu#O8o-iS&B^JX!1wQt8ndjUr4#9Yxsm^d68Zuh08?%r7B&l3b`DcZ3kx2A1-m&L zFTj$Wos-wnl+8@&|NRL=W_25H0K&f>EM{$eR#J9SHpBl19g&lb_dm5u&V@0jSZBhK zo2!;`HO;2Vl8VQ*)ADTm>LrCmEqU4!zDGaACZ{Hi-?og^C65j~Z9H%RVpL2_W^Fu? z5@NZ70D&IQZl>DKyBEQ%EQ|bOG_W%^#GpB5q?~C8$X*eit9i*P#z=2&Cl^lIen>Jva}XaeW`uZG-eWeypRzM zfByym1F`m1vBTIfMum;4{_w8sVwJc4uz7U_^K3di4{QvOINc1cY*%7ZltgI+eR2qo z2R?=s#j1$p&KX;2xDB^=RVOeEcaW9`>v&(vATY+6jH%y+VSuU?Gh^4qAdVhLrL{w6 z5R=vlvuuu4GhmCj4ODEia7K zN;RtCxt4v!*Xj#ng4~&VN Date: Tue, 17 Oct 2023 12:11:20 +0200 Subject: [PATCH 5/7] Blacken code --- jupyter-lab-files/Population Changes.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/jupyter-lab-files/Population Changes.ipynb b/jupyter-lab-files/Population Changes.ipynb index 5985607366..ca44f522ac 100644 --- a/jupyter-lab-files/Population Changes.ipynb +++ b/jupyter-lab-files/Population Changes.ipynb @@ -42,7 +42,9 @@ "\n", "zipped = list(zip(decades, population, population_change))\n", "\n", - "population_df = pd.DataFrame(zipped, columns=(\"Decade\", \"Population(Bn)\", \"Change\"))\n", + "population_df = pd.DataFrame(\n", + " zipped, columns=(\"Decade\", \"Population(Bn)\", \"Change\")\n", + ")\n", "\n", "print(population_df)" ] From 6f5ae6f069dd717c81bb60cf37728a98c02d8d2f Mon Sep 17 00:00:00 2001 From: martin-martin Date: Tue, 17 Oct 2023 12:19:36 +0200 Subject: [PATCH 6/7] Apply readability edits --- jupyter-lab-files/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/jupyter-lab-files/README.md b/jupyter-lab-files/README.md index 84d89cdbf1..9e617b7372 100644 --- a/jupyter-lab-files/README.md +++ b/jupyter-lab-files/README.md @@ -1,13 +1,13 @@ # Using JupyterLab -This folder contains completed notebooks and other files used in the Real Python tutorial on [Using JupyterLab](https://realpython.com/using-jupyterlab/). +This folder contains completed notebooks and other files used in the Real Python tutorial on [using JupyterLab](https://realpython.com/using-jupyterlab/). -None of the files are mandatory to complete the tutorial, howevre you may find them of use to continue your learning by experimenting more with them. +None of the files are mandatory to complete the tutorial, however, you may find them of use to continue your learning by experimenting more with them. ## Setup -The easiest way to incorporate these files into JupyterLab is to copy them into their own samples folder as instructed in the tutorial. They can then be accessed from its File Browser when needed. +The easiest way to incorporate these files into JupyterLab is to copy them into their own `Samples/` folder as instructed in the tutorial. They can then be accessed from its _File Browser_ when needed. ## Usage -After creating and activating your virtual environment, and installing the dependencies, you can load any file into JupyterLab by double-clicking on it. +After creating and activating your virtual environment, installing the dependencies, and starting the JupyterLab server, you can load any file by double-clicking on it from the _File Browser_ in JupyterLab. From 1364e902a5583241911fa85f65b06e37fb2f42e7 Mon Sep 17 00:00:00 2001 From: KateFinegan <95366190+KateFinegan@users.noreply.github.com> Date: Tue, 24 Oct 2023 16:42:23 -0600 Subject: [PATCH 7/7] README LE --- jupyter-lab-files/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/jupyter-lab-files/README.md b/jupyter-lab-files/README.md index 9e617b7372..1462755f5c 100644 --- a/jupyter-lab-files/README.md +++ b/jupyter-lab-files/README.md @@ -2,12 +2,12 @@ This folder contains completed notebooks and other files used in the Real Python tutorial on [using JupyterLab](https://realpython.com/using-jupyterlab/). -None of the files are mandatory to complete the tutorial, however, you may find them of use to continue your learning by experimenting more with them. +None of the files are mandatory to complete the tutorial. However, you may find them of use to continue your learning by experimenting more with them. ## Setup -The easiest way to incorporate these files into JupyterLab is to copy them into their own `Samples/` folder as instructed in the tutorial. They can then be accessed from its _File Browser_ when needed. +The easiest way to incorporate these files into JupyterLab is to copy them into their own `Samples/` folder as instructed in the tutorial. You can then access them from JupyterLab's _file browser_ when needed. ## Usage -After creating and activating your virtual environment, installing the dependencies, and starting the JupyterLab server, you can load any file by double-clicking on it from the _File Browser_ in JupyterLab. +After creating and activating your virtual environment, installing the dependencies, and starting the JupyterLab server, you can load any file by double-clicking on it from the _file browser_ in JupyterLab.