파이썬_Coursea 통계_연습.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # import packages\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "\n",
    "covid_data = pd.read_csv(r'C:\\covid19.csv', encoding='UTF-8') # bring in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39m# Practice Graphs\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[39m# Setting up Graphs\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m plot_objects \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(nrows\u001b[39m=\u001b[39m\u001b[39m3\u001b[39m, ncols\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m, figsize\u001b[39m=\u001b[39m(\u001b[39m18\u001b[39m,\u001b[39m36\u001b[39m)) \u001b[39m# rows and column# of graphs\u001b[39;00m\n\u001b[0;32m      4\u001b[0m fig, ax \u001b[39m=\u001b[39m plot_objects\n\u001b[0;32m      5\u001b[0m fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) \u001b[39m=\u001b[39m plot_objects \u001b[39m# setting up axes or names\u001b[39;00m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "# Practice Graphs\n",
    "# Setting up Graphs\n",
    "plot_objects = plt.subplots(nrows=3, ncols=2, figsize=(18,36)) # rows and column# of graphs\n",
    "fig, ax = plot_objects\n",
    "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plot_objects # setting up axes or names\n",
    "plot_objects\n",
    "\n",
    "\n",
    "# Pie Plot\n",
    "give_labels1 = ['Writing', 'Reading', 'Vocabulary', 'Grammar']\n",
    "give_percentages1 = [10, 20, 30, 40]\n",
    "ax1.set_title('What part of English is most difficult?') # set title\n",
    "ax1.pie(give_percentages1, labels = give_labels1, autopct='%.1f%%') # ax1 뒤에 바로 pie, auto pct: show %\n",
    "\n",
    "\n",
    "# Dot Plot\n",
    "give_percentages2 = [30, 20, 50]\n",
    "give_labels2 = ['Elementary', 'Intermediate' ,'Expert']\n",
    "ax2.set_title('Level of students')\n",
    "ax2.set_xlabel('Percentage')\n",
    "ax2.set_ylabel('Level')\n",
    "ax2.scatter(give_percentages2, give_labels2) # basic operation done\n",
    "\n",
    "\n",
    "ax2.set_xlim([0,100]) # tweaks, changes axis limits\n",
    "\n",
    "\n",
    "# Bar Plot\n",
    "give_labels3 = ['Outlining', 'Writing the First Draft', 'Proofreading', 'Brainstorming for Ideas']\n",
    "give_numbers3 = [10, 20, 15, 5]\n",
    "ax3.bar(give_labels3, give_numbers3) # Basic setup complete, give_labels3 are xticks\n",
    "\n",
    "\n",
    "ax3.set_xticklabels(ax3.get_xticklabels(), rotation=45, ha='right') ## When ticks overlaps with each other, 1) rotate ticks 45 degrees \n",
    "                                                                    ## When labels are dates, use a  special method fig.autofmt_xdate() \n",
    "\n",
    "import textwrap ## or 2) make the text wrap around, source: https://www.dunderdata.com/blog/Automatically%20Wrap%20Graph%20Labels%20in%20Matplotlib%20and%20Seaborn\n",
    "def wrap_labels(ax, width, break_long_words=False):\n",
    "    labels = []\n",
    "    for label in ax.get_xticklabels():\n",
    "        text = label.get_text()\n",
    "        labels.append(textwrap.fill(text, width=width,\n",
    "                      break_long_words=break_long_words))\n",
    "    ax.set_xticklabels(labels, rotation=0)\n",
    "wrap_labels(ax3,15) # run the function, with 15 being the width, adjust according to graph size\n",
    "\n",
    "\n",
    "# Box Plot\n",
    "death_data = covid_data['Deaths']\n",
    "\n",
    "min_death = min(death_data) # calculate\n",
    "Q1 = death_data.quantile(q=0.25)\n",
    "median = death_data.quantile(q=0.5)\n",
    "Q3 = death_data.quantile(q=0.75)\n",
    "max_death = max(death_data)\n",
    "\n",
    "x = [min_death, Q1, median, Q3, max_death]\n",
    "\n",
    "ax4.boxplot(x) # 그래프 검증할 방법이 없을까?\n",
    "ax4.set_title('Covid Deaths around the World')\n",
    "ax4.set_ylabel('Deaths')\n",
    "\n",
    "# Histogram\n",
    "bins = range(0,1000,100) #combine with range\n",
    "ax5.hist(covid_data['New_deaths'], bins=bins, density=True, color='brown')\n",
    "ax5.set_title('Covid New Deaths around the World')\n",
    "ax5.set_ylabel('Density')\n",
    "ax5.set_xlabel('New Death Count')\n",
    "\n",
    "# Scatter Plot\n",
    "# Could there be relation between new_cases and new_deaths?\n",
    "new_cases_data = covid_data['New_cases'][covid_data['New_cases']<=750]\n",
    "new_deaths_data = covid_data['New_deaths'][covid_data['New_cases']<=750] # selecting the new_deaths where new_cases<=750 so that the shapes match, indexing is key in padas\n",
    "\n",
    "ax6.scatter(new_cases_data, new_deaths_data, marker=\"H\", alpha=0.7, s=5)\n",
    "ax6.set_title('Relation between New Cases and New Deaths')\n",
    "ax6.set_xlabel('New cases')\n",
    "ax6.set_ylabel('New deaths')\n",
    "\n",
    "# Save Plot\n",
    "fig.savefig(\"first_figure.png\", dpi=300)\n",
    "\n",
    "# Stpes for plotting data:\n",
    "# 1. Prepare data for plotting data\n",
    "# 2. Plot data using ax. or plt.\n",
    "# 3. Add labels, legends, ticks etc.\n",
    "# 4. Review plot, check if drawn/analyzed correctly\n",
    "# 5. Give additional tweaks (ex) font, padding, color, etc).\n",
    "# 6. Save image onto desktop\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9544997361036416"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Practice Normal Distribution\n",
    "avg = 0\n",
    "s = 1\n",
    "x = np.linspace(avg-3*s, avg+3*s, 100)\n",
    "y = stats.norm.pdf(x, avg, s)\n",
    "plt.plot(x, y)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Practice Normal Approximation\n",
    "# If I want to calculate the area P(a<X<b) in a normal distribution\n",
    "# Note that a must be smaller than b for this to work\n",
    "a = -2\n",
    "b = 2\n",
    "area = stats.norm.cdf(b, avg, s)-stats.norm.cdf(a, avg, s)\n",
    "area"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "3c4f1d380ec591ab9e44a7425913f70353e8b23ef4962a1509ad81b6808d322a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}