Popular Imports From Usage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "98d0a262",
   "metadata": {},
   "source": [
    "# Imported Modules\n",
    "\n",
    "This Jupyter Notebook processes the necessary file under `data_out/submission-fixed-nb_processed.csv`generated from various processing scripts in various phases of the pipeline. The result of this notebook when executed will result in the generation of `Figure 4` as described in the paper.\n",
    "\n",
    "Figure 4 in the paper describes the top 10 popular imported modules ranked by usaged frequency\n",
    "\n",
    "\n",
    "## Required Libraries:\n",
    "\n",
    "- `json`\n",
    "- `matplotlib`\n",
    "- `pandas`\n",
    "- `seaborn`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a323dce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import seaborn as sns\n",
    "\n",
    "matplotlib.rcParams.update({'pdf.fonttype': 42, 'ps.fonttype': 42})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "242276dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pandas.read_csv('./data_out/nb_processed.csv', \n",
    "                     converters={'image_metadata': json.loads, \n",
    "                                 'imports': json.loads, \n",
    "                                 'code_lines': json.loads, \n",
    "                                 'markdown_lines': json.loads})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8dda48b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "imports_used = {}\n",
    "for index, row in df.iterrows():\n",
    "    data = row['imports']\n",
    "    for element in data:\n",
    "        if element not in imports_used:\n",
    "            imports_used[element] = 0\n",
    "        imports_used[element] = imports_used[element] + 1\n",
    "\n",
    "import_df = pandas.DataFrame(data=[[k, v] for k, v in imports_used.items()], \n",
    "                             columns=['Imported Module', 'Number of Occurences'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "13a4e3e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import_df = import_df.sort_values(by=['Number of Occurences'], ascending=False)\n",
    "import_df['Imported Module'] = import_df['Imported Module'].str.strip().replace(' ', '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c3ab6829",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 2000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_description = \"\"\"The figure contains a horizontal bar plot with the x axis indicating the number of occurences of module imports, \n",
    "and the y axis indicating the top 10 imported python modules. The horizontal bars use colors from the colorblind \n",
    "palette provided by seaborn and indicate numpy, matplotlib.pyplot, pandas, os, seaborn, sklearn, tensorflow, time,\n",
    "sys, and random with their respective counts.\"\"\"\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 7))\n",
    "plt.rcParams['font.size'] = 18\n",
    "top_10_results = import_df[:10]\n",
    "top_10_results.loc[top_10_results['Imported Module'].str.contains('sklearn.model_selection train_test_split'), 'Imported Module'] = 'sklearn'\n",
    "sns.barplot(x=\"Number of Occurences\", y=\"Imported Module\", data=top_10_results, ax=ax, palette='colorblind')\n",
    "ax.bar_label(ax.containers[0])\n",
    "ax.tick_params(labelsize=18)\n",
    "ax.set_ylabel('Top 10 Python Modules\\n Imported in Notebooks', fontsize=18)\n",
    "ax.set_xlabel('Number of Occurences of Module Imports', fontsize=18)\n",
    "plt.savefig('./plot_out/fig-4-top-10-import-modules.pdf', bbox_inches='tight')\n",
    "plt.savefig('./plot_out/alt-embedded-images/fig-4-top-10-import-modules.png', metadata={\n",
    "    'alt': image_description,\n",
    "}, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc87908",
   "metadata": {},
   "source": [
    "**Fig 4**: The figure contains a horizontal bar plot with the x axis indicating the number of occurences of module imports, and the y axis indicating the top 10 imported python modules. The horizontal bars use colors from the colorblind palette provided by seaborn and indicate numpy, matplotlib.pyplot, pandas, os, seaborn, sklearn, tensorflow, time, sys, and random with their respective counts.\n",
    "\n",
    "\n",
    "-----\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}