src/juspy/demo_nbs/02_piechart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "104acfd3-d2f4-4148-86df-3918c34c091f",
   "metadata": {},
   "source": [
    "# [juspy](https://pypi.org/project/juspy/) Python Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "249c1bcf-c12e-466d-9809-e1a8afe67545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default</th>\n",
       "      <th>student</th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>729.526495</td>\n",
       "      <td>44361.625074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>817.180407</td>\n",
       "      <td>12106.134700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1073.549164</td>\n",
       "      <td>31767.138947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>529.250605</td>\n",
       "      <td>35704.493935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>785.655883</td>\n",
       "      <td>38463.495879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  default student      balance        income\n",
       "0      No      No   729.526495  44361.625074\n",
       "1      No     Yes   817.180407  12106.134700\n",
       "2      No      No  1073.549164  31767.138947\n",
       "3      No      No   529.250605  35704.493935\n",
       "4      No      No   785.655883  38463.495879"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_name = pd.read_csv(\"https://raw.githubusercontent.com/juspreet51/juspy/main/src/juspy/datasets/default.csv\")\n",
    "df_name.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a7f4deb-e670-4685-bb17-9cdd4d75a139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from juspy import plot as jpplot\n",
    "jpplot.piechart(df_name[\"student\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e0d8ca-c53b-4d86-9806-00bbb23fd7e9",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Click [here](https://pypi.org/project/juspy/) to visit PyPi official page"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}