stacked_bar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Plot stacked bar data read from csv file\n",
    "\n",
    "# Render plots inline\n",
    "%matplotlib inline\n",
    "from os.path import expandvars\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (15, 5)\n",
    "\n",
    "# change dir to jupyter project folder\n",
    "import os\n",
    "os.chdir(expandvars('$HOME/gitwork/github/jupyter-notebooks'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/fnbp2.csv', encoding='latin1', sep=';', \n",
    "                 parse_dates=['date'], index_col='date' ) # usecols="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>pass</th>\n",
       "      <th>block</th>\n",
       "      <th>fail</th>\n",
       "      <th>notexec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pass  block  fail  notexec\n",
       "date                                  \n",
       "2020-01-01     1      0     0       99\n",
       "2020-01-02     3      0     0       97\n",
       "2020-01-03     5      1     0       94"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first rows\n",
    "df[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(stacked=True, color=['green','blue','red','lightgrey']);"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light",
   "text_representation": {
    "extension": ".py",
    "format_name": "light",
    "format_version": "1.5",
    "jupytext_version": "1.3.2"
   }
  },
  "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}