PYTHON_PLT_HIST_B.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "80a3c8c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "  \n",
    "d = {'CLASS_TYPE': ['A','B','C','D','E'], 'MEN': [20,25,30,19,38], 'WOMEN': [21,31,55,22,39]}\n",
    "\n",
    "df = pd.DataFrame(data=d)\n",
    "\n",
    "\n",
    "classType = df[\"CLASS_TYPE\"]\n",
    "men_count = df[\"MEN\"]\n",
    "women_count = df[\"WOMEN\"]\n",
    "width = 0.35       # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax = fig.add_axes([0,0,1.5,1.5])\n",
    "\n",
    "ax.bar(classType, men_count, width, label='Men')\n",
    "ax.bar(classType , women_count, width, bottom=men_count, label='Women')\n",
    "\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_xlabel('Classes')\n",
    "ax.set_title('Count of Classes By Gender')\n",
    "ax.legend()\n",
    "\n",
    "for x,y1,y2 in zip(classType,women_count,men_count):\n",
    "\n",
    "    label = \"{:.2f}\".format(y1)\n",
    "\n",
    "    plt.annotate(label, # label text\n",
    "                 (x,y1), #  The point (x, y) to annotate\n",
    "                 textcoords=\"offset points\", # offset (in points) from the xy value\n",
    "                 xytext=(0,6), # position (x, y) to place the text at. \n",
    "                 ha='center') # horizontal alignment is center in this case\n",
    "    \n",
    "    label = \"{:.2f}\".format(y2)\n",
    "\n",
    "    plt.annotate(label,\n",
    "                 (x,y2),\n",
    "                 textcoords=\"offset points\",\n",
    "                 xytext=(0,-14),\n",
    "                 ha='center')\n",
    "\n",
    "    label = \"{:.2f}\".format(y1+y2)\n",
    "\n",
    "    plt.annotate(label,\n",
    "                 (x,y1+y2), \n",
    "                 textcoords=\"offset points\", \n",
    "                 xytext=(0,9),\n",
    "                 ha='center') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85b33af4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}