{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('figure', figsize=(10, 8))\n",
    "plt.style.use('seaborn-white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ages = [18, 18, 21, 25, 26, 30, 32, 38, 45, 55]\n",
    "bins= [10, 20, 30, 40, 50, 60]\n",
    "\n",
    "# plt.hist(ages, bins=5, edgecolor='black')\n",
    "# plt.hist(ages, bins=bins, edgecolor='black')\n",
    "\n",
    "# excluding data for age 10\n",
    "plt.hist(ages, bins=bins[1:], edgecolor='black')\n",
    "\n",
    "\n",
    "plt.title('Age of Respondants')\n",
    "plt.xlabel('Ages')\n",
    "plt.ylabel('Total Respondants')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = pd.read_csv(r'examples/data_6.csv')\n",
    "# data.head()\n",
    "\n",
    "ids = data['Responder_id']\n",
    "ages = data['Age']\n",
    "\n",
    "bins = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
    "\n",
    "median_age = 29\n",
    "color = '#fc4f30'\n",
    "\n",
    "\n",
    "# plt.hist(ages, bins=bins, edgecolor='black')\n",
    "plt.hist(ages, bins=bins, edgecolor='black', log=True)\n",
    "plt.axvline(median_age, color=color, \n",
    "            linewidth=2, label='Age Median')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.title('Age of Respondants')\n",
    "plt.xlabel('Ages')\n",
    "plt.ylabel('Total Respondants')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}