Visualization- count of tags in open and closed.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2785: DtypeWarning: Columns (1,3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "openCloseData=pd.read_csv(\"openCloseData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(159998, 35)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openCloseData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist( [openCloseData[openCloseData['closed']==0]['tagCount'], openCloseData[openCloseData['closed']==1]['tagCount'] ] ,label=[\"Open\",\"Closed\"]) \n",
    "plt.xlabel(\"Count of Tags in a post\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Histogram of frequency of count of tags in open and closed questions\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "# It can be seen that more proportion of closed questions(as compared to open questions) have lower count of tags(in range [1,2])\n",
    "# while more proportion of open questions(as compared to closed questions) have higher count of tags(in range [3,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}