Day20(18May19).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File b'DataFiles/StudentsData.csv' does not exist: b'DataFiles/StudentsData.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-c18b11ba60c3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'student'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'rollnumber'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mSheet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'DataFiles/StudentsData.csv'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;31m#readCSV('DataFiles/Income.csv')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-4-c18b11ba60c3>\u001b[0m in \u001b[0;36mSheet\u001b[1;34m(filename, nr, rollnumber)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mSheet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrollnumber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrollnumber\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    700\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    428\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 429\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    430\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 895\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1120\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1121\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1122\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1123\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1124\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1851\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'usecols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1852\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1853\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1854\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1855\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File b'DataFiles/StudentsData.csv' does not exist: b'DataFiles/StudentsData.csv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "def Sheet(filename,nr,rollnumber):\n",
    "    df=pd.read_csv(filename)\n",
    "    df.loc[nr]=rollnumber\n",
    "    df.to_csv(filename,index=False)\n",
    "    return df\n",
    "\n",
    "data=['student','rollnumber']\n",
    "Sheet('DataFiles/StudentsData.csv',1,data)\n",
    "#readCSV('DataFiles/Income.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 1:\n",
    "## Statement :\n",
    "Plotting 2d Graph using Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "plt.plot([1,2,3,4],[1,8,27,64])\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Distancde')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 2:\n",
    "## Statement:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "t=np.arange(0.0,2.0,0.10)\n",
    "v=1+np.sin(2*np.pi*s)\n",
    "plt.plot(t,v)\n",
    "plt.grid()\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Voltage')\n",
    "plt.savefig('Figures/Time-VoltGraph.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 2:\n",
    "## Statement:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "df=pd.read_csv('DataFiles/Income.csv')\n",
    "y=df.values[3]\n",
    "x=df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "years=[]\n",
    "for i in range (2,len(x)):\n",
    "    years.append(x[i])\n",
    "years\n",
    "income=[]\n",
    "for i in range(2,len(y)):\n",
    "    income.append(y[i])\n",
    "\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Income')\n",
    "plt.title('Income of Arizona')\n",
    "plt.plot(years,income)\n",
    "plt.grid()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "width=1/1.5\n",
    "plt.bar(years,income,width)\n",
    "plt.title('Income of Arizona')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Income')\n",
    "plt.savefig('Figures/ArizonaIncomeBarGraph.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "n=100\n",
    "x=np.random.rand(n)\n",
    "y=np.random.rand(n)\n",
    "colors=np.random.rand(n)\n",
    "area=np.pi*(15*np.random.rand(n))**2.12\n",
    "#area=(15*np.random.rand(n))**10\n",
    "\n",
    "plt.scatter(x,y,area,colors,alpha=2)\n",
    "plt.show()\n",
    "#plt.savefig('Figures/Bobbles.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "\n",
    "labels='kite','hogs','dogs','logs'\n",
    "sizes=[12,13,45,10]\n",
    "explode=(0,0.1,0,0)\n",
    "\n",
    "figl,axl=plt.subplots()\n",
    "axl.pie(sizes,explode=explode,labels=labels,shadow=True,startangle=90)\n",
    "axl.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7197"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "def Categories(filename):\n",
    "    df=pd.read_csv(filename)\n",
    "    return df\n",
    "df=Categories('DataFiles/AppleStore.csv')\n",
    "y=df.columns[12]\n",
    "x=df.values\n",
    "len(x)\n",
    "#df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Games',\n",
       " 'Productivity',\n",
       " 'Weather',\n",
       " 'Shopping',\n",
       " 'Reference',\n",
       " 'Games',\n",
       " 'Finance',\n",
       " 'Music',\n",
       " 'Utilities',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Utilities',\n",
       " 'Finance',\n",
       " 'Games',\n",
       " 'Travel',\n",
       " 'Social Networking',\n",
       " 'Travel',\n",
       " 'Music',\n",
       " 'Games',\n",
       " 'Reference',\n",
       " 'Games',\n",
       " 'Sports',\n",
       " 'Business',\n",
       " 'Music',\n",
       " 'Health & Fitness',\n",
       " 'Games',\n",
       " 'Productivity',\n",
       " 'Music',\n",
       " 'Weather',\n",
       " 'Social Networking',\n",
       " 'Health & Fitness',\n",
       " 'Entertainment',\n",
       " 'Games',\n",
       " 'Health & Fitness',\n",
       " 'Music',\n",
       " 'Entertainment',\n",
       " 'Utilities',\n",
       " 'Weather',\n",
       " 'Games',\n",
       " 'Photo & Video',\n",
       " 'Music',\n",
       " 'Navigation',\n",
       " 'Music',\n",
       " 'Navigation',\n",
       " 'Travel',\n",
       " 'Entertainment',\n",
       " 'Games',\n",
       " 'Sports',\n",
       " 'Games',\n",
       " 'Weather',\n",
       " 'Business',\n",
       " 'Education',\n",
       " 'Weather',\n",
       " 'Lifestyle',\n",
       " 'Lifestyle',\n",
       " 'Food & Drink',\n",
       " 'Entertainment',\n",
       " 'Education',\n",
       " 'Health & Fitness',\n",
       " 'Shopping',\n",
       " 'Games',\n",
       " 'Shopping',\n",
       " 'Games',\n",
       " 'Utilities',\n",
       " 'Music',\n",
       " 'Music',\n",
       " 'Finance',\n",
       " 'Reference',\n",
       " 'Food & Drink',\n",
       " 'News',\n",
       " 'Navigation',\n",
       " 'Weather',\n",
       " 'Finance',\n",
       " 'News',\n",
       " 'Utilities',\n",
       " 'Education',\n",
       " 'Utilities',\n",
       " 'Entertainment',\n",
       " 'Health & Fitness',\n",
       " 'Finance',\n",
       " 'Games',\n",
       " 'Book',\n",
       " 'Games',\n",
       " 'Finance',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Music',\n",
       " 'Games',\n",
       " 'Photo & Video',\n",
       " 'Games',\n",
       " 'Social Networking',\n",
       " 'Social Networking',\n",
       " 'Photo & Video',\n",
       " 'Social Networking',\n",
       " 'Games',\n",
       " 'Finance',\n",
       " 'Weather',\n",
       " 'Health & Fitness',\n",
       " 'Health & Fitness',\n",
       " 'News',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Sports',\n",
       " 'Health & Fitness',\n",
       " 'Photo & Video',\n",
       " 'Entertainment',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Utilities',\n",
       " 'Entertainment',\n",
       " 'Entertainment',\n",
       " 'Utilities',\n",
       " 'Games',\n",
       " 'Education',\n",
       " 'Reference',\n",
       " 'News',\n",
       " 'Sports',\n",
       " 'Utilities',\n",
       " 'Entertainment',\n",
       " 'Photo & Video',\n",
       " 'Games',\n",
       " 'Business',\n",
       " 'Games',\n",
       " 'Social Networking',\n",
       " 'Productivity',\n",
       " 'Lifestyle',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Finance',\n",
       " 'Music',\n",
       " 'Productivity',\n",
       " 'Games',\n",
       " 'Productivity',\n",
       " 'Travel',\n",
       " 'Finance',\n",
       " 'Entertainment',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Music',\n",
       " 'Social Networking',\n",
       " 'Weather',\n",
       " 'Shopping',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'News',\n",
       " 'Sports',\n",
       " 'Travel',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Education',\n",
       " 'Games',\n",
       " 'Sports',\n",
       " 'Sports',\n",
       " 'Music',\n",
       " 'News',\n",
       " 'Social Networking',\n",
       " 'Shopping',\n",
       " 'Navigation',\n",
       " 'Games',\n",
       " 'Finance',\n",
       " 'Finance',\n",
       " 'Health & Fitness',\n",
       " 'Photo & Video',\n",
       " 'Food & Drink',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Weather',\n",
       " 'Games',\n",
       " 'Navigation',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Sports',\n",
       " 'Music',\n",
       " 'Food & Drink',\n",
       " 'Sports',\n",
       " 'News',\n",
       " 'Weather',\n",
       " 'Games',\n",
       " 'Productivity',\n",
       " 'Health & Fitness',\n",
       " 'Utilities',\n",
       " 'Medical',\n",
       " 'Photo & Video',\n",
       " 'Entertainment',\n",
       " 'Games',\n",
       " 'Weather',\n",
       " 'Entertainment',\n",
       " 'Sports',\n",
       " 'Productivity',\n",
       " 'Entertainment',\n",
       " 'Games',\n",
       " 'Sports',\n",
       " 'Navigation',\n",
       " 'Finance',\n",
       " 'Navigation',\n",
       " 'Photo & Video',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Games',\n",
       " 'Health & Fitness',\n",
       " 'Photo & Video',\n",
       " 'Food & Drink',\n",
       " 'Games',\n",
       " 'Food & Drink',\n",
       " 'Sports',\n",
       " 'Games',\n",
       " 'Health & Fitness',\n",
       " 'News',\n",
       " 'Shopping',\n",
       " 'Entertainment',\n",
       " 'Games',\n",
       " 'Lifestyle',\n",
       " 'News',\n",
       " 'Lifestyle',\n",
       " 'Business',\n",
       " 'News',\n",
       " 'Travel',\n",
       " 'News',\n",
       " 'Entertainment',\n",
       " 'Entertainment',\n",
       " 'Reference',\n",
       " 'Games',\n",
       " 'Business',\n",
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       " 'Shopping',\n",
       " 'Photo & Video',\n",
       " 'Games',\n",
       " ...]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre=[]\n",
    "for i in range(len(x)):\n",
    "    genre.append(x[i][12])\n",
    "genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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