imdb.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.0 64-bit",
   "metadata": {
    "interpreter": {
     "hash": "68078da1e8f2b70216368c65794038e06b6b70a952cdc317112cc851aad305e6"
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests as rq\n",
    "from bs4 import BeautifulSoup\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "page = rq.get('http://www.imdb.com/chart/top/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(page.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = soup.find('tbody',{'class':'lister-list'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "movierows = target.find_all('tr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "250"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "len(movierows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "for movie in movierows:\n",
    "    name = movie.find('td',{'class':'titleColumn'}).a.text\n",
    "    year = movie.find('span',{'class':'secondaryInfo'}).text[1:-1]\n",
    "    star = movie.find('td',{'class':'ratingColumn imdbRating'}).strong.text\n",
    "    data.append ({\n",
    "        \"title\":name,\"year\":year,\"rating\":star\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(data).to_csv('top_movies.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Q:-top rated movies shows nikalna hai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'showrows' is not defined",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-ba81b2658b8a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\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----> 2\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mshow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mshowrows\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      3\u001b[0m     \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'td'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'titleColumn'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0myear\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'span'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'secondaryInfo'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mstar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'td'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'ratingColumn imdbRating'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrong\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'showrows' is not defined"
     ]
    }
   ],
   "source": [
    "data =[]\n",
    "for show in showrows:\n",
    "    name = show.find('td',{'class':'titleColumn'}).a.text\n",
    "    year = show.find('span',{'class':'secondaryInfo'}).text[1:-1]\n",
    "    star = show.find('td',{'class':'ratingColumn imdbRating'}).strong.text\n",
    "    data.append ({\n",
    "        \"title\":name,\"year\":year,\"rating\":star\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(data).to_csv(\"tob_shows.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}