IMDB top 500 movies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a46186c6",
   "metadata": {},
   "source": [
    "# IMDB top 500 movies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b07daba",
   "metadata": {},
   "source": [
    "## 1st page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85d19462",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as bs\n",
    "import requests\n",
    "\n",
    "url = 'https://www.imdb.com/search/title/?groups=top_1000&sort=user_rating,desc&count=100&ref_=adv_prv'\n",
    "\n",
    "page = requests.get(url)\n",
    "\n",
    "page_1 = bs(page.content, 'html5lib')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa08c186",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_1_titles = page_1.find_all(\"h3\",class_=\"lister-item-header\")\n",
    "\n",
    "titles_of_movies = []\n",
    "for title in page_1_titles:\n",
    "    titles=title.get_text(strip=True)\n",
    "    titles_of_movies.append(titles.split(\"(\")[0].split(\".\")[1])\n",
    "\n",
    "\n",
    "page_1_title = titles_of_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c2a1a0c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The Shawshank Redemption',\n",
       " 'The Godfather',\n",
       " 'The Dark Knight',\n",
       " 'The Lord of the Rings: The Return of the King',\n",
       " \"Schindler's List\"]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles_of_movies[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cabead40",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dfaf88fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_1_years = page_1.find_all(\"span\",class_=\"lister-item-year text-muted unbold\")\n",
    "\n",
    "years_of_movies = []\n",
    "for title in page_1_years:\n",
    "    titles=title.get_text(strip=True)\n",
    "    years_of_movies.append(titles.split(\"(\")[1].split(\")\")[0].replace('I','2000'))\n",
    "\n",
    "page_1_years = years_of_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e004879",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1994', '1972', '2008', '2003', '1993']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_1_years[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "255aa969",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cee6d7ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_1_ratings = page_1.find_all(\"div\",class_=\"inline-block ratings-imdb-rating\")\n",
    "\n",
    "rating_Colum = []\n",
    "for title in page_1_ratings:\n",
    "    rating=title.get_text(strip=True)\n",
    "    rating_Colum.append(rating.split()[:2])\n",
    "\n",
    "ini_list=rating_Colum\n",
    "\n",
    "flatten_list = [j for sub in ini_list for j in sub]\n",
    "\n",
    "Ratings = flatten_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c87872ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['9.3', '9.2', '9.0', '9.0', '9.0']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea7e14f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6708224b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['142', '175', '152', '201', '195']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_1_runtime = []\n",
    "all_ratings = page_1.find_all(\"span\",class_=\"runtime\")\n",
    "for rate in all_ratings:\n",
    "    rating=rate.get_text(strip=True)\n",
    "    page_1_runtime.append(rating[:3])\n",
    "\n",
    "page_1_runtime[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80c21c99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "452018a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Votes:2,602,463|Gross:$28.34M|Top 250:#1',\n",
       " 'Votes:1,797,921|Gross:$134.97M|Top 250:#2',\n",
       " 'Votes:2,573,914|Gross:$534.86M|Top 250:#3',\n",
       " 'Votes:1,787,208|Gross:$377.85M|Top 250:#7',\n",
       " 'Votes:1,323,409|Gross:$96.90M|Top 250:#6']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info = []\n",
    "all_votes = page_1.find_all(\"p\",class_=\"sort-num_votes-visible\")\n",
    "for vt in all_votes:\n",
    "    vote=vt.get_text(strip=True)\n",
    "    info.append(vote[:] )#.replace('|','').replace(',','')\n",
    "\n",
    "info[:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "057c06f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb9120fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['260246', '179792', '257391', '178720', '132340']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_1_votes = []\n",
    "all_votes = page_1.find_all(\"p\",class_=\"sort-num_votes-visible\")\n",
    "for vt in all_votes:\n",
    "    vote=vt.get_text(strip=True)\n",
    "    page_1_votes.append(vote[6:14].replace('|','').replace(',','').replace('G','').replace('T',''))\n",
    "\n",
    "page_1_votes[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7011730e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0882b687",
   "metadata": {},
   "source": [
    "## 2nd page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f94d893e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as bs\n",
    "import requests\n",
    "\n",
    "url = 'https://www.imdb.com/search/title/?groups=top_1000&sort=user_rating,desc&count=100&start=101&ref_=adv_nxt'\n",
    "\n",
    "page = requests.get(url)\n",
    "\n",
    "page_2 = bs(page.content, 'html5lib')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "894224a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_2_titles = page_2.find_all(\"h3\",class_=\"lister-item-header\")\n",
    "\n",
    "titles_of_movies_2 = []\n",
    "for title in page_2_titles:\n",
    "    titles=title.get_text(strip=True)\n",
    "    titles_of_movies_2.append(titles.split(\"(\")[0].split(\".\")[1])\n",
    "\n",
    "\n",
    "page_2_title = titles_of_movies_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa833932",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_2_years = page_2.find_all(\"span\",class_=\"lister-item-year text-muted unbold\")\n",
    "\n",
    "years_of_movies_2 = []\n",
    "for title in page_2_years:\n",
    "    titles=title.get_text(strip=True)\n",
    "    years_of_movies_2.append(titles.split(\"(\")[1].split(\")\")[0].replace('I','2000'))\n",
    "\n",
    "page_2_years = years_of_movies_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78579329",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b07b102b",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_2_ratings = page_2.find_all(\"div\",class_=\"inline-block ratings-imdb-rating\")\n",
    "\n",
    "rating_Colum_2 = []\n",
    "for title in page_1_ratings:\n",
    "    rating=title.get_text(strip=True)\n",
    "    rating_Colum_2.append(rating.split()[:2])\n",
    "\n",
    "ini_list=rating_Colum_2\n",
    "\n",
    "flatten_list = [j for sub in ini_list for j in sub]\n",
    "\n",
    "Ratings_2 = flatten_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2700e2b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "02c88556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['148', '96 ', '165', '103', '153']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_2_runtime = []\n",
    "all_ratings = page_2.find_all(\"span\",class_=\"runtime\")\n",
    "for rate in all_ratings:\n",
    "    rating=rate.get_text(strip=True)\n",
    "    page_2_runtime.append(rating[:3])\n",
    "\n",
    "page_2_runtime[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4a34c56a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['663041', '101972', '189536', '818607', '140341']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_2_votes = []\n",
    "all_votes = page_2.find_all(\"p\",class_=\"sort-num_votes-visible\")\n",
    "for vt in all_votes:\n",
    "    vote=vt.get_text(strip=True)\n",
    "    page_2_votes.append(vote[6:14].replace('|','').replace(',','').replace('T','').replace('G',''))\n",
    "\n",
    "page_2_votes[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a66b6f88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5b056cdc",
   "metadata": {},
   "source": [
    "## 3rd page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ece49dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as bs\n",
    "import requests\n",
    "\n",
    "url = 'https://www.imdb.com/search/title/?groups=top_1000&sort=user_rating,desc&count=100&start=301&ref_=adv_nxt'\n",
    "\n",
    "page = requests.get(url)\n",
    "\n",
    "page_3 = bs(page.content, 'html5lib')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3d2ddf45",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_3_titles = page_3.find_all(\"h3\",class_=\"lister-item-header\")\n",
    "\n",
    "titles_of_movies_3 = []\n",
    "for title in page_3_titles:\n",
    "    titles=title.get_text(strip=True)\n",
    "    titles_of_movies_3.append(titles.split(\"(\")[0].split(\".\")[1])\n",
    "\n",
    "\n",
    "page_3_title = titles_of_movies_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fb6376ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_3_years = page_3.find_all(\"span\",class_=\"lister-item-year text-muted unbold\")\n",
    "\n",
    "years_of_movies_3 = []\n",
    "for title in page_3_years:\n",
    "    titles=title.get_text(strip=True)\n",
    "    years_of_movies_3.append(titles.split(\"(\")[1].split(\")\")[0].replace('I','2000'))\n",
    "\n",
    "page_3_years = years_of_movies_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "feac253d",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_3_ratings = page_3.find_all(\"div\",class_=\"inline-block ratings-imdb-rating\")\n",
    "\n",
    "rating_Colum_3 = []\n",
    "for title in page_3_ratings:\n",
    "    rating=title.get_text(strip=True)\n",
    "    rating_Colum_3.append(rating.split()[:2])\n",
    "\n",
    "ini_list=rating_Colum_3\n",
    "\n",
    "flatten_list = [j for sub in ini_list for j in sub]\n",
    "\n",
    "Ratings_3 = flatten_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb1e7f2a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "47921c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['127', '85 ', '172', '121', '123']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_3_runtime = []\n",
    "all_ratings = page_3.find_all(\"span\",class_=\"runtime\")\n",
    "for rate in all_ratings:\n",
    "    rating=rate.get_text(strip=True)\n",
    "    page_3_runtime.append(rating[:3])\n",
    "\n",
    "page_3_runtime[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "343205f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['175191', '116403', '228658', '59450', '75365']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_3_votes = []\n",
    "all_votes = page_3.find_all(\"p\",class_=\"sort-num_votes-visible\")\n",
    "for vt in all_votes:\n",
    "    vote=vt.get_text(strip=True)\n",
    "    page_3_votes.append(vote[6:14].replace('|','').replace(',','').replace('G',''))\n",
    "\n",
    "page_3_votes[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21227c54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0370de6e",
   "metadata": {},
   "source": [
    "## 4th page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "710a34dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as bs\n",
    "import requests\n",
    "\n",
    "url = 'https://www.imdb.com/search/title/?groups=top_1000&sort=user_rating,desc&count=100&start=401&ref_=adv_nxt'\n",
    "\n",
    "page = requests.get(url)\n",
    "\n",
    "page_4 = bs(page.content, 'html5lib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3fb08580",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_4_titles = page_4.find_all(\"h3\",class_=\"lister-item-header\")\n",
    "\n",
    "titles_of_movies_4 = []\n",
    "for title in page_4_titles:\n",
    "    titles=title.get_text(strip=True)\n",
    "    titles_of_movies_4.append(titles.split(\"(\")[0].split(\".\")[1])\n",
    "\n",
    "\n",
    "page_4_title = titles_of_movies_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bb0df8cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_4_years = page_4.find_all(\"span\",class_=\"lister-item-year text-muted unbold\")\n",
    "\n",
    "years_of_movies_4 = []\n",
    "for title in page_4_years:\n",
    "    titles=title.get_text(strip=True)\n",
    "    years_of_movies_4.append(titles.split(\"(\")[1].split(\")\")[0].replace('I','2000'))\n",
    "\n",
    "page_4_years = years_of_movies_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4198ec26",
   "metadata": {},
   "outputs": [],
   "source": [
    "page_4_ratings = page_4.find_all(\"div\",class_=\"inline-block ratings-imdb-rating\")\n",
    "\n",
    "rating_Colum_4 = []\n",
    "for title in page_4_ratings:\n",
    "    rating=title.get_text(strip=True)\n",
    "    rating_Colum_4.append(rating.split()[:2])\n",
    "\n",
    "ini_list=rating_Colum_4\n",
    "\n",
    "flatten_list = [j for sub in ini_list for j in sub]\n",
    "\n",
    "Ratings_4 = flatten_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cb708ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ad5cd053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['156', '90 ', '189', '84 ', '181']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_4_runtime = []\n",
    "all_ratings = page_4.find_all(\"span\",class_=\"runtime\")\n",
    "for rate in all_ratings:\n",
    "    rating=rate.get_text(strip=True)\n",
    "    page_4_runtime.append(rating[:3])\n",
    "\n",
    "page_4_runtime[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "745557e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['290543', '407086', '154174', '445958', '262045']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_4_votes = []\n",
    "all_votes = page_4.find_all(\"p\",class_=\"sort-num_votes-visible\")\n",
    "for vt in all_votes:\n",
    "    vote=vt.get_text(strip=True)\n",
    "    page_4_votes.append(vote[6:14].replace('|','').replace(',','').replace('G',''))\n",
    "\n",
    "page_4_votes[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0fa6908",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c12a6861",
   "metadata": {},
   "source": [
    "# 5th page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a61d2d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as bs\n",
    "import requests\n",
    "\n",
    "url = 'https://www.imdb.com/search/title/?groups=top_1000&sort=user_rating,desc&count=100&start=501&ref_=adv_nx5'\n",
    "\n",
    "page = requests.get(url)\n",
    "\n",
    "page_5 = bs(page.content, 'html5lib')\n",
    "\n",
    "page_5_titles = page_5.find_all(\"h3\",class_=\"lister-item-header\")\n",
    "\n",
    "titles_of_movies_5 = []\n",
    "for title in page_5_titles:\n",
    "    titles=title.get_text(strip=True)\n",
    "    titles_of_movies_5.append(titles.split(\"(\")[0].split(\".\")[1])\n",
    "\n",
    "\n",
    "page_5_title = titles_of_movies_5\n",
    "\n",
    "page_5_years = page_5.find_all(\"span\",class_=\"lister-item-year text-muted unbold\")\n",
    "\n",
    "years_of_movies_5 = []\n",
    "for title in page_5_years:\n",
    "    titles=title.get_text(strip=True)\n",
    "    years_of_movies_5.append(titles.split(\"(\")[1].split(\")\")[0].replace('I','2000'))\n",
    "\n",
    "page_5_years = years_of_movies_5\n",
    "\n",
    "page_5_ratings = page_5.find_all(\"div\",class_=\"inline-block ratings-imdb-rating\")\n",
    "\n",
    "rating_Colum_5 = []\n",
    "for title in page_5_ratings:\n",
    "    rating=title.get_text(strip=True)\n",
    "    rating_Colum_5.append(rating.split()[:2])\n",
    "\n",
    "ini_list=rating_Colum_5\n",
    "\n",
    "flatten_list = [j for sub in ini_list for j in sub]\n",
    "\n",
    "Ratings_5 = flatten_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "60ec9a9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['87 ', '141', '127', '87 ', '88 ']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_5_runtime = []\n",
    "trash = []\n",
    "all_ratings = page_5.find_all(\"span\",class_=\"runtime\")\n",
    "for rate in all_ratings:\n",
    "    rating=rate.get_text(strip=True)\n",
    "    page_5_runtime.append(rating[:3])\n",
    "    \n",
    "\n",
    "page_5_runtime[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "27f37fe1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['185712', '161854', '619671', '226470', '54811']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_5_votes = []\n",
    "all_votes = page_5.find_all(\"p\",class_=\"sort-num_votes-visible\")\n",
    "for vt in all_votes:\n",
    "    vote=vt.get_text(strip=True)\n",
    "    page_5_votes.append(vote[6:14].replace('|','').replace(',','').replace('G',''))\n",
    "\n",
    "page_5_votes[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f55c734",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "629d8a89",
   "metadata": {},
   "source": [
    "# Dataframe section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "38dcf071",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required libraries \n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bb4d1251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.3</td>\n",
       "      <td>142</td>\n",
       "      <td>260246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>9.2</td>\n",
       "      <td>175</td>\n",
       "      <td>179792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.0</td>\n",
       "      <td>152</td>\n",
       "      <td>257391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>2003</td>\n",
       "      <td>9.0</td>\n",
       "      <td>201</td>\n",
       "      <td>178720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>195</td>\n",
       "      <td>132340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Dangal</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.3</td>\n",
       "      <td>161</td>\n",
       "      <td>184485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Drishyam</td>\n",
       "      <td>2013</td>\n",
       "      <td>8.3</td>\n",
       "      <td>160</td>\n",
       "      <td>40632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Jagten</td>\n",
       "      <td>2012</td>\n",
       "      <td>8.3</td>\n",
       "      <td>115</td>\n",
       "      <td>321691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Jodaeiye Nader az Simin</td>\n",
       "      <td>2011</td>\n",
       "      <td>8.3</td>\n",
       "      <td>123</td>\n",
       "      <td>242213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Incendies</td>\n",
       "      <td>2010</td>\n",
       "      <td>8.3</td>\n",
       "      <td>131</td>\n",
       "      <td>173276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Titles of movies Release year of movie  \\\n",
       "0                        The Shawshank Redemption                  1994   \n",
       "1                                   The Godfather                  1972   \n",
       "2                                 The Dark Knight                  2008   \n",
       "3   The Lord of the Rings: The Return of the King                  2003   \n",
       "4                                Schindler's List                  1993   \n",
       "..                                            ...                   ...   \n",
       "95                                         Dangal                  2016   \n",
       "96                                       Drishyam                  2013   \n",
       "97                                         Jagten                  2012   \n",
       "98                        Jodaeiye Nader az Simin                  2011   \n",
       "99                                      Incendies                  2010   \n",
       "\n",
       "   Ratings Run time   Votes  \n",
       "0      9.3      142  260246  \n",
       "1      9.2      175  179792  \n",
       "2      9.0      152  257391  \n",
       "3      9.0      201  178720  \n",
       "4      9.0      195  132340  \n",
       "..     ...      ...     ...  \n",
       "95     8.3      161  184485  \n",
       "96     8.3      160   40632  \n",
       "97     8.3      115  321691  \n",
       "98     8.3      123  242213  \n",
       "99     8.3      131  173276  \n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_1_df = pd.DataFrame({'Titles of movies':(page_1_title), 'Release year of movie':(page_1_years), 'Ratings': (Ratings), 'Run time': (page_1_runtime), 'Votes': (page_1_votes)})\n",
    "\n",
    "page_1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "184b168c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spider-Man: No Way Home</td>\n",
       "      <td>2021</td>\n",
       "      <td>9.3</td>\n",
       "      <td>148</td>\n",
       "      <td>663041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Up</td>\n",
       "      <td>2009</td>\n",
       "      <td>9.2</td>\n",
       "      <td>96</td>\n",
       "      <td>101972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Taare Zameen Par</td>\n",
       "      <td>2007</td>\n",
       "      <td>9.0</td>\n",
       "      <td>165</td>\n",
       "      <td>189536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>2010</td>\n",
       "      <td>9.0</td>\n",
       "      <td>103</td>\n",
       "      <td>818607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inglourious Basterds</td>\n",
       "      <td>2009</td>\n",
       "      <td>9.0</td>\n",
       "      <td>153</td>\n",
       "      <td>140341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Umberto D</td>\n",
       "      <td>1952</td>\n",
       "      <td>8.3</td>\n",
       "      <td>89</td>\n",
       "      <td>25643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Rashômon</td>\n",
       "      <td>1950</td>\n",
       "      <td>8.3</td>\n",
       "      <td>88</td>\n",
       "      <td>166187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>All About Eve</td>\n",
       "      <td>1950</td>\n",
       "      <td>8.3</td>\n",
       "      <td>138</td>\n",
       "      <td>130014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>The Treasure of the Sierra Madre</td>\n",
       "      <td>1948</td>\n",
       "      <td>8.3</td>\n",
       "      <td>126</td>\n",
       "      <td>123580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>To Be or Not to Be</td>\n",
       "      <td>1942</td>\n",
       "      <td>8.3</td>\n",
       "      <td>99</td>\n",
       "      <td>36405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Titles of movies Release year of movie Ratings Run time  \\\n",
       "0            Spider-Man: No Way Home                  2021     9.3      148   \n",
       "1                                 Up                  2009     9.2      96    \n",
       "2                   Taare Zameen Par                  2007     9.0      165   \n",
       "3                        Toy Story 3                  2010     9.0      103   \n",
       "4               Inglourious Basterds                  2009     9.0      153   \n",
       "..                               ...                   ...     ...      ...   \n",
       "95                         Umberto D                  1952     8.3      89    \n",
       "96                          Rashômon                  1950     8.3      88    \n",
       "97                     All About Eve                  1950     8.3      138   \n",
       "98  The Treasure of the Sierra Madre                  1948     8.3      126   \n",
       "99                To Be or Not to Be                  1942     8.3      99    \n",
       "\n",
       "     Votes  \n",
       "0   663041  \n",
       "1   101972  \n",
       "2   189536  \n",
       "3   818607  \n",
       "4   140341  \n",
       "..     ...  \n",
       "95   25643  \n",
       "96  166187  \n",
       "97  130014  \n",
       "98  123580  \n",
       "99   36405  \n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_2_df = pd.DataFrame({'Titles of movies':(page_2_title), 'Release year of movie':(page_2_years), 'Ratings': (Ratings_2), 'Run time': (page_2_runtime), 'Votes': (page_2_votes)})\n",
    "\n",
    "page_2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4791ff13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cool Hand Luke</td>\n",
       "      <td>1967</td>\n",
       "      <td>8.1</td>\n",
       "      <td>127</td>\n",
       "      <td>175191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Persona</td>\n",
       "      <td>1966</td>\n",
       "      <td>8.1</td>\n",
       "      <td>85</td>\n",
       "      <td>116403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Sound of Music</td>\n",
       "      <td>1965</td>\n",
       "      <td>8.1</td>\n",
       "      <td>172</td>\n",
       "      <td>228658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>La battaglia di Algeri</td>\n",
       "      <td>1966</td>\n",
       "      <td>8.1</td>\n",
       "      <td>121</td>\n",
       "      <td>59450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Man Who Shot Liberty Valance</td>\n",
       "      <td>1962</td>\n",
       "      <td>8.1</td>\n",
       "      <td>123</td>\n",
       "      <td>75365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Secrets &amp; Lies</td>\n",
       "      <td>1996</td>\n",
       "      <td>8.0</td>\n",
       "      <td>136</td>\n",
       "      <td>42953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Twelve Monkeys</td>\n",
       "      <td>1995</td>\n",
       "      <td>8.0</td>\n",
       "      <td>129</td>\n",
       "      <td>610737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Dilwale Dulhania Le Jayenge</td>\n",
       "      <td>1995</td>\n",
       "      <td>8.0</td>\n",
       "      <td>189</td>\n",
       "      <td>68597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Chung Hing sam lam</td>\n",
       "      <td>1994</td>\n",
       "      <td>8.0</td>\n",
       "      <td>102</td>\n",
       "      <td>78850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Andaz Apna Apna</td>\n",
       "      <td>1994</td>\n",
       "      <td>8.0</td>\n",
       "      <td>160</td>\n",
       "      <td>52990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Titles of movies Release year of movie Ratings Run time  \\\n",
       "0                     Cool Hand Luke                  1967     8.1      127   \n",
       "1                            Persona                  1966     8.1      85    \n",
       "2                 The Sound of Music                  1965     8.1      172   \n",
       "3             La battaglia di Algeri                  1966     8.1      121   \n",
       "4   The Man Who Shot Liberty Valance                  1962     8.1      123   \n",
       "..                               ...                   ...     ...      ...   \n",
       "95                    Secrets & Lies                  1996     8.0      136   \n",
       "96                    Twelve Monkeys                  1995     8.0      129   \n",
       "97       Dilwale Dulhania Le Jayenge                  1995     8.0      189   \n",
       "98                Chung Hing sam lam                  1994     8.0      102   \n",
       "99                   Andaz Apna Apna                  1994     8.0      160   \n",
       "\n",
       "     Votes  \n",
       "0   175191  \n",
       "1   116403  \n",
       "2   228658  \n",
       "3    59450  \n",
       "4    75365  \n",
       "..     ...  \n",
       "95   42953  \n",
       "96  610737  \n",
       "97   68597  \n",
       "98   78850  \n",
       "99   52990  \n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_3_df = pd.DataFrame({'Titles of movies':(page_3_title), 'Release year of movie':(page_3_years), 'Ratings': (Ratings_3), 'Run time': (page_3_runtime), 'Votes': (page_3_votes)})\n",
    "\n",
    "page_3_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e98c2b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scent of a Woman</td>\n",
       "      <td>1992</td>\n",
       "      <td>8.0</td>\n",
       "      <td>156</td>\n",
       "      <td>290543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aladdin</td>\n",
       "      <td>1992</td>\n",
       "      <td>8.0</td>\n",
       "      <td>90</td>\n",
       "      <td>407086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JFK</td>\n",
       "      <td>1991</td>\n",
       "      <td>8.0</td>\n",
       "      <td>189</td>\n",
       "      <td>154174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beauty and the Beast</td>\n",
       "      <td>1991</td>\n",
       "      <td>8.0</td>\n",
       "      <td>84</td>\n",
       "      <td>445958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dances with Wolves</td>\n",
       "      <td>1990</td>\n",
       "      <td>8.0</td>\n",
       "      <td>181</td>\n",
       "      <td>262045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Verdens verste menneske</td>\n",
       "      <td>2021</td>\n",
       "      <td>7.9</td>\n",
       "      <td>128</td>\n",
       "      <td>48716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>4 luni, 3 saptamâni si 2 zile</td>\n",
       "      <td>2007</td>\n",
       "      <td>7.9</td>\n",
       "      <td>113</td>\n",
       "      <td>59967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Star Trek</td>\n",
       "      <td>2009</td>\n",
       "      <td>7.9</td>\n",
       "      <td>127</td>\n",
       "      <td>599457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Dallas Buyers Club</td>\n",
       "      <td>2013</td>\n",
       "      <td>7.9</td>\n",
       "      <td>117</td>\n",
       "      <td>480131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>In Bruges</td>\n",
       "      <td>2008</td>\n",
       "      <td>7.9</td>\n",
       "      <td>107</td>\n",
       "      <td>416406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Titles of movies Release year of movie Ratings Run time  \\\n",
       "0                Scent of a Woman                  1992     8.0      156   \n",
       "1                         Aladdin                  1992     8.0      90    \n",
       "2                             JFK                  1991     8.0      189   \n",
       "3            Beauty and the Beast                  1991     8.0      84    \n",
       "4              Dances with Wolves                  1990     8.0      181   \n",
       "..                            ...                   ...     ...      ...   \n",
       "95        Verdens verste menneske                  2021     7.9      128   \n",
       "96  4 luni, 3 saptamâni si 2 zile                  2007     7.9      113   \n",
       "97                      Star Trek                  2009     7.9      127   \n",
       "98             Dallas Buyers Club                  2013     7.9      117   \n",
       "99                      In Bruges                  2008     7.9      107   \n",
       "\n",
       "     Votes  \n",
       "0   290543  \n",
       "1   407086  \n",
       "2   154174  \n",
       "3   445958  \n",
       "4   262045  \n",
       "..     ...  \n",
       "95   48716  \n",
       "96   59967  \n",
       "97  599457  \n",
       "98  480131  \n",
       "99  416406  \n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_4_df = pd.DataFrame({'Titles of movies':(page_4_title), 'Release year of movie':(page_4_years), 'Ratings': (Ratings_4), 'Run time': (page_4_runtime), 'Votes': (page_4_votes)})\n",
    "\n",
    "page_4_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab3f9f0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e2eb813f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Man from Earth</td>\n",
       "      <td>2007</td>\n",
       "      <td>7.9</td>\n",
       "      <td>87</td>\n",
       "      <td>185712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Letters from Iwo Jima</td>\n",
       "      <td>2006</td>\n",
       "      <td>7.9</td>\n",
       "      <td>141</td>\n",
       "      <td>161854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Life of Pi</td>\n",
       "      <td>2012</td>\n",
       "      <td>7.9</td>\n",
       "      <td>127</td>\n",
       "      <td>619671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fantastic Mr</td>\n",
       "      <td>2009</td>\n",
       "      <td>7.9</td>\n",
       "      <td>87</td>\n",
       "      <td>226470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bin-jip</td>\n",
       "      <td>2004</td>\n",
       "      <td>7.9</td>\n",
       "      <td>88</td>\n",
       "      <td>54811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Dunkirk</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>106</td>\n",
       "      <td>637013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Hidden Figures</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>127</td>\n",
       "      <td>224273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Hunt for the Wilderpeople</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>101</td>\n",
       "      <td>128268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Paddington 2</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>103</td>\n",
       "      <td>75088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Manchester by the Sea</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>137</td>\n",
       "      <td>275780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Titles of movies Release year of movie Ratings Run time   Votes\n",
       "0          The Man from Earth                  2007     7.9      87   185712\n",
       "1       Letters from Iwo Jima                  2006     7.9      141  161854\n",
       "2                  Life of Pi                  2012     7.9      127  619671\n",
       "3                Fantastic Mr                  2009     7.9      87   226470\n",
       "4                     Bin-jip                  2004     7.9      88    54811\n",
       "..                        ...                   ...     ...      ...     ...\n",
       "95                    Dunkirk                  2017     7.8      106  637013\n",
       "96             Hidden Figures                  2016     7.8      127  224273\n",
       "97  Hunt for the Wilderpeople                  2016     7.8      101  128268\n",
       "98               Paddington 2                  2017     7.8      103   75088\n",
       "99      Manchester by the Sea                  2016     7.8      137  275780\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_5_df = pd.DataFrame({'Titles of movies':(page_5_title), 'Release year of movie':(page_5_years), 'Ratings': (Ratings_5), 'Run time': (page_5_runtime), 'Votes': (page_5_votes)})\n",
    "\n",
    "page_5_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3313d105",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e166f6b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [page_1_df, page_2_df, page_3_df,page_4_df,page_5_df]\n",
    "\n",
    "IMDb = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "01ddd97b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.3</td>\n",
       "      <td>142</td>\n",
       "      <td>260246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>9.2</td>\n",
       "      <td>175</td>\n",
       "      <td>179792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.0</td>\n",
       "      <td>152</td>\n",
       "      <td>257391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>2003</td>\n",
       "      <td>9.0</td>\n",
       "      <td>201</td>\n",
       "      <td>178720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>195</td>\n",
       "      <td>132340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Dunkirk</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>106</td>\n",
       "      <td>637013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Hidden Figures</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>127</td>\n",
       "      <td>224273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Hunt for the Wilderpeople</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>101</td>\n",
       "      <td>128268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Paddington 2</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>103</td>\n",
       "      <td>75088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Manchester by the Sea</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>137</td>\n",
       "      <td>275780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Titles of movies Release year of movie  \\\n",
       "0                        The Shawshank Redemption                  1994   \n",
       "1                                   The Godfather                  1972   \n",
       "2                                 The Dark Knight                  2008   \n",
       "3   The Lord of the Rings: The Return of the King                  2003   \n",
       "4                                Schindler's List                  1993   \n",
       "..                                            ...                   ...   \n",
       "95                                        Dunkirk                  2017   \n",
       "96                                 Hidden Figures                  2016   \n",
       "97                      Hunt for the Wilderpeople                  2016   \n",
       "98                                   Paddington 2                  2017   \n",
       "99                          Manchester by the Sea                  2016   \n",
       "\n",
       "   Ratings Run time   Votes  \n",
       "0      9.3      142  260246  \n",
       "1      9.2      175  179792  \n",
       "2      9.0      152  257391  \n",
       "3      9.0      201  178720  \n",
       "4      9.0      195  132340  \n",
       "..     ...      ...     ...  \n",
       "95     7.8      106  637013  \n",
       "96     7.8      127  224273  \n",
       "97     7.8      101  128268  \n",
       "98     7.8      103   75088  \n",
       "99     7.8      137  275780  \n",
       "\n",
       "[500 rows x 5 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7df6c4dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cb41642b",
   "metadata": {},
   "source": [
    "# EDA on scrapped website "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "38eb45c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required library \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a01b4f8",
   "metadata": {},
   "source": [
    "## inspection of dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "51be7877",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>497</td>\n",
       "      <td>98</td>\n",
       "      <td>14</td>\n",
       "      <td>120</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Drishyam</td>\n",
       "      <td>2000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>130</td>\n",
       "      <td>166187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>129</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Titles of movies Release year of movie Ratings Run time   Votes\n",
       "count               500                   500     500      500     500\n",
       "unique              497                    98      14      120     499\n",
       "top            Drishyam                  2000     8.0      130  166187\n",
       "freq                  2                    20     129       14       2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## checking summary of all columns\n",
    "IMDb.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a45a782d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5)\n",
      "2500\n"
     ]
    }
   ],
   "source": [
    "## checking size of the dataset\n",
    "print(IMDb.shape)\n",
    "print(IMDb.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2f4b4dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the dimensions of data\n",
    "IMDb.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a0195332",
   "metadata": {},
   "outputs": [],
   "source": [
    "## after checking data type wee understand some columns need to change their dtypes for analysis \n",
    "IMDb['Run time']=IMDb['Run time'].astype(str).astype(int)\n",
    "\n",
    "IMDb['Ratings']=IMDb['Ratings'].astype(str).astype(float)\n",
    "\n",
    "IMDb['Votes']=IMDb['Votes'].astype(str).astype(int)\n",
    "\n",
    "IMDb['Release year of movie']=IMDb['Release year of movie'].astype(str).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "301e6b7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[142 175 152 201 195 202  96 164 154 148 179 139 178 161 153 136 145 124\n",
      " 133 130 169 125 189 116 127 137 118 121 207 141 132 106 112 168 151 150\n",
      " 155 119 110  88  89 147 117 165 109 102  87 160 126 122 158 181 149 105\n",
      " 170 135  98 101 113 134 115 146 143  95 123 131 103 108 104  81  99 229\n",
      " 129 218 128 107  68 138 156 167 163 186 321 140  97 180 111 100 162 114\n",
      "  91 172  85 212  72  94 144 196 159  93 242 120 188  90  84 205 174  92\n",
      "  86  75  76 184 176 194  83  82 197  80 220  64]\n",
      "[9.3 9.2 9.  8.9 8.8 8.7 8.6 8.5 8.4 8.3 8.1 8.  7.9 7.8]\n",
      "[260246 179792 257391 178720 132340 123860 768854 191204 199477 228353\n",
      " 161405 204986 180827 201074 746254 111041 186829 112550 125963 987816\n",
      " 192697 174495 744098 737789 135494 126524 679753 159773 107370 139316\n",
      " 133182  54627 340558 447018  26906 753320 817305 836287 109140  39934\n",
      " 130040 129346 809813 146203 110101 106517 113275 103088 255905 268893\n",
      " 116842 654290 858746 324511 656654 482678 561905 238386 182272  88889\n",
      "  85756 120656  29053 254123 497797 106698 102658 482739 150494 166187\n",
      "  35245 388720 117730 108998 384604 565117 121232 113568 384521 102087\n",
      "  75942 702836 395020 947634 247898 991144  43373 482394 122740 194234\n",
      " 218556 220993  40004 126890  39153 184485  40632 321691 242213 173276\n",
      " 663041 101972 189536 818607 140341 985774 745933 839988 825040 942988\n",
      " 970044 637911 995199 727876 344898 816063 102894 258464 813618 654861\n",
      " 312818 289317  76666 178972 322916 395985  31988 238409  77103 161395\n",
      " 155003 435992 155833 172123 124959  50759 557290  59556  42501  89949\n",
      "  77641 468912  37735  94218  80144  67072 692925  94178  36063 207213\n",
      " 465398 126684  53546  47387  32910 138926 135182 947084  85850 573443\n",
      " 658795 110297 143467 351439 382569 915062 108966 102252 973373 573294\n",
      " 104491 574110  73807 508846 959193 405709 741402 860715 123861 412152\n",
      " 347751 237851 803561 533688  28731 321288  28453 252614 241639 121359\n",
      " 263283 218091 173220  60728  60416  25643 130014 123580  36405 175191\n",
      " 116403 228658  59450  75365  29125  29996 116204 236660  38740 106105\n",
      " 182617  48542  51736  34110 153368  65481  35561  32700 169977  34686\n",
      "  63560  32725 135655  92166 392392 114683  36432 102780  61048  33475\n",
      "  50740  33836  30927 172002 569080 233360 832884  56291 313048 485384\n",
      "  51301 119097  45661  55795 753423 114739  57690 545954 597151 782801\n",
      " 501033  82684 673114 100429 381592 222140 583790  51849 114708 835931\n",
      " 750093 103263 136158  94054  46652 498773 539672 629138  27728 761978\n",
      " 107311  39603  62298 647792 740664  82403  81395 186165 123900 437144\n",
      " 716258  47930 147559  36502 787224 309233  52788  89732  73093  39398\n",
      "  63762  53517 145995  93395  42953 610737  68597  78850  52990 290543\n",
      " 407086 154174 445958 262045 506321 182420 422360  70303  33658 163938\n",
      " 104280 166946  26782  80491  34662 394438  71953 263464 253621  45970\n",
      " 156359 129611  33918  90165  47506  47906  43286 213945 212039 178321\n",
      "  50870 270539  74710  53135  25736 116882  32878  55398  37918  36149\n",
      "  81383  72459  61938  65853 103802  31661  88913  88641  62791  55554\n",
      " 137203 103503  37183  36910  26369  25732  39843 157254  57269  62942\n",
      " 596206  74529 297293  30297  25631  47578  56638  54583  57224  92699\n",
      " 707747 372338 160830 674026  38577  87243 122206 155174 701536 523650\n",
      " 521352  25270 240222 661054  99651  53102  30531 105769  33600 215224\n",
      " 673054 303255  52388 351678  48716  59967 599457 480131 416406 185712\n",
      " 161854 619671 226470  54811  32804  45339 103317 548855  68416  54069\n",
      " 450735 618198 180517 111761  46997 540325  52639 493962 266801 273193\n",
      " 109086 349542 663043 569675 114114  30284 260966  61218 140976  34104\n",
      " 224128 100593 328181 215306  31171 481031  73762 100807 305017  27980\n",
      "  71164 199218 138731 146578 401037  35432 196826 139297 114046  43343\n",
      "  76927 101416  30193  84095  31366  75511  26485  76025 214278  76447\n",
      "  31374  75147 133741  31787  49137  70579  88434  26607 107201 132720\n",
      "  31238  55457 141481  47480 100297  85040  26341  36800  47073  70305\n",
      "  26199  68737  28855  50786  29416  84883  46047  95920 322161  26583\n",
      "  28325 261024  59517 164593 637013 224273 128268  75088 275780]\n",
      "[    1994     1972     2008     2003     1993     1974     1957     2021\n",
      "     2010     2002     1999     2001     1966     2020     1990     1980\n",
      "     1975     2022     2014     1998     1997     1995     1991     1977\n",
      "     1962     1954     1946     2019     2011     2006     2000     1988\n",
      "     1985     1979     1968     1960     1942     1936     1931     2018\n",
      " 20002000     2016     2012     2009     1986     1984     1981     1963\n",
      "     1964     1950     1940     2017     2013     2007     2004     1992\n",
      "     1987     1983     1973     1971     1961     1959     1958     1955\n",
      "     1952     1948     1944     1941     1927     1921     2015     2005\n",
      "     1989     1982     1976     1969     1965     1953     1967     1951\n",
      "     1949     1939     1937     1934     1930     1928     1996     1956\n",
      "     1947     1945     1925     1920     1970     1943     1938     1933\n",
      "     1932     1922]\n"
     ]
    }
   ],
   "source": [
    "##  this operation for checking any xna or negative value.\n",
    "\n",
    "print(IMDb['Run time'].unique()) \n",
    "print(IMDb['Ratings'].unique())    \n",
    "print(IMDb['Votes'].unique())   \n",
    "print(IMDb['Release year of movie'].unique()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a5accc3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 500 entries, 0 to 99\n",
      "Data columns (total 5 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Titles of movies       500 non-null    object \n",
      " 1   Release year of movie  500 non-null    int32  \n",
      " 2   Ratings                500 non-null    float64\n",
      " 3   Run time               500 non-null    int32  \n",
      " 4   Votes                  500 non-null    int32  \n",
      "dtypes: float64(1), int32(3), object(1)\n",
      "memory usage: 17.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking data type of every column\n",
    "IMDb.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "161494f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.3</td>\n",
       "      <td>142</td>\n",
       "      <td>260246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>9.2</td>\n",
       "      <td>175</td>\n",
       "      <td>179792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.0</td>\n",
       "      <td>152</td>\n",
       "      <td>257391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>2003</td>\n",
       "      <td>9.0</td>\n",
       "      <td>201</td>\n",
       "      <td>178720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>195</td>\n",
       "      <td>132340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Titles of movies  Release year of movie  \\\n",
       "0                       The Shawshank Redemption                   1994   \n",
       "1                                  The Godfather                   1972   \n",
       "2                                The Dark Knight                   2008   \n",
       "3  The Lord of the Rings: The Return of the King                   2003   \n",
       "4                               Schindler's List                   1993   \n",
       "\n",
       "   Ratings  Run time   Votes  \n",
       "0      9.3       142  260246  \n",
       "1      9.2       175  179792  \n",
       "2      9.0       152  257391  \n",
       "3      9.0       201  178720  \n",
       "4      9.0       195  132340  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## checking all top 5 rows\n",
    "IMDb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd10a21d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8624b1f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Dunkirk</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>106</td>\n",
       "      <td>637013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Hidden Figures</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>127</td>\n",
       "      <td>224273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Hunt for the Wilderpeople</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>101</td>\n",
       "      <td>128268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Paddington 2</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>103</td>\n",
       "      <td>75088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Manchester by the Sea</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>137</td>\n",
       "      <td>275780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Titles of movies  Release year of movie  Ratings  Run time  \\\n",
       "95                    Dunkirk                   2017      7.8       106   \n",
       "96             Hidden Figures                   2016      7.8       127   \n",
       "97  Hunt for the Wilderpeople                   2016      7.8       101   \n",
       "98               Paddington 2                   2017      7.8       103   \n",
       "99      Manchester by the Sea                   2016      7.8       137   \n",
       "\n",
       "     Votes  \n",
       "95  637013  \n",
       "96  224273  \n",
       "97  128268  \n",
       "98   75088  \n",
       "99  275780  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##  checking all bottom 5 columns\n",
    "IMDb.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4382f2f2",
   "metadata": {},
   "source": [
    "### As per above operations we get :\n",
    "* There are 500 rows and 5 columns in the data set. There are no null values in any of the columns.\n",
    "* there are only int,float and object types are available.\n",
    "* And dataframe is 2 dimension."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be2ba6ce",
   "metadata": {},
   "source": [
    "## Dealing with null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b32d6afc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Titles of movies         0\n",
       "Release year of movie    0\n",
       "Ratings                  0\n",
       "Run time                 0\n",
       "Votes                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## checking amount of null values in dataframe\n",
    "IMDb.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a970f8a",
   "metadata": {},
   "source": [
    "there is no any null value in it"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ca64f2",
   "metadata": {},
   "source": [
    "## Dealing with outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "57f44f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAE9CAYAAAD5xEDLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAek0lEQVR4nO3df3Ac533f8c/3DjgAAnCgKIoOSIqk9SOpbVWRZVVjKbLkH5okVi0qcTJj2a0SdVSLqeMmzozryvWPSUq2VhS3ddPGHdJOpm4mshOP1ZTRxEksRz8sy1ZM2aZMW9YPSiIkUhZFOQQIEsABd0//2N3DHnh3wAG7d7uP368ZDO7253N7i/3geXb3WXPOCQAA+KHQ6wIAAIDkEOwAAHiEYAcAwCMEOwAAHiHYAQDwCMEOAIBH+tYy84YNG9z27dsTKgqAZh599NHjzrlze12OleK4AKSv3XFhTcG+fft27d+/fy2LALAMMzvc6zJ0guMCkL52xwWa4gEA8AjBDgCARwh2AAA8QrADAOARgh0AAI8Q7AAAeIRgBwDAIwQ7AAAeIdgBAPAIwQ4AgEcIdgAAPEKwAwDgEYIdAACPEOwAAHiEYAcAwCMEOwAAHiHYAQDwCMEOAIBHCHYAADxCsAMA4BGCHQAAjxDsAAB4hGAHAMAjBDsAAB7p63UBmjlw4IAOHjy47HSvec1rdNlll7Uc/8gjj+jpp59Osmg98Y53vENjY2O9LgbgjSNHjuj+++/veL4bbrhB5XJZTzzxhPbv39/x/O9+97tVKFCfQroyGeyHDh3Svffeq2JpoOU01cqc5ufn2wb7/v37tf/RR1XsL6VRzNS5alW16oLe+ta3EuxAgo4fP657771Xhb6SrGDLTu9qNdUW5nXttdeqXC7r2WefDY5R/QPS8rOrVl2Qq1Z10003JVB6oL1MBrskyUznXvevWo5+5aEvampqqu0ipqamVDr7p7T+jTcmXbqumDn6lCa/+9VeFwPw1ror/rlK61617HSzLx3WiUe/rEqlIkn13xve9muyQnHZ+aefflTTT35rbYUFVii3bULWP7hssE9OTclKQ10qEQBfWTGoA83Pz9d/mxVWFOpAt+U22AsDQ5pcJthPTp1UkWAHsEZWDAI8XmOPwh7ImvwGe2lIJ0+ebDm+Wq3q9OlTKgwQ7ADWJgrxeLAXCHZkVK6DfW52tt40ttT09HQ43WA3iwXAQ1Y4M9hVpBke2ZTfYA9r4q3Os0fDCzTFA1ijqCm+4Rx7gRo7sim/wR4Gdqvm+Hqw0xQPYI2a1diNGjsyKvfBTo0dQOqaXBUvauzIqPwG+zJN8VFNnmAHsFbRxXNzc3OLv7nVDRmV32BfQVO8WUHW37r3OgBYCTOTFYr1Gvsct7shw3Ib7FbsU6HY17YpvjgwJLMV9PcIAMsoFPu4jx25kN9gN1NhYKhtU7xxqxuAhFhxscY+X5mnKR6Zldtgl4JuZds2xfcT7ACSYfEa+zw1dmRXroO9UGrdrezk1BS3ugFITmEx2OdpikeG5T7Y2zXFc0U8gMQUiqpUKnLO0UENMi3fwT4wpOmTJ+Wcaxg+NzenytwcwQ4gMVbo0/z8vKrVqpxzdFCDzMp3sJeGtLCwoNnZ2Ybh9XvYaYoHkJRiUXOVSr05nho7sirfwd6ikxp6nQOQtOjiuejKeM6xI6vyHewtupWlxg4gaVbo09xcpd77HMGOrMp5sAe3sy295Y0aO4CkWbFP8/OLNXYe24qsynmwN+9Wln7iASTNikVVKvOcY0fmeRHszc6xW7GPpjIAySn0aWGec+zIvlwHuxWLKvQPND3HTj/xAJIUNMXPc44dmZfrYJek4sBQ03PsNMMDSFIU5KdPnw7e0xSPjMp9sFv/4Bk19slJ+okHkCwLH/py6tSp4D0XzyGj8h/spSFNTi45x36SfuIBJCuqsU9PTze8B7Im98G+tL945xz9xANIXBTkUY1dNMUjo/If7AODOnVqWrVaTZI0MzOjWrVKsANIVHROnRo7si7/wV4aknOu/l90vXMamuIBJCg6p845dmSdF8EuLQY6ndMASEXsHLsVCjLL/eETnsr9nhnVzKNApztZAGmImuJPnTpFMzwyLf/BvqTGvtgUz+1uAJITNb1PT0+rQLAjw3If7EWa4gF0QUONnSvikWG5D3YrDUhmDU3xxf6BemcSAJCEqPndOVc/3w5kUf6D3Qrqi93LPjVF5zQAkhc/r07FAVmW+2CXJCsNNjTF050sgKTFb29zBDsyzJtgj5riJ6emZJxfB5C02Hl1ropHlnkR7IVYf/E82Q1AGsys3gTPxXPIMm+CferklGq1mk6dOsU5dgCpKPQFgU6NHVnmR7APDGl2ZkYnTpyQnKPGDiAVUaDTnSyyzI9gD4P8yJEjwXs6pwGQgqgJnqZ4ZJmfwU6NHUAaopo6TfHIMD+CfaAx2IsEO4A0GE3xyD4/gr0UNL2/8MIL4XuCHUAKilwVj+zzJNiDID969KhkFnQzCwAJW7x4jmBHdnkR7NZXkhWKqlQq6isN8ZxkAKmoXzxHsCPDvEhAM1MxPM9uJa6IB5AOqzfFc44d2eVFsEuqdyNLd7IA0kKNHXngUbAHNXUunAOQlnqgE+zIMG+CPQp0OqcBkJb6xXNcFY8M8y/YqbEDSEv0EBjuY0eGeRTsNMUDSBe3uyEP/An2AWrsANJFUzzyIPVgv+uuu3TXXXelvRpVjr+gvpGz1TdydurrQj50a99D5/L63VR+/KL6Rs+ptxD6Jq/fCxql/m/nxMRE2quQJFVnT8lKg+obWdeV9SH7urXvoXN5/W5qlRlZf8nbYM/r94JG3jTFAwAAgh0AAK8Q7AAAeIRgBwDAIwQ7AAAeIdgBAPAIwQ4AgEcIdgAAPEKwAwDgEYIdAACPEOwAAHiEYAcAwCMEOwAAHiHYAQDwCMEOAIBHCHYAADxCsAMA4BGCHQAAjxDsAAB4hGAHAMAjBDsAAB4h2AEA8AjBDgCARwh2AAA8QrADAOARgh0AAI8Q7AAAeIRgBwDAIwQ7AAAeIdgBAPAIwQ4AgEcIdgAAPEKwAwDgEYIdAACPEOwAAHiEYAcAwCMEOwAAHiHYAQDwCMEOAIBHCHYAADxCsAMA4BGCHQAAjxDsAAB4hGAHAMAjBDsAAB4h2AEA8AjBDgCARwh2AAA8QrADAOARgh1Yo8OHD+t973ufnn/++a7MB6Rpdna2vl+eOHFCu3bt0u7duzUxMaHdu3dr165dmpyclCSdOHFCn/jEJ+rv48MmJibOGNdqnlY6mdYXSXxmgh1Yo71792pmZkZ79uzpynxAml588cX6frlv3z4988wzOnTokPbs2aNDhw7pmWee0b59+yRJ+/bt01NPPVV/Hx+2Z8+eM8a1mqeVTqb1RRKfmWAH1uDw4cM6evSoJOnIkSMrrn2vdj4gTbOzs6pUKpKC/fKBBx6oj4v2V0l68MEHNTExoYceekjOOX3ta1/T5OSkTpw4UR929OjRhnGSGsbHhzfTybS+SOoz9yVcrjMcO3ZMs7OzuuOOO1Y8z/HjxyXnUixVPsy88KQk6TOf+YxKpVKPS5M/ExMTGhwcTHUde/fubXi/Z88e7d69O7X5fLGa40KSpqenu7q+uWMTkqQ777xTZtbVdXfi8OHDDe9rtVrT6RYWFrRnz576+Fqtpn379sk5d8Y80bibb75Z+/btO2Oem2++uek6OpnWF0l95o5r7GZ2m5ntN7P9L7/8cscrBHwSr8VIQS0nzfmyiuPCT56jR4+qWq1KkqrVqh5++GF94xvfqA+LROMkNYyPD2+mk2l9kdRn7rjG7pzbK2mvJF1++eXLVqs3btwoSbr99ttXvI67775bf3XPPZ0WzTtDW35alePP673vfa/Gx8d7XZzc6UZtcNOmTQ0hvXnz5lTny6puHBeSdODAAX3qU5/q2voGNm7V/ImX9KEPfUjFYrFr6+3Uzp07603xy9m0aZNeeuklVatVFYtFXXXVVXLO6cEHH2wI92icJF155ZX18fHhzXQyrS+S+sycYwfW4Lbbbmt4v3PnzlTnA9K0tAJRKDSPiL6+Pu3cubM+vlAoaMeOHdqxY8cZ80TjJDWMjw9vppNpfZHUZybYgTXYtm2bNm3aJCmodZ933nmpzgekaXBwsH49z+bNm3XttdfWx0X7qyRdc8012rp1q66++mqZmd70pjdpbGxM69atqw/btGlTwzhJDePjw5vpZFpfJPWZCXZgjW677TYNDQ11XOte7XxAmsbHx+v75Y4dO3T++efrggsu0M6dO3XBBRfo/PPPb6iBX3TRRQ01y2jYzp07zxjXap5WOpnWF0l85tSvigd8t23bNn3605/u2nxAmgYHBxv2y4997GP11x/96Ecbpl23bp0+/OEPtxy2dFyreVrpZFpfJPGZqbEDAOARgh0AAI8Q7AAAeIRgBwDAIwQ7AAAeIdgBAPAIwQ4AgEcIdgAAPEKwAwDgEYIdAACPEOwAAHiEYAcAwCMEOwAAHiHYAQDwCMEOAIBHCHYAADxCsAMA4BGCHQAAjxDsAAB4hGAHAMAjBDsAAB4h2AEA8AjBDgCARwh2AAA8QrADAOARgh0AAI8Q7AAAeIRgBwDAIwQ7AAAeIdgBAPAIwQ4AgEcIdgAAPEKwAwDgEYIdAACPEOwAAHiEYAcAwCMEOwAAHiHYAQDwCMEOAIBHCHYAADxCsAMA4BGCHQAAjxDsAAB4hGAHAMAjBDsAAB4h2AEA8AjBDgCARwh2AAA80pf2CrZu3Zr2KiRJhdKg5n/8ouZPHFP/uo1dWSeyrVv7HjqX1+/G+vq1MHlc1dlTKg4O97o4icvr94JGqQf7e97znrRXIUkaOHer5n70rKozJwl2SOrevofO5fW7GThniyrHJuQWKpL8C/a8fi9o5E1TfG1uJvhdmelxSQD4ylUXGn4DWeRPsIeBXp0j2AGkw9WqwW+CHRnmT7BTYweQsnqNPQx4IIv8CfZKFOyzPS4JAG/VaIpH9nkT7G4+CHRHjR1ASlw1bIqvEezILn+CPQx0R40dQEq4eA554EWwO+e0wDl2ACmr19SrnGNHdvkR7PNzknMaHh5WtTLLhS0AUkGNHXngRbBHtfQtW7aE72mOB5ACzrEjB/wI9rAZfvPmzcF7muMBpMDVr4qnVRDZ5UewV5YEO53UAEgDHdQgB7wI9io1dgDdUO+ghmBHdnkR7LXKjGSm8fHx8D3n2AEkz9FBDXLAm2AfPmtYIyMjKhQK1NgBpKJ+bp07b5BhngT7rEbLoyoUChoZGeUcO4BU1LjdDTngRbC7yozWjY1JksrlUWrsABLnXI2nuyEXvAn20dFRSdLY2Bj9xQNIXPwWNzrBQpZ5Eey1yozK5bIkaXR0lP7iASQvVks3ropHhuU+2F2tqmplrh7s5XJZVWrsABIWv8WNDmqQZbkP9ujWtqgpfnR0VLWFebnqfC+LBcAzUZgXi0XuY0emeRDsQe08Cvao5s697ACSFF0wNzw8zMVzyLT8B3t4a1u8KT4+HACSENXSR0ZGCHZkWv6DvdIY7FHNnfPsAJIUhfnIyEj9fnYgi7wLdmrsANIQnWMfHh6WnOOWN2RW/oN9blaFYlFDQ0OS4ufYCXYACaotnmOXuDIe2ZX/YA87pzEzSdLAwID6SyWCHUCi4k3xEk94Q3Z5Eezl0XLDsNFR+osHkKwzgp3z7MgoP4K9PNowrDxapsYOIFFuSVO8CHZkVO6DXZXZ+nn1yNhYWZrnPnYAyWm4eE70F4/syn2wV2P9xEdGR0fpoAZAolxtQTLTWWedFbynxo6MynWw1xbmVVuYr9+7HimXy6rOnZZzrkclA+AbV62q1N+vUqkUvifYkU35DvYl97BHyuWyXK0mt1DpRbEAeMhV59XfX1J/f3/wnqvikVFeBntUg+fKeACJqVbVX4rX2DnHjmzKd7DPNT7ZLUInNQCS5moLKvWXaIpH5uU72Ns0xcfHA8BauWpVpVJ/vSleNMUjo7wI9qU19npTPFfGA0iIqy1oYGCApnhkXu6DvVQqaWBgoGE459gBJK66oFKJpnhkX76DfW5GI0u6k5Wkvr4+DQ4N0RQPIDGuVlWpVFKxWFShUOCqeGRWvoO9MhP0MtcE3coCSFRtoX5+vb+/RI0dmZXrYHeVWY2Vmwf72FiZpngAyalW683w/f39nGNHZuU82GfOuHAuUi6X5egvHkBCXG2hHuylgRJN8cis3Aa7c07VuTP7iY+UyzTFA0hObSEW7DTFI8PyG+wLFTlXaxnso6Ojqs7NyLlal0sGwEeuuniOvVTq57GtyKzcBnt0/rxVU/zivexzXSsTAD+5Wk3O1RZr7KUSj21FZuU32Fv0Oheh9zkASYnOpy/W2EvU2JFZuQ/2dhfPSXRSA2Dtoivgo86wqLEjy/Ib7HPta+yLTfEEO4C1cdV5SUtq7FwVj4zKb7CHgT0yMtJ0PE3xABIT1s4bzrHTFI+MynWwD501rL6+vqbjh4eHZWY0xQNYsyjEF3ue66cpHpmV32Cfm1G53Pz8uiQVCgUNj4xSYwewZlGwU2NHHuQ32CszLbuTjZTLBDuAtXNNmuJr1QU553pZLKCp5u3YWeCcFk5PtR49d1rl8ua2i1g3NqaXj7zcdjlZxmkEIF212dMrOj5UZ05Kagx2OafqqUmpsHz9iP400E3ZDXZJx++/q+34Vre6xcfPT/5g2eUA+Ml04tt/29H08XPsknT8wS8kXiZgrTIZ7G94wxu0cePGZafbsmVL2/HXXXedLr744qSK1TNjY2O9LgLglfPOO0+33nprx/OtX79eknTJJZdoeHi44/nNrON5gE5lMti3bdumbdu2rXk5F154oS688MIESgTAJ+vXr9fVV1+96vnHx8c1Pj6eYImA5OT24jkAAHAmgh0AAI8Q7AAAeIRgBwDAIwQ7AAAeIdgBAPAIwQ4AgEcIdgAAPEKwAwDgEYIdAACPEOwAAHiEYAcAwCMEOwAAHiHYAQDwCMEOAIBHCHYAADxCsAMA4BGCHQAAjxDsAAB4hGAHAMAjBDsAAB4h2AEA8AjBDgCARwh2AAA8QrADAOARc86tfmazlyUdDt9ukHQ8iUKtQRbKIGWjHJTBnzJsc86dm1Rh0rbkuJAlWdgXmslquSTKthrdKlfL48Kagr1hQWb7nXOXJ7KwHJchK+WgDJQBjbL6PWS1XBJlW40slIumeAAAPEKwAwDgkSSDfW+Cy1qtLJRBykY5KEOAMiCS1e8hq+WSKNtq9LxciZ1jBwAAvUdTPAAAHlk22M3sZ8zsu7GfKTP7wJJp/l1s/EEzq5rZ+nDcc2b2vXDc/tUW1Mx+x8y+Hy7/82Y2uGS8mdkfmtnTZvaYmV0WG/eLZvZEOO72FMvwL8J1P2ZmD5vZz8bGdWs7vNnMJmPfx8dj47q1HbqxP/x2uOzvL90fw/Hd2B+WK0Pq+wMCZvYnZnbMzA7Ghq03s6+Y2VPh77Nj4z4cfv9PmNkv9KBsv2tmR2J/J9d3u2xmdp6Z3Wdmj4f78G+Hw3u+3dqUrafbzcwGzewfzOxAWK7fC4f3fJs1cM6t+EdSUdKPFNw/12qaGyT9fez9c5I2dLKeJsvcLOlZSUPh+7+QdMuSaa6X9GVJJumNkh6JlfmQpPMllSQdkPTalMpwlaSzw9dvj8rQ5e3wZkn3tPjuurIdurA/XCzpoKSzJPVJulfSRV3eH1ZShlT3B34atvU1ki6TdDA27E5Jt4evb5f0++Hr14bf+4CkV4f7Q7HLZftdSR9sMm3XyiZpXNJl4etRSU+G6+/5dmtTtp5ut/B4MhK+7pf0SHh86fk2i/902hT/NkmHnHPtOp94t6TPd7jcleiTNGRmfQoOpkeXjL9R0v9xgW9KWmdm45KukPS0c+4Z51xF0hfCaRMvg3PuYefcP4ZvvylpyyrXs+oytNG17bBEGvvDayR90zl32jm3IOkBSb+8ZJq094dly9Cl/QGSnHMPSvrxksE3Svpc+Ppzkn4pNvwLzrk559yzkp5WsF90s2ytdK1szrkXnXPfDl+flPS4gn/ce77d2pStla6ULTyeTIdv+8Mfpwxss7hOg/0mtTlIm9lZkn5R0pdig52kvzOzR83sts6LKDnnjkj6pKQJSS9KmnTO/d2SyTZLej72/oVwWKvhaZQh7lYFNcb6ItSd7SBJV4ZNRV82s9eFw7q+HdLaHxTUlK8xs3PCdVwv6bwl06S6P6ywDHGJ7w9Y1quccy9KQVBI2hgOT2ofWKv3h6dp/iTWdNuTspnZdkmvV1ADzdR2W1I2qcfbzcyKZvZdScckfcU5l7lttuJgN7OSpB2Svthmshskfd05F//v9Oecc5cpaIr8TTO7ptNChl/ejQqaMjZJGjazf7l0siazujbD0yhDNO1bFBzI/31scLe2w7cVnCr5WUn/Q9JfRrM3WWSq20Ep7Q/Ouccl/b6kr0j6GwVNXQtLi9ps1jbD0yhDUJCU9gesWiL7wBr9L0kXSLpUwT/I/yUc3vWymdmIgn++P+Ccm2o3aZNh3S5bz7ebc67qnLtUQQvcFWZ2cZvJe7KvdVJjf7ukbzvnXmozzRk1eufc0fD3MUn/V6trhrhO0rPOuZedc/OS7lZw/jLuBTXWmLYoaCJuNTyNMsjMLpH0WUk3OudeiYZ3azs456aipiLn3F9L6jezDerydgiltT/IOffHzrnLnHPXKGjmfGrJJGnvDyspQ9r7A9p7KTz9ovD3sXB4YvvAajnnXgoDoibpM1r8/rtaNjPrVxCcf+acuzscnInt1qxsWdluYVlOSLpfQatkJrZZpJNgb3uu1MzGJF0r6f/Fhg2b2Wj0WtLPK2jC7NSEpDea2VlmZgrO9T++ZJp9kn7NAm9U0ET8oqRvSbrIzF4dtjrcFE6beBnMbKuCoLvZOfdkbHjXtoOZ/VQ4TmZ2hYLv+BV1cTuE605zf5CZbQx/b5X0Tp25b6a9Pyxbhi7sD2hvn6RfD1//uhb3xX2SbjKzATN7taSLJP1DNwsWhUDol7X4/XetbOHf7x9Letw5919jo3q+3VqVrdfbzczONbN14eshBRWdHyoD26yBW8EVdgoukHpF0lhs2G9I+o3Y+1sUXCQQn+98BU2UByR9X9JHVrK+FmX4PQUb8KCkP1VwlWG9DAqaPP5IwVWH35N0eWze6xVcVXko5TJ8VtI/Svpu+LO/B9vh/eE6Dii4YOuqbm+HLu0PX5P0g3BZb1u6T3Zpf1iuDKnvD/zUv4vPK2ianVdQS7pV0jmSvqqgJeWrktbHpv9I+P0/IentPSjbn4b75WMKDv7j3S6bpKsVNAs/FttHr8/CdmtTtp5uN0mXSPpOuP6Dkj4eDu/5Nov/0PMcAAAeoec5AAA8QrADAOARgh0AAI8Q7AAAeIRgBwDAIwR7jljwlLToiWl/Fd1P2Wb6S63x6Uc7bA1PMwPQe50eBzpY7gcs6B45ev/XSS0b3cXtbjliZtPOuZHw9eckPemc+09tpr9Fwf3b7+9SEQGkrNPjQAfLfU7B8eL4WpeF3qLGnl/fUPgwATO7woLnfX8n/P0zYa9q/1HSu8L/7t9lZreY2f8M5/nfFjyv/GEze8bMfjUcXjCzT1vwrOF7wv/ao3F3mNkPLHgAwyd79LkBLIofB+43s8vD1xvCoFb4d3+3mf2NBc8Lv3PpQszstxQ8++E+M7svHPZcuJztZvZDM/ts2ErwZ2Z2nZl9PVzeFeH0wxY8mOVb4bFotU+PxBoR7DlkZkUFXblGXaH+UNI1zrnXS/q4pP/sgkeSflzSnzvnLnXO/XmTRY0r6OHpHZLuCIe9U9J2Sf9U0r+WdGW4zvUKunB8nXPuEkm7U/hoAFaoyXGgnUslvUvB3/W7zKzhSYTOuT9U0If5W5xzb2ky/4WS/ruCntf+iaT3KDh2fFDSfwin+Yikv3fO/TNJb5H0B2GXyegygj1fhix4XOArktYreLKYJI1J+qKZHZT03yS9rvnsZ/hL51zNOfcDSa8Kh10t6Yvh8B9Jui8cPiVpVtJnzeydkk6v+dMAWI1Wx4F2vuqcm3TOzSroBnlbh+t81jn3PRc8fOX74fKcgu5dt4fT/Lyk28Oy3S9pUNLWDteDBBDs+TLjgscFbpNUkvSb4fBdku5zzl2s4FGpgytc3lzstS353cA5t6DgSUpfkvRLCh5VCqD7Wh0HFrR4TF96DIj/rVcl9XW4zvj8tdj7WmxZJulXwhbCS51zW13weGN0GcGeQ865SUm/JemDFjzacEzSkXD0LbFJT0oa7XDxD0n6lfBc+6skvVmqPxd5zAWPgv2AgqY9AD3S5DjwnKQ3hKN/dRWLXM3xIu5vJf3b8MlsMrPXr2FZWAOCPaecc99R8HSwmyTdKekTZvZ1ScXYZPdJem108dwKF/0lBU+gOihpj6RHJE0q+IO/x8wek/SApN9J5IMAWLUlx4FPSvo3ZvawpA2rWNxeSV+OLp5bhV2S+iU9Fp4W3LXK5WCNuN0NZzCzEefctJmdo+DZwT8Xnm8HAGRcp+dZ8JPhnrBjipKkXYQ6AOQHNXYAADzCOXYAADxCsAMA4BGCHQAAjxDsAAB4hGAHAMAjBDsAAB75/3B6lJdX/Kn2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw box plots for indepent variables with continuous values\n",
    "columns = ['Ratings', 'Run time']\n",
    "plt.figure(figsize=(18,5))\n",
    "\n",
    "i = 1\n",
    "for column in columns:\n",
    "    plt.subplot(1,4,i)\n",
    "    sns.boxplot(x=column, data=IMDb, palette=\"Blues\", notch = True)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24f412b9",
   "metadata": {},
   "source": [
    "as per above operation we understand there is some values are outliner but we dont needd to fix it, because rating and run time has possibility to be outliner"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a147be",
   "metadata": {},
   "source": [
    "## Binning of continuos variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e3ab3ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    MEDIUM\n",
       "1    MEDIUM\n",
       "2    MEDIUM\n",
       "3    MEDIUM\n",
       "4    MEDIUM\n",
       "Name: Votes, dtype: category\n",
       "Categories (5, object): ['VERY_LOW' < 'LOW' < 'MEDIUM' < 'HIGH' < 'VERY_HIGH']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Binning 'Votes' based on quantiles\n",
    "VotesInBins = pd.qcut(IMDb.Votes, q=[0, 0.25, 0.5, 0.75, 0.95, 1], labels=['VERY_LOW', 'LOW', \"MEDIUM\", 'HIGH', 'VERY_HIGH'])\n",
    "VotesInBins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9a78f909",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                Titles of movies  Release year of movie  \\\n",
      "0                       The Shawshank Redemption                   1994   \n",
      "1                                  The Godfather                   1972   \n",
      "2                                The Dark Knight                   2008   \n",
      "3  The Lord of the Rings: The Return of the King                   2003   \n",
      "4                               Schindler's List                   1993   \n",
      "\n",
      "   Ratings  Run time   Votes VotesInBins  \n",
      "0      9.3       142  260246      MEDIUM  \n",
      "1      9.2       175  179792      MEDIUM  \n",
      "2      9.0       152  257391      MEDIUM  \n",
      "3      9.0       201  178720      MEDIUM  \n",
      "4      9.0       195  132340      MEDIUM  \n"
     ]
    }
   ],
   "source": [
    "# Add new column to the DataFrame\n",
    "IMDb = IMDb.assign(VotesInBins =VotesInBins)\n",
    "print(IMDb[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5fdbdc38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VERY_LOW     125\n",
      "LOW          125\n",
      "MEDIUM       125\n",
      "HIGH         100\n",
      "VERY_HIGH     25\n",
      "Name: VotesInBins, dtype: int64\n",
      "VERY_LOW     0.25\n",
      "LOW          0.25\n",
      "MEDIUM       0.25\n",
      "HIGH         0.20\n",
      "VERY_HIGH    0.05\n",
      "Name: VotesInBins, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(IMDb.VotesInBins.value_counts())  ## in amount of value\n",
    "print(IMDb.VotesInBins.value_counts(normalize=True))  ##  to see in percenVotesInBinstage format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8d1d4b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Moderate_Film\n",
       "1            Movie\n",
       "2            Movie\n",
       "3       Full_Movie\n",
       "4       Full_Movie\n",
       "Name: Run time, dtype: category\n",
       "Categories (5, object): ['Short_Film' < 'Film' < 'Moderate_Film' < 'Movie' < 'Full_Movie']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Binning 'Run time' based on quantiles\n",
    "RuntimeInBins = pd.qcut(IMDb['Run time'], q=[0, 0.25, 0.5, 0.75, 0.95, 1], labels=['Short_Film', 'Film', \"Moderate_Film\", 'Movie', 'Full_Movie'])\n",
    "RuntimeInBins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "444fc10e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add new column to the DataFrame\n",
    "\n",
    "IMDb.insert(loc = 4,\n",
    "          column = 'Run time In Bins',\n",
    "          value = RuntimeInBins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "268d9f12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Short_Film       131\n",
      "Moderate_Film    125\n",
      "Film             122\n",
      "Movie             99\n",
      "Full_Movie        23\n",
      "Name: Run time In Bins, dtype: int64\n",
      "Short_Film       0.262\n",
      "Moderate_Film    0.250\n",
      "Film             0.244\n",
      "Movie            0.198\n",
      "Full_Movie       0.046\n",
      "Name: Run time In Bins, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(IMDb['Run time In Bins'].value_counts())  ## in amount of value\n",
    "print(IMDb['Run time In Bins'].value_counts(normalize=True))  ##  to see in percenVotesInBinstage format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db8c8a62",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "62d0ea93",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "26477d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Run time In Bins</th>\n",
       "      <th>Votes</th>\n",
       "      <th>VotesInBins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.3</td>\n",
       "      <td>142</td>\n",
       "      <td>Moderate_Film</td>\n",
       "      <td>260246</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>9.2</td>\n",
       "      <td>175</td>\n",
       "      <td>Movie</td>\n",
       "      <td>179792</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.0</td>\n",
       "      <td>152</td>\n",
       "      <td>Movie</td>\n",
       "      <td>257391</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>2003</td>\n",
       "      <td>9.0</td>\n",
       "      <td>201</td>\n",
       "      <td>Full_Movie</td>\n",
       "      <td>178720</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>195</td>\n",
       "      <td>Full_Movie</td>\n",
       "      <td>132340</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Titles of movies  Release year of movie  \\\n",
       "0                       The Shawshank Redemption                   1994   \n",
       "1                                  The Godfather                   1972   \n",
       "2                                The Dark Knight                   2008   \n",
       "3  The Lord of the Rings: The Return of the King                   2003   \n",
       "4                               Schindler's List                   1993   \n",
       "\n",
       "   Ratings  Run time Run time In Bins   Votes VotesInBins  \n",
       "0      9.3       142    Moderate_Film  260246      MEDIUM  \n",
       "1      9.2       175            Movie  179792      MEDIUM  \n",
       "2      9.0       152            Movie  257391      MEDIUM  \n",
       "3      9.0       201       Full_Movie  178720      MEDIUM  \n",
       "4      9.0       195       Full_Movie  132340      MEDIUM  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cae97536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Votes'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "IMDb.groupby([\"VotesInBins\"])[\"Votes\"].sum().plot.pie(shadow = True,autopct = '%1.0f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "336050f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(data = IMDb,\n",
    "               x = \"VotesInBins\",\n",
    "               y = \"Release year of movie\", \n",
    "               ci = 95\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1a16fc64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Run time'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "IMDb.groupby([\"Run time In Bins\"])[\"Run time\"].sum().plot.pie(shadow = True,autopct = '%1.0f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "62a0f7e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Run time In Bins'>"
      ]
     },
     "execution_count": 61,
     "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": [
    "IMDb.groupby(\"Run time In Bins\")[\"Votes\"].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64cc9b4",
   "metadata": {},
   "source": [
    "## Bivariate and Multivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5533fb0f",
   "metadata": {},
   "source": [
    "### Numeric- numeric analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "95e474b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Run time In Bins</th>\n",
       "      <th>Votes</th>\n",
       "      <th>VotesInBins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.3</td>\n",
       "      <td>142</td>\n",
       "      <td>Moderate_Film</td>\n",
       "      <td>260246</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>9.2</td>\n",
       "      <td>175</td>\n",
       "      <td>Movie</td>\n",
       "      <td>179792</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.0</td>\n",
       "      <td>152</td>\n",
       "      <td>Movie</td>\n",
       "      <td>257391</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>2003</td>\n",
       "      <td>9.0</td>\n",
       "      <td>201</td>\n",
       "      <td>Full_Movie</td>\n",
       "      <td>178720</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>195</td>\n",
       "      <td>Full_Movie</td>\n",
       "      <td>132340</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Titles of movies  Release year of movie  \\\n",
       "0                       The Shawshank Redemption                   1994   \n",
       "1                                  The Godfather                   1972   \n",
       "2                                The Dark Knight                   2008   \n",
       "3  The Lord of the Rings: The Return of the King                   2003   \n",
       "4                               Schindler's List                   1993   \n",
       "\n",
       "   Ratings  Run time Run time In Bins   Votes VotesInBins  \n",
       "0      9.3       142    Moderate_Film  260246      MEDIUM  \n",
       "1      9.2       175            Movie  179792      MEDIUM  \n",
       "2      9.0       152            Movie  257391      MEDIUM  \n",
       "3      9.0       201       Full_Movie  178720      MEDIUM  \n",
       "4      9.0       195       Full_Movie  132340      MEDIUM  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9ea0c649",
   "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": [
    "#plot the scatter plot of Votes and Release year of movie variable in IMDb\n",
    "plt.scatter(IMDb.Votes, IMDb['Release year of movie'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cb54c1f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a59664ff",
   "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": [
    "###  gets a visualisation of the numeric data.\n",
    "sns.heatmap(IMDb[['Ratings','Votes','Run time','Release year of movie']].corr(), annot=True, cmap='Reds')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5e3bb29",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9ed4515e",
   "metadata": {},
   "source": [
    "## Multivariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "84d65fb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titles of movies</th>\n",
       "      <th>Release year of movie</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Run time</th>\n",
       "      <th>Run time In Bins</th>\n",
       "      <th>Votes</th>\n",
       "      <th>VotesInBins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.3</td>\n",
       "      <td>142</td>\n",
       "      <td>Moderate_Film</td>\n",
       "      <td>260246</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>9.2</td>\n",
       "      <td>175</td>\n",
       "      <td>Movie</td>\n",
       "      <td>179792</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.0</td>\n",
       "      <td>152</td>\n",
       "      <td>Movie</td>\n",
       "      <td>257391</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>2003</td>\n",
       "      <td>9.0</td>\n",
       "      <td>201</td>\n",
       "      <td>Full_Movie</td>\n",
       "      <td>178720</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>195</td>\n",
       "      <td>Full_Movie</td>\n",
       "      <td>132340</td>\n",
       "      <td>MEDIUM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Titles of movies  Release year of movie  \\\n",
       "0                       The Shawshank Redemption                   1994   \n",
       "1                                  The Godfather                   1972   \n",
       "2                                The Dark Knight                   2008   \n",
       "3  The Lord of the Rings: The Return of the King                   2003   \n",
       "4                               Schindler's List                   1993   \n",
       "\n",
       "   Ratings  Run time Run time In Bins   Votes VotesInBins  \n",
       "0      9.3       142    Moderate_Film  260246      MEDIUM  \n",
       "1      9.2       175            Movie  179792      MEDIUM  \n",
       "2      9.0       152            Movie  257391      MEDIUM  \n",
       "3      9.0       201       Full_Movie  178720      MEDIUM  \n",
       "4      9.0       195       Full_Movie  132340      MEDIUM  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2322004c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ratings</th>\n",
       "      <th>7.8</th>\n",
       "      <th>7.9</th>\n",
       "      <th>8.0</th>\n",
       "      <th>8.1</th>\n",
       "      <th>8.3</th>\n",
       "      <th>8.4</th>\n",
       "      <th>8.5</th>\n",
       "      <th>8.6</th>\n",
       "      <th>8.7</th>\n",
       "      <th>8.8</th>\n",
       "      <th>8.9</th>\n",
       "      <th>9.0</th>\n",
       "      <th>9.2</th>\n",
       "      <th>9.3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Run time In Bins</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Short_Film</th>\n",
       "      <td>181843.666667</td>\n",
       "      <td>138723.04878</td>\n",
       "      <td>139699.974359</td>\n",
       "      <td>121140.272727</td>\n",
       "      <td>100363.750000</td>\n",
       "      <td>290184.300000</td>\n",
       "      <td>297970.636364</td>\n",
       "      <td>199902.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>982621.500000</td>\n",
       "      <td>832514.0</td>\n",
       "      <td>793730.500000</td>\n",
       "      <td>101972.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <td>322161.000000</td>\n",
       "      <td>206296.20000</td>\n",
       "      <td>292389.225806</td>\n",
       "      <td>59274.500000</td>\n",
       "      <td>281952.000000</td>\n",
       "      <td>392787.411765</td>\n",
       "      <td>438032.090909</td>\n",
       "      <td>283179.833333</td>\n",
       "      <td>125963.00</td>\n",
       "      <td>727876.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>865853.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderate_Film</th>\n",
       "      <td>197350.500000</td>\n",
       "      <td>259158.72000</td>\n",
       "      <td>182764.482759</td>\n",
       "      <td>88731.000000</td>\n",
       "      <td>102008.142857</td>\n",
       "      <td>383058.700000</td>\n",
       "      <td>203694.833333</td>\n",
       "      <td>307170.916667</td>\n",
       "      <td>469924.20</td>\n",
       "      <td>449682.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Movie</th>\n",
       "      <td>NaN</td>\n",
       "      <td>147865.00000</td>\n",
       "      <td>253651.086957</td>\n",
       "      <td>117755.333333</td>\n",
       "      <td>88373.666667</td>\n",
       "      <td>256041.315789</td>\n",
       "      <td>210344.357143</td>\n",
       "      <td>127176.200000</td>\n",
       "      <td>423628.75</td>\n",
       "      <td>390950.000000</td>\n",
       "      <td>195340.5</td>\n",
       "      <td>195756.000000</td>\n",
       "      <td>179792.0</td>\n",
       "      <td>663041.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Full_Movie</th>\n",
       "      <td>NaN</td>\n",
       "      <td>86243.00000</td>\n",
       "      <td>149109.714286</td>\n",
       "      <td>236660.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80625.000000</td>\n",
       "      <td>252133.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>344898.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144973.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ratings                     7.8           7.9            8.0            8.1  \\\n",
       "Run time In Bins                                                              \n",
       "Short_Film        181843.666667  138723.04878  139699.974359  121140.272727   \n",
       "Film              322161.000000  206296.20000  292389.225806   59274.500000   \n",
       "Moderate_Film     197350.500000  259158.72000  182764.482759   88731.000000   \n",
       "Movie                       NaN  147865.00000  253651.086957  117755.333333   \n",
       "Full_Movie                  NaN   86243.00000  149109.714286  236660.000000   \n",
       "\n",
       "Ratings                     8.3            8.4            8.5            8.6  \\\n",
       "Run time In Bins                                                               \n",
       "Short_Film        100363.750000  290184.300000  297970.636364  199902.000000   \n",
       "Film              281952.000000  392787.411765  438032.090909  283179.833333   \n",
       "Moderate_Film     102008.142857  383058.700000  203694.833333  307170.916667   \n",
       "Movie              88373.666667  256041.315789  210344.357143  127176.200000   \n",
       "Full_Movie                  NaN            NaN   80625.000000  252133.000000   \n",
       "\n",
       "Ratings                 8.7            8.8       8.9            9.0       9.2  \\\n",
       "Run time In Bins                                                                \n",
       "Short_Film              NaN  982621.500000  832514.0  793730.500000  101972.0   \n",
       "Film              125963.00  727876.000000       NaN  865853.500000       NaN   \n",
       "Moderate_Film     469924.20  449682.666667       NaN            NaN       NaN   \n",
       "Movie             423628.75  390950.000000  195340.5  195756.000000  179792.0   \n",
       "Full_Movie              NaN  344898.000000       NaN  144973.333333       NaN   \n",
       "\n",
       "Ratings                9.3  \n",
       "Run time In Bins            \n",
       "Short_Film             NaN  \n",
       "Film                   NaN  \n",
       "Moderate_Film     260246.0  \n",
       "Movie             663041.0  \n",
       "Full_Movie             NaN  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MultivariateAnalysis = pd.pivot_table(data=IMDb, index='Run time In Bins', columns='Ratings', values='Votes')\n",
    "MultivariateAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a63eb647",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "84fb5c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Ratings', ylabel='Run time In Bins'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,12))\n",
    "sns.heatmap(MultivariateAnalysis, annot=True, cmap='RdYlGn',center = 0.9, fmt='.1f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f222a01a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35dbd047",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}