1_Cleaning_Data.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "1-Cleaning Data",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJ8ZgqufGK-1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import altair as alt\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import time\n",
        "import re"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P4Ydyz8nGVvG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "goodm = []\n",
        "badm = []\n",
        "medm = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MV8K2f93H2F7",
        "colab_type": "text"
      },
      "source": [
        "We web scrapped from 3 websites off of imdb to get the top 250 movies, bottom 250 movies, as well as 300 mediocre movies. We wanted to get the list of movies from all the same website so that the judging of the movie ranking would be consistent. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsS0OoSlHR_F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(1, 4):\n",
        "  response = requests.get(\"https://www.imdb.com/list/ls068082370/?sort=list_order,asc&st_dt=&mode=detail&page=%d\"%i) \n",
        "  soup =  BeautifulSoup(response.content, \"html.parser\")\n",
        "  goodm += soup.find_all(\"div\", class_ = \"lister-item mode-detail\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FyEnmJI7HSPo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(1, 4):\n",
        "  response = requests.get(\"https://www.imdb.com/list/ls006266952/?sort=list_order,asc&st_dt=&mode=detail&page=%d\"%i)\n",
        "  soup = BeautifulSoup(response.content, \"html.parser\")\n",
        "  medm += soup.find_all(\"div\", class_ = \"lister-item mode-detail\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vuAt8ujPHSxt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(1, 251, 50):\n",
        "  response = requests.get(\"https://www.imdb.com/search/title/?groups=bottom_250&sort=user_rating,asc&start=%d&ref_=adv_nxt\"%i)\n",
        "  soup = BeautifulSoup(response.content, \"html.parser\")\n",
        "  badm += soup.find_all(\"div\", class_ = \"lister-item-content\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEyFUiMCJLFM",
        "colab_type": "text"
      },
      "source": [
        "Our dataframe will consist of the features:\n",
        "\n",
        "types: which group the movie was webscrapped from: good, mediocre, bad\n",
        "\n",
        "title: the title of the movie\n",
        "\n",
        "year: the year the movie was made\n",
        "\n",
        "rating: the rating given to the movie: PG-13, R, PG, etc\n",
        "\n",
        "runtime: the runtime of the movie\n",
        "\n",
        "genre: the genre of the movie: action, comedy, adventure, etc\n",
        "\n",
        "star-rating: the rating given by the user\n",
        "\n",
        "metascore: the rating given by the critic\n",
        "\n",
        "description: the description of the movie\n",
        "\n",
        "director: the director of the movie\n",
        "\n",
        "cast: the list of actors and actresses in the movie\n",
        "\n",
        "votes: the number of people that voted for the movie\n",
        "\n",
        "gross: the earnings of that movie with adjustment for inflation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGzzG6BbGYqM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_movies = pd.DataFrame(columns = [\"types\", \"title\", \"year\", \"rating\",\n",
        "                                    \"runtime\", \"genre\", \"star-rating\",\n",
        "                                    \"metascore\", \"description\", \"director\", \n",
        "                                    \"cast\", \"votes\", \"gross\"])\n",
        "types = []\n",
        "title =[]\n",
        "year = []\n",
        "rating = []\n",
        "runtime = []\n",
        "genre = []\n",
        "star_rating = []\n",
        "metascore = []\n",
        "description = []\n",
        "directors = []\n",
        "cast = []\n",
        "votes = []\n",
        "gross = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZszH92u-Kto0",
        "colab_type": "text"
      },
      "source": [
        "The three chunks below show the code for webscrapping from the website. Even though the pages were all from imdb, the pages were coded differently which was why we needed to do 3 individual for loops for each page we scrapped. We also did some data cleaning in here as well since some of the variables had extra symbols or letters next to the values. We also adjusted for inflation in the gross variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EBkmwgzGd2w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for div in goodm:\n",
        "  title_tags = div.find_all(\"a\", href = re.compile(\"title\"))\n",
        "  title.append(title_tags[1].text)\n",
        "\n",
        "  yr = div.find(\"span\", class_ = \"lister-item-year text-muted unbold\").text\n",
        "  yr = yr.replace(\"(I) \",\"\")\n",
        "  yr = yr.replace(\"(\",\"\")\n",
        "  yr = yr.replace(\")\",\"\")\n",
        "  year.append(yr)\n",
        "\n",
        "  rating.append((\"-1\" if div.find(\"span\", class_ = \"certificate\") is None \n",
        "                 else div.find(\"span\", class_ = \"certificate\").text))\n",
        "  \n",
        "  rt = (div.find(\"span\", class_ = \"runtime\").text)\n",
        "  rt = rt.replace(\" min\", \"\")\n",
        "  runtime.append(rt)\n",
        "\n",
        "  g = (div.find(\"span\", class_ = \"genre\").text)\n",
        "  g = g.replace(\"\\n\",\"\")\n",
        "  g = g.replace(\" \", \"\")\n",
        "  genre.append(g)\n",
        "  \n",
        "  star_rating.append(div.find(\"span\", class_ = \"ipl-rating-star__rating\").text)\n",
        "  \n",
        "  ms = ((\"nan\" if div.find(\"span\", class_ = \"metascore\") is None\n",
        "                    else div.find(\"span\", class_ = \"metascore\").text))\n",
        "  ms = ms.replace(\" \",\"\")\n",
        "  metascore.append(ms)\n",
        "\n",
        "  d = (div.find(\"p\", class_ = \"\").text)\n",
        "  d = d.replace(\"\\n\",\"\")\n",
        "  description.append(d)\n",
        "\n",
        "  spans = div.find_all(\"span\", class_ = \"\")\n",
        "\n",
        "  v = (spans[1].text)\n",
        "\n",
        "  gr = (spans[-1].text)\n",
        "  gr = gr.replace(\"$\",\"\")\n",
        "  gr = gr.replace(\"M\",\"\")\n",
        "  if gr == v:\n",
        "    gr = \"nan\"\n",
        "  if gr != \"nan\":\n",
        "    gr = float(gr)\n",
        "    gr = gr * (10 ** 6)\n",
        "    response = requests.get(\"https://inflation-api.herokuapp.com/api/?value=\"+ str(gr) +\"&year=\"+yr)\n",
        "    resp = response.json()\n",
        "    gr = resp[\"response\"][\"adjustedValue\"]\n",
        "  v = v.replace(\",\",\"\")\n",
        "  votes.append(v)\n",
        "  gross.append(gr)\n",
        "  \n",
        "\n",
        "  name_tags = div.find_all(\"a\", href = re.compile(\"name\"))\n",
        "\n",
        "  director = name_tags[0].text\n",
        "  directors.append(director)\n",
        "  members = name_tags[1:]\n",
        "  for i in range(len(members)):\n",
        "    members[i] = members[i].text\n",
        "  members = \" \".join(members)\n",
        "  cast.append(members)\n",
        "  types.append(\"good\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFjMpIKBGd_s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for div in medm:\n",
        "  title_tags = div.find_all(\"a\", href = re.compile(\"title\"))\n",
        "  title.append(title_tags[1].text)\n",
        "\n",
        "  yr = div.find(\"span\", class_ = \"lister-item-year text-muted unbold\").text\n",
        "  yr = yr.replace(\"(I) \",\"\")\n",
        "  yr = yr.replace(\"(\",\"\")\n",
        "  yr = yr.replace(\")\",\"\")\n",
        "  year.append(yr)\n",
        "\n",
        "  rating.append((\"-1\" if div.find(\"span\", class_ = \"certificate\") is None \n",
        "                 else div.find(\"span\", class_ = \"certificate\").text))\n",
        "  \n",
        "  rt = (div.find(\"span\", class_ = \"runtime\").text)\n",
        "  rt = rt.replace(\" min\", \"\")\n",
        "  runtime.append(rt)\n",
        "\n",
        "  g = (div.find(\"span\", class_ = \"genre\").text)\n",
        "  g = g.replace(\"\\n\",\"\")\n",
        "  g = g.replace(\" \", \"\")\n",
        "  genre.append(g)\n",
        "  \n",
        "  star_rating.append(div.find(\"span\", class_ = \"ipl-rating-star__rating\").text)\n",
        "  \n",
        "  ms = ((\"nan\" if div.find(\"span\", class_ = \"metascore\") is None\n",
        "                    else div.find(\"span\", class_ = \"metascore\").text))\n",
        "  ms = ms.replace(\" \",\"\")\n",
        "  metascore.append(ms)\n",
        "\n",
        "  d = (div.find(\"p\", class_ = \"\").text)\n",
        "  d = d.replace(\"\\n\",\"\")\n",
        "  description.append(d)\n",
        "\n",
        "  spans = div.find_all(\"span\", class_ = \"\")\n",
        "\n",
        "  v = (spans[1].text)\n",
        "\n",
        "  gr = (spans[-1].text)\n",
        "  gr = gr.replace(\"$\",\"\")\n",
        "  gr = gr.replace(\"M\",\"\")\n",
        "  if gr == v:\n",
        "    gr = \"nan\"\n",
        "  if gr != \"nan\":\n",
        "    gr = float(gr)\n",
        "    gr = gr * (10 ** 6)\n",
        "    response = requests.get(\"https://inflation-api.herokuapp.com/api/?value=\"+ str(gr) +\"&year=\"+yr)\n",
        "    resp = response.json()\n",
        "    gr = resp[\"response\"][\"adjustedValue\"]\n",
        "  v = v.replace(\",\",\"\")\n",
        "  votes.append(v)\n",
        "  gross.append(gr)\n",
        "  \n",
        "\n",
        "  name_tags = div.find_all(\"a\", href = re.compile(\"name\"))\n",
        "\n",
        "  director = name_tags[0].text\n",
        "  directors.append(director)\n",
        "  members = name_tags[1:]\n",
        "  for i in range(len(members)):\n",
        "    members[i] = members[i].text\n",
        "  members = \" \".join(members)\n",
        "  cast.append(members)\n",
        "  types.append(\"med\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drmx1sRTGplD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for div in badm:\n",
        "  title_tags = div.find_all(\"a\", href = re.compile(\"title\"))\n",
        "  title.append(title_tags[0].text)\n",
        "\n",
        "  yr = div.find(\"span\", class_ = \"lister-item-year text-muted unbold\").text\n",
        "  yr = yr.replace(\"(I) \",\"\")\n",
        "  yr = yr.replace(\"(\",\"\")\n",
        "  yr = yr.replace(\")\",\"\")\n",
        "  year.append(yr)\n",
        "\n",
        "  rating.append((\"nan\" if div.find(\"span\", class_= \"certificate\") is None \n",
        "                 else div.find(\"span\", class_=\"certificate\").text))\n",
        "  \n",
        "  rt = (div.find(\"span\", class_ = \"runtime\").text)\n",
        "  rt = rt.replace(\" min\", \"\")\n",
        "  runtime.append(rt)\n",
        "\n",
        "  g = (div.find(\"span\", class_ = \"genre\").text)\n",
        "  g = g.replace(\"\\n\",\"\")\n",
        "  g = g.replace(\" \", \"\")\n",
        "  genre.append(g)\n",
        "  \n",
        "  star_rating.append(\"nan\" if div.find(\"strong\") is None\n",
        "                     else div.find(\"strong\").text)\n",
        "  \n",
        "  ms = ((\"nan\" if div.find(\"span\", class_ = \"metascore\") is None\n",
        "                    else div.find(\"span\", class_ = \"metascore\").text))\n",
        "  ms = ms.replace(\" \",\"\")\n",
        "  metascore.append(ms)\n",
        "\n",
        "  d = (div.find(\"p\", class_ = \"\").text)\n",
        "  d = d.replace(\"\\n\",\"\")\n",
        "  description.append(d)\n",
        "\n",
        "  spans = div.select('span[name^=\\\"nv\\\"]')\n",
        "\n",
        "  if len(spans) > 0:\n",
        "    v = (spans[0].text)\n",
        "    gr = \"nan\"\n",
        "    if len(spans) > 1:\n",
        "      gr = (spans[1].text)\n",
        "      gr = gr.replace(\"$\",\"\")\n",
        "      gr = gr.replace(\"M\",\"\")\n",
        "    if gr != \"nan\":\n",
        "      gr = float(gr)\n",
        "      gr = gr * (10 ** 6)\n",
        "      response = requests.get(\"https://inflation-api.herokuapp.com/api/?value=\"+ str(gr) +\"&year=\"+yr)\n",
        "      resp = response.json()\n",
        "      gr = resp[\"response\"][\"adjustedValue\"]\n",
        "    v = v.replace(\",\",\"\")\n",
        "    votes.append(v)\n",
        "    gross.append(gr)\n",
        "  else:\n",
        "    votes.append(\"nan\")\n",
        "    gross.append(\"nan\")\n",
        "  \n",
        "\n",
        "  name_tags = div.find_all(\"a\", href = re.compile(\"name\"))\n",
        "\n",
        "  director = name_tags[0].text\n",
        "  directors.append(director)\n",
        "  members = name_tags[1:]\n",
        "  for i in range(len(members)):\n",
        "    members[i] = members[i].text\n",
        "  members = \" \".join(members)\n",
        "  cast.append(members)\n",
        "  types.append(\"bad\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oi9Y8QzILbv3",
        "colab_type": "text"
      },
      "source": [
        "Once we webscrapped all the data for the variables, we assigned them into the dataframe. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08uVZdmCGyUE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_movies[\"types\"] = types\n",
        "df_movies[\"title\"] = title\n",
        "df_movies[\"year\"] = year\n",
        "df_movies[\"rating\"] = rating\n",
        "df_movies[\"runtime\"] = runtime\n",
        "df_movies[\"genre\"] = genre\n",
        "df_movies[\"star-rating\"] = star_rating\n",
        "df_movies[\"metascore\"] = metascore\n",
        "df_movies[\"description\"] = description\n",
        "df_movies[\"director\"] = directors\n",
        "df_movies[\"cast\"] = cast\n",
        "df_movies[\"votes\"] = votes\n",
        "df_movies[\"gross\"] = gross"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxo56URpLgau",
        "colab_type": "text"
      },
      "source": [
        "We had to change a lot of the features to a numeric type for future plots and analysis. We also noticed that the variable year had other letters and words attached, so we had to rid of those. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1cOB84lG1TV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_movies[\"star-rating\"] = df_movies[\"star-rating\"].astype(float)\n",
        "df_movies[\"metascore\"] = df_movies[\"metascore\"].astype(float)\n",
        "df_movies[\"votes\"] = df_movies[\"votes\"].astype(int)\n",
        "df_movies[\"gross\"] = df_movies[\"gross\"].astype(float)\n",
        "df_movies[\"runtime\"] = df_movies[\"runtime\"].astype(int)\n",
        "df_movies[\"types\"] = df_movies[\"types\"].astype('category')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bomc_8ThG4Qe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_movies[\"year\"] = df_movies[\"year\"].str.replace(\"I\",\"\")\n",
        "df_movies[\"year\"] = df_movies[\"year\"].str.replace(\" \",\"\")\n",
        "df_movies[\"year\"] = df_movies[\"year\"].str.replace(\"TVMovie\",\"\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5InrdKrHAb8",
        "colab_type": "code",
        "outputId": "7d2eecc9-3ec2-46b8-a9ec-f2bbf9d39127",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "source": [
        "df_movies"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>types</th>\n",
              "      <th>title</th>\n",
              "      <th>year</th>\n",
              "      <th>rating</th>\n",
              "      <th>runtime</th>\n",
              "      <th>genre</th>\n",
              "      <th>star-rating</th>\n",
              "      <th>metascore</th>\n",
              "      <th>description</th>\n",
              "      <th>director</th>\n",
              "      <th>cast</th>\n",
              "      <th>votes</th>\n",
              "      <th>gross</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>good</td>\n",
              "      <td>The Shawshank Redemption</td>\n",
              "      <td>1994</td>\n",
              "      <td>R</td>\n",
              "      <td>142</td>\n",
              "      <td>Drama</td>\n",
              "      <td>9.3</td>\n",
              "      <td>80.0</td>\n",
              "      <td>Two imprisoned men bond over a number of y...</td>\n",
              "      <td>Frank Darabont</td>\n",
              "      <td>Tim Robbins Morgan Freeman Bob Gunton William ...</td>\n",
              "      <td>2200597</td>\n",
              "      <td>4.608596e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>good</td>\n",
              "      <td>The Godfather</td>\n",
              "      <td>1972</td>\n",
              "      <td>R</td>\n",
              "      <td>175</td>\n",
              "      <td>Crime,Drama</td>\n",
              "      <td>9.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>The aging patriarch of an organized crime ...</td>\n",
              "      <td>Francis Ford Coppola</td>\n",
              "      <td>Marlon Brando Al Pacino James Caan Diane Keaton</td>\n",
              "      <td>1516220</td>\n",
              "      <td>7.763191e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>good</td>\n",
              "      <td>The Dark Knight</td>\n",
              "      <td>2008</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>152</td>\n",
              "      <td>Action,Crime,Drama</td>\n",
              "      <td>9.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>When the menace known as the Joker wreaks ...</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>Christian Bale Heath Ledger Aaron Eckhart Mich...</td>\n",
              "      <td>2182182</td>\n",
              "      <td>5.987053e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>good</td>\n",
              "      <td>The Godfather: Part II</td>\n",
              "      <td>1974</td>\n",
              "      <td>R</td>\n",
              "      <td>202</td>\n",
              "      <td>Crime,Drama</td>\n",
              "      <td>9.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>The early life and career of Vito Corleone...</td>\n",
              "      <td>Francis Ford Coppola</td>\n",
              "      <td>Al Pacino Robert De Niro Robert Duvall Diane K...</td>\n",
              "      <td>1062183</td>\n",
              "      <td>2.801075e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>good</td>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>1994</td>\n",
              "      <td>R</td>\n",
              "      <td>154</td>\n",
              "      <td>Crime,Drama</td>\n",
              "      <td>8.9</td>\n",
              "      <td>94.0</td>\n",
              "      <td>The lives of two mob hitmen, a boxer, a ga...</td>\n",
              "      <td>Quentin Tarantino</td>\n",
              "      <td>John Travolta Uma Thurman Samuel L. Jackson Br...</td>\n",
              "      <td>1729312</td>\n",
              "      <td>1.755137e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>795</th>\n",
              "      <td>bad</td>\n",
              "      <td>The Twilight Saga: New Moon</td>\n",
              "      <td>2009</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>130</td>\n",
              "      <td>Drama,Fantasy,Romance</td>\n",
              "      <td>4.7</td>\n",
              "      <td>44.0</td>\n",
              "      <td>PG-13|130 min|Drama, Fantasy, Romance</td>\n",
              "      <td>Chris Weitz</td>\n",
              "      <td>Kristen Stewart Robert Pattinson Taylor Lautne...</td>\n",
              "      <td>255321</td>\n",
              "      <td>3.332654e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>796</th>\n",
              "      <td>bad</td>\n",
              "      <td>Zoolander 2</td>\n",
              "      <td>2016</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>101</td>\n",
              "      <td>Action,Adventure,Comedy</td>\n",
              "      <td>4.7</td>\n",
              "      <td>34.0</td>\n",
              "      <td>PG-13|101 min|Action, Adventure, Comedy       ...</td>\n",
              "      <td>Ben Stiller</td>\n",
              "      <td>Ben Stiller Owen Wilson Penélope Cruz Will Fer...</td>\n",
              "      <td>62148</td>\n",
              "      <td>2.885000e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>797</th>\n",
              "      <td>bad</td>\n",
              "      <td>God's Not Dead</td>\n",
              "      <td>2014</td>\n",
              "      <td>PG</td>\n",
              "      <td>113</td>\n",
              "      <td>Drama</td>\n",
              "      <td>4.7</td>\n",
              "      <td>16.0</td>\n",
              "      <td>PG|113 min|Drama</td>\n",
              "      <td>Harold Cronk</td>\n",
              "      <td>Shane Harper Kevin Sorbo David A.R. White Dean...</td>\n",
              "      <td>38989</td>\n",
              "      <td>6.186379e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>798</th>\n",
              "      <td>bad</td>\n",
              "      <td>Meet the Blacks</td>\n",
              "      <td>2016</td>\n",
              "      <td>R</td>\n",
              "      <td>94</td>\n",
              "      <td>Comedy,Horror</td>\n",
              "      <td>4.8</td>\n",
              "      <td>26.0</td>\n",
              "      <td>R|94 min|Comedy, Horror</td>\n",
              "      <td>Deon Taylor</td>\n",
              "      <td>Mike Epps Zulay Henao Alex Henderson Bresha Webb</td>\n",
              "      <td>35249</td>\n",
              "      <td>9.090000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>799</th>\n",
              "      <td>bad</td>\n",
              "      <td>Nothing to Lose</td>\n",
              "      <td>2018</td>\n",
              "      <td>PG</td>\n",
              "      <td>134</td>\n",
              "      <td>Biography,Drama</td>\n",
              "      <td>5.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PG|134 min|Biography, Drama</td>\n",
              "      <td>Alexandre Avancini</td>\n",
              "      <td>Petrônio Gontijo Day Mesquita Beth Goulart Dal...</td>\n",
              "      <td>22794</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>800 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    types                        title  ...    votes         gross\n",
              "0    good     The Shawshank Redemption  ...  2200597  4.608596e+07\n",
              "1    good                The Godfather  ...  1516220  7.763191e+08\n",
              "2    good              The Dark Knight  ...  2182182  5.987053e+08\n",
              "3    good       The Godfather: Part II  ...  1062183  2.801075e+08\n",
              "4    good                 Pulp Fiction  ...  1729312  1.755137e+08\n",
              "..    ...                          ...  ...      ...           ...\n",
              "795   bad  The Twilight Saga: New Moon  ...   255321  3.332654e+08\n",
              "796   bad                  Zoolander 2  ...    62148  2.885000e+07\n",
              "797   bad               God's Not Dead  ...    38989  6.186379e+07\n",
              "798   bad              Meet the Blacks  ...    35249  9.090000e+06\n",
              "799   bad              Nothing to Lose  ...    22794           NaN\n",
              "\n",
              "[800 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    }
  ]
}