Movies_Data_Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "import numpy as np\n",
    "import ast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BEST MOVIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "oscars_csv_file = \"Resources/oscar.csv\" #dont forget to edit\n",
    "oscars_df = pd.read_csv(oscars_csv_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Year</th>\n",
       "      <th>Ceremony</th>\n",
       "      <th>Award</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Name</th>\n",
       "      <th>Film</th>\n",
       "      <th>Film2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>1944</td>\n",
       "      <td>17</td>\n",
       "      <td>Best Motion Picture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Double Indemnity</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>Paramount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>1944</td>\n",
       "      <td>17</td>\n",
       "      <td>Best Motion Picture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gaslight</td>\n",
       "      <td>Metro-Goldwyn-Mayer</td>\n",
       "      <td>Metro-Goldwyn-Mayer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>1944</td>\n",
       "      <td>17</td>\n",
       "      <td>Best Motion Picture</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Going My Way</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>Paramount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>1944</td>\n",
       "      <td>17</td>\n",
       "      <td>Best Motion Picture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Since You Went Away</td>\n",
       "      <td>Selznick International Pictures</td>\n",
       "      <td>Selznick International Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>1944</td>\n",
       "      <td>17</td>\n",
       "      <td>Best Motion Picture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>20th Century-Fox</td>\n",
       "      <td>20th Century-Fox</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Ceremony                Award  Winner                  Name  \\\n",
       "1752  1944        17  Best Motion Picture     NaN     Double Indemnity    \n",
       "1753  1944        17  Best Motion Picture     NaN             Gaslight    \n",
       "1754  1944        17  Best Motion Picture     1.0         Going My Way    \n",
       "1755  1944        17  Best Motion Picture     NaN  Since You Went Away    \n",
       "1756  1944        17  Best Motion Picture     NaN               Wilson    \n",
       "\n",
       "                                 Film                            Film2  \n",
       "1752                        Paramount                        Paramount  \n",
       "1753              Metro-Goldwyn-Mayer              Metro-Goldwyn-Mayer  \n",
       "1754                        Paramount                        Paramount  \n",
       "1755  Selznick International Pictures  Selznick International Pictures  \n",
       "1756                 20th Century-Fox                 20th Century-Fox  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_movie = oscars_df.loc[(oscars_df['Award'] == 'Best Motion Picture') | (oscars_df['Award'] == 'Best Picture')]\n",
    "best_movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Year</th>\n",
       "      <th>Name</th>\n",
       "      <th>Film</th>\n",
       "      <th>Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>1944</td>\n",
       "      <td>Double Indemnity</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>1944</td>\n",
       "      <td>Gaslight</td>\n",
       "      <td>Metro-Goldwyn-Mayer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>1944</td>\n",
       "      <td>Going My Way</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>1944</td>\n",
       "      <td>Since You Went Away</td>\n",
       "      <td>Selznick International Pictures</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>1944</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>20th Century-Fox</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year                 Name                             Film  Winner\n",
       "1752  1944     Double Indemnity                        Paramount     NaN\n",
       "1753  1944             Gaslight              Metro-Goldwyn-Mayer     NaN\n",
       "1754  1944         Going My Way                        Paramount     1.0\n",
       "1755  1944  Since You Went Away  Selznick International Pictures     NaN\n",
       "1756  1944               Wilson                 20th Century-Fox     NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning the data\n",
    "best_movie = best_movie[['Year', 'Name', 'Film', 'Winner']]\n",
    "\n",
    "best_movie = best_movie.apply(lambda x: x.str.strip() if x.dtype == \"object\" else x)\n",
    "\n",
    "best_movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_movie = best_movie.rename(columns = {'Year' : 'release_year', \n",
    "                                          'Name':'title', \n",
    "                                          'Film':'production_companies',\n",
    "                                          'Winner':'win'})\n",
    "\n",
    "best_movie['win'] = np.where(best_movie['win']==1.0, 'Y', 'N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>release_year</th>\n",
       "      <th>title</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>1944</td>\n",
       "      <td>Double Indemnity</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>1944</td>\n",
       "      <td>Gaslight</td>\n",
       "      <td>Metro-Goldwyn-Mayer</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>1944</td>\n",
       "      <td>Going My Way</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>1944</td>\n",
       "      <td>Since You Went Away</td>\n",
       "      <td>Selznick International Pictures</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>1944</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>20th Century-Fox</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     release_year                title             production_companies win\n",
       "1752         1944     Double Indemnity                        Paramount   N\n",
       "1753         1944             Gaslight              Metro-Goldwyn-Mayer   N\n",
       "1754         1944         Going My Way                        Paramount   Y\n",
       "1755         1944  Since You Went Away  Selznick International Pictures   N\n",
       "1756         1944               Wilson                 20th Century-Fox   N"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "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>imdb_id</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0114709</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>30/10/1995</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0113497</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>15/12/1995</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0113228</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>22/12/1995</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0114885</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>22/12/1995</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0113041</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>10/02/1995</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id                        title release_date release_year\n",
       "0  tt0114709                    Toy Story   30/10/1995         1995\n",
       "1  tt0113497                      Jumanji   15/12/1995         1995\n",
       "2  tt0113228             Grumpier Old Men   22/12/1995         1995\n",
       "3  tt0114885            Waiting to Exhale   22/12/1995         1995\n",
       "4  tt0113041  Father of the Bride Part II   10/02/1995         1995"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the imdb_id variables\n",
    "movie_ids = pd.read_csv(\"movies_metadata.csv\")\n",
    "movie_ids = movie_ids[['imdb_id', 'title', 'release_date']]\n",
    "#for windows use:\n",
    "movie_ids['release_year'] = movie_ids['release_date'].str[-4:]\n",
    "#for macs use:\n",
    "#movie_ids['release_year'] = movie_ids['release_date'].str[:4]\n",
    "movie_ids.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding the IDs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>release_year</th>\n",
       "      <th>title</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>win</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>release_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1944</td>\n",
       "      <td>Double Indemnity</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>N</td>\n",
       "      <td>tt0036775</td>\n",
       "      <td>24/04/1944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  release_year             title production_companies win    imdb_id  \\\n",
       "0         1944  Double Indemnity            Paramount   N  tt0036775   \n",
       "\n",
       "  release_date  \n",
       "0   24/04/1944  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_movie_ids = pd.merge(best_movie, \n",
    "                          movie_ids, on=['title', 'release_year'], how='left')\n",
    "best_movie = best_movie_ids\n",
    "best_movie.loc[best_movie['title'] == \"Double Indemnity\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RATINGS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1425941529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1425942435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>858</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1425941523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1221</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1425941546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1246</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1425941556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1      110     1.0  1425941529\n",
       "1       1      147     4.5  1425942435\n",
       "2       1      858     5.0  1425941523\n",
       "3       1     1221     5.0  1425941546\n",
       "4       1     1246     5.0  1425941556"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the ratings data\n",
    "ratings_csv_file = 'Resources/ratings.csv' #dont forget to edit\n",
    "ratings_df = pd.read_csv(ratings_csv_file)\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>userId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>147</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>858</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1221</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1246</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId  rating  userId\n",
       "0      110     1.0       1\n",
       "1      147     4.5       1\n",
       "2      858     5.0       1\n",
       "3     1221     5.0       1\n",
       "4     1246     5.0       1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selectiong relevant variables\n",
    "clean_ratings_df = ratings_df[['movieId', 'rating', 'userId']].copy()\n",
    "clean_ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>movieId</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "      <td>862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>113497</td>\n",
       "      <td>8844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>113228</td>\n",
       "      <td>15602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>114885</td>\n",
       "      <td>31357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>113041</td>\n",
       "      <td>11862.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId  imdbId   tmdbId\n",
       "0        1  114709    862.0\n",
       "1        2  113497   8844.0\n",
       "2        3  113228  15602.0\n",
       "3        4  114885  31357.0\n",
       "4        5  113041  11862.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the movie id\n",
    "links_csv_file = ('Resources/links.csv') #dont forget to edit\n",
    "links_df = pd.read_csv(links_csv_file)\n",
    "links_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>movieId</th>\n",
       "      <th>imdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>113497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>113228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>114885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>113041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId  imdbId\n",
       "0        1  114709\n",
       "1        2  113497\n",
       "2        3  113228\n",
       "3        4  114885\n",
       "4        5  113041"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning IDs\n",
    "links_clean_df = links_df[[\"movieId\",\"imdbId\"]].copy()\n",
    "links_clean_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>movieId</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>rating</th>\n",
       "      <th>userId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId  imdbId  rating  userId\n",
       "0        1  114709     4.0     8.0\n",
       "1        1  114709     4.5     9.0\n",
       "2        1  114709     4.0    12.0\n",
       "3        1  114709     4.0    20.0\n",
       "4        1  114709     4.0    24.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reviews with movie ids\n",
    "reviews_w_movie_ids = pd.merge(links_clean_df, clean_ratings_df, on ='movieId', how = 'left')\n",
    "reviews_w_movie_ids.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>imdb_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>reviewer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt114709</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt113497</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt113228</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt114885</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt113041</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    imdb_id  rating  reviewer_id\n",
       "0  tt114709     4.0          8.0\n",
       "1  tt113497     4.5          9.0\n",
       "2  tt113228     4.0         12.0\n",
       "3  tt114885     4.0         20.0\n",
       "4  tt113041     4.0         24.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making the movie ids the same across all tables\n",
    "reviews_w_movie_ids['imdbId'] = 'tt' + links_df['imdbId'].astype(str)\n",
    "reviews_w_movie_ids = reviews_w_movie_ids.rename(columns = {'userId' : 'reviewer_id', \n",
    "                                                            'imdbId':'imdb_id'})\n",
    "reviews_w_movie_ids = reviews_w_movie_ids[['imdb_id','rating', 'reviewer_id']]\n",
    "reviews_w_movie_ids.head()\n",
    "ratings = reviews_w_movie_ids\n",
    "ratings = ratings.dropna()\n",
    "ratings = ratings.drop_duplicates()\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ALL MOVIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>adult</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
       "      <td>30000000</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
       "      <td>http://toystory.disney.com/toy-story</td>\n",
       "      <td>862</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>30/10/1995</td>\n",
       "      <td>373554033.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>False</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65000000</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8844</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>en</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>...</td>\n",
       "      <td>15/12/1995</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Roll the dice and unleash the excitement!</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>False</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15602</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>en</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>A family wedding reignites the ancient feud be...</td>\n",
       "      <td>...</td>\n",
       "      <td>22/12/1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>False</td>\n",
       "      <td>6.5</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31357</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>en</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
       "      <td>...</td>\n",
       "      <td>22/12/1995</td>\n",
       "      <td>81452156.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Friends are the people who let you be yourself...</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>False</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11862</td>\n",
       "      <td>tt0113041</td>\n",
       "      <td>en</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Just when George Banks has recovered from his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>10/02/1995</td>\n",
       "      <td>76578911.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>False</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult                              belongs_to_collection    budget  \\\n",
       "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
       "1  False                                                NaN  65000000   \n",
       "2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
       "3  False                                                NaN  16000000   \n",
       "4  False  {'id': 96871, 'name': 'Father of the Bride Col...         0   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1                                                  t   \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
       "4                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "\n",
       "                               homepage     id    imdb_id original_language  \\\n",
       "0  http://toystory.disney.com/toy-story    862  tt0114709                en   \n",
       "1                                   NaN   8844  tt0113497                en   \n",
       "2                                   NaN  15602  tt0113228                en   \n",
       "3                                   NaN  31357  tt0114885                en   \n",
       "4                                   NaN  11862  tt0113041                en   \n",
       "\n",
       "                original_title  \\\n",
       "0                    Toy Story   \n",
       "1                      Jumanji   \n",
       "2             Grumpier Old Men   \n",
       "3            Waiting to Exhale   \n",
       "4  Father of the Bride Part II   \n",
       "\n",
       "                                            overview  ...  release_date  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...  ...    30/10/1995   \n",
       "1  When siblings Judy and Peter discover an encha...  ...    15/12/1995   \n",
       "2  A family wedding reignites the ancient feud be...  ...    22/12/1995   \n",
       "3  Cheated on, mistreated and stepped on, the wom...  ...    22/12/1995   \n",
       "4  Just when George Banks has recovered from his ...  ...    10/02/1995   \n",
       "\n",
       "       revenue runtime                                   spoken_languages  \\\n",
       "0  373554033.0    81.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "1  262797249.0   104.0  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   \n",
       "2          0.0   101.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "3   81452156.0   127.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "4   76578911.0   106.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "\n",
       "     status                                            tagline  \\\n",
       "0  Released                                                NaN   \n",
       "1  Released          Roll the dice and unleash the excitement!   \n",
       "2  Released  Still Yelling. Still Fighting. Still Ready for...   \n",
       "3  Released  Friends are the people who let you be yourself...   \n",
       "4  Released  Just When His World Is Back To Normal... He's ...   \n",
       "\n",
       "                         title  video vote_average vote_count  \n",
       "0                    Toy Story  False          7.7     5415.0  \n",
       "1                      Jumanji  False          6.9     2413.0  \n",
       "2             Grumpier Old Men  False          6.5       92.0  \n",
       "3            Waiting to Exhale  False          6.1       34.0  \n",
       "4  Father of the Bride Part II  False          5.7      173.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_csv_file = \"movies_metadata.csv\"#dont forget to edit this\n",
    "movie_df = pd.read_csv(movie_csv_file)\n",
    "movie_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>budget</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>production_companies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000000</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>30/10/1995</td>\n",
       "      <td>373554033.0</td>\n",
       "      <td>[{\"name\": \"Pixar Animation Studios\", \"id\": 3}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65000000</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>15/12/1995</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>[{\"name\": \"TriStar Pictures\", \"id\": 559}, {\"na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>22/12/1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[{\"name\": \"Warner Bros.\", \"id\": 6194}, {\"name\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16000000</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>22/12/1995</td>\n",
       "      <td>81452156.0</td>\n",
       "      <td>[{\"name\": \"Twentieth Century Fox Film Corporat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tt0113041</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>10/02/1995</td>\n",
       "      <td>76578911.0</td>\n",
       "      <td>[{\"name\": \"Sandollar Productions\", \"id\": 5842}...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     budget    imdb_id                        title release_date      revenue  \\\n",
       "0  30000000  tt0114709                    Toy Story   30/10/1995  373554033.0   \n",
       "1  65000000  tt0113497                      Jumanji   15/12/1995  262797249.0   \n",
       "2         0  tt0113228             Grumpier Old Men   22/12/1995          0.0   \n",
       "3  16000000  tt0114885            Waiting to Exhale   22/12/1995   81452156.0   \n",
       "4         0  tt0113041  Father of the Bride Part II   10/02/1995   76578911.0   \n",
       "\n",
       "                                production_companies  \n",
       "0     [{\"name\": \"Pixar Animation Studios\", \"id\": 3}]  \n",
       "1  [{\"name\": \"TriStar Pictures\", \"id\": 559}, {\"na...  \n",
       "2  [{\"name\": \"Warner Bros.\", \"id\": 6194}, {\"name\"...  \n",
       "3  [{\"name\": \"Twentieth Century Fox Film Corporat...  \n",
       "4  [{\"name\": \"Sandollar Productions\", \"id\": 5842}...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting relevant variables\n",
    "clean_movie_df = movie_df[['budget','imdb_id', 'title', 'release_date','revenue', 'production_companies']]\n",
    "clean_movie_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "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>budget</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>production_companies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000000</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>30/10/1995</td>\n",
       "      <td>373554033.0</td>\n",
       "      <td>Pixar Animation Studios</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65000000</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>15/12/1995</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>TriStar Pictures, Teitler Film, Interscope Com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>22/12/1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Warner Bros., Lancaster Gate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16000000</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>22/12/1995</td>\n",
       "      <td>81452156.0</td>\n",
       "      <td>Twentieth Century Fox Film Corporation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tt0113041</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>10/02/1995</td>\n",
       "      <td>76578911.0</td>\n",
       "      <td>Sandollar Productions, Touchstone Pictures</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     budget    imdb_id                        title release_date      revenue  \\\n",
       "0  30000000  tt0114709                    Toy Story   30/10/1995  373554033.0   \n",
       "1  65000000  tt0113497                      Jumanji   15/12/1995  262797249.0   \n",
       "2         0  tt0113228             Grumpier Old Men   22/12/1995          0.0   \n",
       "3  16000000  tt0114885            Waiting to Exhale   22/12/1995   81452156.0   \n",
       "4         0  tt0113041  Father of the Bride Part II   10/02/1995   76578911.0   \n",
       "\n",
       "                                production_companies  \n",
       "0                            Pixar Animation Studios  \n",
       "1  TriStar Pictures, Teitler Film, Interscope Com...  \n",
       "2                       Warner Bros., Lancaster Gate  \n",
       "3             Twentieth Century Fox Film Corporation  \n",
       "4         Sandollar Productions, Touchstone Pictures  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('name ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('id ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('1 ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('2 ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('3 ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('4 ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('5 ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('6 ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('7 ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('8 ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('9 ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('9 ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('0 ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace(': ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('\"\"\" ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace(', \"\" ?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('\"?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('\\[?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('\\]?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('\\{?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace('\\}?' , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace(\"'''?\" , '')\n",
    "clean_movie_df['production_companies'] = clean_movie_df.production_companies.str.replace(\"', ?\" , '')\n",
    "\n",
    "clean_movie_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "budget                  45332\n",
       "imdb_id                 45332\n",
       "title                   45332\n",
       "release_date            45332\n",
       "revenue                 45332\n",
       "production_companies    45332\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_movies = clean_movie_df.dropna()\n",
    "all_movies = all_movies.drop_duplicates()\n",
    "all_movies.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPORTING THE DATASETS\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to Local DataBase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter user name:postgres\n",
      "Password:magic_table\n"
     ]
    }
   ],
   "source": [
    "user_name = input('Enter user name:')\n",
    "password = input('Password:')\n",
    "rds_connection_string = \"{0}:{1}@localhost:5432/Magic_Table_db\".format(user_name, password)\n",
    "engine = create_engine(f'postgresql://{rds_connection_string}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['all_movies', 'best_movies', 'ratings']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine.table_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use pandas to load csv converted DataFrame into database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load all_movies to SQL data base\n",
    "all_movies.to_sql(name='all_movies', con=engine, if_exists='append', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check to see if load was successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>budget</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>production_companies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000000.0</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>30/10/1995</td>\n",
       "      <td>373554033.0</td>\n",
       "      <td>Pixar Animation Studios</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65000000.0</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>15/12/1995</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>TriStar Pictures, Teitler Film, Interscope Com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>22/12/1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Warner Bros., Lancaster Gate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16000000.0</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>22/12/1995</td>\n",
       "      <td>81452156.0</td>\n",
       "      <td>Twentieth Century Fox Film Corporation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>tt0113041</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>10/02/1995</td>\n",
       "      <td>76578911.0</td>\n",
       "      <td>Sandollar Productions, Touchstone Pictures</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       budget    imdb_id                        title release_date  \\\n",
       "0  30000000.0  tt0114709                    Toy Story   30/10/1995   \n",
       "1  65000000.0  tt0113497                      Jumanji   15/12/1995   \n",
       "2         0.0  tt0113228             Grumpier Old Men   22/12/1995   \n",
       "3  16000000.0  tt0114885            Waiting to Exhale   22/12/1995   \n",
       "4         0.0  tt0113041  Father of the Bride Part II   10/02/1995   \n",
       "\n",
       "       revenue                               production_companies  \n",
       "0  373554033.0                            Pixar Animation Studios  \n",
       "1  262797249.0  TriStar Pictures, Teitler Film, Interscope Com...  \n",
       "2          0.0                       Warner Bros., Lancaster Gate  \n",
       "3   81452156.0             Twentieth Century Fox Film Corporation  \n",
       "4   76578911.0         Sandollar Productions, Touchstone Pictures  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from all_movies', con=engine).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "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>release_year</th>\n",
       "      <th>title</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>win</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>release_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1944</td>\n",
       "      <td>Double Indemnity</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>N</td>\n",
       "      <td>tt0036775</td>\n",
       "      <td>24/04/1944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1944</td>\n",
       "      <td>Gaslight</td>\n",
       "      <td>Metro-Goldwyn-Mayer</td>\n",
       "      <td>N</td>\n",
       "      <td>tt0036855</td>\n",
       "      <td>04/05/1944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1944</td>\n",
       "      <td>Going My Way</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>Y</td>\n",
       "      <td>tt0036872</td>\n",
       "      <td>15/05/1944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1944</td>\n",
       "      <td>Since You Went Away</td>\n",
       "      <td>Selznick International Pictures</td>\n",
       "      <td>N</td>\n",
       "      <td>tt0037280</td>\n",
       "      <td>20/07/1944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1944</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>20th Century-Fox</td>\n",
       "      <td>N</td>\n",
       "      <td>tt0037465</td>\n",
       "      <td>01/08/1944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  release_year                title             production_companies win  \\\n",
       "0         1944     Double Indemnity                        Paramount   N   \n",
       "1         1944             Gaslight              Metro-Goldwyn-Mayer   N   \n",
       "2         1944         Going My Way                        Paramount   Y   \n",
       "3         1944  Since You Went Away  Selznick International Pictures   N   \n",
       "4         1944               Wilson                 20th Century-Fox   N   \n",
       "\n",
       "     imdb_id release_date  \n",
       "0  tt0036775   24/04/1944  \n",
       "1  tt0036855   04/05/1944  \n",
       "2  tt0036872   15/05/1944  \n",
       "3  tt0037280   20/07/1944  \n",
       "4  tt0037465   01/08/1944  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_movie.to_sql(name='best_movies', con=engine, if_exists='append', index=False)\n",
    "pd.read_sql_query('select * from best_movies', con=engine).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>imdb_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>reviewer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt114709</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt113497</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt113228</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt114885</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt113041</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    imdb_id  rating reviewer_id\n",
       "0  tt114709     4.0         8.0\n",
       "1  tt113497     4.5         9.0\n",
       "2  tt113228     4.0        12.0\n",
       "3  tt114885     4.0        20.0\n",
       "4  tt113041     4.0        24.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.to_sql(name='ratings', con=engine, if_exists='append', index=False)\n",
    "pd.read_sql_query('select * from ratings', con=engine).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}