PostgreSQL_Tables.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config_sql import password, database\n",
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(f\"postgresql://postgres:{password}@localhost:5432/{database}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['netflix_movie',\n",
       " 'top_imdb',\n",
       " 'movie',\n",
       " 'movie_actor',\n",
       " 'movie_director',\n",
       " 'utelly_movie_streaming',\n",
       " 'streaming_service',\n",
       " 'google_streaming_service',\n",
       " 'google_movie_streaming']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine.table_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IMDb Rank</th>\n",
       "      <th>Movie Title</th>\n",
       "      <th>Year Released</th>\n",
       "      <th>IMDb Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>1974</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>1957</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>245</td>\n",
       "      <td>246</td>\n",
       "      <td>Butch Cassidy and the Sundance Kid</td>\n",
       "      <td>1969</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>246</td>\n",
       "      <td>247</td>\n",
       "      <td>Aladdin</td>\n",
       "      <td>1992</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>247</td>\n",
       "      <td>248</td>\n",
       "      <td>PK</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248</td>\n",
       "      <td>249</td>\n",
       "      <td>Akira</td>\n",
       "      <td>1988</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>249</td>\n",
       "      <td>250</td>\n",
       "      <td>Throne of Blood</td>\n",
       "      <td>1957</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     IMDb Rank                         Movie Title  Year Released  IMDb Rating\n",
       "0            1            The Shawshank Redemption           1994          9.2\n",
       "1            2                       The Godfather           1972          9.1\n",
       "2            3              The Godfather: Part II           1974          9.0\n",
       "3            4                     The Dark Knight           2008          9.0\n",
       "4            5                        12 Angry Men           1957          8.9\n",
       "..         ...                                 ...            ...          ...\n",
       "245        246  Butch Cassidy and the Sundance Kid           1969          8.0\n",
       "246        247                             Aladdin           1992          8.0\n",
       "247        248                                  PK           2014          8.0\n",
       "248        249                               Akira           1988          8.0\n",
       "249        250                     Throne of Blood           1957          8.0\n",
       "\n",
       "[250 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_250 = pd.read_csv('Output/Top_250_IMDb.csv')\n",
    "imdb_250"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_imdb = pd.read_csv('Output/csv_files/top_imdb.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>The Godfather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0071562</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0468569</td>\n",
       "      <td>The Dark Knight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0050083</td>\n",
       "      <td>12 Angry Men</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id                     title\n",
       "0  tt0111161  The Shawshank Redemption\n",
       "1  tt0068646             The Godfather\n",
       "2  tt0071562    The Godfather: Part II\n",
       "3  tt0468569           The Dark Knight\n",
       "4  tt0050083              12 Angry Men"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_imdb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_imdb.to_csv('SQL_Table/table_1_top_imdb.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_imdb.to_sql(name='top_imdb', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>The Godfather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0071562</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0468569</td>\n",
       "      <td>The Dark Knight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0050083</td>\n",
       "      <td>12 Angry Men</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id                     title\n",
       "0  tt0111161  The Shawshank Redemption\n",
       "1  tt0068646             The Godfather\n",
       "2  tt0071562    The Godfather: Part II\n",
       "3  tt0468569           The Dark Knight\n",
       "4  tt0050083              12 Angry Men"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from top_imdb', con=engine).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie = pd.read_csv('Output/csv_files/movie.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(245, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie = movie.dropna(how = 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(223, 8)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie['runtime'] = movie['runtime'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>imdb_rank</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>runtime</th>\n",
       "      <th>rated</th>\n",
       "      <th>imdb_rating</th>\n",
       "      <th>production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>0</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>142</td>\n",
       "      <td>R</td>\n",
       "      <td>9.3</td>\n",
       "      <td>Columbia Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>1</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>175</td>\n",
       "      <td>R</td>\n",
       "      <td>9.2</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0071562</td>\n",
       "      <td>2</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>1974</td>\n",
       "      <td>202</td>\n",
       "      <td>R</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0468569</td>\n",
       "      <td>3</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>152</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Warner Bros. Pictures/Legendary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0050083</td>\n",
       "      <td>4</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>1957</td>\n",
       "      <td>96</td>\n",
       "      <td>Approved</td>\n",
       "      <td>8.9</td>\n",
       "      <td>Criterion Collection</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id  imdb_rank                     title  year  runtime     rated  \\\n",
       "0  tt0111161          0  The Shawshank Redemption  1994      142         R   \n",
       "1  tt0068646          1             The Godfather  1972      175         R   \n",
       "2  tt0071562          2    The Godfather: Part II  1974      202         R   \n",
       "3  tt0468569          3           The Dark Knight  2008      152     PG-13   \n",
       "4  tt0050083          4              12 Angry Men  1957       96  Approved   \n",
       "\n",
       "   imdb_rating                       production  \n",
       "0          9.3                Columbia Pictures  \n",
       "1          9.2               Paramount Pictures  \n",
       "2          9.0               Paramount Pictures  \n",
       "3          9.0  Warner Bros. Pictures/Legendary  \n",
       "4          8.9             Criterion Collection  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "del movie['imdb_rank']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_rank = pd.merge(movie, imdb_250, left_on=\"title\", right_on=\"Movie Title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>runtime</th>\n",
       "      <th>rated</th>\n",
       "      <th>imdb_rating</th>\n",
       "      <th>production</th>\n",
       "      <th>IMDb Rank</th>\n",
       "      <th>Movie Title</th>\n",
       "      <th>Year Released</th>\n",
       "      <th>IMDb Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>142</td>\n",
       "      <td>R</td>\n",
       "      <td>9.3</td>\n",
       "      <td>Columbia Pictures</td>\n",
       "      <td>1</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>175</td>\n",
       "      <td>R</td>\n",
       "      <td>9.2</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0071562</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>1974</td>\n",
       "      <td>202</td>\n",
       "      <td>R</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>3</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>1974</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0468569</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>152</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Warner Bros. Pictures/Legendary</td>\n",
       "      <td>4</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0050083</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>1957</td>\n",
       "      <td>96</td>\n",
       "      <td>Approved</td>\n",
       "      <td>8.9</td>\n",
       "      <td>Criterion Collection</td>\n",
       "      <td>5</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>1957</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id                     title  year  runtime     rated  imdb_rating  \\\n",
       "0  tt0111161  The Shawshank Redemption  1994      142         R          9.3   \n",
       "1  tt0068646             The Godfather  1972      175         R          9.2   \n",
       "2  tt0071562    The Godfather: Part II  1974      202         R          9.0   \n",
       "3  tt0468569           The Dark Knight  2008      152     PG-13          9.0   \n",
       "4  tt0050083              12 Angry Men  1957       96  Approved          8.9   \n",
       "\n",
       "                        production  IMDb Rank               Movie Title  \\\n",
       "0                Columbia Pictures          1  The Shawshank Redemption   \n",
       "1               Paramount Pictures          2             The Godfather   \n",
       "2               Paramount Pictures          3    The Godfather: Part II   \n",
       "3  Warner Bros. Pictures/Legendary          4           The Dark Knight   \n",
       "4             Criterion Collection          5              12 Angry Men   \n",
       "\n",
       "   Year Released  IMDb Rating  \n",
       "0           1994          9.2  \n",
       "1           1972          9.1  \n",
       "2           1974          9.0  \n",
       "3           2008          9.0  \n",
       "4           1957          8.9  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_rank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_rank = movie_rank.rename(columns={\"IMDb Rank\": \"imdb_rank\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie = movie_rank[[\"imdb_id\", \"imdb_rank\", \"title\", \"year\", \"runtime\", \"rated\", \"imdb_rating\",\"production\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>imdb_rank</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>runtime</th>\n",
       "      <th>rated</th>\n",
       "      <th>imdb_rating</th>\n",
       "      <th>production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>1</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>142</td>\n",
       "      <td>R</td>\n",
       "      <td>9.3</td>\n",
       "      <td>Columbia Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>175</td>\n",
       "      <td>R</td>\n",
       "      <td>9.2</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0071562</td>\n",
       "      <td>3</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>1974</td>\n",
       "      <td>202</td>\n",
       "      <td>R</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0468569</td>\n",
       "      <td>4</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>152</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Warner Bros. Pictures/Legendary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0050083</td>\n",
       "      <td>5</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>1957</td>\n",
       "      <td>96</td>\n",
       "      <td>Approved</td>\n",
       "      <td>8.9</td>\n",
       "      <td>Criterion Collection</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id  imdb_rank                     title  year  runtime     rated  \\\n",
       "0  tt0111161          1  The Shawshank Redemption  1994      142         R   \n",
       "1  tt0068646          2             The Godfather  1972      175         R   \n",
       "2  tt0071562          3    The Godfather: Part II  1974      202         R   \n",
       "3  tt0468569          4           The Dark Knight  2008      152     PG-13   \n",
       "4  tt0050083          5              12 Angry Men  1957       96  Approved   \n",
       "\n",
       "   imdb_rating                       production  \n",
       "0          9.3                Columbia Pictures  \n",
       "1          9.2               Paramount Pictures  \n",
       "2          9.0               Paramount Pictures  \n",
       "3          9.0  Warner Bros. Pictures/Legendary  \n",
       "4          8.9             Criterion Collection  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie.to_csv('SQL_Table/table_2_movie.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie.to_sql(name='movie', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>imdb_rank</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>runtime</th>\n",
       "      <th>rated</th>\n",
       "      <th>imdb_rating</th>\n",
       "      <th>production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>1</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>142</td>\n",
       "      <td>R</td>\n",
       "      <td>9.3</td>\n",
       "      <td>Columbia Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>175</td>\n",
       "      <td>R</td>\n",
       "      <td>9.2</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0071562</td>\n",
       "      <td>3</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>1974</td>\n",
       "      <td>202</td>\n",
       "      <td>R</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0468569</td>\n",
       "      <td>4</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>152</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Warner Bros. Pictures/Legendary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0050083</td>\n",
       "      <td>5</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>1957</td>\n",
       "      <td>96</td>\n",
       "      <td>Approved</td>\n",
       "      <td>8.9</td>\n",
       "      <td>Criterion Collection</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id  imdb_rank                     title  year  runtime     rated  \\\n",
       "0  tt0111161          1  The Shawshank Redemption  1994      142         R   \n",
       "1  tt0068646          2             The Godfather  1972      175         R   \n",
       "2  tt0071562          3    The Godfather: Part II  1974      202         R   \n",
       "3  tt0468569          4           The Dark Knight  2008      152     PG-13   \n",
       "4  tt0050083          5              12 Angry Men  1957       96  Approved   \n",
       "\n",
       "   imdb_rating                       production  \n",
       "0          9.3                Columbia Pictures  \n",
       "1          9.2               Paramount Pictures  \n",
       "2          9.0               Paramount Pictures  \n",
       "3          9.0  Warner Bros. Pictures/Legendary  \n",
       "4          8.9             Criterion Collection  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from movie', con=engine).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>actor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>Tim Robbins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>Morgan Freeman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>Bob Gunton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>William Sadler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>Marlon Brando</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id           actor\n",
       "0  tt0111161     Tim Robbins\n",
       "1  tt0111161  Morgan Freeman\n",
       "2  tt0111161      Bob Gunton\n",
       "3  tt0111161  William Sadler\n",
       "4  tt0068646   Marlon Brando"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_actor = pd.read_csv('Output/csv_files/movie_actor.csv')\n",
    "movie_actor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_actor = movie_actor.dropna(how = 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_actor.to_csv('SQL_Table/table_3_movie_actor.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_actor.to_sql(name='movie_actor', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>imdb_id</th>\n",
       "      <th>actor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>Tim Robbins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>Morgan Freeman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>Bob Gunton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>William Sadler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>Marlon Brando</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id           actor\n",
       "0  tt0111161     Tim Robbins\n",
       "1  tt0111161  Morgan Freeman\n",
       "2  tt0111161      Bob Gunton\n",
       "3  tt0111161  William Sadler\n",
       "4  tt0068646   Marlon Brando"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from movie_actor', con=engine).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>director</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>Frank Darabont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0071562</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0468569</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0050083</td>\n",
       "      <td>Sidney Lumet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id              director\n",
       "0  tt0111161        Frank Darabont\n",
       "1  tt0068646  Francis Ford Coppola\n",
       "2  tt0071562  Francis Ford Coppola\n",
       "3  tt0468569     Christopher Nolan\n",
       "4  tt0050083          Sidney Lumet"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_director = pd.read_csv('Output/csv_files/movie_director.csv')\n",
    "movie_director.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_director = movie_director.dropna(how = 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_director.to_csv('SQL_Table/table_4_movie_director.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_director.to_sql(name='movie_director', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>director</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>Frank Darabont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0071562</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0468569</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0050083</td>\n",
       "      <td>Sidney Lumet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id              director\n",
       "0  tt0111161        Frank Darabont\n",
       "1  tt0068646  Francis Ford Coppola\n",
       "2  tt0071562  Francis Ford Coppola\n",
       "3  tt0468569     Christopher Nolan\n",
       "4  tt0050083          Sidney Lumet"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from movie_director', con=engine).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>service_id</th>\n",
       "      <th>service_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ss_1</td>\n",
       "      <td>Amazon Instant Video</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ss_2</td>\n",
       "      <td>Amazon Prime Video</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ss_3</td>\n",
       "      <td>AtomTickets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ss_4</td>\n",
       "      <td>DisneyPlus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ss_5</td>\n",
       "      <td>FandangoMovies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>ss_6</td>\n",
       "      <td>Google Play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>ss_7</td>\n",
       "      <td>HBO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>ss_8</td>\n",
       "      <td>Hulu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>ss_9</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>ss_10</td>\n",
       "      <td>iTunes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  service_id          service_name\n",
       "0       ss_1  Amazon Instant Video\n",
       "1       ss_2    Amazon Prime Video\n",
       "2       ss_3           AtomTickets\n",
       "3       ss_4            DisneyPlus\n",
       "4       ss_5        FandangoMovies\n",
       "5       ss_6           Google Play\n",
       "6       ss_7                   HBO\n",
       "7       ss_8                  Hulu\n",
       "8       ss_9               Netflix\n",
       "9      ss_10                iTunes"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "streaming_service = pd.read_csv('Output/csv_files/streaming_service.csv')\n",
    "streaming_service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "streaming_service = streaming_service.dropna(how = 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "streaming_service.to_csv('SQL_Table/table_5_streaming_service.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "streaming_service.to_sql(name='streaming_service', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>service_id</th>\n",
       "      <th>service_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ss_1</td>\n",
       "      <td>Amazon Instant Video</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ss_2</td>\n",
       "      <td>Amazon Prime Video</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ss_3</td>\n",
       "      <td>AtomTickets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ss_4</td>\n",
       "      <td>DisneyPlus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ss_5</td>\n",
       "      <td>FandangoMovies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  service_id          service_name\n",
       "0       ss_1  Amazon Instant Video\n",
       "1       ss_2    Amazon Prime Video\n",
       "2       ss_3           AtomTickets\n",
       "3       ss_4            DisneyPlus\n",
       "4       ss_5        FandangoMovies"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from streaming_service', con=engine).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>service_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>ss_9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>ss_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>ss_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>ss_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>ss_6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id service_id\n",
       "0  tt0111161       ss_9\n",
       "1  tt0111161       ss_6\n",
       "2  tt0111161       ss_1\n",
       "3  tt0111161      ss_10\n",
       "4  tt0068646       ss_6"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utelly_movie_streaming = pd.read_csv('Output/csv_files/utelly_movie_streaming.csv')\n",
    "utelly_movie_streaming.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "utelly_movie_streaming = utelly_movie_streaming.dropna(how = 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "utelly_movie_streaming.to_csv('SQL_Table/table_6_utelly_movie_streaming.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "utelly_movie_streaming.to_sql(name='utelly_movie_streaming', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>service_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>ss_9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>ss_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>ss_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>ss_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0068646</td>\n",
       "      <td>ss_6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id service_id\n",
       "0  tt0111161       ss_9\n",
       "1  tt0111161       ss_6\n",
       "2  tt0111161       ss_1\n",
       "3  tt0111161      ss_10\n",
       "4  tt0068646       ss_6"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from utelly_movie_streaming', con=engine).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>service_id</th>\n",
       "      <th>service_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>gg_1</td>\n",
       "      <td>Amazon Prime Video</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>gg_2</td>\n",
       "      <td>Cinemax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>gg_3</td>\n",
       "      <td>Crackle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>gg_4</td>\n",
       "      <td>Disney+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>gg_5</td>\n",
       "      <td>Google Play Movies &amp; TV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  service_id             service_name\n",
       "0       gg_1       Amazon Prime Video\n",
       "1       gg_2                  Cinemax\n",
       "2       gg_3                  Crackle\n",
       "3       gg_4                  Disney+\n",
       "4       gg_5  Google Play Movies & TV"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "google_streaming_service = pd.read_csv('Output/csv_files/google_streaming_service.csv')\n",
    "google_streaming_service.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "google_streaming_service = google_streaming_service.dropna(how = 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "google_streaming_service.to_csv('SQL_Table/table_7_google_streaming_service.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "google_streaming_service.to_sql(name='google_streaming_service', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>service_id</th>\n",
       "      <th>service_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>gg_1</td>\n",
       "      <td>Amazon Prime Video</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>gg_2</td>\n",
       "      <td>Cinemax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>gg_3</td>\n",
       "      <td>Crackle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>gg_4</td>\n",
       "      <td>Disney+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>gg_5</td>\n",
       "      <td>Google Play Movies &amp; TV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  service_id             service_name\n",
       "0       gg_1       Amazon Prime Video\n",
       "1       gg_2                  Cinemax\n",
       "2       gg_3                  Crackle\n",
       "3       gg_4                  Disney+\n",
       "4       gg_5  Google Play Movies & TV"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from google_streaming_service', con=engine).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>title</th>\n",
       "      <th>google_service_id</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>gg_17</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>gg_9</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>gg_6</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>gg_16</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>gg_1</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      title google_service_id price\n",
       "0  The Shawshank Redemption             gg_17  3.99\n",
       "1  The Shawshank Redemption              gg_9  3.99\n",
       "2  The Shawshank Redemption              gg_6  3.99\n",
       "3  The Shawshank Redemption             gg_16  3.99\n",
       "4  The Shawshank Redemption              gg_1  3.99"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "google_movie_streaming = pd.read_csv('Output/csv_files/google_movie_streaming.csv')\n",
    "google_movie_streaming.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "google_movie_streaming = google_movie_streaming.dropna(how = 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "google_movie_streaming.to_csv('SQL_Table/table_8_google_movie_streaming.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "google_movie_streaming.to_sql(name='google_movie_streaming', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>google_service_id</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>gg_17</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>gg_9</td>\n",
       "      <td>3.99</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>gg_6</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>gg_16</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>gg_1</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      title google_service_id price\n",
       "0  The Shawshank Redemption             gg_17  3.99\n",
       "1  The Shawshank Redemption              gg_9  3.99\n",
       "2  The Shawshank Redemption              gg_6  3.99\n",
       "3  The Shawshank Redemption             gg_16  3.99\n",
       "4  The Shawshank Redemption              gg_1  3.99"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from google_movie_streaming', con=engine).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>netflix_id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>553500</td>\n",
       "      <td>tt0060196</td>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>70131314</td>\n",
       "      <td>tt1375666</td>\n",
       "      <td>Inception</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>20557937</td>\n",
       "      <td>tt0133093</td>\n",
       "      <td>The Matrix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>60010110</td>\n",
       "      <td>tt0088763</td>\n",
       "      <td>Back to the Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>60031884</td>\n",
       "      <td>tt0064116</td>\n",
       "      <td>Once Upon a Time in the West</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   netflix_id    imdb_id                           title\n",
       "0      553500  tt0060196  The Good, the Bad and the Ugly\n",
       "1    70131314  tt1375666                       Inception\n",
       "2    20557937  tt0133093                      The Matrix\n",
       "3    60010110  tt0088763              Back to the Future\n",
       "4    60031884  tt0064116    Once Upon a Time in the West"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_movie = pd.read_csv('Output/csv_files/netflix_movie.csv')\n",
    "netflix_movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "netflix_movie = netflix_movie.dropna(how = 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "netflix_movie.to_csv('SQL_Table/table_9_netflix_movie.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "netflix_movie.to_sql(name='netflix_movie', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>netflix_id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>553500</td>\n",
       "      <td>tt0060196</td>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>70131314</td>\n",
       "      <td>tt1375666</td>\n",
       "      <td>Inception</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>20557937</td>\n",
       "      <td>tt0133093</td>\n",
       "      <td>The Matrix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>60010110</td>\n",
       "      <td>tt0088763</td>\n",
       "      <td>Back to the Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>60031884</td>\n",
       "      <td>tt0064116</td>\n",
       "      <td>Once Upon a Time in the West</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   netflix_id    imdb_id                           title\n",
       "0      553500  tt0060196  The Good, the Bad and the Ugly\n",
       "1    70131314  tt1375666                       Inception\n",
       "2    20557937  tt0133093                      The Matrix\n",
       "3    60010110  tt0088763              Back to the Future\n",
       "4    60031884  tt0064116    Once Upon a Time in the West"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from netflix_movie', con=engine).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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