SQL Queries, Analysis and Visualization using Jupyter Notebook.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "id": "bf0cfc9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import sqlite3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e885b41c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# maximum number of rows to display\n",
    "pd.options.display.max_rows = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b139769a",
   "metadata": {},
   "source": [
    "**Connecting to the database**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "219c9c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = sqlite3.connect('sqlite-sakila.db')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "347aa104",
   "metadata": {},
   "source": [
    "We can now query the database directly in this environment using pandas' read_sql method. We will first see what tables are available in the dataset. This can be done by accessing the sqlite_master table, which is a table containing the information about the database schema. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0c09e1f2",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>tbl_name</th>\n",
       "      <th>rootpage</th>\n",
       "      <th>sql</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>table</td>\n",
       "      <td>actor</td>\n",
       "      <td>actor</td>\n",
       "      <td>2</td>\n",
       "      <td>CREATE TABLE actor (\\n  actor_id numeric NOT N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>table</td>\n",
       "      <td>country</td>\n",
       "      <td>country</td>\n",
       "      <td>5</td>\n",
       "      <td>CREATE TABLE country (\\n  country_id SMALLINT ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>table</td>\n",
       "      <td>city</td>\n",
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       "      <td>7</td>\n",
       "      <td>CREATE TABLE city (\\n  city_id int NOT NULL,\\n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>table</td>\n",
       "      <td>address</td>\n",
       "      <td>address</td>\n",
       "      <td>10</td>\n",
       "      <td>CREATE TABLE address (\\n  address_id int NOT N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>table</td>\n",
       "      <td>language</td>\n",
       "      <td>language</td>\n",
       "      <td>13</td>\n",
       "      <td>CREATE TABLE language (\\n  language_id SMALLIN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>table</td>\n",
       "      <td>category</td>\n",
       "      <td>category</td>\n",
       "      <td>15</td>\n",
       "      <td>CREATE TABLE category (\\n  category_id SMALLIN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>table</td>\n",
       "      <td>customer</td>\n",
       "      <td>customer</td>\n",
       "      <td>19</td>\n",
       "      <td>CREATE TABLE customer (\\n  customer_id INT NOT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>table</td>\n",
       "      <td>film</td>\n",
       "      <td>film</td>\n",
       "      <td>24</td>\n",
       "      <td>CREATE TABLE film (\\n  film_id int NOT NULL,\\n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>table</td>\n",
       "      <td>film_actor</td>\n",
       "      <td>film_actor</td>\n",
       "      <td>28</td>\n",
       "      <td>CREATE TABLE film_actor (\\n  actor_id INT NOT ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>table</td>\n",
       "      <td>film_category</td>\n",
       "      <td>film_category</td>\n",
       "      <td>33</td>\n",
       "      <td>CREATE TABLE film_category (\\n  film_id INT NO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>table</td>\n",
       "      <td>film_text</td>\n",
       "      <td>film_text</td>\n",
       "      <td>37</td>\n",
       "      <td>CREATE TABLE film_text (\\n  film_id SMALLINT N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>table</td>\n",
       "      <td>inventory</td>\n",
       "      <td>inventory</td>\n",
       "      <td>39</td>\n",
       "      <td>CREATE TABLE inventory (\\n  inventory_id INT N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>table</td>\n",
       "      <td>staff</td>\n",
       "      <td>staff</td>\n",
       "      <td>43</td>\n",
       "      <td>CREATE TABLE staff (\\n  staff_id SMALLINT NOT ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>table</td>\n",
       "      <td>store</td>\n",
       "      <td>store</td>\n",
       "      <td>48</td>\n",
       "      <td>CREATE TABLE store (\\n  store_id INT NOT NULL,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>table</td>\n",
       "      <td>payment</td>\n",
       "      <td>payment</td>\n",
       "      <td>52</td>\n",
       "      <td>CREATE TABLE payment (\\n  payment_id int NOT N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>table</td>\n",
       "      <td>rental</td>\n",
       "      <td>rental</td>\n",
       "      <td>56</td>\n",
       "      <td>CREATE TABLE rental (\\n  rental_id INT NOT NUL...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     type           name       tbl_name  rootpage  \\\n",
       "0   table          actor          actor         2   \n",
       "1   table        country        country         5   \n",
       "2   table           city           city         7   \n",
       "3   table        address        address        10   \n",
       "4   table       language       language        13   \n",
       "5   table       category       category        15   \n",
       "6   table       customer       customer        19   \n",
       "7   table           film           film        24   \n",
       "8   table     film_actor     film_actor        28   \n",
       "9   table  film_category  film_category        33   \n",
       "10  table      film_text      film_text        37   \n",
       "11  table      inventory      inventory        39   \n",
       "12  table          staff          staff        43   \n",
       "13  table          store          store        48   \n",
       "14  table        payment        payment        52   \n",
       "15  table         rental         rental        56   \n",
       "\n",
       "                                                  sql  \n",
       "0   CREATE TABLE actor (\\n  actor_id numeric NOT N...  \n",
       "1   CREATE TABLE country (\\n  country_id SMALLINT ...  \n",
       "2   CREATE TABLE city (\\n  city_id int NOT NULL,\\n...  \n",
       "3   CREATE TABLE address (\\n  address_id int NOT N...  \n",
       "4   CREATE TABLE language (\\n  language_id SMALLIN...  \n",
       "5   CREATE TABLE category (\\n  category_id SMALLIN...  \n",
       "6   CREATE TABLE customer (\\n  customer_id INT NOT...  \n",
       "7   CREATE TABLE film (\\n  film_id int NOT NULL,\\n...  \n",
       "8   CREATE TABLE film_actor (\\n  actor_id INT NOT ...  \n",
       "9   CREATE TABLE film_category (\\n  film_id INT NO...  \n",
       "10  CREATE TABLE film_text (\\n  film_id SMALLINT N...  \n",
       "11  CREATE TABLE inventory (\\n  inventory_id INT N...  \n",
       "12  CREATE TABLE staff (\\n  staff_id SMALLINT NOT ...  \n",
       "13  CREATE TABLE store (\\n  store_id INT NOT NULL,...  \n",
       "14  CREATE TABLE payment (\\n  payment_id int NOT N...  \n",
       "15  CREATE TABLE rental (\\n  rental_id INT NOT NUL...  "
      ]
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     "metadata": {},
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   "source": [
    "# Looking at the all tables preset in our database \n",
    "tables = pd.read_sql('''\n",
    "                        select * \n",
    "                        from sqlite_master\n",
    "                        where type = 'table' ''',conn)\n",
    "tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c351b881",
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>actor_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>last_update</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PENELOPE</td>\n",
       "      <td>GUINESS</td>\n",
       "      <td>2021-03-06 15:51:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NICK</td>\n",
       "      <td>WAHLBERG</td>\n",
       "      <td>2021-03-06 15:51:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ED</td>\n",
       "      <td>CHASE</td>\n",
       "      <td>2021-03-06 15:51:59</td>\n",
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       "      <th>3</th>\n",
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       "      <td>JENNIFER</td>\n",
       "      <td>DAVIS</td>\n",
       "      <td>2021-03-06 15:51:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>JOHNNY</td>\n",
       "      <td>LOLLOBRIGIDA</td>\n",
       "      <td>2021-03-06 15:51:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>BELA</td>\n",
       "      <td>WALKEN</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>REESE</td>\n",
       "      <td>WEST</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>MARY</td>\n",
       "      <td>KEITEL</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>JULIA</td>\n",
       "      <td>FAWCETT</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>THORA</td>\n",
       "      <td>TEMPLE</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     actor_id first_name     last_name          last_update\n",
       "0           1   PENELOPE       GUINESS  2021-03-06 15:51:59\n",
       "1           2       NICK      WAHLBERG  2021-03-06 15:51:59\n",
       "2           3         ED         CHASE  2021-03-06 15:51:59\n",
       "3           4   JENNIFER         DAVIS  2021-03-06 15:51:59\n",
       "4           5     JOHNNY  LOLLOBRIGIDA  2021-03-06 15:51:59\n",
       "..        ...        ...           ...                  ...\n",
       "195       196       BELA        WALKEN  2021-03-06 15:52:00\n",
       "196       197      REESE          WEST  2021-03-06 15:52:00\n",
       "197       198       MARY        KEITEL  2021-03-06 15:52:00\n",
       "198       199      JULIA       FAWCETT  2021-03-06 15:52:00\n",
       "199       200      THORA        TEMPLE  2021-03-06 15:52:00\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accessing the record of actor table\n",
    "pd.read_sql('''\n",
    "    select * \n",
    "    from actor;''',conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4adcdfc1",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>film_id</th>\n",
       "      <th>title</th>\n",
       "      <th>description</th>\n",
       "      <th>release_year</th>\n",
       "      <th>language_id</th>\n",
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       "      <th>rental_rate</th>\n",
       "      <th>length</th>\n",
       "      <th>replacement_cost</th>\n",
       "      <th>rating</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ACADEMY DINOSAUR</td>\n",
       "      <td>A Epic Drama of a Feminist And a Mad Scientist...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ACE GOLDFINGER</td>\n",
       "      <td>A Astounding Epistle of a Database Administrat...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
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       "      <td>48</td>\n",
       "      <td>12.99</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ADAPTATION HOLES</td>\n",
       "      <td>A Astounding Reflection of a Lumberjack And a ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>2.99</td>\n",
       "      <td>50</td>\n",
       "      <td>18.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>Trailers,Deleted Scenes</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   film_id             title  \\\n",
       "0        1  ACADEMY DINOSAUR   \n",
       "1        2    ACE GOLDFINGER   \n",
       "2        3  ADAPTATION HOLES   \n",
       "\n",
       "                                         description release_year  \\\n",
       "0  A Epic Drama of a Feminist And a Mad Scientist...         2006   \n",
       "1  A Astounding Epistle of a Database Administrat...         2006   \n",
       "2  A Astounding Reflection of a Lumberjack And a ...         2006   \n",
       "\n",
       "   language_id original_language_id  rental_duration  rental_rate  length  \\\n",
       "0            1                 None                6         0.99      86   \n",
       "1            1                 None                3         4.99      48   \n",
       "2            1                 None                7         2.99      50   \n",
       "\n",
       "   replacement_cost rating                  special_features  \\\n",
       "0             20.99     PG  Deleted Scenes,Behind the Scenes   \n",
       "1             12.99      G           Trailers,Deleted Scenes   \n",
       "2             18.99  NC-17           Trailers,Deleted Scenes   \n",
       "\n",
       "           last_update  \n",
       "0  2021-03-06 15:52:00  \n",
       "1  2021-03-06 15:52:00  \n",
       "2  2021-03-06 15:52:00  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aceessing the records of film table \n",
    "pd.read_sql('''\n",
    "    select *\n",
    "    from film''',conn).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96189c43",
   "metadata": {},
   "outputs": [
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       "      <td>2005-05-25 11:30:37.000</td>\n",
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       "      <td>3</td>\n",
       "      <td>2005-06-15 00:54:12.000</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2005-06-15 18:02:53.000</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2005-06-15 21:08:46.000</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16044</th>\n",
       "      <td>16045</td>\n",
       "      <td>2005-08-21 17:43:42.000</td>\n",
       "      <td>4.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16045</th>\n",
       "      <td>16046</td>\n",
       "      <td>2005-08-21 21:41:57.000</td>\n",
       "      <td>1.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16046</th>\n",
       "      <td>16047</td>\n",
       "      <td>2005-08-23 06:09:44.000</td>\n",
       "      <td>8.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16047</th>\n",
       "      <td>16048</td>\n",
       "      <td>2005-08-23 11:08:46.000</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16048</th>\n",
       "      <td>16049</td>\n",
       "      <td>2005-08-23 11:25:00.000</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16049 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       payment_id             payment_date  amount\n",
       "0               1  2005-05-25 11:30:37.000    2.99\n",
       "1               2  2005-05-28 10:35:23.000    0.99\n",
       "2               3  2005-06-15 00:54:12.000    5.99\n",
       "3               4  2005-06-15 18:02:53.000    0.99\n",
       "4               5  2005-06-15 21:08:46.000    9.99\n",
       "...           ...                      ...     ...\n",
       "16044       16045  2005-08-21 17:43:42.000    4.99\n",
       "16045       16046  2005-08-21 21:41:57.000    1.99\n",
       "16046       16047  2005-08-23 06:09:44.000    8.99\n",
       "16047       16048  2005-08-23 11:08:46.000    2.99\n",
       "16048       16049  2005-08-23 11:25:00.000    2.99\n",
       "\n",
       "[16049 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing the specific columns from payment table\n",
    "pd.read_sql('''\n",
    "    select \n",
    "    payment_id,\n",
    "    payment_date,\n",
    "    amount \n",
    "    from payment''',conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e5464e1b",
   "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>Total_no_rows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_no_rows\n",
       "0          16049"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select count(*) as 'Total_no_rows'\n",
    "    from payment''',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ac3d7d",
   "metadata": {},
   "source": [
    "* so we can say that the payment table contains 16049 rows \n",
    "* now let's have a look on the specific column of payment table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "74194968",
   "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>count(rental_id)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count(rental_id)\n",
       "0             16044"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select count(rental_id)\n",
    "    from payment''',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43468fe",
   "metadata": {},
   "source": [
    "* In this case the total rows is lesser than the previous case.\n",
    "* beacuse count doesnot include the null value into the counting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eb279908",
   "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>payment_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>staff_id</th>\n",
       "      <th>rental_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>payment_date</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>424</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2005-06-18 04:56:12.000</td>\n",
       "      <td>2021-03-06 15:56:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7011</td>\n",
       "      <td>259</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2005-08-23 06:13:16.000</td>\n",
       "      <td>2021-03-06 15:56:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10840</td>\n",
       "      <td>401</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>0.99</td>\n",
       "      <td>2005-07-12 06:26:10.000</td>\n",
       "      <td>2021-03-06 15:57:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14675</td>\n",
       "      <td>546</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2005-07-30 21:16:20.000</td>\n",
       "      <td>2021-03-06 15:57:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15458</td>\n",
       "      <td>577</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>0.99</td>\n",
       "      <td>2005-05-27 00:46:39.000</td>\n",
       "      <td>2021-03-06 15:58:04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   payment_id  customer_id  staff_id rental_id  amount  \\\n",
       "0         424           16         1      None    1.99   \n",
       "1        7011          259         2      None    1.99   \n",
       "2       10840          401         1      None    0.99   \n",
       "3       14675          546         1      None    3.99   \n",
       "4       15458          577         2      None    0.99   \n",
       "\n",
       "              payment_date          last_update  \n",
       "0  2005-06-18 04:56:12.000  2021-03-06 15:56:00  \n",
       "1  2005-08-23 06:13:16.000  2021-03-06 15:56:55  \n",
       "2  2005-07-12 06:26:10.000  2021-03-06 15:57:27  \n",
       "3  2005-07-30 21:16:20.000  2021-03-06 15:57:58  \n",
       "4  2005-05-27 00:46:39.000  2021-03-06 15:58:04  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now retriveing the data where rental_id is null in payment table \n",
    "\n",
    "pd.read_sql('''\n",
    "    select *\n",
    "    from payment \n",
    "    where rental_id is NULL''',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5074bc",
   "metadata": {},
   "source": [
    "* So, the payment table containg 5 rows where the rental_id is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "727e2df4",
   "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>Total_rented_out_movies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_rented_out_movies\n",
       "0                      183"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of the movies which are currently\n",
    "# rented out \n",
    "\n",
    "pd.read_sql('''\n",
    "        select count(*) as 'Total_rented_out_movies'\n",
    "        from rental\n",
    "        where return_date is null''',conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c9650ed5",
   "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>film_id</th>\n",
       "      <th>title</th>\n",
       "      <th>description</th>\n",
       "      <th>release_year</th>\n",
       "      <th>language_id</th>\n",
       "      <th>original_language_id</th>\n",
       "      <th>rental_duration</th>\n",
       "      <th>rental_rate</th>\n",
       "      <th>length</th>\n",
       "      <th>replacement_cost</th>\n",
       "      <th>rating</th>\n",
       "      <th>special_features</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ACADEMY DINOSAUR</td>\n",
       "      <td>A Epic Drama of a Feminist And a Mad Scientist...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>0.99</td>\n",
       "      <td>86</td>\n",
       "      <td>20.99</td>\n",
       "      <td>PG</td>\n",
       "      <td>Deleted Scenes,Behind the Scenes</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ACE GOLDFINGER</td>\n",
       "      <td>A Astounding Epistle of a Database Administrat...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>48</td>\n",
       "      <td>12.99</td>\n",
       "      <td>G</td>\n",
       "      <td>Trailers,Deleted Scenes</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   film_id             title  \\\n",
       "0        1  ACADEMY DINOSAUR   \n",
       "1        2    ACE GOLDFINGER   \n",
       "\n",
       "                                         description release_year  \\\n",
       "0  A Epic Drama of a Feminist And a Mad Scientist...         2006   \n",
       "1  A Astounding Epistle of a Database Administrat...         2006   \n",
       "\n",
       "   language_id original_language_id  rental_duration  rental_rate  length  \\\n",
       "0            1                 None                6         0.99      86   \n",
       "1            1                 None                3         4.99      48   \n",
       "\n",
       "   replacement_cost rating                  special_features  \\\n",
       "0             20.99     PG  Deleted Scenes,Behind the Scenes   \n",
       "1             12.99      G           Trailers,Deleted Scenes   \n",
       "\n",
       "           last_update  \n",
       "0  2021-03-06 15:52:00  \n",
       "1  2021-03-06 15:52:00  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the film table dataset \n",
    "pd.read_sql('''\n",
    "    select * \n",
    "    from film''',conn).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c8afc7",
   "metadata": {},
   "source": [
    "**MIN(), MAX(), AVERAGE() and Aggregation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "50d58c05",
   "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>min(length)</th>\n",
       "      <th>max(length)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   min(length)  max(length)\n",
       "0           46          185"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating the min and max movie length\n",
    "pd.read_sql('''\n",
    "        select min(length),max(length)\n",
    "        from film\n",
    "        where rating  = 'PG' ''',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a461d543",
   "metadata": {},
   "source": [
    "* In the film table the minimum and maximum length of the movie is 46 and 185 respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "88913560",
   "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>rating</th>\n",
       "      <th>min(length)</th>\n",
       "      <th>max(length)</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>G</td>\n",
       "      <td>47</td>\n",
       "      <td>185</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NC-17</td>\n",
       "      <td>46</td>\n",
       "      <td>184</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PG</td>\n",
       "      <td>46</td>\n",
       "      <td>185</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PG-13</td>\n",
       "      <td>46</td>\n",
       "      <td>185</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>49</td>\n",
       "      <td>185</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  rating  min(length)  max(length)  Average\n",
       "0      G           47          185    111.0\n",
       "1  NC-17           46          184    113.0\n",
       "2     PG           46          185    112.0\n",
       "3  PG-13           46          185    120.0\n",
       "4      R           49          185    119.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calulating the min, max, avg length of movie using group by function\n",
    "\n",
    "pd.read_sql('''\n",
    "    select rating,\n",
    "    min(length),\n",
    "    max(length),\n",
    "    round(avg(length)) as Average\n",
    "    from film\n",
    "    group by rating''',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395360fa",
   "metadata": {},
   "source": [
    "**How many movies are there in each rating category?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "194571d2",
   "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>rating</th>\n",
       "      <th>no of movies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PG-13</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NC-17</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PG</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  rating  no of movies\n",
       "0  PG-13           223\n",
       "1  NC-17           210\n",
       "2      R           195\n",
       "3     PG           194\n",
       "4      G           178"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select rating,\n",
    "    count(*) as 'no of movies'\n",
    "    from film\n",
    "    group by rating\n",
    "    order by count(*) desc''',conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "233c2309",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4e6a4618",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql('''\n",
    "    select rating,\n",
    "    count(*) as 'no of movies'\n",
    "    from film\n",
    "    group by rating\n",
    "    order by count(*) desc''',conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "79372677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "ax = sns.barplot(x= 'rating' , y= 'no of movies',data = df)\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.title('Number of movies in each Rating category',fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dcc63c4",
   "metadata": {},
   "source": [
    "**What is the total number of movies in each category held in inventory?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "67d88e51",
   "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>inventory_id</th>\n",
       "      <th>film_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:52:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:52:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   inventory_id  film_id  store_id          last_update\n",
       "0             1        1         1  2021-03-06 15:52:08\n",
       "1             2        1         1  2021-03-06 15:52:08"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the inventory table dataset\n",
    "pd.read_sql('''\n",
    "    select * \n",
    "    from inventory''',conn).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a9ebe9f1",
   "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>rating</th>\n",
       "      <th>no of movies in inventory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>G</td>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R</td>\n",
       "      <td>904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PG</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NC-17</td>\n",
       "      <td>944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PG-13</td>\n",
       "      <td>1018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  rating  no of movies in inventory\n",
       "0      G                        791\n",
       "1      R                        904\n",
       "2     PG                        924\n",
       "3  NC-17                        944\n",
       "4  PG-13                       1018"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select rating,\n",
    "    count(*) as 'no of movies in inventory'\n",
    "    from \n",
    "    inventory as i\n",
    "    inner join\n",
    "    film as f\n",
    "    on i.film_id = f.film_id\n",
    "    group by rating \n",
    "    order by count(*)\n",
    "    ''',conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c6629db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the above data \n",
    "\n",
    "df = pd.read_sql('''\n",
    "    select rating,\n",
    "    count(*) as 'no of movies in inventory'\n",
    "    from \n",
    "    inventory as i\n",
    "    inner join\n",
    "    film as f\n",
    "    on i.film_id = f.film_id\n",
    "    group by rating \n",
    "    order by count(*) desc\n",
    "    ''',conn)\n",
    "\n",
    "plt.figure(figsize = (12,6))\n",
    "ax = sns.barplot(x = 'rating', y ='no of movies in inventory',data  = df , color= 'orange')\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.title('No of movies in inventory in each category',fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a02c4471",
   "metadata": {},
   "source": [
    "**Which movies of rating 'PG' are held in highest quantity in the inventory?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1c81cd05",
   "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>inventory_id</th>\n",
       "      <th>film_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:52:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:52:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   inventory_id  film_id  store_id          last_update\n",
       "0             1        1         1  2021-03-06 15:52:08\n",
       "1             2        1         1  2021-03-06 15:52:08"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select * \n",
    "    from inventory''',conn).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "df5fbdd3",
   "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>rating</th>\n",
       "      <th>no of movies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACADEMY DINOSAUR</td>\n",
       "      <td>PG</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BOUND CHEAPER</td>\n",
       "      <td>PG</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BUCKET BROTHERHOOD</td>\n",
       "      <td>PG</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EXPENDABLE STALLION</td>\n",
       "      <td>PG</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FROST HEAD</td>\n",
       "      <td>PG</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>PRIVATE DROP</td>\n",
       "      <td>PG</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>PUNK DIVORCE</td>\n",
       "      <td>PG</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>RECORDS ZORRO</td>\n",
       "      <td>PG</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>TEQUILA PAST</td>\n",
       "      <td>PG</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>TREATMENT JEKYLL</td>\n",
       "      <td>PG</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   title rating  no of movies\n",
       "0       ACADEMY DINOSAUR     PG             8\n",
       "1          BOUND CHEAPER     PG             8\n",
       "2     BUCKET BROTHERHOOD     PG             8\n",
       "3    EXPENDABLE STALLION     PG             8\n",
       "4             FROST HEAD     PG             8\n",
       "..                   ...    ...           ...\n",
       "178         PRIVATE DROP     PG             2\n",
       "179         PUNK DIVORCE     PG             2\n",
       "180        RECORDS ZORRO     PG             2\n",
       "181         TEQUILA PAST     PG             2\n",
       "182     TREATMENT JEKYLL     PG             2\n",
       "\n",
       "[183 rows x 3 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select title,rating ,\n",
    "    count(*) as 'no of movies'\n",
    "    from inventory i\n",
    "    inner join\n",
    "    film  f\n",
    "    on i.film_id = f.film_id\n",
    "    where rating == 'PG'\n",
    "    group by title\n",
    "    order by count(*) desc,title asc  ''',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee11c885",
   "metadata": {},
   "source": [
    "**What movies has each actor starred in and who has starred in the most movies?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e2a9c6da",
   "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>actor_id</th>\n",
       "      <th>film_id</th>\n",
       "      <th>last_update</th>\n",
       "      <th>film_id</th>\n",
       "      <th>title</th>\n",
       "      <th>description</th>\n",
       "      <th>release_year</th>\n",
       "      <th>language_id</th>\n",
       "      <th>original_language_id</th>\n",
       "      <th>rental_duration</th>\n",
       "      <th>rental_rate</th>\n",
       "      <th>length</th>\n",
       "      <th>replacement_cost</th>\n",
       "      <th>rating</th>\n",
       "      <th>special_features</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:52:45</td>\n",
       "      <td>1</td>\n",
       "      <td>ACADEMY DINOSAUR</td>\n",
       "      <td>A Epic Drama of a Feminist And a Mad Scientist...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>0.99</td>\n",
       "      <td>86</td>\n",
       "      <td>20.99</td>\n",
       "      <td>PG</td>\n",
       "      <td>Deleted Scenes,Behind the Scenes</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>2021-03-06 15:52:45</td>\n",
       "      <td>23</td>\n",
       "      <td>ANACONDA CONFESSIONS</td>\n",
       "      <td>A Lacklusture Display of a Dentist And a Denti...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>0.99</td>\n",
       "      <td>92</td>\n",
       "      <td>9.99</td>\n",
       "      <td>R</td>\n",
       "      <td>Trailers,Deleted Scenes</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   actor_id  film_id          last_update  film_id                 title  \\\n",
       "0         1        1  2021-03-06 15:52:45        1      ACADEMY DINOSAUR   \n",
       "1         1       23  2021-03-06 15:52:45       23  ANACONDA CONFESSIONS   \n",
       "\n",
       "                                         description release_year  \\\n",
       "0  A Epic Drama of a Feminist And a Mad Scientist...         2006   \n",
       "1  A Lacklusture Display of a Dentist And a Denti...         2006   \n",
       "\n",
       "   language_id original_language_id  rental_duration  rental_rate  length  \\\n",
       "0            1                 None                6         0.99      86   \n",
       "1            1                 None                3         0.99      92   \n",
       "\n",
       "   replacement_cost rating                  special_features  \\\n",
       "0             20.99     PG  Deleted Scenes,Behind the Scenes   \n",
       "1              9.99      R           Trailers,Deleted Scenes   \n",
       "\n",
       "           last_update  \n",
       "0  2021-03-06 15:52:00  \n",
       "1  2021-03-06 15:52:00  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select * \n",
    "    from film_actor fa\n",
    "    inner join\n",
    "    film f\n",
    "    on fa.film_id = f.film_id''',conn).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "2a7769a2",
   "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>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACADEMY DINOSAUR</td>\n",
       "      <td>PENELOPE GUINESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANACONDA CONFESSIONS</td>\n",
       "      <td>PENELOPE GUINESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANGELS LIFE</td>\n",
       "      <td>PENELOPE GUINESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BULWORTH COMMANDMENTS</td>\n",
       "      <td>PENELOPE GUINESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEAPER CLYDE</td>\n",
       "      <td>PENELOPE GUINESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5457</th>\n",
       "      <td>TELEGRAPH VOYAGE</td>\n",
       "      <td>THORA TEMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5458</th>\n",
       "      <td>TROJAN TOMORROW</td>\n",
       "      <td>THORA TEMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5459</th>\n",
       "      <td>VIRGINIAN PLUTO</td>\n",
       "      <td>THORA TEMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5460</th>\n",
       "      <td>WARDROBE PHANTOM</td>\n",
       "      <td>THORA TEMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5461</th>\n",
       "      <td>WRONG BEHAVIOR</td>\n",
       "      <td>THORA TEMPLE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5462 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      title              Name\n",
       "0          ACADEMY DINOSAUR  PENELOPE GUINESS\n",
       "1      ANACONDA CONFESSIONS  PENELOPE GUINESS\n",
       "2               ANGELS LIFE  PENELOPE GUINESS\n",
       "3     BULWORTH COMMANDMENTS  PENELOPE GUINESS\n",
       "4             CHEAPER CLYDE  PENELOPE GUINESS\n",
       "...                     ...               ...\n",
       "5457       TELEGRAPH VOYAGE      THORA TEMPLE\n",
       "5458        TROJAN TOMORROW      THORA TEMPLE\n",
       "5459        VIRGINIAN PLUTO      THORA TEMPLE\n",
       "5460       WARDROBE PHANTOM      THORA TEMPLE\n",
       "5461         WRONG BEHAVIOR      THORA TEMPLE\n",
       "\n",
       "[5462 rows x 2 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# movie title with their starring actor name \n",
    "pd.read_sql('''\n",
    "    select title,\n",
    "    first_name || ' ' || last_name AS Name,\n",
    "    \n",
    "    \n",
    "    from\n",
    "    (select * \n",
    "    from film_actor fa\n",
    "    inner join\n",
    "    film f\n",
    "    on fa.film_id = f.film_id) as t1\n",
    "    inner join \n",
    "    actor as a\n",
    "    on t1.actor_id = a.actor_id''',conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "157bc508",
   "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>Name</th>\n",
       "      <th>no of movies starred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WOODY HOFFMAN</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JENNIFER DAVIS</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HELEN VOIGHT</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NICK WAHLBERG</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SISSY SOBIESKI</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>SISSY SOBIESKI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>MARY KEITEL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>CARY MCCONAUGHEY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>ANGELA HUDSON</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>MICHELLE MCCONAUGHEY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>997 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name  no of movies starred\n",
       "0           WOODY HOFFMAN                    15\n",
       "1          JENNIFER DAVIS                    13\n",
       "2            HELEN VOIGHT                    13\n",
       "3           NICK WAHLBERG                    13\n",
       "4          SISSY SOBIESKI                    13\n",
       "..                    ...                   ...\n",
       "992        SISSY SOBIESKI                     1\n",
       "993           MARY KEITEL                     1\n",
       "994      CARY MCCONAUGHEY                     1\n",
       "995         ANGELA HUDSON                     1\n",
       "996  MICHELLE MCCONAUGHEY                     1\n",
       "\n",
       "[997 rows x 2 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the number of the movies starred by each actor and also most movies starred by  the actor \n",
    "pd.read_sql('''\n",
    "    select \n",
    "    first_name || ' ' || last_name AS Name,\n",
    "    count(title) as 'no of movies starred'\n",
    "    \n",
    "    \n",
    "    from\n",
    "    (select * \n",
    "    from film_actor fa\n",
    "    inner join\n",
    "    film f\n",
    "    on fa.film_id = f.film_id) as t1\n",
    "    inner join \n",
    "    actor as a\n",
    "    on t1.actor_id = a.actor_id\n",
    "    group by title\n",
    "    order by count(title) desc  ''',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c25a9fa",
   "metadata": {},
   "source": [
    "**Calculate only actors who have starred in movies more than 10 times.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "86d91de3",
   "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>Name</th>\n",
       "      <th>no of movies starred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WOODY HOFFMAN</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JENNIFER DAVIS</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HELEN VOIGHT</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NICK WAHLBERG</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SISSY SOBIESKI</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JOHNNY LOLLOBRIGIDA</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ED CHASE</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BETTE NICHOLSON</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ELVIS MARX</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RIP CRAWFORD</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BETTE NICHOLSON</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GRACE MOSTEL</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KARL BERRY</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KARL BERRY</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>JENNIFER DAVIS</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>JULIA MCQUEEN</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ED CHASE</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>JOHNNY LOLLOBRIGIDA</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KIRSTEN PALTROW</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SANDRA KILMER</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NICK WAHLBERG</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CUBA OLIVIER</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>JENNIFER DAVIS</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ED CHASE</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>KARL BERRY</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CUBA OLIVIER</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>TIM HACKMAN</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name  no of movies starred\n",
       "0         WOODY HOFFMAN                    15\n",
       "1        JENNIFER DAVIS                    13\n",
       "2          HELEN VOIGHT                    13\n",
       "3         NICK WAHLBERG                    13\n",
       "4        SISSY SOBIESKI                    13\n",
       "5   JOHNNY LOLLOBRIGIDA                    13\n",
       "6              ED CHASE                    13\n",
       "7       BETTE NICHOLSON                    12\n",
       "8            ELVIS MARX                    12\n",
       "9          RIP CRAWFORD                    12\n",
       "10      BETTE NICHOLSON                    12\n",
       "11         GRACE MOSTEL                    12\n",
       "12           KARL BERRY                    12\n",
       "13           KARL BERRY                    11\n",
       "14       JENNIFER DAVIS                    11\n",
       "15        JULIA MCQUEEN                    11\n",
       "16             ED CHASE                    11\n",
       "17  JOHNNY LOLLOBRIGIDA                    11\n",
       "18      KIRSTEN PALTROW                    11\n",
       "19        SANDRA KILMER                    11\n",
       "20        NICK WAHLBERG                    11\n",
       "21         CUBA OLIVIER                    11\n",
       "22       JENNIFER DAVIS                    11\n",
       "23             ED CHASE                    11\n",
       "24           KARL BERRY                    11\n",
       "25         CUBA OLIVIER                    11\n",
       "26          TIM HACKMAN                    11"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select \n",
    "    first_name || ' ' || last_name AS Name,\n",
    "    count(title) as 'no of movies starred'\n",
    "    \n",
    "    \n",
    "    from\n",
    "    (select * \n",
    "    from film_actor fa\n",
    "    inner join\n",
    "    film f\n",
    "    on fa.film_id = f.film_id) as t1\n",
    "    inner join \n",
    "    actor as a\n",
    "    on t1.actor_id = a.actor_id\n",
    "    group by title\n",
    "    having count(title)>10\n",
    "    order by count(title) desc  ''',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c78411a",
   "metadata": {},
   "source": [
    "* So,in our dataset we are having 27 actors who have starred in more than 10 movies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "7eaec4ee",
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rental_id</th>\n",
       "      <th>rental_date</th>\n",
       "      <th>inventory_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>return_date</th>\n",
       "      <th>staff_id</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2005-05-24 22:53:30.000</td>\n",
       "      <td>367</td>\n",
       "      <td>130</td>\n",
       "      <td>2005-05-26 22:04:30.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:53:41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rental_id              rental_date  inventory_id  customer_id  \\\n",
       "0          1  2005-05-24 22:53:30.000           367          130   \n",
       "\n",
       "               return_date  staff_id          last_update  \n",
       "0  2005-05-26 22:04:30.000         1  2021-03-06 15:53:41  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select * \n",
    "    from rental r''',conn).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "f8715877",
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>inventory_id</th>\n",
       "      <th>film_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:52:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   inventory_id  film_id  store_id          last_update\n",
       "0             1        1         1  2021-03-06 15:52:08"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select *\n",
    "    from inventory i''',conn).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "f233e35c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rental_ID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Return</th>\n",
       "      <th>HoldPeriod</th>\n",
       "      <th>RentalDuration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>BLANKET BEVERLY</td>\n",
       "      <td>2005-05-24</td>\n",
       "      <td>2005-05-26</td>\n",
       "      <td>1.97</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>FREAKY POCUS</td>\n",
       "      <td>2005-05-24</td>\n",
       "      <td>2005-05-28</td>\n",
       "      <td>3.87</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>GRADUATE LORD</td>\n",
       "      <td>2005-05-24</td>\n",
       "      <td>2005-06-01</td>\n",
       "      <td>7.96</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>LOVE SUICIDES</td>\n",
       "      <td>2005-05-24</td>\n",
       "      <td>2005-06-03</td>\n",
       "      <td>9.11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IDOLS SNATCHERS</td>\n",
       "      <td>2005-05-24</td>\n",
       "      <td>2005-06-02</td>\n",
       "      <td>8.23</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15856</th>\n",
       "      <td>16045</td>\n",
       "      <td>COMANCHEROS ENEMY</td>\n",
       "      <td>2005-08-23</td>\n",
       "      <td>2005-08-25</td>\n",
       "      <td>2.06</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15857</th>\n",
       "      <td>16046</td>\n",
       "      <td>VOYAGE LEGALLY</td>\n",
       "      <td>2005-08-23</td>\n",
       "      <td>2005-08-27</td>\n",
       "      <td>3.82</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15858</th>\n",
       "      <td>16047</td>\n",
       "      <td>ILLUSION AMELIE</td>\n",
       "      <td>2005-08-23</td>\n",
       "      <td>2005-08-25</td>\n",
       "      <td>1.17</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15859</th>\n",
       "      <td>16048</td>\n",
       "      <td>HUNCHBACK IMPOSSIBLE</td>\n",
       "      <td>2005-08-23</td>\n",
       "      <td>2005-08-31</td>\n",
       "      <td>7.95</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15860</th>\n",
       "      <td>16049</td>\n",
       "      <td>MOB DUFFEL</td>\n",
       "      <td>2005-08-23</td>\n",
       "      <td>2005-08-30</td>\n",
       "      <td>6.09</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15861 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rental_ID                 Title        Rent      Return  HoldPeriod  \\\n",
       "0              1       BLANKET BEVERLY  2005-05-24  2005-05-26        1.97   \n",
       "1              2          FREAKY POCUS  2005-05-24  2005-05-28        3.87   \n",
       "2              3         GRADUATE LORD  2005-05-24  2005-06-01        7.96   \n",
       "3              4         LOVE SUICIDES  2005-05-24  2005-06-03        9.11   \n",
       "4              5       IDOLS SNATCHERS  2005-05-24  2005-06-02        8.23   \n",
       "...          ...                   ...         ...         ...         ...   \n",
       "15856      16045     COMANCHEROS ENEMY  2005-08-23  2005-08-25        2.06   \n",
       "15857      16046        VOYAGE LEGALLY  2005-08-23  2005-08-27        3.82   \n",
       "15858      16047       ILLUSION AMELIE  2005-08-23  2005-08-25        1.17   \n",
       "15859      16048  HUNCHBACK IMPOSSIBLE  2005-08-23  2005-08-31        7.95   \n",
       "15860      16049            MOB DUFFEL  2005-08-23  2005-08-30        6.09   \n",
       "\n",
       "       RentalDuration  \n",
       "0                   7  \n",
       "1                   7  \n",
       "2                   7  \n",
       "3                   6  \n",
       "4                   5  \n",
       "...               ...  \n",
       "15856               5  \n",
       "15857               6  \n",
       "15858               4  \n",
       "15859               4  \n",
       "15860               4  \n",
       "\n",
       "[15861 rows x 6 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "\n",
    "SELECT\n",
    "    r.rental_id AS Rental_ID\n",
    "    ,f.title AS Title\n",
    "    ,strftime('%Y-%m-%d', r.rental_date) AS Rent\n",
    "    ,strftime('%Y-%m-%d', r.return_date) AS Return\n",
    "    ,ROUND(JULIANDAY(r.return_date) - JULIANDAY(r.rental_date), 2) AS HoldPeriod\n",
    "    ,f.rental_duration AS RentalDuration\n",
    "FROM rental AS r\n",
    "JOIN inventory AS i ON r.inventory_id = i.inventory_id\n",
    "JOIN film AS f ON i.film_id = f.film_id\n",
    "WHERE r.return_date IS NOT NULL;''',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bcb3459",
   "metadata": {},
   "source": [
    "* Simply going over the results for HoldPeriod and RentalDuration, we can see that there have been overdue rentals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "c6c1f780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rental_ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>email</th>\n",
       "      <th>Title</th>\n",
       "      <th>Overdue_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>MURRELL, MANUEL</td>\n",
       "      <td>MANUEL.MURRELL@sakilacustomer.org</td>\n",
       "      <td>GRADUATE LORD</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>PURDY, ANDREW</td>\n",
       "      <td>ANDREW.PURDY@sakilacustomer.org</td>\n",
       "      <td>LOVE SUICIDES</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>HANSEN, DELORES</td>\n",
       "      <td>DELORES.HANSEN@sakilacustomer.org</td>\n",
       "      <td>IDOLS SNATCHERS</td>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>WALTERS, CASSANDRA</td>\n",
       "      <td>CASSANDRA.WALTERS@sakilacustomer.org</td>\n",
       "      <td>SWARM GOLD</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>ISOM, DANNY</td>\n",
       "      <td>DANNY.ISOM@sakilacustomer.org</td>\n",
       "      <td>HANGING DEEP</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8101</th>\n",
       "      <td>16039</td>\n",
       "      <td>WOOD, LORI</td>\n",
       "      <td>LORI.WOOD@sakilacustomer.org</td>\n",
       "      <td>CAPER MOTIONS</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8102</th>\n",
       "      <td>16040</td>\n",
       "      <td>SIMS, VANESSA</td>\n",
       "      <td>VANESSA.SIMS@sakilacustomer.org</td>\n",
       "      <td>SCORPION APOLLO</td>\n",
       "      <td>6.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8103</th>\n",
       "      <td>16043</td>\n",
       "      <td>SEAL, KARL</td>\n",
       "      <td>KARL.SEAL@sakilacustomer.org</td>\n",
       "      <td>STING PERSONAL</td>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8104</th>\n",
       "      <td>16048</td>\n",
       "      <td>HAMILTON, GLADYS</td>\n",
       "      <td>GLADYS.HAMILTON@sakilacustomer.org</td>\n",
       "      <td>HUNCHBACK IMPOSSIBLE</td>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8105</th>\n",
       "      <td>16049</td>\n",
       "      <td>CAUSEY, PHILIP</td>\n",
       "      <td>PHILIP.CAUSEY@sakilacustomer.org</td>\n",
       "      <td>MOB DUFFEL</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8106 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rental_ID                Name                                 email  \\\n",
       "0             3     MURRELL, MANUEL     MANUEL.MURRELL@sakilacustomer.org   \n",
       "1             4       PURDY, ANDREW       ANDREW.PURDY@sakilacustomer.org   \n",
       "2             5     HANSEN, DELORES     DELORES.HANSEN@sakilacustomer.org   \n",
       "3             7  WALTERS, CASSANDRA  CASSANDRA.WALTERS@sakilacustomer.org   \n",
       "4            10         ISOM, DANNY         DANNY.ISOM@sakilacustomer.org   \n",
       "...         ...                 ...                                   ...   \n",
       "8101      16039          WOOD, LORI          LORI.WOOD@sakilacustomer.org   \n",
       "8102      16040       SIMS, VANESSA       VANESSA.SIMS@sakilacustomer.org   \n",
       "8103      16043          SEAL, KARL          KARL.SEAL@sakilacustomer.org   \n",
       "8104      16048    HAMILTON, GLADYS    GLADYS.HAMILTON@sakilacustomer.org   \n",
       "8105      16049      CAUSEY, PHILIP      PHILIP.CAUSEY@sakilacustomer.org   \n",
       "\n",
       "                     Title  Overdue_days  \n",
       "0            GRADUATE LORD          0.96  \n",
       "1            LOVE SUICIDES          3.11  \n",
       "2          IDOLS SNATCHERS          3.23  \n",
       "3               SWARM GOLD          0.89  \n",
       "4             HANGING DEEP          1.95  \n",
       "...                    ...           ...  \n",
       "8101         CAPER MOTIONS          1.90  \n",
       "8102       SCORPION APOLLO          6.17  \n",
       "8103        STING PERSONAL          4.20  \n",
       "8104  HUNCHBACK IMPOSSIBLE          3.95  \n",
       "8105            MOB DUFFEL          2.09  \n",
       "\n",
       "[8106 rows x 5 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "\n",
    "SELECT\n",
    "    r.rental_id AS Rental_ID\n",
    "    ,c.last_name || ', ' || c.first_name AS Name\n",
    "    ,c.email\n",
    "    ,f.title AS Title\n",
    "    ,ROUND(JULIANDAY(r.return_date) - JULIANDAY(r.rental_date)-f.rental_duration,2) AS Overdue_days\n",
    "FROM rental AS r\n",
    "JOIN inventory AS i ON r.inventory_id = i.inventory_id\n",
    "JOIN film AS f ON i.film_id = f.film_id\n",
    "join customer as c on r.customer_id = c.customer_id\n",
    "WHERE r.return_date IS NOT NULL\n",
    "AND Overdue_days >0''',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e28e76",
   "metadata": {},
   "source": [
    "* By looking at the result we can say that a large number of rental have been overdue."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc6d29ee",
   "metadata": {},
   "source": [
    "**Calculate the customer who have most overdue rental**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "51bf8419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer_ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>email</th>\n",
       "      <th>Overdue_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>295</td>\n",
       "      <td>BATES, DAISY</td>\n",
       "      <td>DAISY.BATES@sakilacustomer.org</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>137</td>\n",
       "      <td>KENNEDY, RHONDA</td>\n",
       "      <td>RHONDA.KENNEDY@sakilacustomer.org</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>526</td>\n",
       "      <td>SEAL, KARL</td>\n",
       "      <td>KARL.SEAL@sakilacustomer.org</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer_ID             Name                              email  \\\n",
       "0          295     BATES, DAISY     DAISY.BATES@sakilacustomer.org   \n",
       "1          137  KENNEDY, RHONDA  RHONDA.KENNEDY@sakilacustomer.org   \n",
       "2          526       SEAL, KARL       KARL.SEAL@sakilacustomer.org   \n",
       "\n",
       "   Overdue_Count  \n",
       "0             28  \n",
       "1             27  \n",
       "2             27  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"\"\"\n",
    "\n",
    "SELECT\n",
    "    c.customer_id AS Customer_ID\n",
    "    ,c.last_name || ', ' || c.first_name AS Name\n",
    "    ,c.email\n",
    "    ,COUNT(*) AS Overdue_Count\n",
    "FROM rental AS r\n",
    "JOIN inventory AS i ON r.inventory_id = i.inventory_id\n",
    "JOIN film AS f ON i.film_id = f.film_id\n",
    "JOIN customer AS c ON r.customer_id = c.customer_id\n",
    "WHERE r.return_date IS NOT NULL\n",
    "AND ROUND(JULIANDAY(r.return_date) - JULIANDAY(r.rental_date), 2) - f.rental_duration > 0\n",
    "GROUP BY Name\n",
    "ORDER BY Overdue_Count DESC, Name ASC;\n",
    "\n",
    "\"\"\", conn).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77e138e1",
   "metadata": {},
   "source": [
    "* From the above we get the result that BATES,DAISY haviing the most overdue count  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1452b446",
   "metadata": {},
   "source": [
    "**Payments per rental, payments per date, payments per store**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e853d0",
   "metadata": {},
   "source": [
    "Since we saw that there have been a lot of overdue rentals, let's now check if there have been extra payments or overdue taxes in cases when a movie has been returned after the maximum rental duration.\n",
    "\n",
    "Here we use a LEFT JOIN because we want to return all payments even if for some reason they are not associated with a rental. Let's group by rental to see if there have been multiple payments for 1 rental:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "4253eddd",
   "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>rental_id</th>\n",
       "      <th>payment_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16049.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16048.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16047.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16046.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16040</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16041</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16042</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16043</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16044</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16045 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       rental_id  payment_counts\n",
       "0            NaN               5\n",
       "1        16049.0               1\n",
       "2        16048.0               1\n",
       "3        16047.0               1\n",
       "4        16046.0               1\n",
       "...          ...             ...\n",
       "16040        5.0               1\n",
       "16041        4.0               1\n",
       "16042        3.0               1\n",
       "16043        2.0               1\n",
       "16044        1.0               1\n",
       "\n",
       "[16045 rows x 2 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select p.rental_id,\n",
    "    count(*) as payment_counts \n",
    "    from payment p\n",
    "    LEFT JOIN rental r\n",
    "    on p.rental_id = r.rental_id\n",
    "    group by p.rental_id\n",
    "    order by payment_counts desc''',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "244f49e8",
   "metadata": {},
   "source": [
    "* As we see, there haven't been multiple payments for one rental and hence no extra payment has been charged for overdue rentals. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce55905",
   "metadata": {},
   "source": [
    "**calculate the total payments i.e. sales for each month.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "ae90913c",
   "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>payment_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>staff_id</th>\n",
       "      <th>rental_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>payment_date</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2005-05-25 11:30:37.000</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>573.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>2005-05-28 10:35:23.000</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   payment_id  customer_id  staff_id  rental_id  amount  \\\n",
       "0           1            1         1       76.0    2.99   \n",
       "1           2            1         1      573.0    0.99   \n",
       "\n",
       "              payment_date          last_update  \n",
       "0  2005-05-25 11:30:37.000  2021-03-06 15:55:57  \n",
       "1  2005-05-28 10:35:23.000  2021-03-06 15:55:57  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select *\n",
    "    from payment''',conn).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "a394ffa5",
   "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>date</th>\n",
       "      <th>Total_monthly_sale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-05</td>\n",
       "      <td>4824.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-06</td>\n",
       "      <td>9631.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-07</td>\n",
       "      <td>28373.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-08</td>\n",
       "      <td>24072.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-02</td>\n",
       "      <td>514.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      date  Total_monthly_sale\n",
       "0  2005-05             4824.43\n",
       "1  2005-06             9631.88\n",
       "2  2005-07            28373.89\n",
       "3  2005-08            24072.13\n",
       "4  2006-02              514.18"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql('''\n",
    "    select \n",
    "    strftime('%Y-%m',payment_date) as date,\n",
    "    sum(amount) as Total_monthly_sale \n",
    "    From payment\n",
    "    group by date \n",
    "    order by date asc''',conn)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e0adef",
   "metadata": {},
   "source": [
    "* In the months of july and august the monthly sale is maximum "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "2f63b4b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the above result \n",
    "plt.figure(figsize = (12,6))\n",
    "ax = sns.barplot(x = 'date',y='Total_monthly_sale',data = df)\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.title('Sales per month',fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af6cff0",
   "metadata": {},
   "source": [
    "**since there are two stores, let's see how much sales each store has generated:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "6e95fe88",
   "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>staff_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>address_id</th>\n",
       "      <th>picture</th>\n",
       "      <th>email</th>\n",
       "      <th>store_id</th>\n",
       "      <th>active</th>\n",
       "      <th>username</th>\n",
       "      <th>password</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Hillyer</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>Mike.Hillyer@sakilastaff.com</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mike</td>\n",
       "      <td>8cb2237d0679ca88db6464eac60da96345513964</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jon</td>\n",
       "      <td>Stephens</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>Jon.Stephens@sakilastaff.com</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Jon</td>\n",
       "      <td>8cb2237d0679ca88db6464eac60da96345513964</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   staff_id first_name last_name  address_id picture  \\\n",
       "0         1       Mike   Hillyer           3    None   \n",
       "1         2        Jon  Stephens           4    None   \n",
       "\n",
       "                          email  store_id  active username  \\\n",
       "0  Mike.Hillyer@sakilastaff.com         1       1     Mike   \n",
       "1  Jon.Stephens@sakilastaff.com         2       1      Jon   \n",
       "\n",
       "                                   password          last_update  \n",
       "0  8cb2237d0679ca88db6464eac60da96345513964  2021-03-06 15:52:00  \n",
       "1  8cb2237d0679ca88db6464eac60da96345513964  2021-03-06 15:52:00  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select * \n",
    "    from staff''',conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "4c932bfd",
   "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>payment_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>staff_id</th>\n",
       "      <th>rental_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>payment_date</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2005-05-25 11:30:37.000</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>573.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>2005-05-28 10:35:23.000</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   payment_id  customer_id  staff_id  rental_id  amount  \\\n",
       "0           1            1         1       76.0    2.99   \n",
       "1           2            1         1      573.0    0.99   \n",
       "\n",
       "              payment_date          last_update  \n",
       "0  2005-05-25 11:30:37.000  2021-03-06 15:55:57  \n",
       "1  2005-05-28 10:35:23.000  2021-03-06 15:55:57  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('''\n",
    "    select *\n",
    "    from payment''',conn).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "bb606351",
   "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>store_id</th>\n",
       "      <th>Total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>33489.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>33927.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   store_id  Total_sales\n",
       "0         1     33489.47\n",
       "1         2     33927.04"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql('''\n",
    "    select \n",
    "    s.store_id,\n",
    "    sum(p.amount) as Total_sales\n",
    "    from staff s\n",
    "    join payment p\n",
    "    on s.staff_id = p.staff_id\n",
    "    group by s.store_id\n",
    "    ''',conn)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "4b5f00f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the above result using pie chart \n",
    "\n",
    "labels = ['1','2']\n",
    "plt.pie(data = df ,x='Total_sales',labels = labels, autopct = '%0.0f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1650623",
   "metadata": {},
   "source": [
    "* both the stores are having nearly equal sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb829c8f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}