Movies Project Pt 2.1 - TMDB API Test Extraction 2000-2001.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b2a22ce9",
   "metadata": {},
   "source": [
    "# TMDB API Initial Test Extraction\n",
    "\n",
    "- Extract Revenue, Budget and Certification for Movies Using the TMDB Application Programing Interface (API)\n",
    "- Years being requested by Stakeholders: 2000 and 2001"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b81cfe78",
   "metadata": {},
   "source": [
    "### Data Source\n",
    "\n",
    "The Movie Database (TMDB) - (https://www.themoviedb.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a2520b1",
   "metadata": {},
   "source": [
    "Imports/Installaltions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2842219",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tmdbsimple in c:\\users\\daisy\\anaconda3\\envs\\dojo-env\\lib\\site-packages (2.9.1)\n",
      "Requirement already satisfied: requests in c:\\users\\daisy\\anaconda3\\envs\\dojo-env\\lib\\site-packages (from tmdbsimple) (2.28.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\daisy\\anaconda3\\envs\\dojo-env\\lib\\site-packages (from requests->tmdbsimple) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\daisy\\anaconda3\\envs\\dojo-env\\lib\\site-packages (from requests->tmdbsimple) (1.26.13)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in c:\\users\\daisy\\anaconda3\\envs\\dojo-env\\lib\\site-packages (from requests->tmdbsimple) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\daisy\\anaconda3\\envs\\dojo-env\\lib\\site-packages (from requests->tmdbsimple) (2022.9.24)\n",
      "Requirement already satisfied: tqdm in c:\\users\\daisy\\anaconda3\\envs\\dojo-env\\lib\\site-packages (4.64.1)\n",
      "Requirement already satisfied: colorama in c:\\users\\daisy\\anaconda3\\envs\\dojo-env\\lib\\site-packages (from tqdm) (0.4.5)\n"
     ]
    }
   ],
   "source": [
    "#installations\n",
    "!pip install tmdbsimple\n",
    "!pip install tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa78255a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Additional Imports\n",
    "#os - for saving and loading files\n",
    "#json - to work with json files\n",
    "#time - to add a short pause to not overwhelm the server\n",
    "import os, json, time\n",
    "\n",
    "#python package - to help download from TMDB API\n",
    "import tmdbsimple as tmdb\n",
    "\n",
    "#progress bar\n",
    "from tqdm.notebook import tqdm_notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a9d925",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29f8232f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to add movie certification to the .info\n",
    "def get_movie_with_rating(movie_id):\n",
    "  \"\"\"Adapted from source = https://github.com/celiao/tmdbsimple\"\"\"\n",
    "  \n",
    "  #get the movie object for the current id\n",
    "  movie = tmdb.Movies(movie_id)\n",
    "\n",
    "  #save the .info .releases dictinoaries\n",
    "  movie_info = movie.info()\n",
    "  releases = movie.releases()\n",
    "\n",
    "  #loop through countries in releases to get only US\n",
    "  for c in releases['countries']:\n",
    "      #if the country abbreviation==US\n",
    "      if c['iso_3166_1'] == 'US':\n",
    "          #save a 'certification' key in the info with the certification\n",
    "          movie_info['certification'] = c['certification']\n",
    "  return movie_info\n",
    "\n",
    "#Appends a list of records (new_data) to a json file (filename)\n",
    "def write_json(new_data, filename): \n",
    "    \"\"\"Adapted from: https://www.geeksforgeeks.org/append-to-json-file-using-python/\"\"\"     \n",
    "    \n",
    "    with open(filename,'r+') as file:\n",
    "        # First we load existing data into a dict.\n",
    "        file_data = json.load(file)\n",
    "        \n",
    "        ## Choose extend or append\n",
    "        if (type(new_data) == list) & (type(file_data) == list):\n",
    "            file_data.extend(new_data)\n",
    "        else:\n",
    "            file_data.append(new_data)\n",
    "        # Sets file's current position at offset.\n",
    "        file.seek(0)\n",
    "        # convert back to json.\n",
    "        json.dump(file_data, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e176862",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_and_fix_json(JSON_FILE):\n",
    "    \"\"\"Attempts to read in json file of records and fixes the final character\n",
    "    to end with a ] if it errors.\n",
    "    \n",
    "    Args:\n",
    "        JSON_FILE (str): filepath of JSON file\n",
    "        \n",
    "    Returns:\n",
    "        DataFrame: the corrected data from the bad json file\n",
    "    \"\"\"\n",
    "    try: \n",
    "        previous_df =  pd.read_json(JSON_FILE)\n",
    "    \n",
    "    ## If read_json throws an error\n",
    "    except:\n",
    "        \n",
    "\n",
    "        ## manually open the json file\n",
    "        with open(JSON_FILE,'r+') as f:\n",
    "            ## Read in the file as a STRING\n",
    "            bad_json = f.read()\n",
    "            \n",
    "            ## if the final character doesn't match first, select the right bracket\n",
    "            first_char = bad_json[0]\n",
    "            final_brackets = {'[':']', \n",
    "                           \"{\":\"}\"}\n",
    "            ## Select expected final brakcet\n",
    "            final_char = final_brackets[first_char]\n",
    "            \n",
    "            ## if the last character in file doen't match the first char, add it\n",
    "            if bad_json[-1] != final_char:\n",
    "                good_json = bad_json[:-1]\n",
    "                good_json+=final_char\n",
    "            else:\n",
    "                raise Exception('ERROR is not due to mismatched final bracket.')\n",
    "            \n",
    "            ## Rewind to start of file and write new good_json to disk\n",
    "            f.seek(0)\n",
    "            f.write(good_json)\n",
    "           \n",
    "        ## Load the json file again now that its fixed\n",
    "        previous_df =  pd.read_json(JSON_FILE)\n",
    "        \n",
    "    return previous_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3dfcc5",
   "metadata": {},
   "source": [
    "### Uploading my API credentials for TMDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "80a5bbce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['api-key'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#login credentials from json file\n",
    "with open('/Users/Daisy/.secret/tmdb_api.json', 'r') as f: \n",
    "    login = json.load(f)\n",
    "## Display the keys of the loaded dict\n",
    "login.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8069ab5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting API key variable \n",
    "tmdb.API_KEY =  login['api-key']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75352513",
   "metadata": {},
   "source": [
    "### Uploading basics data\n",
    "We need to upload the basics dataset because this is the list of movies that our Stakeholders want to include in their database. Therefore, we will use this dataset to help us extract the financial data for only these specific titles from the API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ba22a93",
   "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>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0035423</td>\n",
       "      <td>movie</td>\n",
       "      <td>Kate &amp; Leopold</td>\n",
       "      <td>Kate &amp; Leopold</td>\n",
       "      <td>0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118</td>\n",
       "      <td>Comedy,Fantasy,Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0062336</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Tango of the Widower and Its Distorting Mi...</td>\n",
       "      <td>El tango del viudo y su espejo deformante</td>\n",
       "      <td>0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst titleType                                       primaryTitle  \\\n",
       "0  tt0035423     movie                                     Kate & Leopold   \n",
       "1  tt0062336     movie  The Tango of the Widower and Its Distorting Mi...   \n",
       "2  tt0069049     movie                         The Other Side of the Wind   \n",
       "\n",
       "                               originalTitle  isAdult  startYear  endYear  \\\n",
       "0                             Kate & Leopold        0     2001.0      NaN   \n",
       "1  El tango del viudo y su espejo deformante        0     2020.0      NaN   \n",
       "2                 The Other Side of the Wind        0     2018.0      NaN   \n",
       "\n",
       "   runtimeMinutes                  genres  \n",
       "0             118  Comedy,Fantasy,Romance  \n",
       "1              70                   Drama  \n",
       "2             122                   Drama  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Open basics dataset and preview\n",
    "basics = pd.read_csv(\"Data/title_basics.csv.gz\", low_memory = False)\n",
    "basics.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a49d7d4",
   "metadata": {},
   "source": [
    "### TEST Run to see if loop works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b26f793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adult</th>\n",
       "      <th>backdrop_path</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>...</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>certification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>/9BBTo63ANSmhC4e6r62OJFuK2GL.jpg</td>\n",
       "      <td>{'id': 86311, 'name': 'The Avengers Collection...</td>\n",
       "      <td>220000000</td>\n",
       "      <td>[{'id': 878, 'name': 'Science Fiction'}, {'id'...</td>\n",
       "      <td>https://www.marvel.com/movies/the-avengers</td>\n",
       "      <td>24428</td>\n",
       "      <td>tt0848228</td>\n",
       "      <td>en</td>\n",
       "      <td>The Avengers</td>\n",
       "      <td>...</td>\n",
       "      <td>1518815515</td>\n",
       "      <td>143</td>\n",
       "      <td>[{'english_name': 'English', 'iso_639_1': 'en'...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Some assembly required.</td>\n",
       "      <td>The Avengers</td>\n",
       "      <td>False</td>\n",
       "      <td>7.707</td>\n",
       "      <td>28547</td>\n",
       "      <td>PG-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>/qom1SZSENdmHFNZBXbtJAU0WTlC.jpg</td>\n",
       "      <td>None</td>\n",
       "      <td>29000000</td>\n",
       "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 18, ...</td>\n",
       "      <td>http://www.newline.com/properties/notebookthe....</td>\n",
       "      <td>11036</td>\n",
       "      <td>tt0332280</td>\n",
       "      <td>en</td>\n",
       "      <td>The Notebook</td>\n",
       "      <td>...</td>\n",
       "      <td>115603229</td>\n",
       "      <td>123</td>\n",
       "      <td>[{'english_name': 'English', 'iso_639_1': 'en'...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Behind every great love is a great story.</td>\n",
       "      <td>The Notebook</td>\n",
       "      <td>False</td>\n",
       "      <td>7.878</td>\n",
       "      <td>10308</td>\n",
       "      <td>PG-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult                     backdrop_path  \\\n",
       "0  False  /9BBTo63ANSmhC4e6r62OJFuK2GL.jpg   \n",
       "1  False  /qom1SZSENdmHFNZBXbtJAU0WTlC.jpg   \n",
       "\n",
       "                               belongs_to_collection     budget  \\\n",
       "0  {'id': 86311, 'name': 'The Avengers Collection...  220000000   \n",
       "1                                               None   29000000   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 878, 'name': 'Science Fiction'}, {'id'...   \n",
       "1  [{'id': 10749, 'name': 'Romance'}, {'id': 18, ...   \n",
       "\n",
       "                                            homepage     id    imdb_id  \\\n",
       "0         https://www.marvel.com/movies/the-avengers  24428  tt0848228   \n",
       "1  http://www.newline.com/properties/notebookthe....  11036  tt0332280   \n",
       "\n",
       "  original_language original_title  ...     revenue  runtime  \\\n",
       "0                en   The Avengers  ...  1518815515      143   \n",
       "1                en   The Notebook  ...   115603229      123   \n",
       "\n",
       "                                    spoken_languages    status  \\\n",
       "0  [{'english_name': 'English', 'iso_639_1': 'en'...  Released   \n",
       "1  [{'english_name': 'English', 'iso_639_1': 'en'...  Released   \n",
       "\n",
       "                                     tagline         title  video  \\\n",
       "0                    Some assembly required.  The Avengers  False   \n",
       "1  Behind every great love is a great story.  The Notebook  False   \n",
       "\n",
       "   vote_average vote_count certification  \n",
       "0         7.707      28547         PG-13  \n",
       "1         7.878      10308         PG-13  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loop to loop thru list of movie ids and save errors in dataframe\n",
    "#test function on movie ids: tt0848228 (\"The Avengers\") and tt0332280 (\"The Notebook\")\n",
    "test_ids = [\"tt0848228\", \"tt0332280\"]\n",
    "results = []\n",
    "errors = []\n",
    "for movie_id in test_ids:\n",
    "    \n",
    "    try:\n",
    "        movie_info = get_movie_with_rating(movie_id)\n",
    "        results.append(movie_info)\n",
    "        \n",
    "    except Exception as e: \n",
    "        errors.append([movie_id, e])\n",
    "    \n",
    "pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "945649a9",
   "metadata": {},
   "source": [
    "### Create folder if doesn't exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e15deebb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.ipynb_checkpoints',\n",
       " 'title_akas.csv.gz',\n",
       " 'title_basics.csv.gz',\n",
       " 'title_ratings.csv.gz']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FOLDER = \"Data/\"\n",
    "os.makedirs(FOLDER, exist_ok=True)\n",
    "os.listdir(FOLDER)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5865617b",
   "metadata": {},
   "source": [
    "### Creating Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e7e113ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#years to extract from Loop\n",
    "YEARS_TO_GET = [2001,2000]\n",
    "\n",
    "#defining empty list to store errors\n",
    "errors = [ ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "424b3f76",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa1ce06950f5410e8c5041ab1507f058",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "YEARS:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2eacd03dedfb4e059ebc5b784d1b7cd8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Movies from 2001:   0%|          | 0/1570 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31d3911af9ce4455b1afa24774a6fde5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Movies from 2000:   0%|          | 0/1448 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Total errors: 455\n"
     ]
    }
   ],
   "source": [
    "# Start of OUTER loop\n",
    "for YEAR in tqdm_notebook(YEARS_TO_GET, desc='YEARS', position=0):\n",
    "    #Defining the JSON file to store results for year\n",
    "    JSON_FILE = f'{FOLDER}tmdb_api_results_{YEAR}.json'\n",
    "\n",
    "    #check if file exists\n",
    "    file_exists = os.path.isfile(JSON_FILE)\n",
    "    #if it does not exist: create it\n",
    "    if file_exists == False:\n",
    "    #save an empty dict with just \"imdb_id\" to the new json file.\n",
    "        with open(JSON_FILE,'w') as f:\n",
    "            json.dump([{'imdb_id':0}],f)\n",
    "  \n",
    "    #saving new year as the current df\n",
    "    df = basics.loc[ basics['startYear']==YEAR].copy()\n",
    "  \n",
    "    #saving movie ids to list\n",
    "    movie_ids = df['tconst'].copy()\n",
    "\n",
    "    #load existing data from json into a dataframe called \"previous_df\"\n",
    "    previous_df = pd.read_json(JSON_FILE)\n",
    "    \n",
    "    #filter out any ids that are already in the JSON_FILE\n",
    "    movie_ids_to_get = movie_ids[~movie_ids.isin(previous_df['imdb_id'])]\n",
    "\n",
    "        #INNER Loop - using functions to get index and movie id from list\n",
    "    for movie_id in tqdm_notebook(movie_ids_to_get,\n",
    "                                  desc=f'Movies from {YEAR}',\n",
    "                                  position=1,\n",
    "                                  leave=True):\n",
    "        try:\n",
    "            #retrieve then data for the movie id\n",
    "            temp = get_movie_with_rating(movie_id)  \n",
    "            #append/extend results to existing file using a pre-made function\n",
    "            write_json(temp,JSON_FILE)\n",
    "            #short 20 ms sleep to prevent overwhelming server\n",
    "            time.sleep(0.02)\n",
    "        \n",
    "        #saving errors in a list   \n",
    "        except Exception as e:\n",
    "            #continue\n",
    "            errors.append([movie_id, e])\n",
    "\n",
    "    #Finish OUTER Loop\n",
    "    #Save the year's results as csv.gz files\n",
    "    final_year_df = pd.read_json(JSON_FILE)\n",
    "    final_year_df.to_csv(f\"{FOLDER}final_movies_{YEAR}.csv.gz\", compression=\"gzip\", index=False)\n",
    "\n",
    "#After loops finish print errors\n",
    "print(f\"- Total errors: {len(errors)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bc358dd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}