ETL_clean_wiki_movies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import re\n",
    "\n",
    "from sqlalchemy import create_engine\n",
    "import psycopg2\n",
    "\n",
    "# from config import db_password\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Add the clean movie function that takes in the argument, \"movie\".\n",
    "def clean_movie(movie):\n",
    "    movie = dict(movie) #create a non-destructive copy\n",
    "    alt_titles = {}\n",
    "    # combine alternate titles into one list\n",
    "    for key in ['Also known as','Arabic','Cantonese','Chinese','French',\n",
    "                'Hangul','Hebrew','Hepburn','Japanese','Literally',\n",
    "                'Mandarin','McCune-Reischauer','Original title','Polish',\n",
    "                'Revised Romanization','Romanized','Russian',\n",
    "                'Simplified','Traditional','Yiddish']:\n",
    "        if key in movie:\n",
    "            alt_titles[key] = movie[key]\n",
    "            movie.pop(key)\n",
    "    if len(alt_titles) > 0:\n",
    "        movie['alt_titles'] = alt_titles\n",
    "\n",
    "    # merge column names\n",
    "    def change_column_name(old_name, new_name):\n",
    "        if old_name in movie:\n",
    "            movie[new_name] = movie.pop(old_name)\n",
    "    change_column_name('Adaptation by', 'Writer(s)')\n",
    "    change_column_name('Country of origin', 'Country')\n",
    "    change_column_name('Directed by', 'Director')\n",
    "    change_column_name('Distributed by', 'Distributor')\n",
    "    change_column_name('Edited by', 'Editor(s)')\n",
    "    change_column_name('Length', 'Running time')\n",
    "    change_column_name('Original release', 'Release date')\n",
    "    change_column_name('Music by', 'Composer(s)')\n",
    "    change_column_name('Produced by', 'Producer(s)')\n",
    "    change_column_name('Producer', 'Producer(s)')\n",
    "    change_column_name('Productioncompanies ', 'Production company(s)')\n",
    "    change_column_name('Productioncompany ', 'Production company(s)')\n",
    "    change_column_name('Released', 'Release Date')\n",
    "    change_column_name('Release Date', 'Release date')\n",
    "    change_column_name('Screen story by', 'Writer(s)')\n",
    "    change_column_name('Screenplay by', 'Writer(s)')\n",
    "    change_column_name('Story by', 'Writer(s)')\n",
    "    change_column_name('Theme music composer', 'Composer(s)')\n",
    "    change_column_name('Written by', 'Writer(s)')\n",
    "\n",
    "    return movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 Add the function that takes in three arguments;\n",
    "# Wikipedia data, Kaggle metadata, and MovieLens rating data (from Kaggle)\n",
    "\n",
    "def extract_transform_load():\n",
    "    # Read in the kaggle metadata and MovieLens ratings CSV files as Pandas DataFrames.\n",
    "    kaggle_metadata = pd.read_csv(os.path.join('Archive','movies_metadata.csv'), low_memory=False)\n",
    "    ratings = pd.read_csv(os.path.join('Archive','ratings.csv'))    \n",
    "    \n",
    "    # Open and read the Wikipedia data JSON file.\n",
    "    with open(os.path.join('Archive','wikipedia-movies.json'), mode='r') as file:\n",
    "        wiki_movies_raw = json.load(file)      \n",
    "    \n",
    "    # 3. Write a list comprehension to filter out TV shows.\n",
    "    wiki_tv = [tvshows for tvshows in wiki_movies_raw \n",
    "                if 'Television series' in tvshows]   \n",
    "\n",
    "    # 4. Write a list comprehension to iterate through the cleaned wiki movies list\n",
    "    # and call the clean_movie function on each movie.\n",
    "    clean_movies = [clean_movie(movie) for movie in wiki_movies_raw]    \n",
    "\n",
    "    # 5. Read in the cleaned movies list from Step 4 as a DataFrame.\n",
    "    wiki_movies_df = pd.DataFrame(clean_movies)\n",
    "\n",
    "    # 6. Write a try-except block to catch errors while extracting the IMDb ID using a regular expression string and\n",
    "    #  dropping any imdb_id duplicates. If there is an error, capture and print the exception.\n",
    "    try:\n",
    "        wiki_movies_df['imdb_id'] = wiki_movies_df['imdb_link'].str.extract(r'(tt\\d{7})')\n",
    "        wiki_movies_df.drop_duplicates(subset='imdb_id', inplace=True)        \n",
    "    except: \n",
    "        print(\"No link to extract\")\n",
    "        \n",
    "    #  7. Write a list comprehension to keep the columns that don't have null values from the wiki_movies_df DataFrame.\n",
    "    wiki_columns_to_keep = [column for column in wiki_movies_df.columns if wiki_movies_df[column].isnull().sum() < len(wiki_movies_df) * 0.9]\n",
    "    wiki_movies_df = wiki_movies_df[wiki_columns_to_keep]\n",
    "\n",
    "    # 8. Create a variable that will hold the non-null values from the “Box office” column.\n",
    "    box_office = wiki_movies_df['Box office'].dropna()\n",
    "    \n",
    "    # 9. Convert the box office data created in Step 8 to string values using the lambda and join functions.\n",
    "    box_office = box_office.apply(lambda x: ' '.join(x) if type(x) == list else x)\n",
    "\n",
    "    # 10. Write a regular expression to match the six elements of \"form_one\" of the box office data.\n",
    "    form_one = r'\\$\\d+\\.?\\d*\\s*[mb]illion'\n",
    "    \n",
    "    # 11. Write a regular expression to match the three elements of \"form_two\" of the box office data.\n",
    "    form_two = r'\\$\\d{1,3}(?:,\\d{3})+'\n",
    "\n",
    "    # 12. Add the parse_dollars function.\n",
    "    \n",
    "    def parse_dollars(s):\n",
    "        # if s is not a string, return NaN\n",
    "        if type(s) != str:\n",
    "            return np.nan\n",
    "\n",
    "        # if input is of the form $###.# million\n",
    "        if re.match(r'\\$\\s*\\d+\\.?\\d*\\s*milli?on', s, flags=re.IGNORECASE):\n",
    "\n",
    "            # remove dollar sign and \" million\"\n",
    "            s = re.sub('\\$|\\s|[a-zA-Z]','', s)\n",
    "\n",
    "            # convert to float and multiply by a million\n",
    "            value = float(s) * 10**6\n",
    "\n",
    "            # return value\n",
    "            return value\n",
    "\n",
    "        # if input is of the form $###.# billion\n",
    "        elif re.match(r'\\$\\s*\\d+\\.?\\d*\\s*billi?on', s, flags=re.IGNORECASE):\n",
    "\n",
    "            # remove dollar sign and \" billion\"\n",
    "            s = re.sub('\\$|\\s|[a-zA-Z]','', s)\n",
    "\n",
    "            # convert to float and multiply by a billion\n",
    "            value = float(s) * 10**9\n",
    "\n",
    "            # return value\n",
    "            return value\n",
    "\n",
    "        # if input is of the form $###,###,###\n",
    "        elif re.match(r'\\$\\s*\\d{1,3}(?:[,\\.]\\d{3})+(?!\\s[mb]illion)', s, flags=re.IGNORECASE):\n",
    "\n",
    "            # remove dollar sign and commas\n",
    "            s = re.sub('\\$|,','', s)\n",
    "\n",
    "            # convert to float\n",
    "            value = float(s)\n",
    "\n",
    "            # return value\n",
    "            return value\n",
    "\n",
    "        # otherwise, return NaN\n",
    "        else:\n",
    "            return np.nan   \n",
    "        \n",
    "    # 13. Clean the box office column in the wiki_movies_df DataFrame.\n",
    "    wiki_movies_df['box_office'] = box_office.str.extract(f'({form_one}|{form_two})', flags=re.IGNORECASE)[0].apply(parse_dollars)\n",
    "    wiki_movies_df.drop('Box office', axis=1, inplace=True)\n",
    "    \n",
    "    # 14. Clean the budget column in the wiki_movies_df DataFrame.\n",
    "    # First, we need to preprocess the budget data. Create a budget variable\n",
    "    budget = wiki_movies_df['Budget'].dropna()\n",
    "    \n",
    "    # Convert any lists to strings:\n",
    "    budget = budget.map(lambda x: ' '.join(x) if type(x) == list else x)\n",
    "    \n",
    "    # Then remove any values between a dollar sign and a hyphen (for budgets given in ranges):\n",
    "    budget = budget.str.replace(r'\\$.*[-—–](?![a-z])', '$', regex=True)\n",
    "    \n",
    "    # Use the same pattern matches that you created to parse the box office data, and apply them without modifications \n",
    "    # to the budget data. Then, look at what's left.\n",
    "    matches_form_one = budget.str.contains(form_one, flags=re.IGNORECASE, na=False)\n",
    "    matches_form_two = budget.str.contains(form_two, flags=re.IGNORECASE, na=False)\n",
    "    budget[~matches_form_one & ~matches_form_two]\n",
    "    \n",
    "    # Another issue is discovered: citation references (the numbers in square brackets).\n",
    "    # Remove those with a regular expression.\n",
    "    budget = budget.str.replace(r'\\[\\d+\\]\\s*', '')\n",
    "    budget[~matches_form_one & ~matches_form_two]\n",
    "    \n",
    "    # Ready to parse the budget values\n",
    "    wiki_movies_df['budget'] = budget.str.extract(f'({form_one}|{form_two})', flags=re.IGNORECASE)[0].apply(parse_dollars)\n",
    "    \n",
    "    # Drop the original Budget column\n",
    "    wiki_movies_df.drop('Budget', axis=1, inplace=True)\n",
    "      \n",
    "    # 15. Clean the release date column in the wiki_movies_df DataFrame.\n",
    "    # Parse Release Date\n",
    "    # Parsing the release date will follow a similar pattern to parsing box office and budget, but with different forms.\n",
    "\n",
    "    # First, make a variable that holds the non-null values of Release date in the DataFrame, converting lists to strings:\n",
    "    release_date = wiki_movies_df['Release date'].dropna().apply(lambda x: ' '.join(x) if type(x) == list else x)\n",
    "    \n",
    "    # The forms we'll be parsing are:\n",
    "\n",
    "    # 1 Full month name, one- to two-digit day, four-digit year (i.e., January 1, 2000)\n",
    "    # 2 Four-digit year, two-digit month, two-digit day, with any separator (i.e., 2000-01-01)\n",
    "    # 3 Full month name, four-digit year (i.e., January 2000)\n",
    "    # 4 Four-digit year\n",
    "    date_form_one = r'(?:January|February|March|April|May|June|July|August|September|October|November|December)\\s[123]?\\d,\\s\\d{4}'\n",
    "    date_form_two = r'\\d{4}.[01]\\d.[0123]\\d'\n",
    "    date_form_three = r'(?:January|February|March|April|May|June|July|August|September|October|November|December)\\s\\d{4}'\n",
    "    date_form_four = r'\\d{4}'\n",
    "    \n",
    "    # And then we can extract the dates with:\n",
    "    release_date.str.extract(f'({date_form_one}|{date_form_two}|{date_form_three}|{date_form_four})', flags=re.IGNORECASE)\n",
    "    \n",
    "    # Instead of creating our own function to parse the dates, we'll use the built-in to_datetime() method in Pandas. Since \n",
    "    # there are different date formats, set the infer_datetime_format option to True. The date formats we've targeted are \n",
    "    # among those that the to_datetime() function can recognize, which explains the infer_datetime_format=True argument below.\n",
    "    wiki_movies_df['release_date'] = pd.to_datetime(release_date.str.extract(f'({date_form_one}|{date_form_two}|{date_form_three}|{date_form_four})')[0], infer_datetime_format=True)  \n",
    "    \n",
    "    # 16. Clean the running time column in the wiki_movies_df DataFrame.\n",
    "    # Parse Running Time\n",
    "    # First, make a variable that holds the non-null values of Release date in the DataFrame, converting lists to strings:\n",
    "    running_time = wiki_movies_df['Running time'].dropna().apply(lambda x: ' '.join(x) if type(x) == list else x)\n",
    "    running_time\n",
    "    \n",
    "    # It looks like most of the entries just look like \"100 minutes.\" Let's see how many running times look exactly like \n",
    "    # that by using string boundaries.\n",
    "    running_time.str.contains(r'^\\d*\\s*minutes$', flags=re.IGNORECASE, na=False).sum()\n",
    "    \n",
    "    # The above code returns 6,528 entries. Let's get a sense of what the other 366 entries look like.\n",
    "    running_time[running_time.str.contains(r'^\\d*\\s*minutes$', flags=re.IGNORECASE, na=False) != True]\n",
    "    \n",
    "    # Let's make this more general by only marking the beginning of the string, and accepting other abbreviations of \n",
    "    # \"minutes\" by only searching up to the letter \"m.\n",
    "    running_time.str.contains(r'^\\d*\\s*m', flags=re.IGNORECASE, na=False).sum()\n",
    "    \n",
    "    # That accounts for 6,877 entries. The remaining 17 follow:\n",
    "    running_time[running_time.str.contains(r'^\\d*\\s*m', flags=re.IGNORECASE, na=False) != True]\n",
    "    \n",
    "    # We can match all of the hour + minute patterns with one regular expression pattern. Our pattern follows:\n",
    "    # 1 Start with one or more digits.\n",
    "    # 2 Have an optional space after the digit and before the letter \"h.\"\n",
    "    # 3 Capture all the possible abbreviations of \"hour(s).\" To do this, we'll make every letter in \"hours\" optional except \n",
    "    #   the \"h.\"\n",
    "    # 4 Have an optional space after the \"hours\" marker.\n",
    "    # 5 Have an optional number of digits for minutes.\n",
    "    # As a pattern, this looks like \"\\d+\\s*ho?u?r?s?\\s*\\d*\".\n",
    "\n",
    "    # We only want to extract digits, and we want to allow for both possible patterns. Therefore, we'll add capture \n",
    "    # groups around the \\d instances as well as add an alternating character. Our code will look like the following.\n",
    "    running_time_extract = running_time.str.extract(r'(\\d+)\\s*ho?u?r?s?\\s*(\\d*)|(\\d+)\\s*m')\n",
    "    \n",
    "    # Unfortunately, this new DataFrame is all strings, we'll need to convert them to numeric values. Because we may have \n",
    "    # captured empty strings, we'll use the to_numeric() method and set the errors argument to 'coerce'. Coercing the \n",
    "    # errors will turn the empty strings into Not a Number (NaN), then we can use fillna() to change all the NaNs to zeros.\n",
    "    running_time_extract = running_time_extract.apply(lambda col: pd.to_numeric(col, errors='coerce')).fillna(0)\n",
    "    \n",
    "    # Now we can apply a function that will convert the hour capture groups and minute capture groups to minutes if the \n",
    "    # pure minutes capture group is zero, and save the output to wiki_movies_df:\n",
    "    wiki_movies_df['running_time'] = running_time_extract.apply(lambda row: row[0]*60 + row[1] if row[2] == 0 else row[2], axis=1)\n",
    "    \n",
    "    # Finally, we can drop Running time from the dataset with the following code:\n",
    "    wiki_movies_df.drop('Running time', axis=1, inplace=True)\n",
    "    \n",
    "    # Return three variables. The first is the wiki_movies_df DataFrame\n",
    "    return wiki_movies_df, kaggle_metadata, ratings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 17. Create the path to your file directory and variables for the three files.\n",
    "file_dir = '/Users/rfnichol/OneDrive - COOPER TIRE & RUBBER COMPANY/Personal/Data Analytics Boot Camp\\\n",
    "    /Module_8_ETL/Module_8_files/Movies_ETL/archive'\n",
    "# The Wikipedia data\n",
    "wiki_file = f'{file_dir}/wikipedia.movies.json'\n",
    "# The Kaggle metadata\n",
    "kaggle_file = f'{file_dir}/movies_metadata.csv'\n",
    "# The MovieLens rating data.\n",
    "ratings_file = f'{file_dir}/ratings.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rfnichol\\Anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel_launcher.py:117: FutureWarning: The default value of regex will change from True to False in a future version.\n"
     ]
    }
   ],
   "source": [
    "# 18. Set the three variables equal to the function created in D1.\n",
    "wiki_file, kaggle_file, ratings_file = extract_transform_load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 19. Set the wiki_movies_df equal to the wiki_file variable. \n",
    "wiki_movies_df = wiki_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>title</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>...</th>\n",
       "      <th>Editor(s)</th>\n",
       "      <th>Composer(s)</th>\n",
       "      <th>Producer(s)</th>\n",
       "      <th>Production company(s)</th>\n",
       "      <th>Writer(s)</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>box_office</th>\n",
       "      <th>budget</th>\n",
       "      <th>release_date</th>\n",
       "      <th>running_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Adventures_o...</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>https://www.imdb.com/title/tt0098987/</td>\n",
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       "      <td>[Andrew Dice Clay, Wayne Newton, Priscilla Pre...</td>\n",
       "      <td>Oliver Wood</td>\n",
       "      <td>[July 11, 1990, (, 1990-07-11, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>Michael Tronick</td>\n",
       "      <td>[Cliff Eidelman, Yello]</td>\n",
       "      <td>[Steve Perry, Joel Silver]</td>\n",
       "      <td>Silver Pictures</td>\n",
       "      <td>[David Arnott, James Cappe]</td>\n",
       "      <td>tt0098987</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://en.wikipedia.org/wiki/After_Dark,_My_S...</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>https://www.imdb.com/title/tt0098994/</td>\n",
       "      <td>After Dark, My Sweet</td>\n",
       "      <td>[the novel, After Dark, My Sweet, by, Jim Thom...</td>\n",
       "      <td>[Jason Patric, Rachel Ward, Bruce Dern, George...</td>\n",
       "      <td>Mark Plummer</td>\n",
       "      <td>[May 17, 1990, (, 1990-05-17, ), (Cannes Film ...</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>Howard E. Smith</td>\n",
       "      <td>Maurice Jarre</td>\n",
       "      <td>[Ric Kidney, Robert Redlin]</td>\n",
       "      <td>Avenue Pictures</td>\n",
       "      <td>[James Foley, Robert Redlin]</td>\n",
       "      <td>tt0098994</td>\n",
       "      <td>2700000.0</td>\n",
       "      <td>6000000.0</td>\n",
       "      <td>1990-05-17</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Air_America_(film)</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>https://www.imdb.com/title/tt0099005/</td>\n",
       "      <td>Air America</td>\n",
       "      <td>[Air America, by, Christopher Robbins]</td>\n",
       "      <td>[Mel Gibson, Robert Downey Jr., Nancy Travis, ...</td>\n",
       "      <td>Roger Deakins</td>\n",
       "      <td>[August 10, 1990, (, 1990-08-10, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>[English, Lao]</td>\n",
       "      <td>...</td>\n",
       "      <td>[John Bloom, Lois Freeman-Fox]</td>\n",
       "      <td>Charles Gross</td>\n",
       "      <td>Daniel Melnick</td>\n",
       "      <td>[Carolco Pictures, IndieProd Company]</td>\n",
       "      <td>[John Eskow, Richard Rush]</td>\n",
       "      <td>tt0099005</td>\n",
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       "      <td>35000000.0</td>\n",
       "      <td>1990-08-10</td>\n",
       "      <td>113.0</td>\n",
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       "      <td>https://en.wikipedia.org/wiki/Alice_(1990_film)</td>\n",
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       "      <td>https://www.imdb.com/title/tt0099012/</td>\n",
       "      <td>Alice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Alec Baldwin, Blythe Danner, Judy Davis, Mia ...</td>\n",
       "      <td>Carlo Di Palma</td>\n",
       "      <td>[December 25, 1990, (, 1990-12-25, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>Susan E. Morse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Robert Greenhut</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>tt0099012</td>\n",
       "      <td>7331647.0</td>\n",
       "      <td>12000000.0</td>\n",
       "      <td>1990-12-25</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Almost_an_Angel</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>https://www.imdb.com/title/tt0099018/</td>\n",
       "      <td>Almost an Angel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Paul Hogan, Elias Koteas, Linda Kozlowski]</td>\n",
       "      <td>Russell Boyd</td>\n",
       "      <td>December 19, 1990</td>\n",
       "      <td>US</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>David Stiven</td>\n",
       "      <td>Maurice Jarre</td>\n",
       "      <td>John Cornell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paul Hogan</td>\n",
       "      <td>tt0099018</td>\n",
       "      <td>6939946.0</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>1990-12-19</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url    year  \\\n",
       "0  https://en.wikipedia.org/wiki/The_Adventures_o...  1990.0   \n",
       "1  https://en.wikipedia.org/wiki/After_Dark,_My_S...  1990.0   \n",
       "2   https://en.wikipedia.org/wiki/Air_America_(film)  1990.0   \n",
       "3    https://en.wikipedia.org/wiki/Alice_(1990_film)  1990.0   \n",
       "4      https://en.wikipedia.org/wiki/Almost_an_Angel  1990.0   \n",
       "\n",
       "                               imdb_link                            title  \\\n",
       "0  https://www.imdb.com/title/tt0098987/  The Adventures of Ford Fairlane   \n",
       "1  https://www.imdb.com/title/tt0098994/             After Dark, My Sweet   \n",
       "2  https://www.imdb.com/title/tt0099005/                      Air America   \n",
       "3  https://www.imdb.com/title/tt0099012/                            Alice   \n",
       "4  https://www.imdb.com/title/tt0099018/                  Almost an Angel   \n",
       "\n",
       "                                            Based on  \\\n",
       "0                        [Characters, by Rex Weiner]   \n",
       "1  [the novel, After Dark, My Sweet, by, Jim Thom...   \n",
       "2             [Air America, by, Christopher Robbins]   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "                                            Starring  Cinematography  \\\n",
       "0  [Andrew Dice Clay, Wayne Newton, Priscilla Pre...     Oliver Wood   \n",
       "1  [Jason Patric, Rachel Ward, Bruce Dern, George...    Mark Plummer   \n",
       "2  [Mel Gibson, Robert Downey Jr., Nancy Travis, ...   Roger Deakins   \n",
       "3  [Alec Baldwin, Blythe Danner, Judy Davis, Mia ...  Carlo Di Palma   \n",
       "4        [Paul Hogan, Elias Koteas, Linda Kozlowski]    Russell Boyd   \n",
       "\n",
       "                                        Release date        Country  \\\n",
       "0                  [July 11, 1990, (, 1990-07-11, )]  United States   \n",
       "1  [May 17, 1990, (, 1990-05-17, ), (Cannes Film ...  United States   \n",
       "2                [August 10, 1990, (, 1990-08-10, )]  United States   \n",
       "3              [December 25, 1990, (, 1990-12-25, )]  United States   \n",
       "4                                  December 19, 1990             US   \n",
       "\n",
       "         Language  ...                       Editor(s)  \\\n",
       "0         English  ...                 Michael Tronick   \n",
       "1         English  ...                 Howard E. Smith   \n",
       "2  [English, Lao]  ...  [John Bloom, Lois Freeman-Fox]   \n",
       "3         English  ...                  Susan E. Morse   \n",
       "4         English  ...                    David Stiven   \n",
       "\n",
       "               Composer(s)                  Producer(s)  \\\n",
       "0  [Cliff Eidelman, Yello]   [Steve Perry, Joel Silver]   \n",
       "1            Maurice Jarre  [Ric Kidney, Robert Redlin]   \n",
       "2            Charles Gross               Daniel Melnick   \n",
       "3                      NaN              Robert Greenhut   \n",
       "4            Maurice Jarre                 John Cornell   \n",
       "\n",
       "                   Production company(s)                     Writer(s)  \\\n",
       "0                        Silver Pictures   [David Arnott, James Cappe]   \n",
       "1                        Avenue Pictures  [James Foley, Robert Redlin]   \n",
       "2  [Carolco Pictures, IndieProd Company]    [John Eskow, Richard Rush]   \n",
       "3                                    NaN                   Woody Allen   \n",
       "4                                    NaN                    Paul Hogan   \n",
       "\n",
       "     imdb_id  box_office      budget  release_date  running_time  \n",
       "0  tt0098987  21400000.0  20000000.0    1990-07-11         102.0  \n",
       "1  tt0098994   2700000.0   6000000.0    1990-05-17         114.0  \n",
       "2  tt0099005  57718089.0  35000000.0    1990-08-10         113.0  \n",
       "3  tt0099012   7331647.0  12000000.0    1990-12-25         106.0  \n",
       "4  tt0099018   6939946.0  25000000.0    1990-12-19          95.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 20. Check that the wiki_movies_df DataFrame looks like this. \n",
    "wiki_movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['url',\n",
       " 'year',\n",
       " 'imdb_link',\n",
       " 'title',\n",
       " 'Based on',\n",
       " 'Starring',\n",
       " 'Cinematography',\n",
       " 'Release date',\n",
       " 'Country',\n",
       " 'Language',\n",
       " 'Director',\n",
       " 'Distributor',\n",
       " 'Editor(s)',\n",
       " 'Composer(s)',\n",
       " 'Producer(s)',\n",
       " 'Production company(s)',\n",
       " 'Writer(s)',\n",
       " 'imdb_id',\n",
       " 'box_office',\n",
       " 'budget',\n",
       " 'release_date',\n",
       " 'running_time']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 21. Check that wiki_movies_df DataFrame columns are correct. \n",
    "wiki_movies_df.columns.to_list()"
   ]
  }
 ],
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