How to Extract TV Shows Data on TMDB Using Python?

TMDB is a huge database of film and television data that is entirely powered by its users. TMDB is a website that offers a free API gateway (method) for academics who want to get access to movie information.

A database of popular TV series can be found on the website www.themoviedb.org/tv. In this blog, we will use web scraping services to extract TV shows data from the TMDB website. Web Scraping data is the method of collecting information from websites using Python, BeautifulSoup, and Pandas programing language.

To extract TMDB information from the website, you will require Python packages Requests and BeautifulSoup.

Follow the below steps to extract the TMDB data using Python script.

Step 1: Download the webpage using requests.

Step 2: Use BeautifulSoup to read the HTML code.

Step 3: You will check the websites that will include various kinds of information on TV shows. The data will include user scores, will display individual page URLs, and launch dates for every program. You can also scrape latest season episodes, latest season, tagline, cast, and Genre from every show’s page.

Step 4: Create Python groups and dictionaries with the data you have collected.

Step 5: Data from various pages are extracted and combined.

Step 6: Finally, you will need to save the information to a CSV file.

Downloading the Webpage using "requests"

For downloading the webpage, use the requests library. Also, pip will be used to install the library.

!pip install requests --upgrade --quiet

For downloading a webpage, you can choose to employ the get function from requests. Requests.get will replay with a response object that will contain the information from the website and other details.

import requests # The library is now installed and imported. # sometimes websites stop you from extracting the data for some reason. It can be due to some authentication errors. needed_headers = {'User-Agent': "Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.131 Safari/537.36"} response = requests.get("https://www.themoviedb.org/tv", headers = needed_headers

For a successful response, use the status_code property. The HTTP status code for a successful response will be between 200 and 299.

response.status_code 200 The request was successful. We can get the contents of the page using response.text. dwn_content = response.text len(dwn_content) 223531

Check first 500 characters of the data that you downloaded as above.

dwn_content[:500] ' \n \n \n Popular TV Shows — The Movie Database (TMDB) \n \n \n \n \n \n \n a CSV file with name shows-page-9.csv has been created

You will be able to extract all of the data from a website and save it to CSV file. In other words, we can do this and extract 20 shows that are visible on the website page, but for the remaining of the websites you will need to click "load more". We'll finish off by writing a function that scrapes the desired number of pages. Let's have a look at the remaining few stages. The final step in finishing our function is to generate a final CSV file that contains all 200 rows.

In the final function, you will consider a list of Dataframes and convert it to CSV using concat() function.

The concat() method combines a list of Dataframes into a single large dataframe that may then be translated to a CSV file.

import pandas as pd base_link = "https://www.themoviedb.org/tv" def scrape_top_200_shows(base_link): dataframe_list = [] # we are going to keep range up to 11 because we just need up to 200 TV shows for now. for i in range(1,11): create_page_df(i, dataframe_list) # here we are using concat function so that we can merge the each dataframe that we got from the each page. total_dataframe = pd.concat(dataframe_list, ignore_index = True) # with the simple command of to_csv() we can create a csv file of all the pages we extracted. csv_complete = total_dataframe.to_csv('shows-data/Total-dataframe.csv', index= None) print(" \n a CSV file named Total-dataframe.csv with all the scraped shows has been created")

Let's place the last function to the test now that we've completed all of the functions.

scrape_top_200_shows(base_link) scraping page 1 :- https://www.themoviedb.org/tv?page=1 ---> a CSV file with name shows-page-1.csv has been created scraping page 2 :- https://www.themoviedb.org/tv?page=2 ---> a CSV file with name shows-page-2.csv has been created scraping page 3 :- https://www.themoviedb.org/tv?page=3 ---> a CSV file with name shows-page-3.csv has been created scraping page 4 :- https://www.themoviedb.org/tv?page=4 ---> a CSV file with name shows-page-4.csv has been created scraping page 5 :- https://www.themoviedb.org/tv?page=5 ---> a CSV file with name shows-page-5.csv has been created scraping page 6 :- https://www.themoviedb.org/tv?page=6 ---> a CSV file with name shows-page-6.csv has been created scraping page 7 :- https://www.themoviedb.org/tv?page=7 ---> a CSV file with name shows-page-7.csv has been created scraping page 8 :- https://www.themoviedb.org/tv?page=8 ---> a CSV file with name shows-page-8.csv has been created scraping page 9 :- https://www.themoviedb.org/tv?page=9 ---> a CSV file with name shows-page-9.csv has been created scraping page 10 :- https://www.themoviedb.org/tv?page=10 ---> a CSV file with name shows-page-10.csv has been created a CSV file named Total-dataframe.csv with all the scraped shows has been created

We were able to effectively develop a function as well as all of the necessary csv files. Let's take one more glance at how our entire CSV file looks with the aid of pandas. read csv()

pd.read_csv('shows-data/Total-dataframe.csv')[0:50]

That is all there is to it. Now, all we need is a single function that we developed at the very end. We were able to scrape 10 pages/200 shows successfully. We could have scraped a lot more pages, but we kept it at a modest level to keep it legible.

Requests were used to download the webpage.

Beautifulsoup was used to parse the HTML source code.

Summary

We looked at the website with the TV show schedule. We extracted the title, User Score, show's individual page URL, and launch date for each program.

We gathered information on the program from each unique page URL. Current season, Current season Episodes, Tagline, Genre, and Cast are just a few examples.

Into python lists and dictionaries, I compiled the retrieved data.

Data from different pages was extracted and blended.

The extracted data was saved to a CSV file called Total-dataframe.csv.

If you are looking to extract TV Shows data on TMDB using Python then contact Scraping Intelligence or request for a quote!!

Know more : https://www.websitescraper.com/how-web-scraping-is-used-to-scrape-website-using-infinite-scrolling.php

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