Web Scraping IMDB movie rating using Python

Last Updated : 07 Nov, 2025

We can scrape the IMDb movie ratings and their details with the help of the BeautifulSoup library of Python.

Modules Needed

Below is the list of modules required to scrape from IMDB.

requests: Requests library is an integral part of Python for making HTTP requests to a specified URL. Whether it be REST APIs or Web Scraping, requests must be learned for proceeding further with these technologies. When one makes a request to a URI, it returns a response.
html5lib: A pure-python library for parsing HTML. It is designed to conform to the WHATWG HTML specification, as is implemented by all major web browsers.
bs4: BeautifulSoup object is provided by Beautiful Soup which is a web scraping framework for Python. Web scraping is the process of extracting data from the website using automated tools to make the process faster.
pandas: Pandas is a library made over the NumPy library which provides various data structures and operators to manipulate the numerical data.

Step by Step Implementation

Steps to implement web scraping in python to extract IMDb movie ratings and its ratings:

1. Import the required modules.

Python

from bs4 import BeautifulSoup
import requests
import pandas as pd

2. Access the HTML content from the IMDb Top 250 movies page

Python

url = 'https://www.imdb.com/chart/top/'
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")

3. Extract movie details using HTML tags, each li tag represents a movie block containing title, year, and rating details.

Python

movies = soup.select("li.ipc-metadata-list-summary-item")

4. Create a list to store movie data

Python

movie_data = []

for movie in movies:
    title = movie.select_one("h3.ipc-title__text").text.strip()
    year = movie.select_one("span.cli-title-metadata-item").text.strip()
    rating_tag = movie.select_one("span.ipc-rating-star--rating")
    rating = rating_tag.text.strip() if rating_tag else "N/A"
    
    movie_data.append({
        "Title": title,
        "Year": year,
        "Rating": rating
    })

5. Display the extracted data

Python

for movie in movie_data:
    print(f"{movie['Title']} ({movie['Year']}) - Rating: {movie['Rating']}")

6. Save the data into a CSV file

Python

df = pd.DataFrame(movie_data)
df.to_csv("imdb_top_250_movies.csv", index=False)
print("IMDb data saved successfully to imdb_top_250_movies.csv!")

Complete Code Implementation

Python

from bs4 import BeautifulSoup
import requests
import pandas as pd

# Downloading IMDb Top 250 movie data
url = 'https://www.imdb.com/chart/top/'
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")

# Extract all movie containers
movies = soup.select("li.ipc-metadata-list-summary-item")

# Create a list to store movie details
movie_data = []

# Loop through each movie block and extract info
for movie in movies:
    title = movie.select_one("h3.ipc-title__text").text.strip()
    year = movie.select_one("span.cli-title-metadata-item").text.strip()
    rating_tag = movie.select_one("span.ipc-rating-star--rating")
    rating = rating_tag.text.strip() if rating_tag else "N/A"
    
    movie_data.append({
        "Title": title,
        "Year": year,
        "Rating": rating
    })

# Print movie data in terminal
for movie in movie_data:
    print(f"{movie['Title']} ({movie['Year']}) - Rating: {movie['Rating']}")

# Save the list as a DataFrame and export to CSV
df = pd.DataFrame(movie_data)
df.to_csv("imdb_top_250_movies.csv", index=False)
print("IMDb data saved successfully to imdb_top_250_movies.csv!")

Output

Title Year Rating
1. The Shawshank Redemption N/A 9.3 (3.1M)
2. The Godfather N/A 9.2 (2.2M)
3. The Dark Knight N/A 9.1 (3.1M)
4. The Godfather: Part II N/A 9.0 (1.5M)
5. 12 Angry Men N/A 9.0 (955K)
IMDb data saved successfully to imdb_top_250_movies.csv!

Along with this in the terminal, a .csv file with a given name is saved in the same file and the data in the .csv file will be as shown in the following image.