How to Scrape IMDb Top Box Office Movies Data using Python?

Different Libraries for Data Scrapping

We all understand that in Python, you have various libraries for various objectives. We will use the given libraries:

BeautifulSoup: It is utilized for web scraping objectives for pulling data out from XML and HTML files. It makes a parse tree using page source codes, which can be utilized to scrape data in a categorized and clearer manner.

Requests: It allows you to send HTTP/1.1 requests with Python. Using it, it is easy to add content including headers, multipart files, form data, as well as parameters through easy Python libraries. This also helps in accessing response data from Python in a similar way.

Pandas: It is a software library created for Python programming language to do data analysis and manipulation. Particularly, it provides data operations and structures to manipulate numerical tables as well as time series.

For scraping data using data extraction with Python, you have to follow some basic steps:

1: Finding the URL:

Here, we will extract IMDb website data to scrape the movie title, gross, weekly growth, as well as total weeks for the finest box office movies in the US. This URL for a page is https://www.imdb.com/chart/boxoffice/?ref_=nv_ch_cht

2: Reviewing the Page

Do right-click on that element as well as click on the “Inspect” option.

3: Get the Required Data to Scrape

Here, we will go to scrape data including movies title, weekly growth, and name, gross overall, and total weeks are taken for it that is in “div” tag correspondingly.

4: Writing the Code

For doing that, you can utilize Jupiter book or Google Colab. We are utilizing Google Colab here:

Import libraries:

import requests
from bs4 import BeautifulSoup
import pandas as pd

Make empty arrays and we would utilize them in the future to store data of a particular column.

TitleName=[]
Gross=[]
Weekend=[]
Week=[]

Just open the URL as well as scrape data from a website.

url = "https://www.imdb.com/chart/boxoffice/?ref_=nv_ch_cht"
r = requests.get(url).content

With the use of Find as well as Find All techniques in BeautifulSoup, we scrape data as well as store that in a variable.

soup = BeautifulSoup(r, "html.parser")
list = soup.find("tbody", {"class":""}).find_all("tr")
x = 1
for i in list:
title = i.find("td",{"class":"titleColumn"})
gross = i.find("span",{"class":"secondaryInfo"})
weekend = i.find("td",{"class":"ratingColumn"})
week=i.find("td",{"class":"weeksColumn"}

With the append option, we store all the information in an Array, which we have made before.

TitleName.append(title.text)
Gross.append(gross.text)
Weekend.append(weekend.text)
Week.append(week.text)

5. Storing Data in the Sheet. We Store Data in the CSV Format

df=pd.DataFrame({'Movie Title':TitleName, 'Weekend':Weekend, 'Gross':Gross, 'Week':Week})
df.to_csv('DS-PR1-18IT012.csv', index=False, encoding='utf-8')

6. It’s Time to Run the Entire Code

All the information is saved as IMDbRating.csv within the path of a Python file.

For more information, contact 3i Data Scraping or ask for a free quote about IMDb Top Box Office Movies Data Scraping services.

Originally published at https://www.3idatascraping.com.

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3i Data Scraping is an Experienced Web Scraping Service Provider in the USA. We offering a Complete Range of Data Extraction from Websites and Online Outsource.

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3i Data Scraping

3i Data Scraping

3i Data Scraping is an Experienced Web Scraping Service Provider in the USA. We offering a Complete Range of Data Extraction from Websites and Online Outsource.

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