Let's dive in..

The tools we're going to use are:

• Request will allow us to send HTTP requests to get HTML files
• BeautifulSoup will help us parse the HTML files
• Pandas will help us assemble the data into a DataFrame to clean and analyze it
• csv(optional)- If you want to share data in csv file format

Let's Begin..

In this tutorial, we're going to scrape IMDB website, which we can get title, year, ratings, genre etc..

First, we'll import the tools to build our scraper

 python
import requests
from bs4 import BeautifulSoup
import pandas as pd
import csv

Getting the contents of webpage into results variable

 python
url = "https://www.imdb.com/search/title/?groups=top_1000"
results = requests.get(url)

In order to make content easy to understand, we are using BeautifulSoup and the content is stored in soup variable

 python
soup = BeautifulSoup(results.text, "html.parser")

And now initializing the lists to store data

 python 
titles = []        #Stores the title of movie
years = []         #Stores the launch year of the movie
time = []          #Stores movie duration
imdb_ratings = []  #Stores the rating of the movie
genre = []         #Stores details regarding genre of the movie
votes = []         #Store the no.of votes for the movie

Now find the right movie container by inspecting it, and hover over the movie div, which looks like below image

And we can see 50 div with class names:lister-item mode-advanced
So, find all div's with that classname by

 python
movie_div = soup.find_all("div", class_="lister-item mode-advanced")

find_all attribute extracts all the div's which has class
name:"lister-item mode-advanced"

Now get into each lister-item mode-advanced div and get the title, year, ratings, genre, movie duration

So we iterate every div to get title, year, ratings etc..

 python
for movieSection in movie_div:

Extracting the title

From image, we can see that the movie name is placed under div>h3>a

name = movieSection.h3.a.text  #we're iterating those divs using <b>movieSection<b> variable
titles.append(name) #appending the movie names into <b>titles</b> list  

Extracting Year

From image, we can see that the movie launch year is placed under div>h3>span(class name="lister-item-year") and we extract it using text keyword

 python
year = movieSection.h3.find("span", class_="lister-item-year").text
years.append(year)   #appending into years list

Similarly, we can get ratings, genre, movieDuration w.r.t classname

ratings = movieSection.strong.text
imdb_ratings.append(ratings)   #appending ratings into list
category = movieSection.find("span", class_="genre").text.strip()
genre.append(category)         #appending category into Genre list
runTime = movieSection.find("span", class_="runtime").text
time.append(runTime)           #appending runTime into time list

Extracting votes

As from the image, we can see that we have two span tags with classname="nv". So, for votings we need to consider nv[0] and for gross collections nv[1]

nv = movieSection.find_all("span", attrs={"name": "nv"})
vote = nv[0].text
votes.append(vote)

Now we will build a DataFrame with pandas
To store the data we have to create nicely into a table, so that we can really understand
And we can do it..

 python
movies = pd.DataFrame(
    {
        "Movie": titles,
        "Year": years,
        "RunTime": time,
        "imdb": imdb_ratings,
        "Genre": genre,
        "votes": votes,
    }
)

And now let's print the dataframe

As we can see on row 16 and 25, there is some inconsistent of data. So we need to clean

 python
 movies["Year"] = movies["Year"].str.extract("(\\d+)").astype(int) #Extracting only numerical values. so we can commit "I"
 movies["RunTime"] = movies["RunTime"].str.replace("min", "minutes") #replacing <b>min</b> with <b>minutes</b>
 movies["votes"] = movies["votes"].str.replace(",", "").astype(int) #removing "," to make it more clear

And now after cleaning we will see, how it looks

 python
print(movies)

You can also export the data in .csv file format.
In order to export,
Create a file with .csv file extension

 python
movies.to_csv(r"C:\Users\Aleti Sunil\Downloads\movies.csv", index=False, header=True)

You can get Final code from my Github repo

In my next part, I’ll explain how to loop through all of the pages of this IMDb list to grab all of the 1,000 movies, which will involve a few alterations to the final code we have here

Hope it's useful
A ❤️ would be Awesome 😊

HappyCoding