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Web Scraping with Python: A How-To Guide


Most businesses (and people) today more or less understand the implications of data on their business.

ERP systems enable companies to crunch their internal data and make decisions accordingly.

Which would have been enough by and itself if the creation of web data did not rise exponentially as we speak. Some sources estimate it to be 328.77 million terabytes of data every day!

And, your business operated in a vacuum, of course.

Web scraping is the process of extracting data from the web to monitor various events that can impact your business.

If you run a logistics business, scraping climate data can help you make informed decisions. Think of information on upcoming hurricanes and storms from the news.

Some businesses generate most of their sales online. If you fall in that segment, collecting and monitoring social media data enables you to gauge customer sentiment.

For its part, Grepsr specializes in managed data acquisition from the web.

With over a decade of experience in the field we come with proven expertise to solve the most pesky data extraction use cases and enable businesses to ingest high-volume data in byte-sized pieces. 

Read about data extraction with PHP here:

Data extraction with Python

In one of our older articles, we explained how you can build your own crawler and scrape the web using PHP.

This time, we will focus on data extraction with Python.

Python, much like PHP, is a programming language used worldwide for a variety of applications.

From simple scripting purposes to generating complex language models for artificial intelligence and machine learning (like ChatGPT and various other LLMs), developers often turn to Python for its simplicity.

Its syntax is easy to learn, beginner friendly and a robust community of plugins and libraries has helped it encroach every task relating to computers.

Web scraping is no different. We can most certainly carry out data extraction with Python.

In this lesson we will use Python to write a crawler to scrape IMDB’s top 250 movies and preserve the data in a CSV file.

We have divided this article into the following sections for effective navigation: 

  1. Prerequisites
  2. Definitions
  3. Setup
  4. Writing the scraper
  5. Final words
Data to make or break your business
Get high-priority web data for your business, when you want it.

Prerequisites to start web scraping with Python

Similar to the PHP article, before getting started, we will need certain tools and basic knowledge about a few terms. We will then set up a project directory, and install different packages and libraries that are of use in this project.

Since Python is a “Jack of all trades” language, all of its feature set is available on almost every platform.

After that, we will go through each line of the Python code and reflect on what it does and why it does that. At the end of this tutorial, you will have learned the basics of Python programming, at least when it comes to web scraping. You will generate a CSV file that you can use for data analysis and data processing.

Basics on Python


These are pre-packaged python programs that are designed to carry out a small operation or function. You can find them distributed over the internet under a license, free of cost or with a proprietary right.

Package manager

The Package Managers help manage the libraries we use inside our Python program.

Bear in mind that recklessly installing Python packages by replacing or completely overriding current install can potentially bork your system, which is why we use a concept called ‘environment’.

Here are a few package managers you can use: pip, conda, pipenv, etc. You can also use OS package managers to install Python packages.


Most modern operating systems use Python in their core processes and applications. Uncontrolled installation of different packages can break one’s system and prevent it from booting or performing routine tasks.

To prevent this from happening we use a containerization logic known as environment. An environment is a sandbox container in a computer system where new versions of a particular file (or files) can be installed. The environment does not communicate with the installation but fulfills the requirements of a certain program.

Simply put, the environment is similar to a virtual machine. To prevent our system from disintegrating, we create a virtual machine and execute our program by installing packages to this virtual machine instead.

This way, the new packages cannot override the system, and the application gets all the packages it needs. Conda, venv, and pipenv are some examples of environment managers for Python.


We use the concept of sandboxing when we want to run an application without interference from other systems. It is a security measure, and we use it during Python programming to boost the security of our application. Which also helps in solidifying the overall security of our system.


We use Lxml to parse HTML documents and generate a DOM of the HTML document. Creating a DOM is a matter of convenience.  It helps us to search for the data we want.


Pandas is another Python library used extensively in data science and data processing. It comes in handy when processing large rows of data and obtaining actionable insights. We will use this library to generate a CSV of the data we scrape from IMDB.


It enables our program to search through the HTML DOM using CSS selectors. They are an extension to the LXML library. You can use these together to quickly generate a searchable HTML DOM using CSS Selector.

Project setup for data extraction with Python

We create a directory where we house our main program and also the final output CSV file. To create a project directory we simply use our operating system to create a new folder and open a text editor in it. For this tutorial, we will be using Visual Studio Code from Microsoft.

Once this is done, we will install an environment manager to prevent new packages from interfering with our system.

Open the terminal in our text editor and type in the following:

pip install pipenv

This installs the environment manager called pipenv. We could have used conda and others but their setup is a bit more hasslesome compared to pipenv.

Now create a new file named requirements.txt.

We write down the name of the packages we are to install in this file. The contents of requirements.txt file will be:

  • pandas
  • requests
  • Lxml
  • Cssselect

Once this step is completed, write down the following command to install all of these in one go.

pipenv install -r requirements.txt

This command reads the requirements.txt file and installs each of the libraries after creating a Python environment with the same name as the folder. We generate two new files: Pipfile and Pipfile.lock.

This signals that our packages are installed and we are ready for the next step.

Now we need our program to know that we want to use the packages installed in the new environment rather than from the system so we run the following command to set the executable location.

pipenv shell

Now create a new file named to finally start writing the program.

Writing the crawler in Python

Unlike the PHP crawler our scraping technique will be a bit different this time. Rather than stopping at the first 250 pages, we will go one step further and extract the movie summary for each of the listed 250 movies.

So our column names for this scraping session will be: Title, Rating, and Summary.

We want to extract IMDB’s top 250 movies of all time from this page. Please read our previous blog “How to Perform Web Scraping with PHP” to learn about the detailed structure of this page. 

Here, we will only focus on the next page from where we are to extract the summary text for each of the 250 movies.

Once we click on one of the movie, we can see we reach a web page similar to this one:

Summary text source page of the movie Shawshank Redemption

Here we can see our required summary lies at the bottom part of our webpage. Inspecting it through our developer console tool, we can see the exact CSS tag the summary exists in.

Go through the developer’s console to find the source text within the page

We can directly use this information during our scraping, but let us find out if there is an easier method of dealing with this information. Let’s search for the summary text for this movie in the page source.

The script tag contains data in a JSON format

Moreover, we find out that the summary text is being pulled from a script tag which contains data in a JSON format. It is a more desirable format because JSON will always have its content in a more expected format unlike HTML body which can change as the web page is updated.

Therefore, it’s best to extract the data from the JSON object. Once you get an idea of the scraper’s workflow, you can begin writing it.

Begin the python script by importing all the installed packages using the keyword import similar to:

import pandas as pd
import requests as re 
import lxml.html as ext
import json

To start scraping, we define a header variable which contains all the required headers for the website.

The headers are important since they determine the format of handshakes the source website and our code will have, so that a smooth data transfer between the two agents can occur.

Resultantly, we can pull the respective headers from the developer console’s Network tab. Just search for your desired page and right click on the web page, then click and copy as cURL.

Paste the contents on another file temporarily to check all the headers.

Save the headers as cURL
headers = {
	'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/ Safari/537.36 Edg/111.0.1661.51',
	'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
	'accept-language': 'en-US,en;q=0.9'
The headers to put in our code

With this defined, we use the requests library to send a get request to the website of IMDB containing the top 250 movies.

response = re.get(‘’, headers=headers)

It will put the response content body in the response variable. Now we will need to generate an HTML DOM document of the respective response so that we can easily use lxml and cssselect to search for our required data. Additionally, we can do that with the following line of code:

tree = ext.fromstring(response.text)
movies = [ext.tostring(x) for x in tree.cssselect('table[data-caller-name="chart-top250movie"] > tbody > tr')]

Once this is done, all the information we need about the 250 movies is stored in the movies variable as a list. Now, it is only a matter of iterating over this variable to go further inside each movie’s web page and getting the summary. You can do that with the code block below:

url = '{}'.format(tree.cssselect('a')[1].get('href'))
response = req.get(url,  headers=headers)

Consequently, it will receive a web page that contains the movie summary for our movies. Now, as mentioned before, we will need to extract the JSON object from the script tag in this new web page. We can do that by using the JSON library as shown below:

script = json.loads(tree.cssselect('script[type="application/ld+json"]')[0].text_content())

Further, this new script variable contains the JSON object from the website. Place the information in a neat array and you will get an appropriate row data for one movie. Do it this way: 

row = {
    	'Title': script['name'],
    	'Rating': rating,
    	'Summary': script['description']

Iterating this process over 250 times will get us our required data. However, without storing this data somewhere intermediary we can’t use the data for further processing and analysis, which is where our pandas library comes in.

During each iteration we store the row information in a dataframe row of our pandas object using the following code:

all_data = pd.concat([all_data, pd.DataFrame([row])], ignore_index=True)

After the iterations are complete, we can export the pandas object to a CSV to send the data for downstream analysis.

Following is the entirety of the Python code:

import pandas as pd
import requests as req
import lxml.html as ext
import json
url = ''
headers = {
	'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/ Safari/537.36 Edg/111.0.1661.51',
	'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
	'accept-language': 'en-US,en;q=0.9'
response = req.get(url, headers=headers)
tree = ext.fromstring(response.text)
movies = [ext.tostring(x) for x in tree.cssselect('table[data-caller-name="chart-top250movie"] > tbody > tr')]
all_data = pd.DataFrame()
for idx, movie in enumerate(movies):
	print('Processing {}'.format(idx))
	tree = ext.fromstring(movie)
	url = '{}'.format(tree.cssselect('a')[1].get('href'))
	rating = tree.cssselect('td.ratingColumn.imdbRating strong')[0].text_content()
	response = req.get(url, headers=headers)
	tree = ext.fromstring(response.text)
	script = json.loads(tree.cssselect('script[type="application/ld+json"]')[0].text_content())
	row = {
    	'Title': script['name'],
    	'Rating': rating,
    	'Summary': script['description']
	all_data = pd.concat([all_data, pd.DataFrame([row])], ignore_index=True)
all_data.to_csv('final_data.csv', index = False)

Use the following dataset as you see fit. Binge-watch over the weekend, maybe?

Top IMBD movies of all time.

Final words

In Python, we enter each movies page and extract the summaries of the movies.

Unlink in PHP, we didn’t have to define headers in our code. Mainly because the internal page this time was a lot more trickier and further shelled by the web server.

In this article we used the basic of methods to access the data without causing needless errors.

Today, most websites on the internet have strict anti-bot measures in place. For any serious and long term data extraction projects, simple bypasses like these fail to work.

Therefore, companies relying on large-scale data extraction projects on a recurring frequency choose Grepsr for their needs.

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Track Changes in Your CSV Data Using Python and Pandas

So you’ve set up your online shop with your vendors’ data obtained via Grepsr’s extension, and you’re receiving their inventory listings as a CSV file regularly. Now you need to periodically monitor the data for changes on the vendors’ side — new additions, removals, price changes, etc. While your website automatically updates all this information when you […]

A Look Back at Grepsr’s 2020

A brief look at Grepsr's achievements in data extraction and industry reach in 2020, and a glimpse into 2021 plans.

Our Newly Redesigned Website is Live!

We’ve redesigned our website to make it easier for you to find what you’re looking for

data mining during covid

Role of Data Mining During the COVID-19 Outbreak

How web scraping and data mining can help predict, track and contain current and future disease outbreaks

Grepsr’s 2019 — A Year (and Decade) in Review

Time flies when you’re having fun

Introducing Grepsr’s New Slack-like Support

Making our data acquisition specialists more accessible to busy professionals

Introducing Grepsr’s Data Quality Report

Quality assured data to help you make the best business decisions

Report History/Activity on the Grepsr App

A walk-through detailing your report history and how to access (and download) your report’s data from historic crawl runs

Data Retention in Grepsr

New policy announcement

Automate Future Crawls Using Scheduler

Configure and enable schedules to automate future crawls

Data Delivery via Email

Have your Grepsr files automatically delivered by email

Data Delivery via Dropbox

Have your Grepsr files synced automatically to your Dropbox

Data Delivery via FTP

Have your Grepsr files synced automatically to your FTP/SFTP server

Data Delivery via Webhooks

Get notified as soon as your Grepsr data is ready

Data Delivery via Google Drive

Have your Grepsr files synced automatically to your Google Drive

Data Delivery via Amazon S3

Have your Grepsr files synced automatically to your Amazon S3 bucket

Data Delivery via Box

Have your Grepsr files synced automatically to your Box account

Data Delivery via File Feed

Under File Feed, there are two URLs — marked ‘Latest’ and ‘All’. Here’s a brief demo:

Customized Data Extraction via Grepsr Concierge

Although Grepsr for Chrome is a powerful tool in itself, it sometimes lacks the capability to extract data from some websites that are poorly structured, where data fields are hidden, and so on. Here we give you a simple demonstration on how you can get data from these complex websites via our custom platform — Grepsr Concierge. […]

Common Issues and Tips to Get the Best out of Grepsr

We know how annoying it is when you’ve spent time setting up Grepsr for Chrome to collect your data fields, and then you get back partial or no data at all.

Grepsr — the Numbers That Matter

Our stats since the start of 2018

Feeds & Endpoint API for Your Data in Grepsr

In our last post, we showed you how to automate your data delivery process in the Grepsr app. This time let’s have a quick look at data feeds and endpoints[*]. Your scraped data’s Endpoint API is the final stop it makes in its journey— starting from the host website, then to your Grepsr account via our crawler, and […]

Automate Your Data Delivery on the Grepsr App

I’m sure you’ve already got the hang of Grepsr for Chrome by now. If you’re like some of our users who are inquiring about data delivery on the app, then this blog is for you! Once you’ve set up your project and the app starts to extract your data, depending on the volume of data requested, it might […]

Kick-Start Your E-commerce Venture with Grepsr

400+ million entrepreneurs worldwide are attempting to start 300+ million companies, according to the Global Entrepreneurship Monitor. Approximately a hundred million new businesses start every year around the world, while a similar number also fold. What sets successful firms apart are the innovations and resources they utilize that help them stay healthy and relevant. Grepsr […]

How to Use Grepsr Browser Tool to Scrape the Web for Free

A beginner’s guide to your favorite DIY web scraping tool Just over a year ago, we introduced the all new Grepsr along with a beta launch of Chrome extension to fill the gap that Kimono Labs, a widely popular scraping tool, left since it’s closure. Now after a year of iteration on both the UI and UX along with shipping […]

Our Kimono Labs Replacement (Grepsr for Chrome) Levels Up

We’ve recently made a number of improvements to make Grepsr for Chrome that little bit easier, and more handy to use. We’ve also received tons of feature requests (keep ’em coming!), so we thought we’d share couple of our favorites that have most recently made it into Grepsr for Chrome. Infinite Scrolling and Enhanced Pagination Support From […]

Importance of Web Scraping in the Age of Big Data

Big Data has become an internet buzz lately. Not a day goes by without a mention of Big Data in many articles published by media or tech companies around the world.

Web Crawling Software or Web Crawling Service

Some people ask us if we are a “service” or a “software”. We simply tell them – we are a service, with killer software that runs behind the scenes! 🙂 Also, lot of our customers ask us, why go for a Web Crawling Service over a Web Crawling Software? The answer is pretty straight forward. […]

Managed Data Extraction Service

Grepsr is what we like to call, “Managed Data Extraction Service”. Here are some of the reasons why we call it “managed”: We let you focus on your business and use the data — worrying about technical details of extraction is our job, and we will do it for you. We let you describe your […]