How to Save Python Web Scraping Data to CSV Files Read it later

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Have you ever thought about how you could extract data from a website in a structured format? Python web scraping is the answer to your problem. With web scraping, you can extract data from any website of your choice and save it in a CSV file format for further analysis. In this blog, we will discuss Python web scraping to CSV and how you can achieve it using Python.

Before learning how to save web scraped data, take a closer look at how to scrape web in python using scrapy, beautiful soup and selenium.

What is CSV?

CSV stands for Comma Separated Values. It is a file format used to store data in a tabular form.

Each row in a CSV file represents a record, and each column represents a field.

CSV files are commonly used for storing and exchanging data between different software applications.

Check out our other blogs on Python development, including Python decorator, generator, enumerate function, and list comprehension to enhance your Python skills.

Python Web Scraping to CSV

Python provides several libraries for web scraping, such as BeautifulSoup, Scrapy, and Selenium.

In this blog, we will be using BeautifulSoup for web scraping and Pandas for saving the data to a CSV file.

Installing libraries for Web Scraping in Python

To get started with web scraping in Python, you’ll need to install a few libraries. The most popular libraries for web scraping in Python are Beautiful Soup and Requests.

Beautiful Soup is a Python library for parsing HTML and XML documents. It provides a simple API for navigating and searching the document tree. Requests is a library for sending HTTP requests in Python. It makes it easy to send requests and handle responses.

You can install both libraries using pip, which is a package manager for Python.

To install Beautiful Soup and Requests, open a terminal or command prompt and enter the following commands:

pip install beautifulsoup4
pip install requests

Once you’ve installed these libraries, you’re ready to start scraping websites.

Scraping Website with Beautiful Soup

To scrape a website with Beautiful Soup, you’ll need to send an HTTP request to the website and parse the HTML response.


import requests
from bs4 import BeautifulSoup
url = ''
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
title = soup.title.string

In this example, we’re using the Requests library to send an HTTP GET request to the website.

The response is then parsed with Beautiful Soup, and the title of the website is extracted from the HTML document.

Save Scraped Data to CSV

Now that we know how to scrape a website with Python, let’s look at how to save the scraped data to a CSV file.

CSV files are a common format for storing tabular data, and they can be easily read and manipulated with a spreadsheet program like Microsoft Excel or Google Sheets.

To save scraped data to a CSV file in Python, we’ll use the built-in CSV module.

Let’s take an example that scrapes the title and URL of the top 10 Google search results for a given query and saves the results to a CSV file:

import csv
from urllib.request import urlopen
from bs4 import BeautifulSoup
html = urlopen('')
bs = BeautifulSoup(html, 'html.parser')
table = bs.findAll('table', {'class': 'wikitable'})[0]
rows = table.findAll('tr')
csvFile = open('Text-Editor-Data.csv', 'wt+')
writer = csv.writer(csvFile)
    for row in rows:
        csvRow = []
        for cell in row.findAll(['td', 'th']):

This will Save the First Row of the table into our CSV file i.e Text-Editor-Data.csv.

Best Practices for Saving Web Scraping Data to CSV

Saving web scraped data to a CSV file in Python can be a crucial step in your data analysis process.

You should follow certain best practices to ensure that you save your data correctly and efficiently.

  1. Encoding: It is important to choose the correct encoding for your CSV file, especially if you are working with non-English characters. The most commonly used encoding for CSV files is UTF-8, which can handle a wide range of characters.
  2. Delimiter: Using a comma as a delimiter in your CSV file can affect the readability and efficiency of your data. Most software applications can easily read a comma, making it the most widely used delimiter.
  3. Column Headers: Including column headers in your CSV file can make it easier to understand and analyze your data. When creating your CSV file, make sure to include column headers that accurately describe the data in each column.
  4. Data Cleaning: It is important to clean your data before saving it to a CSV file. This can involve removing unnecessary characters, removing duplicates, and formatting the data to ensure consistency.
  5. File Path: When saving your CSV file, it is important to choose an appropriate file path that makes it easy to locate and access your data.

Wrapping Up

In conclusion, web scraping is a powerful tool for collecting data from the internet, and saving that data to a CSV file can make it easier to analyze and use for various purposes.

Through this blog, we have covered the basic process of web scraping, and best practices for saving the scraped data to a CSV file. By following the steps outlined in this blog, you can easily start web scraping and saving data to a CSV file using Python.

Remember to always practice ethical web scraping, respect the websites you are scraping, and follow any legal restrictions and terms of use.

We hope this blog helped you in your web scraping journey and equipped you with the knowledge and tools necessary to scrape data and save it to a CSV file using Python. Happy web scraping!


  1. Beautiful Soup Documentation
  2. Requests Documentation
  3. Pandas Documentation
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