How to Use Pandas for Web Scraping and Saving Data (2 examples)

Introduction

Web scraping is the process of extracting data from websites. While libraries like BeautifulSoup and Scrapy are popular for web scraping, Pandas offers a simpler approach for certain tasks, particularly when data is contained in tables or CSV files accessible via a URL. Pandas can read this data directly into a DataFrame, making it ready for analysis or processing.

Pandas, a highly versatile library in Python, is primarily known for data manipulation and analysis. Interestingly, with its powerful data handling capabilities, it can also be leveraged for web scraping tasks. This tutorial will guide you through using Pandas for web scraping and how to store that data efficiently, with two practical examples.

Prerequisites

• Basic understanding of Python
• Python installed on your system
• Pandas library installed (pip install pandas)
• Optional: Requests library installed for advanced examples (pip install requests)

Example 1: Extracting Table Data from a Web Page

In our first example, we’ll scrape tabular data directly from a web page into a DataFrame. This method is straightforward when the data is presented in a table format on the website.

import pandas as pd

# URL containing the table you want to scrape
data_url = 'https://example.com/table'

# Use read_html to extract tables from the URL
# The result is a list of DataFrame objects
# We assume there's only one table, hence [0]
table = pd.read_html(data_url)[0]

table.head()

The above code fetches the table data from the specified URL and loads it into a DataFrame. The head() method displays the first few rows of the table, giving you a glimpse of the data.

Example 2: Scraping and Saving Data with More Control

For more complex scraping tasks, where more control over the HTTP request is required or the data isn’t in a straightforward table format, combining Pandas with the Requests library can be powerful. Here, we’ll extract data from a web page and manipulate it into a structured DataFrame, then save it as a CSV file.

import requests
import pandas as pd
from io import StringIO

# Target URL
url = 'https://example.com/customdata'

# Headers to mimic a browser visit
headers = {'User-Agent': 'Mozilla/5.0'}

# Sending GET request
response = requests.get(url, headers=headers)

# Assume the response contains CSV-formatted data
csv_data = response.text

# Creating a DataFrame from the CSV data
df = pd.read_csv(StringIO(csv_data))

df.head()

# Saving the DataFrame to a CSV file
df.to_csv('scraped_data.csv', index=False)

This example demonstrates fetching content from a page, converting it into a DataFrame, and saving it. This method is useful when dealing with CSV data embedded or generated by web pages.

Conclusion

Pandas, while not a dedicated web scraping tool, offers a simple yet powerful approach for certain web scraping tasks, especially when dealing with data in tabular form or accessible via CSV. These examples showcase just a glimpse of what’s possible with Pandas in the realm of data extraction and manipulation.