Pandas is an open-source Python library used for data manipulation and analysis. It is a powerful tool for handling structured data, such as spreadsheets and SQL tables. One of the most useful features of pandas is its ability to read and write data from various file formats, including CSV, Excel, and SQL databases.

Pandas can also be used for web scraping by reading data from HTML tables on web pages. The library provides a method called read_html() that can parse HTML tables and return a list of data frames. Data frames are two-dimensional tables with labeled rows and columns, similar to spreadsheets.

Here’s an example code snippet that uses pandas to scrape a table from a web page:

import pandas as pd

# URL of the web page containing the table
url = 'https://www.example.com/table.html'

# Read the HTML table into a list of data frames
dfs = pd.read_html(url)

# Extract the first data frame (assuming only one table on the page)
df = dfs[0]

# Print the first 5 rows of the data frame
print(df.head())

In this code snippet, we first import the pandas library using the import statement. We then specify the URL of the web page containing the table we want to scrape. We use the pd.read_html() method to read the HTML table into a list of data frames. Since read_html() can potentially return multiple data frames (if there are multiple tables on the page), we extract the first data frame using…