Scraping HTML Tables with Pandas - FriendlyUsers Tech Blog

Here’s an example of a blog post explaining how to scrape HTML tables using Pandas and read_html() method, along with a section on accessing class attributes in HTML elements:

Scraping HTML Tables with Pandas and read_html Pandas is a powerful data analysis and manipulation library in Python, and one of its great features is the ability to easily extract data from HTML tables and transform them into DataFrames. One of the quickest ways to do this is using the read_html() method, which automatically converts HTML tables into a list of DataFrames.

Here’s an example of how to use read_html() to extract data from a simple HTML table:

import pandas as pd

# Scrape the table from a webpage
tables = pd.read_html("https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States")

# The first table in the list is our desired table
df = tables[0]

# Display the first five rows of the DataFrame
print(df.head())

The read_html() method will search for all tables in the HTML content and return them as a list of DataFrames. In this example, we only want the first table, so we extract it using tables[0]. The resulting DataFrame will contain the data from the HTML table, with each row corresponding to a row in the HTML table and each column corresponding to a table cell.

Accessing Class Attributes in HTML Elements

In addition to extracting data from HTML tables, you may also want to extract information from specific HTML elements based on their class attributes. To do this, you can use a library such as BeautifulSoup, which provides a way to parse HTML and extract specific elements based on their attributes.

Here’s an example of how to use BeautifulSoup to extract information from a table based on the class attribute of its cells:

import requests
from bs4 import BeautifulSoup

# Request the page
response = requests.get("https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States")

# Parse the HTML content
soup = BeautifulSoup(response.content, "html.parser")

# Find the table cells with the class attribute "flagicon"
flag_cells = soup.find_all("td", class_="flagicon")

# Extract the text from each cell
flags = [cell.text for cell in flag_cells]

# Print the extracted flags
print(flags)

In this example, we use requests.get() to retrieve the content of the webpage, and then parse it using BeautifulSoup. Next, we use the soup.find_all() method to find all td elements with a class attribute of “flagicon”. Finally, we extract the text from each of these cells and store it in the flags list.

In conclusion, with Pandas and BeautifulSoup, you can easily extract data from HTML tables and specific elements based on their class attributes. These tools are incredibly useful for data analysis and manipulation, and with a bit of Python knowledge, you can quickly extract the information you need from webpages.