Converting Scrape HTML Tables to Pandas DataFrames

Introduction

In this article, we will explore the process of converting scraped HTML tables into pandas dataframes. We’ll cover the use of BeautifulSoup and requests libraries to scrape the HTML content, followed by the conversion using the read_html function from pandas.

Background

BeautifulSoup is a Python library used for parsing HTML and XML documents. It creates a parse tree from page source code that can be used to extract data in a hierarchical and more readable manner. The requests library is used for making HTTP requests, which allows us to retrieve web pages or resources.

Pandas DataFrames are two-dimensional data structures with labels as row and column indices. They offer efficient data analysis capabilities such as filtering, sorting, grouping, and merging.

Prerequisites

Before we dive into the conversion process, make sure you have installed the required libraries:

pip install beautifulsoup4 requests pandas

Scraping HTML Tables with BeautifulSoup

To scrape an HTML table, you can use BeautifulSoup’s find_all method to locate all tables on a webpage. In this example, we’ll be using the Wikipedia World Golf Ranking page.

import requests
from bs4 import BeautifulSoup

# Set headers for User-Agent
headers = {'User-Agent':'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36'}

# Send GET request to the webpage
response = requests.get('https://en.wikipedia.org/wiki/Official_World_Golf_Ranking', headers=headers)

# Parse HTML content using BeautifulSoup
soup = BeautifulSoup(response.text, 'html.parser')

Finding All Tables on a Webpage

Once you have parsed the HTML content, you can use the find_all method to locate all tables on the webpage. In this case, we’re only interested in the first table.

# Find all tables on the webpage
html_table = soup.find_all("table")[0]

Inspecting the Table Element

After finding the table element, you can inspect its properties using the print function. This will help you understand how to proceed with converting it into a pandas dataframe.

# Print the HTML table element
print(html_table)
print(type(html_table))

The Error: ‘NoneType’ Object is Not Callable

When we try to convert the scraped HTML table into a pandas dataframe using pd.read_html, we encounter an error. This is because read_html expects an html string, not a BeautifulSoup object.

# The error encountered when trying to convert the scraped HTML table to a pandas dataframe
print("Error:", df)

Fixing the Issue: Converting BeautifulSoup Object to Html String

To fix this issue, we need to convert the BeautifulSoup object into an html string. We can achieve this by using the prettify method.

# Convert the BeautifulSoup object into an html string
html_string = str(html_table)

Converting to Pandas DataFrame Using `read_html`

Finally, we can use the read_html function from pandas to convert our scraped HTML table into a dataframe. We’ll pass the html string as an argument to this function.

import pandas as pd

# Convert the scraped HTML table to a pandas dataframe
df = pd.read_html(html_string)[0]

Displaying the DataFrame

After successfully converting the scraped HTML table into a pandas dataframe, we can display it using various pandas functions such as head, info, or describe.

# Display the first few rows of the dataframe
print(df.head())

Example Use Case: Web Scraping and Data Analysis

Here’s an example use case where we scrape data from a website, convert it into a pandas dataframe, and perform various data analysis tasks:

import requests
from bs4 import BeautifulSoup
import pandas as pd

# Set headers for User-Agent
headers = {'User-Agent':'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36'}

# Send GET request to the webpage
response = requests.get('https://en.wikipedia.org/wiki/Official_World_Golf_Ranking', headers=headers)

# Parse HTML content using BeautifulSoup
soup = BeautifulSoup(response.text, 'html.parser')

# Find all tables on the webpage
html_table = soup.find_all("table")[0]

# Convert the BeautifulSoup object into an html string
html_string = str(html_table)

# Convert the scraped HTML table to a pandas dataframe
df = pd.read_html(html_string)[0]

# Display the first few rows of the dataframe
print(df.head())

# Get the number of rows in the dataframe
print("Number of Rows:", df.shape[0])

# Get the data types of each column
print("Data Types:")
for col in df.columns:
    print(col, ":", df[col].dtype)

Conclusion

In this article, we covered the process of converting scraped HTML tables into pandas dataframes using BeautifulSoup and requests libraries. We discussed how to inspect table elements, fix common errors, and perform various data analysis tasks. With these techniques, you can efficiently scrape data from websites and analyze it using pandas dataframes.

Last modified on 2025-02-13