5 Best Ways to Extract Wikipedia Data in Python

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πŸ’‘ Problem Formulation: Extracting data from Wikipedia can empower various analyses, machine learning models, and data aggregation tasks. For Python developers, the goal is to retrieve structured information such as page content, summary, links, etc., for a given topic. The ideal input would be a Python function with the topic name, and the output would be the structured data related to the topic from Wikipedia.

Method 1: Using Wikipedia-API

This method involves using the Wikipedia-API, a Python wrapper that makes it easier to access and parse data from Wikipedia. The API provides functions for searching Wikipedia, fetching article summaries, content, and obtaining various metadata.

Here’s an example:

# You may need to install wikipedia-api package first. Use: pip install wikipedia-api
import wikipediaapi

wiki_wiki = wikipediaapi.Wikipedia('en')

page = wiki_wiki.page('Python_(programming_language)')
print("Page - Title: %s" % page.title)
print("Page - Summary: %s" % page.summary[0:60])

The output will be the title and the first 60 characters of the summary of the “Python (programming language)” Wikipedia page:

"Page - Title: Python (programming language) Page - Summary: Python is an interpreted high-level general-purpose programming."

This code snippet initiates a Wikipedia API object for the English Wikipedia and fetches the page for “Python (programming language)”. The title and a snippet of the summary are printed out. This method is straightforward and allows for quick retrieval of basic Wikipedia page information.

Method 2: Using wikipedia Python Library

The wikipedia library is a Python package that simplifies the process of accessing and parsing data from Wikipedia. It is built on top of the MediaWiki API and provides convenient functions to search and fetch Wikipedia articles, summaries, and more.

Here’s an example:

# You may need to install the wikipedia package first. Use: pip install wikipedia
import wikipedia

wikipedia.set_lang("en")
summary = wikipedia.summary("Global warming")
print(summary)

The output will be a concise summary of the Wikipedia page for “Global warming.”

This code snippet uses the `wikipedia` library to set the language to English and then fetches a summary of the “Global warming” Wikipedia page. It offers a quick and easy approach for accessing Wikipedia summaries.

Method 3: Using Requests and BeautifulSoup

This method combines the use of the Requests library to make HTTP calls to Wikipedia and BeautifulSoup to parse the returned HTML document. It gives more control and flexibility in accessing and scraping the exact webpage content needed.

Here’s an example:

# You may need to install requests and beautifulsoup4 packages first. Use: pip install requests beautifulsoup4
from bs4 import BeautifulSoup
import requests

response = requests.get("https://en.wikipedia.org/wiki/Machine_learning")
soup = BeautifulSoup(response.content, 'html.parser')
title = soup.find(id="firstHeading").text
print(title)

The output will be the title of the Wikipedia page for “Machine learning.”

In this snippet, an HTTP GET request is sent to the Wikipedia page for “Machine learning”. BeautifulSoup then parses the HTML and extracts the text of the first heading element, which contains the title. This method is more technical but very powerful when you need to scrape specific information not directly accessible through APIs.

Method 4: Using Selenium for Dynamic Content

Selenium is a tool for browser automation, which can be used to retrieve data from Wikipedia pages that load content dynamically with JavaScript.

Here’s an example:

# You may need to install selenium and a webdriver first.
from selenium import webdriver

browser = webdriver.Chrome()
browser.get('https://en.wikipedia.org/wiki/Python_(programming_language)')
title = browser.find_element_by_id('firstHeading').text
print(title)
browser.quit()

The output will be the title of the Wikipedia page for “Python (programming language).”

This example shows how Selenium is used to automate a web browser that fetches the Wikipedia page for “Python (programming language)”. Selenium can obtain data from a webpage just like a human user, which is helpful for dynamic content that requires JavaScript execution.

Bonus One-Liner Method 5: Using Pandas for Tables

For extracting tables directly into DataFrames, Pandas offers a simple one-liner.

Here’s an example:

# You may need to install pandas first. Use: pip install pandas
import pandas as pd

df = pd.read_html('https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)')[0]
print(df.head())

The output will be the first few rows of the table listing countries by GDP (nominal) from Wikipedia.

By calling pd.read_html() with the Wikipedia URL, Pandas fetches the tables from the page and returns a list of DataFrames. This is a very concise method to directly access tabular data from Wikipedia.

Summary/Discussion

  • Method 1: Wikipedia-API. Provides an easy-to-use interface for accessing Wikipedia data. Strengths: Simple to use, returns structured data. Weaknesses: Limited to what the API provides, may not include all available data.
  • Method 2: wikipedia Python Library. A user-friendly package for fetching summaries and full articles. Strengths: Straightforward, convenient functions. Weaknesses: Less control over data scraping details, might not handle disambiguation or error cases gracefully.
  • Method 3: Requests and BeautifulSoup. Allows detailed control over HTTP requests and HTML parsing. Strengths: Very flexible, can extract any part of the webpage. Weaknesses: Requires knowledge of HTML and possibly web scraping ethics and legality.
  • Method 4: Using Selenium. Simulates a real user’s interaction with a browser. Strengths: Can handle dynamic content loaded with JavaScript. Weaknesses: Overhead of running a browser, slower execution, and more resource-heavy.
  • Bonus Method 5: Pandas for Tables. Quick extraction of tabular data directly into DataFrames. Strengths: Extremely simple for tables. Weaknesses: Only applicable to tabular data, may require additional processing for complex tables.