Lesson 3. Wikipedia - Data Visualization

This lesson introduces pandas.read_html, a useful tool for extracting tables from HTML, and continues to explore BeautifulSoup, a Python library designed for parsing XML and HTML documents. We will start by gathering artists found on the List of Rock and Roll Hall of Fame inductees webpage in Wikipedia. We will then assemble discographies for 2-3 of our favorite artists.

Data skills | concepts¶

Search parameters
HTML
Web scraping
Pandas

Learning objectives¶

Extract and store tables and other HTML elements in a structured format
Apply best practices for managing data

This tutorial is designed to support multi-session workshops offered by The Ohio State University Libraries Research Commons. It assumes you already have a basic understanding of Python, including how to iterate through lists and dictionaries to extract data using a for loop. To learn basic Python concepts visit the Python - Mastering the Basics tutorial.

LESSON 3¶

Lesson 1 and Lesson 2 introduced the basic steps for any webscraping or API project:

Review and understand copyright and terms of use.
Check to see if an API is available.
Examine the URL
Inspect the elements
Identify Python libraries for project
Write and test code

Solution

import requests
from bs4 import BeautifulSoup

import requests
from bs4 import BeautifulSoup

url='https://en.wikipedia.org/wiki/Parliament_discography'
response=requests.get(url).text
soup=BeautifulSoup(response, 'html.parser')
headers=soup.find_all('div', {'class':'mw-heading2'})
for header in headers:
    print(header.find('h2').text)

Pandas¶

.read_html()¶

Read HTML tables directly into DataFrames with .read_html() . This extremely useful tool extracts all tables present in specified URL or file, allowing each table to be accessed using standard list indexing and slicing syntax.

The following code instructs Python to go to the Wikipedia List of states and territories of the United States and retrieve the second table.

import pandas as pd

tables=pd.read_html('https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States')
tables[1]

Loading...

BeautifulSoup¶

.find_previous( ) and .find_all_previous( )¶

Similar to .find_next( ) and .find_all_next( ), .find_previous( ) and .find_all_previous( ) gathers the previous instance of a named tag.

Exercise 7: Gather discographies

Gather the discography tables from the discography Wikipedia pages for the 2-3 artists you identified in Exercise 3: Examine the URL.

Identify base url
Store the secondary_url parts for each artist in a list, such as ...

artists=['Cyndi_Lauper_discography','Joe_Cocker_discography','The_White_Stripes_discography']

Create a for loop to iterate through each artist in your list.
Use .read_html to gather the discography tables each artist on your list.

Create an empty list for headers to gather table headers for each artist.
Use requests with BeautifulSoup and .find_previous() to gather the headers for each table and append each header to your headers list. Check to see if your table headers match the html tables you gathered with .read_html

Solution

import requests
from bs4 import BeautifulSoup
import pandas as pd

artists=['Cyndi_Lauper_discography','Joe_Cocker_discography','The_White_Stripes_discography']
base_url='https://en.wikipedia.org/wiki/'


for artist in artists[0:1]:

    url=base_url+artist
    response=requests.get(url).text
    soup=BeautifulSoup(response, 'html.parser')
    
    #FIND TABLES
    html_tables=pd.read_html(url)

    #FIND TABLE_HEADERS
    response = requests.get(url)
    response.encoding = 'utf-8'
    soup = BeautifulSoup(response.content, 'html.parser')
    
    tables=soup.find_all('table')
    # tables=soup.find_all('table', {'class':'wikitable'})
    table_number=0
    
    
    headers=[]
    
    for table in tables:

        if table.find_previous('div',{'class':'mw-heading2'}) is not None:
            h2=table.find_previous('div',{'class':'mw-heading2'}).text.split('[')[0]
        else:
            h2='None'
        if table.find_previous('div',{'class':'mw-heading3'}) is not None:
            h3=table.find_previous('div',{'class':'mw-heading3'}).text.split('[')[0]
            # print(f"table_number_{table_number}: {h3}")
        else:
            h3='None'
        print(f"table_number_{table_number}: {h2}, {h3}")
        table_number += 1

Managing files¶

There are several best practices and considerations for effectively managing research data files. When extracting and locally storing data in individual files, using standardized file naming conventions not only helps you organize and utilize your files efficiently but also facilitates sharing and collaboration with others in future projects.

Use short, descriptive names.
Use _ underscores or - dashes instead of spaces in your file names. Use leading zeros for sequential numbers to ensure proper sorting.

file_001_20250506.txt
file_002_20250506.txt

Use all lowercase for directory and filenames if possible.
Avoid special characters, including ~!@#$%^&*()[]{}?:;<>|\/
Use standardized dates YYYYMMDD to track versions and updates.
Include version control numbers to keep track of projects.

os module¶

The os module tells Python where to find and save files.

os.mkdir(‘path’)¶

Creates a new directory in your project folder or another specified location. Makes a directory in your project folder or another folder you specify. If a directory by the same name already exists in the path specified, os.mkdir will raise an OSError. Use a try-except block to handle the error.

import os

try:
    os.mkdir(artist)
except FileExistsError:
    print(f"Directory '{artist}' already exists.")
except Exception as e:
    print(f"An error occurred: {e}")

Solution

import requests
from bs4 import BeautifulSoup
import pandas as pd
import os

artists=['Cyndi_Lauper_discography','Joe_Cocker_discography','The_White_Stripes_discography']
base_url='https://en.wikipedia.org/wiki/'


for artist in artists:

    url=base_url+artist
    response=requests.get(url).text
    soup=BeautifulSoup(response, 'html.parser')
    
    #FIND TABLES
    html_tables=pd.read_html(url)

    #FIND TABLE_HEADERS
    response = requests.get(url)
    response.encoding = 'utf-8'
    soup = BeautifulSoup(response.content, 'html.parser')
    
    tables=soup.find_all('table')
    # tables=soup.find_all('table', {'class':'wikitable'})
    table_number=0
    
    
    headers=[]
    
    for table in tables:

        if table.find_previous('div',{'class':'mw-heading2'}) is not None:
            h2=table.find_previous('div',{'class':'mw-heading2'}).text.split('[')[0]
        else:
            h2='None'
            
        
        headers.append([h2.lower().replace(' ','_'),table_number])
        print(f"table_number_{table_number}: {h2}")
        table_number += 1   
        
    position=0
    for each_header in headers:
        if each_header[1] != None:
            table_name=each_header[0]
            number=each_header[1]
            artist_directory=artist.replace('_discography','').lower()
            try:
                os.mkdir(artist_directory)
            except FileExistsError:
                print(f"Directory '{artist_directory}' already exists.")
            except Exception as e:
                print(f"An error occurred: {e}")
            filename=artist_directory+'/table_number_'+str(number)+'_'+table_name+'.csv'
            print(filename)
            html_table=html_tables[position]
            html_table.to_csv(filename)
        elif each_header[0] != None:
            table_name=each_header[0].lower().replace(' ','_')
            number=each_header[2]
            print(number)
            print(table_name)
            artist_directory=artist.replace('_discography','').lower()
            try:
                os.mkdir(artist_directory)
            except FileExistsError:
                print(f"Directory '{artist_directory}' already exists.")
            except Exception as e:
                print(f"An error occurred: {e}")
            filename=artist_directory+'/table_number_'+str(number)+'_'+table_name+'.csv'
            print(filename)
            html_table=html_tables[position]
            html_table.to_csv(filename)
        position += 1