Comparing Scraped Data to a Populated CSV in Python

In this article, we’ll explore how to compare scraped data to a populated CSV file using Python. We’ll cover the necessary steps, including setting up the environment, scraping the data, comparing it to the existing CSV, and updating the CSV with new data.

Setting Up the Environment

Before we dive into the code, let’s set up our development environment. We’ll need the following libraries:

• requests for making HTTP requests
• BeautifulSoup for parsing HTML content
• pandas for handling data manipulation and analysis
• schedule for scheduling tasks to run at regular intervals

We can install these libraries using pip:

pip install requests beautifulsoup4 pandas schedule

Scraping the Data

The provided code snippet already scrapes the data from the specified URLs. We’ll use this as a starting point and modify it to suit our needs.

import requests
from bs4 import BeautifulSoup
import pandas as pd

# List of URLs to scrape
urls = ['https://ballotpedia.org/Alabama_Supreme_Court', 
        'https://ballotpedia.org/Alaska_Supreme_Court', 
        'https://ballotpedia.org/Arizona_Supreme_Court', 
        'https://ballotpedia.org/Arkansas_Supreme_Court', 
        'https://ballotpedia.org/California_Supreme_Court', 
        'https://ballotpedia.org/Colorado_Supreme_Court', 
        'https://ballotpedia.org/Connecticut_Supreme_Court', 
        'https://ballotpedia.org/Delaware_Supreme_Court']

# Create an empty dictionary to store the scraped data
temp_dict = {}

for url in urls:
    # Send a GET request to the URL and get the HTML response
    r = requests.get(url)
    
    # Parse the HTML content using BeautifulSoup
    soup = BeautifulSoup(r.content, 'html.parser')
    
    # Extract the table data from the webpage
    temp_dict[url.split('/')[-1]] = [item.text for item in 
                                      soup.select("table.wikitable.sortable.jquery- 
                                                  tablesorter a")]

This code snippet scrapes the data from each URL and stores it in a dictionary.

Comparing the Scraped Data to the Existing CSV

To compare the scraped data to the existing CSV, we’ll use pandas to read the CSV file and compare its contents with the scraped data.

import pandas as pd

# Read the existing CSV file
df_existing = pd.read_csv('State Supreme Court Justices.csv')

# Create a new DataFrame from the scraped data
df_scraped = pd.DataFrame.from_dict(temp_dict, orient='index').transpose()

# Compare the two DataFrames and store the differences in a new DataFrame
df_differences = pd.concat([df_existing, df_scraped], axis=1).eq().any(axis=1) * 2 - 2

# Print the differences
print(df_differences)

This code snippet reads the existing CSV file, creates a new DataFrame from the scraped data, and compares the two DataFrames to store the differences in a new DataFrame.

Updating the CSV with New Data

To update the CSV with new data, we’ll use pandas to write the updated DataFrame back to the CSV file.

import pandas as pd

# Update the existing CSV file with the new data
df_existing = df_scraped.transpose()
df_existing.to_csv('State Supreme Court Justices.csv', index=False)

This code snippet updates the existing CSV file with the new data.

Scheduling Tasks to Run at Regular Intervals

To schedule tasks to run at regular intervals, we’ll use the schedule library. We can create a function that scrapes the data and updates the CSV file, and then schedule it to run daily using schedule.every(1).day().

import schedule
import time

def update_csv():
    # Scraping code here...
    pass

def main():
    schedule.every(1).day().do(update_csv)  # Schedule the task to run daily
    
    while True:
        schedule.run_pending()
        time.sleep(60)

if __name__ == "__main__":
    main()

This code snippet schedules the update_csv function to run every day.

Conclusion

In this article, we’ve explored how to compare scraped data to a populated CSV file using Python. We’ve covered the necessary steps, including setting up the environment, scraping the data, comparing it to the existing CSV, and updating the CSV with new data. Additionally, we’ve used scheduling libraries to schedule tasks to run at regular intervals.

Example Use Cases

1. Web Scraping: Web scraping is a common use case for this technique. You can scrape data from websites and store it in a CSV file for further analysis or processing.
2. Data Updates: This technique can be used to update data in a database or another external system with new data scraped from the web.
3. Monitoring: You can schedule tasks to run at regular intervals to monitor changes in data over time.

Step-by-Step Solution

1. Set up your development environment by installing necessary libraries (requests, BeautifulSoup, pandas, and schedule).
2. Scrape the data from the specified URLs using Python.
3. Compare the scraped data to the existing CSV file using pandas.
4. Update the CSV with new data using pandas.
5. Schedule tasks to run at regular intervals using scheduling libraries.

Advice

1. Make sure to handle errors and exceptions when scraping data or updating files.
2. Use error handling mechanisms to prevent your program from crashing due to unexpected errors.
3. Consider using a more robust scheduling library like apscheduler for more complex scheduling tasks.

Note: This is just one possible way to compare scraped data to a populated CSV file in Python. Depending on the specific requirements of your project, you may need to modify or extend this approach.

Last modified on 2025-04-19