Combining Excel Files After Scraping Basketball Player Data in Python

Learn how to efficiently combine multiple Excel files into one using Python after scraping basketball player statistics. Perfect for data analysis!
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This video is based on the question https://stackoverflow.com/q/62295231/ asked by the user 'pythondazza' ( https://stackoverflow.com/u/13490163/ ) and on the answer https://stackoverflow.com/a/62295374/ provided by the user 'Sy Ker' ( https://stackoverflow.com/u/13656045/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Combining Excel Files After Scraping Basketball Player Data in Python

If you’ve been working with web scraping in Python, you might have run into a scenario where your data output is split into multiple files, especially when you're scraping data spanning several years. A common task is to scrape basketball player data for different seasons – ending up with an Excel file for each year. But wouldn’t it be more efficient to combine all that data into one single Excel or CSV file for analysis? In this guide, we’ll walk through how to achieve that by modifying the existing code you might be using for scraping.

The Problem

Let’s set the scene. You’re scraping basketball player statistics from a website, and your current method saves the data you’ve extracted into individual CSV files for each season. This means if you’ve extracted data for multiple years, you would have a separate file for every year. While this works, it’s not very helpful for wider analysis where you want all data in one place. So, how can we efficiently combine all the data into a single file?

The Solution

Step 1: Setting Up Your Scraping Code

To begin, make sure you already have your code set up to scrape the basketball data. Based on the example you provided, here’s a streamlined version that accumulates the data into a single list as you scrape:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Combining the DataFrames

Now that you have your code set up to scrape and store each year’s data into a list called poop, the next step is to combine all the DataFrames in this list into one unified DataFrame. This can be easily accomplished using the pandas.concat() function:

[[See Video to Reveal this Text or Code Snippet]]

Putting It All Together

Here’s how your complete script might look after combining both the scraping part and the data concatenation part. This revised code will not only collect data over multiple years but also save it all into a single CSV file for your convenience:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By masterfully leveraging the pandas library’s capabilities to concatenate DataFrames, you can streamline your workflow and conduct data analysis much more effectively. Instead of sifting through multiple Excel files, you now have all player statistics in one comprehensive file. Happy coding and scraping!