Handling and Cleaning Data with Python Libraries

This week in BALT 4363, I dove into Chapter 3 of Data Toolkit: Python + Hands-On Math by Todd Kelsey. The main focus was on learning how to handle and clean data using two important Python libraries: Pandas and NumPy. These tools are especially helpful when working with large datasets that need to be cleaned or reorganized before they can be analyzed. Pandas, for example, allows us to import and manipulate data from CSV files, while NumPy helps with mathematical operations like calculating averages and standard deviations. Learning how to use both libraries in Google Colab gave me hands-on experience working with real-world data.

One of the most valuable things I learned was how to handle missing data and remove duplicates. This is a big part of making sure your data is accurate and ready for analysis. I practiced using methods like dropna(), fillna(), and drop_duplicates() on a sample dataset. I also explored the Aquasmart scenario, where a business used these same tools to analyze customer behavior and sales. It was helpful to see how data cleaning techniques can actually impact decision-making in a real company setting. These steps, while simple, are crucial for making sense of messy data and getting clear, useful results.

During the hands-on exercise, I worked with a video game sales dataset from Kaggle. Using Pandas and NumPy, I learned how to group and sort data to find things like top-selling games and average sales by genre. Seeing how a few lines of code could organize and analyze so much information was pretty exciting. I now understand how powerful Python can be—not just for coding, but for turning raw data into insights. This week gave me more confidence in using Python, and I’m starting to see how it fits into the bigger picture of data analysis.