Cleaning Messy Data with Pandas

Cleaning Messy Data with Pandas

Now that we've covered arrays, matrices, and basic Pandas ideas in All About NumPy and Pandas, we are ready for the step that happens before almost every real machine learning model: data cleaning.

Raw data rarely arrives as a perfect rectangular table. It often has:

• missing values
• duplicate rows
• inconsistent category names
• numbers stored as text
• dates in multiple formats
• impossible values, like negative ages

For software engineers, think of cleaning as input validation plus normalization for a dataset. A model is only as trustworthy as the data contract it receives.

By the end of this lesson, you will be able to turn a small messy table into a cleaner DataFrame that is ready for the exploratory analysis coming next in Univariate, Bivariate, Multivariate Analysis.