How to Drop Null Values in pandas

Missing values can derail your analysis. In pandas, you can use the .dropna() method to remove rows or columns containing null values—in other words, missing data—so you can work with clean DataFrames. In this tutorial, you’ll learn how this method’s parameters let you control exactly which data gets removed. As you’ll see, these parameters give you fine-grained control over how much of your data to clean.

Dealing with null values is essential for keeping datasets clean and avoiding the issues they can cause. Missing entries can lead to misinterpreted column data types, inaccurate conclusions, and errors in calculations. Simply put, nulls can cause havoc if they find their way into your calculations.

By the end of this tutorial, you’ll