How to Dynamically Add a Column in a Pandas DataFrame Based on Conditions

Learn how to efficiently add a new column to a Pandas DataFrame that takes values from other columns based on specific conditions.
---

Visit these links for original content and any more details, such as alternate solutions, comments, revision history etc. For example, the original title of the Question was: Add a column in a dataframe which takes the value of a column X if it is not blank , else it takes the value of column Y

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Dynamically Adding a Column in a Pandas DataFrame

When working with data in Python using the Pandas library, you may encounter situations where you need to manipulate your DataFrame to extract insights. A common challenge is creating a new column based on existing columns but with specific conditions.

In this post, we will explore a scenario where we want to create a new column that retrieves values from one column if it's populated and falls back to another column if it isn't.

The Problem Statement

Imagine you have a DataFrame with a column called Descr where values are separated by the delimiter \. You successfully split this column into several new columns (Desc1, Desc2, and Desc3). However, not all these new columns contain values. Your objective is to create a new column, say name, which will:

Take the value from Desc3 if it is not blank (populated).

If Desc3 is blank or null, the value should be taken from Desc1.

Your initial attempt using the apply function faced issues. Specifically, it worked for non-blank values in Desc3, but it failed to return values from Desc1 when Desc3 was empty.

The Solution

Method 1: Using apply

You can define a function that checks the values of Desc3 and Desc1, and then apply this function across the rows of your DataFrame. However, first, we need to ensure we are checking for null values properly:

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

This method is more efficient, especially for larger DataFrames, as it avoids the overhead of applying a function row by row. Instead, it utilizes vectorized operations:

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

Why Does This Work?

Conclusion

By understanding how to effectively handle conditions in Pandas, you can make your data manipulation tasks both simpler and more robust.

Feel free to reach out if you have more questions or if you would like to explore more Pandas tips!