Скачать или смотреть Creating New Columns from Conditions in a Pandas DataFrame

Learn how to leverage `pandas` in Python to create new columns in a DataFrame based on conditions from existing data. Perfect for data wranglers seeking efficiency in their workflow!
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This video is based on the question https://stackoverflow.com/q/68742266/ asked by the user 'Kaustav Dutta' ( https://stackoverflow.com/u/11744404/ ) and on the answer https://stackoverflow.com/a/68742407/ provided by the user 'Anurag Dabas' ( https://stackoverflow.com/u/14289892/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Creating New Columns from Conditions in a Pandas DataFrame: A Step-by-Step Guide

When working with large datasets in Python, especially with libraries like pandas, it’s common to encounter scenarios where the structure of your data requires alteration for better clarity and usability. In this guide, we’ll explore an interesting problem where we want to create new columns based on two different conditions from existing columns in a dataset.

The Problem: Understanding the Dataset

Let’s say you have a dataset structured like this:

state idDist idName320Jammu & Kashmir321Kupwara324Badgam120Arunachal Pradesh1210Dibang ValleyWhat We Want to Achieve

In the dataset above:

The state id is used to identify the state.

If the Dist id is 0, the name refers to the entire state.

If the Dist id is any number other than 0, it refers to a specific district within that state.

Our task is to add two new columns:

state_name: This will be filled with values from the Name column where Dist id is 0 for a given state id.

district_name: This will be filled with values from the Name column where Dist id is not 0.

The expected output should resemble:

state idDist idNamestate_namedistrict_name320Jammu & KashmirJammu & Kashmir-321KupwaraJammu & KashmirKupwara324BadgamJammu & KashmirBadgam120Arunachal PradeshArunachal Pradesh-1210Dibang ValleyArunachal PradeshDibang ValleyThe Solution: Step-by-Step Implementation

Method 1: Using loc and ffill()

Using the loc accessor: This is a powerful way to manipulate DataFrame values based on logical conditions.

Forward fill to propagate states: After filling in the new state names, we'll ensure all rows that belong to the same state are updated adequately.

Here’s how it’s done in code:

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

Method 2: Using np.where()

An alternative method could be using NumPy's where function to get similar results in fewer lines of code.

Here’s the code:

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

Explanation of the Code

For both methods, we first check if the Dist id equals 0 or not.

We fill the state_name and district_name based on these conditions.

Finally, we use the ffill() function to propagate any non-null state_name values downward to fill corresponding values for rows with District IDs.

Final Output

Once you execute the above code, your DataFrame will look like this:

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

With only a few lines of code, you can efficiently restructure your dataset for better readability and analysis.

Happy coding with pandas! Feel free to experiment with the dataset and the provided methods to deepen your understanding.