Скачать или смотреть How to Create a New Column in a Pandas DataFrame Based on Conditional Row Values

Learn how to efficiently create a new column in a Pandas DataFrame by leveraging the values of other rows and columns, ensuring clarity and accuracy in data manipulation.
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This video is based on the question https://stackoverflow.com/q/64067444/ asked by the user 'Chris90' ( https://stackoverflow.com/u/8797830/ ) and on the answer https://stackoverflow.com/a/64067580/ provided by the user 'BENY' ( https://stackoverflow.com/u/7964527/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Create a New Column in a Pandas DataFrame Based on Conditional Row Values

Handling data manipulation tasks in Python, especially when working with Pandas DataFrames, can sometimes get complicated. A common scenario arises when you need to create a new column that depends on values from other columns and rows. In this post, we'll explore a specific problem that many data scientists encounter, and we'll guide you through the solution step-by-step.

The Problem

Imagine you have a DataFrame that looks like this:

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

You need to add a new column where, if the status is True, and the corresponding ref_id exists in the id column of another row with status as False, you should fetch the name from that row. The expected output would be:

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

To achieve this, we will break down the solution into manageable steps.

Solution Steps

Step 1: Set Up Your DataFrame

First, ensure that you have the required libraries installed. You can install Pandas if you haven’t done so already:

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

Next, let's create the DataFrame with the given data:

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Step 2: Create the New Column

Now let's implement the logic to populate the new column named new. Using the map function allows us to map the ref_id values to their corresponding names based on the conditions specified.

Here's the solution code:

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Step 3: View the Updated DataFrame

After executing the code above, you can now view the updated DataFrame with the newly created column new:

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

The output will be as expected:

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

Conclusion

In this guide, we tackled a common data manipulation challenge—creating a new column in a Pandas DataFrame based on the condition of other rows and values. By understanding how to leverage the loc and map functions, you can simplify your data analysis tasks significantly. Remember, practice is key, so try modifying this code to fit various scenarios you may encounter.

If you have any questions or additional tips, feel free to share in the comments below!