How to Assign Labels to DataFrame Columns Based on Dictionary Values in Python

Описание: Learn how to check values between two dictionary-type columns in a pandas DataFrame and assign labels according to specific criteria.
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How to Assign Labels to DataFrame Columns Based on Dictionary Values in Python

Working with data can often lead us to complex challenges, especially when it comes to handling structured data like dictionaries within columns of a pandas DataFrame. Today, we'll tackle a specific problem: how to assign labels to a DataFrame based on the presence of values in two dictionary-type columns.

The Problem

Consider a scenario where you have a pandas DataFrame that contains two columns—diff_2 and nonBreakingChanges—both of which hold dictionary-type data. You need to create a new column called type based on specific conditions:

If both diff_2 and nonBreakingChanges have values and are not empty, assign the label Both.

If only diff_2 has values, assign the label B.

If only nonBreakingChanges has values, assign the label NB.

If neither has values, the label should be None.

With these conditions in mind, let’s break down the solution step-by-step.

Setting Up the DataFrame

First, let's establish how to create a DataFrame that resembles our starting point. Here is a snippet to outline our data:

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

Implementing the Solution

To perform the labeling according to our conditions, we will define a function that checks each row of the DataFrame and returns the appropriate label.

Step 1: Define the Function

Here’s the assign_type function that checks the conditions:

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

Step 2: Apply the Function to the DataFrame

Now let's apply our function to each row of the DataFrame and create the type column:

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

Final Output

After applying the function, your DataFrame will now include a new column, type, that reflects the conditions based on the diff_2 and nonBreakingChanges columns.

Conclusion

By following these steps, you can effectively assign labels in a pandas DataFrame based on dictionary-type columns. This solution can be a powerful addition to your data manipulation toolkit, especially when working with more complex datasets.

Now that you have a detailed understanding, feel free to modify the conditions and expand this solution to suit your specific needs! Happy coding!