Скачать или смотреть How to Set New Columns in a Multi-Column Index with Pandas from a Dictionary

Learn how to efficiently add new columns to a Pandas DataFrame with a multi-column index using a dictionary containing partially specified tuple keys.
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Adding New Columns to Multi-Index DataFrame using Dictionaries in Pandas

When working with multi-index DataFrames in Pandas, it can often be a challenge to manipulate and assign data efficiently. One common scenario involves adding new columns based on partial keys from a dictionary. If you're dealing with a situation where you want to update a multi-index DataFrame from configuration settings stored in an external dictionary, you're in the right place! In this post, we'll demonstrate how to achieve that without hardcoding values directly into your script.

The Problem: Updating a Multi-Index DataFrame

Consider a DataFrame initialized with a multi-column index. You may find yourself wanting to add a new column, let's call it desc1, while only partially specifying the keys for each row. The goal is to do this with minimal hardcoding and enhanced maintainability by utilizing a dictionary as our source of truth.

Here's an example DataFrame:

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

This will yield the following DataFrame:

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

The challenge now is to add the desc1 column based on the following dictionary:

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

The Solution: Using tuples to Slice the MultiIndex

Method 1: Using a Loop through the Dictionary

The first method involves simple iteration over the items in your dictionary. Here's how it works:

Adjust the dictionary: To ensure compatibility with the index, adjust your dictionary keys to tuples of length one for partial matching.

Loop through the items: Use a for loop to assign values in the DataFrame based on the keys in your dictionary.

Here's the complete code for this method:

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

After executing the code, the DataFrame will look like this:

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

Method 2: Using Index Mapping

If you prefer a more concise approach, you might consider using the Index.map functionality provided by Pandas. Here’s how to implement that:

Create a new column by mapping the index of the DataFrame to your dictionary values.

Use forward fill to handle the partial keys appropriately.

Here’s the complete code for this method:

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

Running this code leads to the same DataFrame result as before:

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

Conclusion

In this guide, we've explored two effective methods for setting new columns in a multi-column index in Pandas using a dictionary with partially specified tuple keys. By adapting your code to use flexible methodologies like loops or index mapping, you can maintain clean and easily modifiable code. This not only enhances performance but also improves readability and maintainability for you or your team.

Feel free to choose the method that best suits your coding style and specific application needs!