Скачать или смотреть How to Check List Membership in Pandas and Store Parent Group Names in a Column Python Pandas

Discover how to efficiently check list membership and quickly store parent group names in your Pandas dataframe with straightforward mapping techniques.
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Solving List Membership and Parent Group Mapping in Python Pandas

In data analysis, there are scenarios where you need to verify if the elements of a dataframe column belong to specific predefined categories or groups. This guide will address a common problem encountered while performing this task using Python and the Pandas library. We will illustrate how to check list membership and store the corresponding parent group names in a new column of your dataframe.

The Problem at Hand

You may find yourself with multiple lists representing different categories, and a dataframe containing a column with various products. Your goal is to check each product against the lists and record which list (or group) they belong to in a new column. This is especially useful for organizing data and making your analysis easier.

Example Lists

Consider the following categories of products:

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Sample DataFrame

Let's say your dataframe, df1_1, looks like this:

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The Inefficient Approach

Initially, one might think to use multiple lambda statements to achieve this. An attempt could look like this:

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The problem here is that each application of a lambda function overwrites the previous results, which does not lead to the desired outcome.

A More Efficient Solution

Step 1: Create a Dictionary Mapping

Rather than using redundant lambda functions, we can consolidate our lists into a dictionary for clearer mapping. The key will be the category name, and the value will be a list of products:

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Step 2: Reverse the Mapping

Next, we reverse the mapping process to allow for quick lookups of group names based on the product name:

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Step 3: Apply Mapping to DataFrame

Finally, you can effortlessly use the map function to create the new column GMPL in one step:

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Conclusion

By employing the dictionary mapping approach and using the map function, you can efficiently check list membership and create a new column in your dataframe without overwriting previous results. This method is not only more readable but also significantly improves performance for larger datasets.

Example Final DataFrame

As a result, you’d end up with:

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In this example, where list membership checks and categorization can become complex, the dictionary approach simplifies and enhances your data processing capabilities in Pandas.

Happy coding!