Скачать или смотреть How to Perform a Dataframe Lookup with Lists in Pandas

Learn how to effectively perform a lookup in multiple dataframes using Pandas to get desired results in Python.
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This video is based on the question https://stackoverflow.com/q/64317404/ asked by the user 'Ventoii' ( https://stackoverflow.com/u/12227248/ ) and on the answer https://stackoverflow.com/a/64541186/ provided by the user 'Shubham Sharma' ( https://stackoverflow.com/u/12833166/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Introduction

Working with data can sometimes be a challenge, especially when you have multiple dataframes with lists embedded within them. A common problem arises when you want to extract information from one dataframe based on the values found in another. In this guide, we will look at a scenario where you have two dataframes (df1 and df2) and you want to return a list of corresponding values from df1 for every entry in df2.

This operation requires a bit of understanding of how to manipulate dataframes in Pandas. Let’s dive into the details!

The Problem

Given the two dataframes below:

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Goal

You need to lookup values in ID2 of df2 using values from ID2 in df1 and return corresponding entries from ID1. The desired output in df2 should look like this:

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

The Solution

To achieve the desired result, we can use a combination of explode, groupby, agg, and map functions in Pandas. Here is a step-by-step breakdown of how we can solve this:

Step 1: Explode and Group

First, we will expand the lists in df1 using explode and then group by ID2 to collect all corresponding ID1 values into a list.

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

Step 2: Map the Values

Next, we will map the ID2 entries in df2 to the series m we created. This will fill df2 with the list of ID1 corresponding to each ID2.

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

Step 3: Handle Missing Values

Since some ID2 values in df2 may not have a match in df1, we will encounter NaN. To fix this, we can replace NaN values with empty lists.

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

Final Output

After running the above code, you can print df2 to see the final result:

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

Conclusion

By understanding how to manipulate dataframes in Python's Pandas library, you can efficiently look up and match values across multiple dataframes. The approach outlined here not only helps in the current scenario but can be adapted for a variety of similar tasks in data analysis.

Feel free to reach out if you encounter any challenges or have additional questions! Happy coding!