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Discover a simple method for querying a `Pandas DataFrame` with embedded lists to easily retrieve associated values.
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How to Use a Pandas DataFrame as a Lookup Table with Embedded Lists

Introduction

Are you working with a Pandas DataFrame and find yourself needing to perform lookups on lists embedded within its cells? This can be a common requirement when your data is structured to hold multiple values in a single field. Fortunately, there is an effective and readable solution to this problem. In this post, we will tackle a specific use case and provide you with a step-by-step method that leverages Python's Pandas library.

The Problem

Let's say you have a DataFrame structured like this:

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

You want to be able to query this DataFrame so that when you input a number, it returns the corresponding list. For example:

Inputting 1 should return [a, b, c]

Inputting 6 should return [d, e, f]

The Solution

To achieve this, we can use the map() function in combination with loc from the Pandas library. This approach is not only efficient but also quite intuitive. Let's break it down into clear steps.

Step 1: Setup Your DataFrame

First, ensure that you have your DataFrame created as follows:

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

Step 2: Querying the DataFrame

Now, to perform the lookup based on your input, you can execute the following code:

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

Step 3: Understanding the Code

n = 1: This variable holds the value you want to query.

df.A.map(lambda x: n in x): This line iterates through the lists in column A, checking if n exists in each list. It returns a boolean series.

df.loc[<boolean series>, 'B']: Using loc, we filter the DataFrame to find rows where our boolean series is True, and we extract the corresponding entries from column B.

Example Outputs

For n = 1, the output will be:

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

For n = 6, the output will be:

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

Conclusion

In summary, querying a Pandas DataFrame acting as a lookup table for embedded lists can be done efficiently with the combination of map() and loc. This method is not only readable but also leverages Python's powerful data manipulation capabilities, making it an excellent choice for data analysis tasks.

Feel free to experiment with different inputs and explore additional functionalities of the Pandas library to further enhance your data analysis skills!