Скачать или смотреть Efficiently Search Column Values in Pandas DataFrames with Lists

Learn how to search for values from one DataFrame in another containing lists using Pandas, and avoid issues like duplicates.
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Searching for Values Across DataFrames with Lists in Pandas

When working with data in Python using the Pandas library, you might encounter situations where you need to search for values from one DataFrame within another DataFrame that contains lists. A common challenge is efficiently handling this without running into performance issues or duplicates when dealing with larger datasets. In this guide, we’ll explore a practical solution to this problem using Pandas' powerful features.

The Problem

Imagine you have two DataFrames. The first DataFrame, df1, contains information about certain days along with a column named numsearch that contains numerical values represented as strings. The second DataFrame, df2, includes a column named Numbers, where each entry is a list of strings, and a Score that correlates with each list of numbers. Your goal is to check if the values from df1['numsearch'] exist within any of the lists in df2['Numbers']. If found, you will append the corresponding Score from df2 into df1. If a match is not found, you should indicate that there is "No score".

Example DataFrames

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

The desired output for df1 after performing the search and score assignment should look like this:

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

The Solution

To achieve the above results, we will perform the following steps:

Explode the Lists: The explode() function will break down the lists in df2['Numbers'], turning each number in the lists into its own row. This makes it simpler to map scores to those numbers.

Create a Mapping: We will create a dictionary that maps each number in Numbers to its corresponding Score.

Map the Scores: Using the mapping dictionary, we will assign scores back to df1, and fill in any unmatched values with "No score".

Step-by-Step Implementation

Here’s how you can implement the solution in just a few lines of code:

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

Summary of the Code

df2.explode('Numbers'): This breaks down the lists in the Numbers column.

dict(zip(df2.Numbers, df2.Score)): Creates a dictionary where keys are numbers and values are scores.

df1['Score'] = df1.numsearch.map(score_map).fillna('No Score'): Maps the scores to df1 based on the numsearch column.

Final Output

After running the code, df1 will now contain the scores linked to their respective numsearch values from df2, or "No Score" for those not found. The final DataFrame will look as desired:

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

Conclusion

By using the explode() method along with mapping techniques, you can efficiently search through DataFrames in Pandas — even when dealing with lists. This approach not only eliminates duplicates that might occur with more complex for loops but also leverages the power of Pandas for clean and readable code. If you encounter similar challenges in your data analysis tasks, remember that Pandas offers simple yet effective solutions!