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Discover how to effectively merge two DataFrames in Python Pandas when one DataFrame contains columns with comma-separated values. Perfect for data analysis and manipulation!
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Merging Two DataFrames in Python Pandas on Comma-Separated Values

When working with data in Python’s Pandas library, it's not uncommon to encounter situations where you need to combine multiple DataFrames based on specific criteria. One particularly tricky situation arises when one of your DataFrames has values stored as a string of comma-separated entries. In this guide, we will explore how to merge two DataFrames based on Employee IDs when one DataFrame uses comma-separated strings for the IDs.

Getting Started with the Problem

Imagine you have the following two DataFrames:

df1:

Employee NameEmployeeIDJohn2, 22Kim3df2:

EmployeeIDHours28310You want to merge these DataFrames based on the EmployeeID values found in df2. However, df1 has EmployeeIDs formatted as a string of comma-separated values. The output you expect is a DataFrame combining the names, IDs, and corresponding hours worked as follows:

Expected Output:

Employee NameEmployeeIDHoursJohn2,228Kim310The Solution Explained

To achieve this merge, we can utilize some handy Python techniques, particularly leveraging dictionary mapping and list comprehension. Let's break down the steps:

Step 1: Convert Data Types

Firstly, we ensure that all Employee IDs in df2 are treated as strings to facilitate matching with the splitting function that we'll use later.

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Step 2: Create a Dictionary for Hours

Next, we create a mapping dictionary from df2, where each EmployeeID maps to its corresponding Hours. This can be conveniently done using the set_index method followed by to_dict().

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Step 3: Summing Hours Using a Lambda Function

Now, we need to apply a function to each row in df1 that splits the EmployeeIDs and sums the corresponding hours from the dictionary we created in step 2.

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Using this approach, while iterating through each EmployeeID, we split the string on the comma, check if the individual IDs exist in the dictionary, and sum the hours.

Step 4: Final Output

Now we can print df1 to see the final result:

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This will yield:

Employee NameEmployeeIDHoursJohn2, 228Kim310Alternate Method with Integer Matching

If desired, you can also work directly with integers instead of converting EmployeeIDs to strings. Here’s another version of the dictionary mapping that uses integer matching:

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In this method, we convert y to an integer before checking for its existence in the dictionary. Both approaches will result in the same final DataFrame, offering flexibility in how you handle your data.

Conclusion

Merging DataFrames on comma-separated values can be tricky, but with a bit of preparation and the right functions, it becomes a straightforward task. You can easily adapt the methods discussed here for other similar cases in data manipulation using Pandas.

Happy coding and data analyzing!