How to Efficiently Populate a DataFrame Column with Ranges from Another DataFrame in Python

Discover a quick and elegant solution to fill a DataFrame column with a range of values from another DataFrame using `Pandas`. Learn how to implement this in your Python code for better performance!
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How to Efficiently Populate a DataFrame Column with Ranges from Another DataFrame in Python

Working with data in Pandas can sometimes feel daunting, especially when it comes to manipulating DataFrames effectively. A common task might arise when you want to generate a new DataFrame (Df2) based on a range of values defined in two columns of an existing DataFrame (Df1). This guide will guide you through the problem and provide a streamlined solution to achieve this task efficiently.

The Problem: Creating Df2 from Df1

Let's consider two DataFrames:

Df1

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Df2

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You need to create Df2 by filling the column C with values from a range defined by columns C1 and C2 of Df1.

The Solution: A Quick Approach Using Pandas

A more efficient and elegant way to construct Df2 from Df1 is to utilize the combination of the assign() and explode() functions in Pandas.

Step-by-Step Breakdown

Assign to New Column: Use the assign() method to create a new column C that holds these ranges.

Explode the Results: Finally, utilize explode() to transform the lists in the new column into separate rows.

Here’s how you can implement it in code:

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Output

Running the above code will yield the following DataFrame Df2:

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Conclusion

With these straightforward methods using Pandas, you can efficiently populate a DataFrame column with a range of values from another DataFrame. Instead of getting bogged down by loops and concatenations, leveraging built-in functions results in cleaner, more readable, and performant code. Give it a try and streamline your data manipulation tasks!