How to Dynamically Fill a New Column in Pandas DataFrame Based on Conditions from Other DataFrames

Learn how to create a new column in a Pandas DataFrame based on conditions derived from two other DataFrames. This guide provides a step-by-step solution and example code.
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How to Dynamically Fill a New Column in Pandas DataFrame Based on Conditions from Other DataFrames

When working with data in Python, we often find ourselves needing to analyze and categorize information based on certain criteria. One common scenario is when you have multiple DataFrames and you want to create a new column in one DataFrame based on values found in others. In this post, we'll tackle a specific example where you need to evaluate conditions across two DataFrames (df2 and df3) to fill a new column in a third DataFrame (df1).

The Problem

You have three DataFrames:

df1: This contains the primary data along with multiple numeric columns.

df2: This contains threshold values used to check for a condition called "Repeat".

df3: This holds higher threshold values for a condition called "Repeat with Addition".

Here's a simplified version of each DataFrame:

DataFrames Overview

df1

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df2

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df3

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

The goal is to create a new column in df1 called Repeat Required?, which will display "Repeat", "Repeat with Addition", or "No" based on the following conditions:

If any value in columns A-C is greater than the threshold in df3, then it should be marked as "Repeat with Addition".

If any value in columns A-C is greater than the threshold in df2 but less than the threshold in df3, it should simply be "Repeat".

If neither condition is met, the result should be "No".

The Solution

Step-by-Step Approach

1. Import Necessary Libraries

Before diving into the code, make sure that you have Pandas and NumPy installed and imported:

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

2. Define Your DataFrames

Set up your DataFrames (df1, df2, and df3) as shown previously:

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

3. Setting Up Conditions

Next, we need to determine the conditions for the new column.

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

4. Create the New Column

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5. View the Result

Now you can print the updated df1 to see the results:

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Final Output

Your final DataFrame (df1) should look like this:

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

Conclusion

Now you have the tools and knowledge to tackle similar scenarios that may arise in your data analysis projects. Happy coding!