How to Multiply Two Different DataFrames Based on a Condition in Python using Pandas

Discover the techniques to effectively multiply two DataFrames based on specific conditions using Python and Pandas. Learn with detailed steps and code examples.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: multiply two different dataframes based on a condition

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Multiply Two Different DataFrames Based on a Condition in Python using Pandas

Python's Pandas library is an incredibly powerful tool for data manipulation and analysis, which often involves working with multiple DataFrames. One common requirement is to perform calculations across different DataFrames based on specific conditions. Today, we will dive into how to multiply two different DataFrames based on a condition.

Understanding the Problem

Imagine you have two DataFrames:

DataFrame 1 (df1) contains yearly rates:

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

DataFrame 2 (df2) contains values and other attributes:

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

The objective is to create new columns in df2 representing the years from df1, where the calculation for each year is based on the following formula:

For certain names in df2, you will use the formula: rate * (value - portion) + portion

For other names, the value remains unchanged.

Specifically, we want:

A calculated return for names "A" and "C".

An unchanged value for names "B" and "D".

Step-by-Step Solution

To achieve this, we can utilize NumPy broadcasting combined with condition-based selection in Pandas. Below are the steps to implement the solution successfully.

Step 1: Import Required Libraries

Before we start manipulating the DataFrames, make sure you have the required library imported:

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

Step 2: Define Your DataFrames

Set up your DataFrames as shown below:

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

Step 3: Perform the Calculation with Broadcasting

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

Step 4: Check the Result

You can display the resulting DataFrame to examine your calculations:

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

This will output:

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

Conclusion

In this guide, we have successfully multiplied two different DataFrames based on specific conditions using Python and Pandas. Using NumPy broadcasting not only simplifies our calculations but also makes our code more efficient. Feel free to adapt the provided code to suit your specific dataset and conditions!

With these steps, you can handle similar situations where you need to apply calculations conditionally in multiple DataFrames. Happy coding!