Скачать или смотреть How to Create a New Binary Variable Based on Multiple Binary Columns in Pandas

Learn how to create a new binary variable in a Pandas DataFrame based on the conditions of multiple binary columns. We'll walk through practical examples to simplify the process.
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How to Create a New Binary Variable Based on Multiple Binary Columns in Pandas

When working with data in Python, especially in data analysis tasks, it's common to encounter situations where you need to derive new insights from existing data. A common problem is creating a new binary variable based on the conditions set by multiple binary columns. In this guide, we will explore how to accomplish this using the powerful pandas library.

The Problem at Hand

Imagine you have the following three DataFrames:

Df1: Contains distance values between forests and facilities.

Df2: Contains binary values indicating whether the distance to a facility is less than or equal to a given maximum distance (Dmax).

Df3: Holds supply values for each forest.

You want to create an additional column in Df3 that indicates if any of the facilities are within the defined distance (Dmax) from each forest. Specifically, your goal is to create a binary column called 'Binary' that shows 1 if the forest is within Dmax of any facility, and 0 otherwise.

Here's a quick snapshot of what the DataFrame looks like:

Initial DataFrames

Df2 (Binary representation based on distance):

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

Df3 (Supply values):

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

The Solution

Step 1: Summing Binary Values

The first step is to identify which forests are within Dmax of any facility. Since Df2 contains binary values (1 and 0), you can sum these to find if there is at least one facility within range for each forest.

If the row sequence in Df2 matches that of Df3, you could easily assign a new binary variable in Df3 just by summing the rows of Df2:

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

Step 2: Handling Different Indexes

However, if the row sequences between Df2 and Df3 are different, you'll need to use the following approach to ensure you correlate them correctly:

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

This line will:

Set Df2 index to 'Forest' so you can map it correctly.

Sum the binary values along the rows.

Check if the sum is greater than zero.

Convert the Boolean values into integers (0 or 1).

Conclusion

By following these steps, you will successfully create a new binary variable in your DataFrame that indicates whether each forest is within the designated distance from any facility. This technique is a great example of how to leverage the capabilities of pandas for efficient data manipulation.

This method simplifies data representation and allows you to derive valuable insights quickly. Happy coding!