How to Create a New Column Based on Multiple Conditions in a Pandas DataFrame Easily

Описание: Learn how to create a new column in your Pandas DataFrame by applying multiple conditions from another column. This guide breaks down the steps for beginners and ensures your data is well-structured and analyzed.
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Introduction

Working with data often involves transforming it through conditions and calculations to derive new insights. One common task analysts and developers face is creating a new column in a Pandas DataFrame based on conditions from another column. In this post, we will explore how to achieve this in a structured and easy-to-understand manner.

The Problem

We have a DataFrame with a column A, and we want to create a new column B based on specific conditions related to the values in column A. Our conditions can be defined as follows:

If A(i) <= 10: Set B(i) = 1 if the previous value (A(i-1)) is greater than or equal to the current value and vice versa.

If 10 < A(i) <= 20: Set B(i) = 1 if the difference between the previous value and the current value is greater than or equal to 2.

If A(i) > 20: Set B(i) = 1 if the difference from the previous value is greater than or equal to 5.

Additionally, the first entry in column B should be 2.

Solution Overview

To implement this, we will make use of two useful Pandas functions: shift and select from NumPy. Here’s a step-by-step breakdown of how to accomplish this:

Step 1: Setup Your DataFrame

First, we need to create our DataFrame with the initial values for column A.

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Step 2: Create a New Column for Previous Values

To compare values A(i) with A(i-1), we will create a new column A_prv, which contains the previous values of A:

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Step 3: Define Conditions and Choices

Next, we will specify our conditions and what values we want to assign in the new column B based on these conditions:

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Step 4: Apply Conditions to Create New Column

Finally, we will apply our conditions to the DataFrame to create column B:

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Step 5: Check the Result

Now that we have implemented our conditions and created our new column, let's print the DataFrame to observe the results:

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Final Output

After running the code, you should see the following output that demonstrates how new values in column B were assigned based on the conditions specified:

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Conclusion

In this guide, we have successfully covered how to create a new column in a DataFrame based on multiple conditions from another column using Pandas and NumPy. By laying out the steps clearly and using straightforward language, we aim to help beginners get comfortable with this powerful data manipulation tool. Now you’re ready to implement your own conditions and enhance your data analysis in Python!