Скачать или смотреть Creating a New Column in Pandas Using Multiple Conditions

Learn how to create a new column in a pandas DataFrame based on multiple conditions applied to an existing column. This guide provides clear instructions and example code for Python users.
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Creating a New Column in Pandas Using Multiple Conditions: A Step-by-Step Guide

When working with data in Python, specifically using the pandas library, you often need to manipulate DataFrames to derive new insights. A common task is creating a new column based on conditions applied to existing columns. In this guide, we’ll explore how to accomplish this task effectively, focusing on a specific scenario involving days in a DataFrame.

The Problem: How to Create a New Column Based on Existing Data

Let’s say you’re dealing with a DataFrame that includes a column called days. The challenge is to create a new column named new_col that categorizes each entry based on specific conditions. Here’s an example of what your DataFrame might look like:

days19056755You want to assign values to the new column based on the following conditions:

If days < 180, assign a value y

If 180 < days < 365, assign a value d

Otherwise, assign a value h

The Solution: Using the apply Method

To implement this logic, you can utilize the apply method available in pandas. This allows you to apply a function along the axis of the DataFrame.

Step-by-Step Instructions

Import pandas: First, ensure you have pandas imported in your Python script.

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Create your DataFrame: You need to begin by creating your DataFrame with the days column.

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Define the values: Decide what y, d, and h represent in your context.

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Use the apply method: Here’s where the magic happens. You’ll apply a lambda function that checks each condition for the days column and assigns the appropriate value to new_col.

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Code Summary

Here’s the complete code:

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Expected Output

After executing the above code, your DataFrame df will look like this:

daysnew_col190Medium567High55LowConclusion

Creating a new column based on multiple conditions in a pandas DataFrame is simple and efficient when using the apply method in combination with lambda functions. This method allows for cleaner code and better readability compared to traditional if-else statements.

By following the steps outlined in this guide, you can easily apply similar logic to your DataFrame manipulations and enhance your data analysis workflows. Happy coding!