How Can I Create Conditional Columns in a DataFrame Based on Existing Column Values?

Creating Conditional Columns Based on Existing Column Values

In data analysis, it's often necessary to create new columns whose values are determined based on conditions derived from existing columns. Consider the scenario where you have a DataFrame with two columns: "Type" and "Set," and you want to add a new column called "color" that follows specific rules.

Adding a Color Column Based on Set Values

To create a "color" column where the values are "green" if "Set" is "Z" and "red" otherwise, you can use the following approach:

import numpy as np

df['color'] = np.where(df['Set'] == 'Z', 'green', 'red')

This code utilizes the np.where function, which selects values based on a condition. If the "Set" column value is "Z," the "color" value becomes "green"; otherwise, it becomes "red."

Using np.select for More Complex Conditions

For more complex scenarios where you have multiple conditions, you can use np.select. For instance, suppose you want to assign colors according to the following rules:

• "yellow" if both "Set" is "Z" and "Type" is "A"
• "blue" if "Set" is "Z" and "Type" is "B"
• "purple" if "Type" is "B"
• "black" otherwise

conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')

The np.select function takes a list of conditions and a corresponding list of choices. If the condition is met, the associated choice is selected; otherwise, the default value is used.

These methods provide versatile options for creating conditional columns based on existing column values, allowing you to manipulate and analyze your data efficiently.

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