How Can I Create a New Column in Pandas Based on Values from an Existing Column?

Create a New Column with Values Selected from an Existing Column

Selecting particular values from an existing column to populate a new column is a common data manipulation task. Let's explore two approaches to achieve this in Python using Pandas.

Method 1: np.where for Two Choices

When there are only two options to choose from, the np.where function is a convenient choice. In the example provided, we want to create a color column with 'green' for values in the Set column equal to 'Z' and 'red' otherwise.

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

Method 2: np.select for Multiple Choices

For scenarios with more than two choices, np.select offers greater flexibility. Let's introduce more conditions for the color column:

• 'yellow' for when (df['Set'] == 'Z') & (df['Type'] == 'A')
• 'blue' for when (df['Set'] == 'Z') & (df['Type'] == 'B')
• 'purple' for when (df['Type'] == 'B')
• 'black' for all other cases

The code for this scenario is as follows:

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')

This approach allows for easy customization of the conditions and values to be selected, making it suitable for more complex scenarios.

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