How to Create a New Column with Condition-Based Values in Pandas?

Creating a New Column with Condition-Based Values

This question explores how to add a new column, color, to a given dataframe. The condition is that color should be set to 'green' if the corresponding value in the Set column is 'Z' and 'red' otherwise.

Solution with Numpy Where:

For scenarios with only two choices, the np.where method can be utilized. Here's the code:

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

This approach effectively assigns 'green' to rows where Set is 'Z' and 'red' otherwise.

Solution with Numpy Select:

In cases where there are more than two conditions, np.select can be employed. Let's say color should meet the following criteria:

• 'yellow' when Set is 'Z' and Type is 'A'
• 'blue' when Set is 'Z' and Type is 'B'
• 'purple' when Type is 'B'
• 'black' otherwise

In this scenario, the code would be:

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 solution allows for flexible and granular condition-based value assignment for the new column.

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