How to Create a New Column Based on Conditions in an Existing Column Using Python?

Creating a New Column with Values Based on an Existing Column

In certain data analysis scenarios, you may need to create a new column where the values are selected based on specific conditions in an existing column. This can be achieved using various methods in Python, depending on the number of conditions to check.

Two-Choice Scenarios with np.where

If you only have two choices to select from, the numpy function np.where can be used efficiently. It takes the following form:

df['new_column'] = np.where(condition, value_if_true, value_if_false)

where 'df' is the dataframe, 'condition' is a boolean expression that defines the condition, 'value_if_true' is the value to be assigned if the condition is True, and 'value_if_false' is the value to be assigned if the condition is False.

For example, to create a 'color' column in the provided dataframe where 'color' is 'green' if 'Set' is 'Z' and 'red' otherwise, you can use:

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

Multiple Conditions with np.select

If you have more than two conditions to check, the numpy function np.select can be utilized. It allows for more complex conditional logic. The format is as follows:

df['new_column'] = np.select(conditions, choices, default=None)

where 'conditions' is a list of boolean expressions, 'choices' is a list of values corresponding to each condition, and 'default' is the value to be assigned if none of the conditions are met.

For instance, if 'color' is to be assigned as 'yellow' when ('Set' == 'Z') & ('Type' == 'A'), 'blue' when ('Set' == 'Z') & ('Type' == 'B'), and 'purple' when just ('Type' == 'B'), and 'black' otherwise, you can use:

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')

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