A case statement is a type of statement that goes through conditions and returns a value when the first condition is met.

The easiest way to implement a case statement in a Pandas DataFrame is by using the NumPy where() function, which uses the following basic syntax:

df['new_column'] = np.where(df['col2']<9, 'value1',
                   np.where(df['col2']<12, 'value2',
                   np.where(df['col2']<15, 'value3', 'value4')))

This particular function looks at the value in the column called col2 and returns:

• “value1” if the value in col2 is less than 9
• “value2” if the value in col2 is less than 12
• “value3” if the value in col2 is less than 15
• “value4” if none of the previous conditions are true

The following example shows how to use this function in practice.

Example: Case Statement in Pandas

Suppose we have the following pandas DataFrame:

import pandas as pd
import numpy as np

#create DataFrame
df = pd.DataFrame({'player': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                   'points': [6, 8, 9, 9, 12, 14, 15, 17, 19, 22]})

#view DataFrame
df

	player	points
0	1	6
1	2	8
2	3	9
3	4	9
4	5	12
5	6	14
6	7	15
7	8	17
8	9	19
9	10	22

We can use the following syntax to write a case statement that creates a new column called class whose values are determined by the values in the points column:

#add 'class' column using case-statement logic
df['class'] = np.where(df['points']<9, 'Bad',
              np.where(df['points']<12, 'OK',
              np.where(df['points']<15, 'Good', 'Great')))

#view updated DataFrame
df

	player	points	class
0	1	6	Bad
1	2	8	Bad
2	3	9	OK
3	4	9	OK
4	5	12	Good
5	6	14	Good
6	7	15	Great
7	8	17	Great
8	9	19	Great
9	10	22	Great

The case statement looked at the value in the points column and returned:

• “Bad” if the value in the points column was less than 9
• “OK” if the value in the points column was less than 12
• “Good” if the value in the points column was less than 15
• “Great” if none of the previous conditions are true

Note: You can find the complete documentation for the NumPy where() function .

Additional Resources

The following tutorials explain how to perform other common tasks in Pandas: