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A Case Statement in Pandas is a way to use the Pandas library to create a new column in a dataframe based on a condition. It can be used to apply an expression to multiple columns of data and return a result based on that expression. For example, a case statement can be used to compare the values of two columns and return a third column with a value based on the comparison. It can also be used to create a categorical column from a numerical column by mapping the numerical values to different categories. This is done using the “when” and “otherwise” clauses of the case statement.
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 .
The following tutorials explain how to perform other common tasks in Pandas: