How to Use where() Function in Pandas (With Examples)

---

The where() function can be used to replace certain values in a pandas DataFrame.

This function uses the following basic syntax:

df.where(cond, other=nan)

For every value in a pandas DataFrame where cond is True, the original value is retained.

For every value where cond is False, the original value is replaced by the value specified by the other argument.

The following examples show how to use this syntax in practice with the following pandas DataFrame:

import pandas as pd

#define DataFrame
df = pd.DataFrame({'points': [25, 12, 15, 14, 19, 23, 25, 29],
                   'assists': [5, 7, 7, 9, 12, 9, 9, 4],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})

#view DataFrame
df

points	assists	rebounds
0	25	5	11
1	12	7	8
2	15	7	10
3	14	9	6
4	19	12	6
5	23	9	5
6	25	9	9
7	29	4	12

Example 1: Replace Values in Entire DataFrame

The following code shows how to use the where() function to replace all values that don’t meet a certain condition in an entire pandas DataFrame with a NaN value.

#keep values that are greater than 7, but replace all others with NaN
df.where(df>7)

	points	assists	rebounds
0	25	NaN	11.0
1	12	NaN	8.0
2	15	NaN	10.0
3	14	9.0	NaN
4	19	12.0	NaN
5	23	9.0	NaN
6	25	9.0	9.0
7	29	NaN	12.0

We can also use the other argument to replace values with something other than NaN.

#keep values that are greater than 7, but replace all others with 'low'
df.where(df>7, other='low')

	points	assists	rebounds
0	25	low	11
1	12	low	8
2	15	low	10
3	14	9	low
4	19	12	low
5	23	9	low
6	25	9	9
7	29	low	12

Example 2: Replace Values in Specific Column of DataFrame

The following code shows how to use the where() function to replace all values that don’t meet a certain condition in a specific column of a DataFrame.

#keep values greater than 15 in 'points' column, but replace others with 'low'
df['points'] = df['points'].where(df['points']>15, other='low')

#view DataFrame
df

	points	assists	rebounds
0	25	5	11
1	low	7	8
2	low	7	10
3	low	9	6
4	19	12	6
5	23	9	5
6	25	9	9
7	29	4	12

You can find the complete online documentation for the pandas where() function here.

Additional Resources

The following tutorials explain how to use other common functions in pandas:

How to Use describe() Function in Pandas
How to Use idxmax() Function in Pandas
How to Apply a Function to Selected Columns in Pandas