Many functions in pandas require that you specify an axis along which to apply a certain calculation.
Typically the following rule of thumb applies:
- axis=0: Apply the calculation “column-wise”
- axis=1: Apply the calculation “row-wise”
The following examples show how to use the axis argument in different scenarios with the following pandas DataFrame:
import pandas as pd
#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'B', 'B', 'B', 'B', 'C', 'C'],
'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
team points assists rebounds
0 A 25 5 11
1 A 12 7 8
2 B 15 7 10
3 B 14 9 6
4 B 19 12 6
5 B 23 9 5
6 C 25 9 9
7 C 29 4 12
Example 1: Find Mean Along Different Axes
We can use axis=0 to find the mean of each column in the DataFrame:
#find mean of each column
df.mean(axis=0)
points 20.250
assists 7.750
rebounds 8.375
dtype: float64
The output shows the mean value of each numeric column in the DataFrame.
Notice that pandas automatically avoids calculating the mean of the ‘team’ column because it’s a character column.
We can also use axis=1 to find the mean of each row in the DataFrame:
#find mean of each row
df.mean(axis=1)
0 13.666667
1 9.000000
2 10.666667
3 9.666667
4 12.333333
5 12.333333
6 14.333333
7 15.000000
dtype: float64
From the output we can see:
- The mean value in the first row is 13.667.
- The mean value in the second row is 9.000.
- The mean value in the third row is 10.667.
And so on.
Example 2: Find Sum Along Different Axes
We can use axis=0 to find the sum of specific columns in the DataFrame:
#find sum of 'points' and 'assists' columns
df[['points', 'assists']].sum(axis=0)
points 162
assists 62
dtype: int64
We can also use axis=1 to find the sum of each row in the DataFrame:
#find sum of each row
df.sum(axis=1)
0 41
1 27
2 32
3 29
4 37
5 37
6 43
7 45
dtype: int64
Example 3: Find Max Along Different Axes
We can use axis=0 to find the max value of specific columns in the DataFrame:
#find max of 'points', 'assists', and 'rebounds' columns
df[['points', 'assists', 'rebounds']].max(axis=0)
points 29
assists 12
rebounds 12
dtype: int64
We can also use axis=1 to find the max value of each row in the DataFrame:
#find max of each row
df.max(axis=1)
0 25
1 12
2 15
3 14
4 19
5 23
6 25
7 29
dtype: int64
From the output we can see:
- The max value in the first row is 25.
- The max value in the second row is 12.
- The max value in the third row is 15.
And so on.
Additional Resources
The following tutorials explain how to perform other common operations in pandas:
How to Calculate the Mean of Columns in Pandas
How to Calculate the Sum of Columns in Pandas
How to Find the Max Value of Columns in Pandas