How are NumPy Axes Used?

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Many functions in 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 image shows a visual representation of the axes on a NumPy matrix with 2 rows and 4 columns:

The following examples show how to use the axis argument in different scenarios with the following NumPy matrix:

import numpy as np

#create NumPy matrix
my_matrix = np.matrix([[1, 4, 7, 8], [5, 10, 12, 14]])

#view NumPy matrix
my_matrix

matrix([[ 1,  4,  7,  8],
        [ 5, 10, 12, 14]])

Example 1: Find Mean Along Different Axes

We can use axis=0 to find the mean of each column in the NumPy matrix:

#find mean of each column in matrix
np.mean(my_matrix, axis=0)

matrix([[ 3. ,  7. ,  9.5, 11. ]])

The output shows the mean value of each column in the matrix.

For example:

• The mean value of the first column is (1 + 5) / 2 = 3.
• The mean value of the second column is (4 + 10) / 2 = 7.

And so on.

We can also use axis=1 to find the mean of each row in the matrix:

#find mean of each row in matrix
np.mean(my_matrix, axis=1)

matrix([[ 5.  ],
        [10.25]])

The output shows the mean value of each row in the matrix.

• The mean value in the first row is (1+4+7+8) / 4 = 5.
• The mean value in the second row is (5+10+12+14) / 4 = 10.25.

Example 2: Find Sum Along Different Axes

We can use axis=0 to find the sum of each column in the matrix:

#find sum of each column in matrix
np.sum(my_matrix, axis=0)

matrix([[ 6, 14, 19, 22]])

The output shows the sum of each column in the matrix.

For example:

• The sum of the first column is 1 + 5 = 6.
• The sum of the second column is 4 + 10 = 14.

And so on.

We can also use axis=1 to find the sum of each row in the matrix:

#find sum of each row in matrix
np.sum(my_matrix, axis=1)

matrix([[20],
        [41]])

The output shows the sum of each row in the matrix.

For example:

• The sum of the first row is 1+4+7+8 = 20.
• The sum of the second row is 5+10+12+14 = 41.

The following tutorials explain how to perform other common operations in NumPy: