Python – Get Matrix Mean

When working with matrices in Python, calculating the mean (average) of all elements is a common operation. The NumPy package provides the mean() method to efficiently compute the mean of matrix elements.

Basic Matrix Mean

Here's how to calculate the mean of all elements in a matrix ?

import numpy as np

my_matrix = np.matrix('24 41; 35 25')
print("The matrix is:")
print(my_matrix)

my_result = my_matrix.mean()
print("The mean is:")
print(my_result)

The matrix is:
[[24 41]
 [35 25]]
The mean is:
31.25

Mean Along Specific Axis

You can calculate the mean along rows (axis=1) or columns (axis=0) ?

import numpy as np

matrix = np.matrix('10 20 30; 40 50 60; 70 80 90')
print("Original matrix:")
print(matrix)

# Mean of each row
row_means = matrix.mean(axis=1)
print("\nMean of each row:")
print(row_means)

# Mean of each column
col_means = matrix.mean(axis=0)
print("\nMean of each column:")
print(col_means)

Original matrix:
[[10 20 30]
 [40 50 60]
 [70 80 90]]

Mean of each row:
[[20.]
 [50.]
 [80.]]

Mean of each column:
[[40. 50. 60.]]

Key Parameters

The mean() method accepts these important parameters:

• axis: None (default, all elements), 0 (column-wise), 1 (row-wise)
• dtype: Data type for the result
• out: Output array to store the result

Conclusion

Use matrix.mean() to calculate the overall mean, or specify axis=0 for column means and axis=1 for row means. NumPy's mean method provides efficient computation for matrix statistics.