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Calculate the mean across dimension in a 2D NumPy array

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  • Last Updated : 29 Aug, 2020
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We can find out the mean of each row and column of 2d array using numpy with the function np.mean(). Here we have to provide the axis for finding mean.

Syntax: numpy.mean(arr, axis = None)

For Row mean: axis=1

For Column mean: axis=0



# Importing Library
import numpy as np
# creating 2d array
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# Calculating mean across Rows
row_mean = np.mean(arr, axis=1)
row1_mean = row_mean[0]
print("Mean of Row 1 is", row1_mean)
row2_mean = row_mean[1]
print("Mean of Row 2 is", row2_mean)
row3_mean = row_mean[2]
print("Mean of Row 3 is", row3_mean)
# Calculating mean across Columns
column_mean = np.mean(arr, axis=0)
column1_mean = column_mean[0]
print("Mean of column 1 is", column1_mean)
column2_mean = column_mean[1]
print("Mean of column 2 is", column2_mean)
column3_mean = column_mean[2]
print("Mean of column 3 is", column3_mean)


Mean of Row 1 is 2.0
Mean of Row 2 is 5.0
Mean of Row 3 is 8.0
Mean of column 1 is 4.0
Mean of column 2 is 5.0
Mean of column 3 is 6.0
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