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Calculate the mean across dimension in a 2D NumPy array
  • Last Updated : 29 Aug, 2020

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

Example:



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# 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)

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Output:

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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