Calculate the mean across dimension in a 2D NumPy array
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:
Python3
# Importing Libraryimport numpy as np # creating 2d arrayarr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # Calculating mean across Rowsrow_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 Columnscolumn_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) |
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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