NumPy: Calculate mean across dimension, in a 2D numpy array
Write a NumPy program to calculate mean across dimension, in a 2D numpy array.
Sample Solution:
Python Code:
# Importing the NumPy library
import numpy as np
# Creating a 2x2 NumPy array
x = np.array([[10, 30], [20, 60]])
# Displaying the original array
print("Original array:")
print(x)
# Computing the mean of each column using axis 0 (column-wise)
print("Mean of each column:")
print(x.mean(axis=0))
# Computing the mean of each row using axis 1 (row-wise)
print("Mean of each row:")
print(x.mean(axis=1))
Sample Output:
Original array: [[10 30] [20 60]] Mean of each column: [ 15. 45.] Mean of each row: [ 20. 40.]
Explanation:
In the above code –
x = np.array([[10, 30], [20, 60]]) - The NumPy array x is a 2-dimensional array with shape (2, 2). It has 2 rows and 2 columns. The first row contains the elements 10 and 30, and the second row contains the elements 20 and 60.
x.mean(axis=0) -> This line computes the mean of each column of the x array. The axis=0 argument specifies the axis along which the mean is computed. Since axis=0 is specified, the mean is computed along the rows of the array. Thus, it returns an array with shape (2,) containing the means of the two columns of the x array.
x.mean(axis=1) –> This line computes the mean of each row of the x array. The axis=1 argument specifies the axis along which the mean is computed. Since axis=1 is specified, the mean is computed along the columns of the array. Thus, it returns an array with shape (2,) containing the means of the two rows of the x array.
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