Advanced NumPy Exercises - Subtract the mean of each column from each element of a 3x3 array

NumPy: Advanced Exercise-5 with Solution

Write a NumPy program to create a 3x3 array with random values and subtract the mean of each column from each element.

In NumPy, you can create a 3x3 array with random values using numpy.random.rand. To normalize each column, first calculate the mean of each column using numpy.mean with the axis parameter set to 0. Then, subtract these column means from the respective elements in the array. This procedure centers the data by removing the mean value from each column.

Sample Solution:

Python Code:

import numpy as np
# Generate a 3x3 array with random values
nums = np.random.rand(3, 3)
print("Original array elements:")
print(nums)
# Compute the mean of each column
col_means = np.mean(nums, axis=0)
print("\nMean of each row:")
print(col_means)
# Subtract the mean of each column from each element
print("\nSubtract the mean of each row from each element:")
result = nums - col_means
print(result)

Output:

Original array elements:
[[0.26932492 0.0151877  0.26077975]
 [0.07798544 0.31104472 0.37382605]
 [0.06486036 0.77784151 0.91056512]]

Mean of each row:
[0.13739024 0.36802464 0.51505697]

Subtract the mean of each row from each element:
[[ 0.13193468 -0.35283694 -0.25427722]
 [-0.0594048  -0.05697992 -0.14123093]
 [-0.07252988  0.40981687  0.39550815]]

Explanation:

In the above exercise -

nums = np.random.rand(3, 3): This statement creates a 3x3 array nums filled with random values between 0 and 1.

col_means = np.mean(nums, axis=0): This statement calculates the mean of each column of nums using the np.mean() function and specifying axis=0 as the argument. The resulting array col_means contains the means of each column.

result = nums - col_means: This code subtracts the column means stored in col_means from each element in the corresponding column of nums. This creates a new array result that has been normalized column-wise.

Python-Numpy Code Editor:

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