.randn()

The .randn() function generates an array of random numbers sampled from the standard normal distribution (a Gaussian distribution where mean = 0 and standard deviation = 1). It is commonly used in statistics, machine learning, and data analysis for creating synthetic data and testing algorithms.

Syntax

numpy.random.randn(d0, d1, ..., dn)

Parameters:

• d0, d1, ..., dn: Dimensions of the output array. If no arguments are provided, the .randn() function returns a single random float sampled from the standard normal distribution.

Return value:

• The .randn() function returns an ndarray of shape (d0, d1, ..., dn) filled with random samples from the standard normal distribution.

Example

In this example, the .randn() generates a 2x3 NumPy array filled with random numbers from the standard normal distribution:

import numpy as np

# Generate a 2x3 array of random samples
samples = np.random.randn(2, 3)

# Print the result
print(samples)

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A possible output of this code can be:

[[-1.87894354 -0.05884307  1.0121173 ]
 [ 0.77652245  0.20369627 -0.97778735]]

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Note: The output may change each time the code is run because the values are generated randomly from a standard normal distribution.

Codebyte Example

This codebyte generates a 1-dimensional array with 5 elements and a 3-dimensional array with shape (2, 2, 2) using numpy.random.randn():

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