How to Use np.random.randn in NumPy for Random Normal Data

Use np.random.randn to generate samples from a standard normal distribution (mean 0, variance 1). You can specify the shape by passing dimensions as arguments, like np.random.randn(3, 2) for a 3x2 array of random values.

Syntax

The basic syntax of np.random.randn is to pass one or more integer arguments that define the shape of the output array. It returns samples from a standard normal distribution (mean 0, standard deviation 1).

• np.random.randn(d0, d1, ..., dn): Generates an array of shape (d0, d1, ..., dn).
• If no arguments are given, it returns a single float.

python

import numpy as np

# Generate a single random number from standard normal distribution
single_value = np.random.randn()

# Generate a 1D array of 5 random numbers
array_1d = np.random.randn(5)

# Generate a 2D array of shape 3x2
array_2d = np.random.randn(3, 2)

Example

This example shows how to generate a 3x3 array of random numbers from the standard normal distribution and print it.

python

import numpy as np

# Generate a 3x3 array of random numbers
random_array = np.random.randn(3, 3)
print(random_array)

Output

[[ 0.49671415 -0.1382643   0.64768854]
 [-0.23415337 -0.23413696  1.57921282]
 [ 0.76743473 -0.46947439  0.54256004]]

Common Pitfalls

Common mistakes when using np.random.randn include:

• Confusing randn with rand: randn generates normal distribution values, while rand generates uniform distribution values between 0 and 1.
• Passing a tuple instead of separate arguments: np.random.randn((3,2)) will cause an error; use np.random.randn(3, 2) instead.
• Expecting values outside the normal distribution range: values can be any real number, not limited to 0-1.

python

import numpy as np

# Wrong: passing a tuple causes an error
try:
    np.random.randn((3, 2))
except TypeError as e:
    print(f"Error: {e}")

# Correct usage
correct_array = np.random.randn(3, 2)
print(correct_array)

Output

Error: 'tuple' object cannot be interpreted as an integer
[[ 0.49671415 -0.1382643 ]
 [-0.23415337 -0.23413696]
 [ 1.57921282  0.76743473]]

Quick Reference

Summary tips for using np.random.randn:

• Generates samples from a standard normal distribution (mean 0, std 1).
• Arguments specify the shape of the output array.
• Pass dimensions as separate integers, not as a tuple.
• Returns a float if no arguments are given.

Key Takeaways

Use np.random.randn with integer arguments to get arrays of standard normal random values.

Do not pass a tuple as a single argument; pass dimensions separately.

np.random.randn returns floats from a normal distribution with mean 0 and std 1.

If no arguments are given, it returns a single float value.

Remember np.random.randn differs from np.random.rand which generates uniform values.