Numpy random.randn() Function

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The Numpy random.randn() function used to generate a numpy array of specified shape and fills it with random values as per standard normal distribution.

If positive arguments are provided, randn generates an array of shape (d0, d1, , dn), filled with random floats sampled from a univariate normal (Gaussian) distribution of mean 0 and variance 1. A single float randomly sampled from the distribution is returned if no argument is provided.

Syntax

Following is the syntax of the Numpy random.randn() function −

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

Parameters

Following are the parameters of the Numpy random.randn() function −

• d0, d1, ..., dn - These are the dimensions of the output array.

Return Values

This function returns a NumPy array filled with random floats following a standard normal distribution.

Example

Following is a basic example to generate a single random float using Numpy random.randn() function −

import numpy as np
random_value = np.random.randn()
print("Random Value -", random_value)
print(type(random_value))

Output

Following is the output of the above code −

Random Value - 0.3946743245523318
<class 'float'>

Example : Numpy array using 'random.randn()'

The numpy.random.randn() function can also be used to generate a 1D NumPy array of random values from a standard normal distribution.

In the following example, a 1D array of 4 elements is generated using numpy.random.randn() function −

import numpy as np
numpy_array = np.random.randn(4)
print("1D Array of Random Values - \n", numpy_array)
print(type(numpy_array))

Output

Following is the output of the above code:

1D Array of Random Values - 
 [ 1.30524442 -0.74706264  0.53150742 -1.29810795]
<class 'numpy.ndarray'>

Example : Multi-dimensional Numpy Array

Using the numpy.random.randn() function, we can generate n-dimensional arrays of random values by specifying the desired dimensions as parameters.

In this example, a 3D array with dimensions 3x4x3 is been generated, filled with random values from the standard normal distribution −

import numpy as np
numpy_3d_array = np.random.randn(3, 4, 3)
print("3D Array of Random Values -\n", numpy_3d_array)

Output

Following is the output of the above code −

3D Array of Random Values -
 [[[ 0.62852014  1.41807874  0.83575845]
  [ 1.00913888 -0.62230585  0.67617611]
  [-0.51083778  0.52039257 -0.84454698]
  [ 1.22446679 -0.13410954 -0.33386194]]

 [[ 0.41440124  1.00212288  0.39328479]
  [ 0.51793246  1.03868843  1.23749478]
  [-2.23742862  0.0430593  -0.60495951]
  [ 0.0221033   0.75218868 -0.02696248]]

 [[ 0.4918343   0.72076167 -1.40222382]
  [ 2.43693037 -0.83250305  0.26874105]
  [-0.54347424  2.25655853  0.45657885]
  [-1.01706963  0.6131971   0.45100215]]]

Example : Passing Negative Arguments

When we pass a negative argument to the numpy.random.randn() function, it raises a ValueError.

In the following example, we have passed a -6(negative) number to the numpy.random.randn() function −

#importing numpy array
import numpy as np
#passing a negative integers
my_Array = np.random.randn(-6)
print("Numpy Array",my_Array)

Output

Following is the output of the above code −

Traceback (most recent call last):
  File "/home/cg/root/52187/main.py", line 4, in <module>
    my_Array = np.random.randn(-6)
  File "mtrand.pyx", line 1270, in numpy.random.mtrand.RandomState.randn
  File "mtrand.pyx", line 1431, in numpy.random.mtrand.RandomState.standard_normal
  File "_common.pyx", line 636, in numpy.random._common.cont
ValueError: negative dimensions are not allowed