numpy.random.randn() in Python
The numpy.random.randn() function creates an 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 (if any of the d_i are floats, they are first converted to integers by truncation). A single float randomly sampled from the distribution is returned if no argument is provided.
numpy.random.randn(d0, d1, ..., dn)
d0, d1, ..., dn : [int, optional]Dimension of the returned array we require, If no argument is given a single Python float is returned.
Array of defined shape, filled with random floating-point samples from the standard normal distribution.
Code 1 : randomly constructing 1D array
1D Array filled with randnom values : [-0.51733692 0.48813676 -0.88147002 1.12901958 0.68026197]
Code 2 : randomly constructing 2D array
2D Array filled with random values : [[ 1.33262386 -0.88922967 -0.07056098 0.27340112] [ 1.00664965 -0.68443807 0.43801295 -0.35874714] [-0.19289416 -0.42746963 -1.80435223 0.02751727]]
Code 3 : randomly constructing 3D array
3D Array filled with random values : [[[-0.00416587 -0.66211158] [-0.97254293 -0.68981333]] [[-0.18304476 -0.8371425 ] [ 2.18985366 -0.9740637 ]]]
Code 4 : Manipulations with randomly created array
3D Array filled with random values : [[[ 1.9609643 -1.89882763] [ 0.52252173 0.08159455]] [[-0.6060213 -0.86759247] [ 0.53870235 -0.77388125]]] Array * 3 : [[[ 5.88289289 -5.69648288] [ 1.56756519 0.24478366]] [[-1.81806391 -2.6027774 ] [ 1.61610704 -2.32164376]]] Array * 3 + 2 : [[[-2.73766306 6.80761741] [-1.57909191 -1.64195796]] [[ 0.51019498 1.30017345] [ 3.8107863 -4.07438963]]]
These codes won’t run on online-ID. Please run them on your systems to explore the working.
This article is contributed by Mohit Gupta_OMG 😀. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course