# numpy.random.randn () in Python

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If positive arguments are provided, randn generates an array of the form (d0, d1, ..., dn) filled with random floating point numbers selected from the one-dimensional "normal" (Gaussian) distribution of mean 0 and variance 1 (if any of the d_i is float, they are first converted to integers by truncation). A single randomly chosen floating point number from the distribution is returned if no argument is supplied.

Parameters :

`  d0, d1, .. ., dn:  [int, optional] Dimension of the returned array we require, If no argument is given a single Python float is returned. `

Return:

` Array of defined shape, filled with random floating-point samples from the standard normal distribution. `

Code 1: randomly constructing a 1D array

 ` # Python program illustrating ` ` # numpy.random.randn () method `   ` import ` ` numpy as geek `   ` # 1D Array ` ` array ` ` = ` ` geek.random.randn (` ` 5 ` `) ` ` print ` ` (` ` "1D Array filled with random values:" ` `, array); `

Output:

` 1D Array filled with randnom values: [-0.51733692 0.48813676 -0.88147002 1.12901958 0.68026197] `

Code 2: Randomly build 2D array

 ` # Python program illustrating ` ` # numpy.random.randn () method `   ` import ` ` numpy as geek `   ` # 2D Array ` ` array ` ` = ` ` geek.random.randn (` ` 3 ` `, ` ` 4 ` `) ` ` print ` ` (` ` "2D Array filled with random values:" ` `, array); `

Output:

``` 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 : randomly building a 3-D array            ` # Python program illustrating `  ` # numpy .random.randn () method `      ` import ` ` numpy as geek `    ` # 3D Array `   ` array ` ` = ` ` geek.random. randn (` ` 2 ` `, ` ` 2 ` `, ` ` 2 ` `) `   ` print ` ` (` ` "3D Array filled with random values:" ` `, array); `   Output:    3D Array filled with random values: [[[-0.00416587 -0.66211158] [-0.97254293 -0.68981333]] [[-0.18304476 -0.8371425] [2.18985366 -0.9740637]]]]     Code 4: Manipulating a randomly created array            ` # Python program illustrating `  ` # numpy.random.randn () method `      ` import ` ` numpy as geek `  ` `  ` # 3D Array `   ` array ` ` = ` ` geek.random.randn (` ` 2 ` `, ` ` 2 ` `, ` ` 2 ` `) `  ` print ` ` (` ` "3D Array filled with random values:" ` `, array); `    ` # Multiply values ​​by 3 `   ` print ` ` (` ` "Array * 3:" ` `, array ` ` * ` ` 3 ` `) `     ` # Or we can do it directly, `   ` array ` ` = ` ` geek.random.randn (` ` 2 ` `, ` ` 2 ` `, ` ` 2 ` `) ` ` * ` ` 3 ` ` + ` ` 2 `   ` print ` ` (` `" Array * 3 + 2: "` `, array); `   Output:    3D Array filled with random values: [[[1.9609643 -1.89882763] [0.52252173 0.08159455]] [[-0.6060213 -0.86759247] [0.53870235 -0.77388125]]] Array * 3: [[[5.88289288] [1.51965 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] [3.438107863] / pre>   Links:   randn.html > https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.random.randn.html     Notes:   These codes will not work for online IDs. Please run them on your systems to see how they work. ,  This article is provided by  Mohit Gupta_OMG

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