i would like to get an numpy array , shape 1000 row and 2 column.

1. 1st column will contain - Gaussian distributed variables with standard deviation 2 and mean 1.
2. 2nd column will contain Gaussian distributed variables with mean -1 and standard deviation 0.5.

How to create the array using define value of mean and std?

CodePudding user response：

You can just create two normal distributions with the mean and std for each and stack them.

np.hstack((np.random.normal(1, 2, size=(1000,1)), np.random.normal(-1, 0.5, size=(1000,1))))

CodePudding user response：

You can use numpy's random generators.

import numpy as np

# as per kwinkunks suggestion
rng = np.random.default_rng()

arr1 = rng.normal(1, 2, 1000).reshape(1000, 1)
arr2 = rng.normal(-1, 0.5, 1000).reshape(1000, 1)

arr1[:5]

array([[-2.8428678 ],
       [ 2.52213097],
       [-0.98329961],
       [-0.87854616],
       [ 0.65674208]])

arr2[:5]

array([[-0.85321735],
       [-1.59748405],
       [-1.77794019],
       [-1.02239036],
       [-0.57849622]])

After that, you can concatenate.

np.concatenate([arr1, arr2], axis = 1)

# output
array([[-2.8428678 , -0.85321735],
       [ 2.52213097, -1.59748405],
       [-0.98329961, -1.77794019],
       ...,
       [ 0.84249042, -0.26451526],
       [ 0.6950764 , -0.86348222],
       [ 3.53885426, -0.95546126]])

CodePudding user response：

Use np.random.normal directly:

import numpy as np
np.random.normal([1, -1], [2, 0.5], (1000, 2))