In Python numpy.random.sample() is one of the functionsgenerate that generates floating-point values in an open interval [0.0,1.0). It doesn’t take up any arguments and produces a single random value each time it’s called. This function is often used for statistical and simulation tasks in Python.
Syntax : numpy.random.sample(size=None)
Parameters:
size : [int or tuple of ints, optional] Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.Return: Array of random floats in the interval [0.0, 1.0). or a single such random float if size not provided.
Example 1: Random Sampling for 1D Array
In Example 1, It prints the random float value in the range between [0.0, 1.0) in a 1D Array.
Python3
# Python program explaining# numpy.random.sample() function# importing numpyimport numpy as geek# output random valueout_val = geek.random.sample()print("Output random value : ", out_val) |
Output:
Output random value : 0.48333001584192203
Example 2: Random Sampling for 2D Array
In Example 2, It will print a 2D array in range [0.0, 1.0) and size represents the dimensions of the array, i.e., 3,3.
Python3
# Python program explaining# numpy.random.sample() function# importing numpyimport numpy as geek# output arrayout_arr = geek.random.sample(size=(3, 3))print("Output 2D Array filled with random floats : ", out_arr) |
Output:
Output 2D Array filled with random floats : [[0.88080589 0.6975613 0.24834172]
[0.7624025 0.57821126 0.16190988]
[0.19641213 0.98098179 0.7861734 ]]
Example 3: Random Sampling for 3D Array
In Example 3, it will print a 3D array in range of [0.0,1.0) and the dimensions provide for array are 2,2,3.
Python3
# Python program explaining# numpy.random.sample() function# importing numpyimport numpy as geek# output arrayout_arr = geek.random.sample((2, 2, 3))print("Output 3D Array filled with random floats : ", out_arr) |
Output:
Output 3D Array filled with random floats : [[[0.46531776 0.12490349 0.4788548 ]
[0.17803379 0.46658566 0.42292984]]
[[0.00454164 0.07650314 0.43976311]
[0.11644706 0.52697036 0.11542112]]]
