Random sampling in numpy | randint() function

numpy.random.randint() is one of the function for doing random sampling in numpy. It returns an array of specified shape and fills it with random integers from low (inclusive) to high (exclusive), i.e. in the interval [low, high).

Syntax : numpy.random.randint(low, high=None, size=None, dtype=’l’)

Parameters :
low : [int] Lowest (signed) integer to be drawn from the distribution.But, it works as a highest integer in the sample if high=None.
high : [int, optional] Largest (signed) integer to be drawn from the distribution.
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.
dtype : [optional] Desired output data-type.

Return : Array of random integers in the interval [low, high) or a single such random int if size not provided.

Code #1 :

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# Python program explaining
# numpy.random.randint() function
  
# importing numpy
import numpy as geek
  
# output array
out_arr = geek.random.randint(low = 0, high = 3, size = 5)
print ("Output 1D Array filled with random integers : ", out_arr) 

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Output :

Output 1D Array filled with random integers :  [1 1 0 1 1]

 

Code #2 :

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# Python program explaining
# numpy.random.randint() function
  
# importing numpy
import numpy as geek
  
  
# output array
out_arr = geek.random.randint(low = 4, size =(2, 3))
print ("Output 2D Array filled with random integers : ", out_arr) 

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Output :

Output 2D Array filled with random integers :  [[1 1 0]
 [1 0 3]]

 
Code #3 :

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# Python program explaining
# numpy.random.randint() function
  
# importing numpy
import numpy as geek
  
# output array
out_arr = geek.random.randint(2, 10, (2, 3, 4))
print ("Output 3D Array filled with random integers : ", out_arr) 

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Output :

Output 3D Array filled with random integers :  [[[4 8 5 7]
  [6 5 6 7]
  [4 3 4 3]]

 [[2 9 2 2]
  [3 2 2 3]
  [6 8 3 2]]]


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