Numpy to generate random number between 0 and 1

In this post we will discuss how to quickly generate random numbers and float between 0 and 1 or between a range using numpy.

Why we are using numpy to generate random numbers? Because numpy arrays can be easily integrated with Pandas and we can generate dataframe columns with these random numbers too.

Numpy has these three functions that can be used to generate the random number and floats between a range

• numpy.random.uniform
• numpy.random.randint
• numpy.random.sample

1. Generate Random number between 0 and 1

a) numpy.random.uniform():

np.random.uniform(low, high, size)

low: Lower boundary of the output array, All values generated will be greater than or equal to low. The default value is 0

high: Upper boundary of the output array, All values generated will be less than or equal to high. The default value is 1.0

size: Output shape or length of array

1. Generate 20 random float between 0 and 1:

import numpy as np
np.random.uniform(0, 1, size=20)

Out:
  array([0.69203392, 0.34019562, 0.74442452, 0.9980623 , 0.35615059,
         0.33135049, 0.13165237, 0.43444599, 0.52700622, 0.64007571,
         0.13452034, 0.5095588 , 0.82088138, 0.26085551, 0.00696426,
         0.40988165, 0.43614663, 0.35560554, 0.97111713, 0.71243258])

b) numpy.random.sample():

It returns the random floats in the half-open interval [0.0, 1.0)

np.random.sample(size)

size: Output shape or length of array

1. Generate 20 random float between 0 and 1:

np.random.sample(20)

By default it outputs values between 0 and 1

Out:
array([0.83622401, 0.27656984, 0.51956385, 0.58562562, 0.81212898,
       0.52548523, 0.7759998 , 0.44796633, 0.73089862, 0.99077859,
       0.79913039, 0.7595591 , 0.948321  , 0.99220847, 0.37780327,
       0.87530571, 0.13907738, 0.00785441, 0.53615096, 0.61039024])

2. Generate random number between range

a) Random Integer between range

numpy.random.randint returns random integers from low (inclusive) to high (exclusive)

np.random.randint(low, high, size, dtype)

low: Lowest (signed) integers to be drawn from the distribution, if high= None then this parameter is one above the highest such integer

high: If provided, one above the largest (signed) integer to be drawn from the distribution

size: Output shape or length of array. 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

1.  Generate 20 random integer between 0 and 10

np.random.randint(10, high=None, 20)

Out:

array([7, 4, 6, 0, 7, 0, 9, 9, 3, 2, 9, 6, 8, 8, 0, 1, 1, 6, 1, 8])

2. Generate random 10 integer between 5 and 20

np.random.randint(low=5, high=20, size= 10)
#or
np.random.randint(5, 20, 10)

Out:

array([ 5,  7, 13,  7, 19,  6, 11, 12, 17,  7])

3. Generate a 2D array between 0 and 8 of size 2x4

np.random.randint(8, size=(2, 4))

Out:

array([[6, 7, 6, 6],
       [0, 5, 3, 0]])

4. Generate 1D array between 1 and 3 different upper bounds

np.random.randint(1, [3, 5, 10])

Out:

array([1, 4, 6])

5. Generate 1D array between 3 lower bounds and 3 upper bounds

np.random.randint([1, 3, 4], [3, 5, 10])

Out:

array([2, 3, 4])

6. Generate 2d array of size 2x3, where

np.random.randint([8,0, 8],[[10], [9]])

Out:

array([[9, 6, 9],
       [8, 1, 8]])

7. Generate another 2D array of size 3x2 

np.random.randint([0,8],[[9], [20], [10]])

Out:

array([[ 3,  8],
       [ 6, 10],
       [ 7,  9]])

b) Random float/decimals between range

1. Generate 20 float values between 0 and 10 using random.uniform 

np.random.uniform(0, 10, 20)

Out:

array([2.52003182, 7.92110332, 1.50855153, 9.90953893, 2.03089333,
       1.70043576, 5.4130004 , 2.76550054, 3.66412313, 0.43711276,
       6.86533095, 7.87814796, 5.87564616, 1.2567026 , 7.95171546,
       3.30952517, 9.29987975, 1.10395478, 9.94582268, 5.83766228])

2. Generate 2D 2x2 float array  

np.random.uniform([0,8],[[9], [20]])

Out:

array([[ 2.56164838,  8.77214394],
       [ 3.93668152, 13.50095773]])

3. Generate 20 float values between 5 and 10 using random.sample

Random.sample returns random floats between 0 and 1, so in order to get the values between a range, in this case it is between 5 and 10

We would multiply the random values by upper bounds and add lower bound to it

(high-low) * np.random.sample(size=20) + low

Let’s put the low and high value as 5 and 10 respectively.

(10-5) * np.random.sample(size=20) + 5

Out:

array([5.30887252, 5.68872096, 8.84929092, 6.92724732, 5.45294085,
       7.11049378, 8.53911988, 9.43878303, 7.81624595, 7.77538502,
       6.88726948, 8.08815515, 5.81105502, 9.65732916, 7.23508785,
       8.0566595 , 9.36133479, 8.2490744 , 5.60270196, 6.72205368])

3. Pandas create dataframe of random numbers

import pandas as pd
df = pd.DataFrame(np.random.uniform(0,100,size=(5, 4)), columns=list('ABCD'))

Out:

	A	B	C	D
0	10.761108	0.856031	18.205962	7.594729
1	14.689869	12.605781	9.522636	0.307509
2	11.682856	11.915566	2.554456	1.212645
3	1.757673	10.225725	4.179541	8.477603
4	0.429727	2.289190	14.142173	14.396024