How To Generate Random Numbers Using NumPy ?
Table Of Contents:
- What Are Random Numbers?
- Examples Of Random Number Generators.
(1) What Are Random Numbers?
- As the name suggests, a random number is a number chosen by chance — i.e., randomly, from a set of numbers.
- All the numbers in a specified distribution have an equal probability of being chosen randomly.
(2) Random Numbers?
Syntax: Default Random Number Generators
np.random.default_rng()Example: Initializing The Generator
rng = np.random.default_rng()Using ‘random()’ Function To Generate Numbers
Syntax:
numpy.random.random(size=None)Parameters:
- size : int or tuple of ints, optional – Output shape. If the given shape is, e.g.,
(m, n, k), thenm * n * ksamples are drawn. Default is None, in which case a single value is returned.
Returns:
- out : float or ndarray of floats – Array of random floats of shape size (unless
size=None, in which case a single float is returned).
Example-1:Random Decimal Number Generator
rng.random()Output:
0.03584145017167317Example-2: Generating 10 Random Numbers
rng.random(10)Output:
array([0.90486713, 0.40078114, 0.44952358, 0.18589626, 0.3175533 ,
0.89347518, 0.03356966, 0.59438873, 0.33121976, 0.4530089 ])Example-3: Generating multi-dimensional Random Numbers
rng.random((3, 2))Output:
array([[0.77440885, 0.75916156],
[0.42323151, 0.76035767],
[0.42136301, 0.39422448]])Example-4: Reproducing The Same Random Numbers
- If you want to generate the same random numbers repeatedly, then you can use the seeding technique.
- To seed the generator you need to pass an integer value to the Generator function.
inport numpy as np
rng_seed = np.random.default_rng(14245)
rng_seed.random()Output:
0.944980436348342Note:
- When we run the code again, we will get the same random number.
Example-4: Generating Random Integers
- We can use the integer() method to generate the random integers.
Syntax:
random.Generator.integers(low, high=None, size=None, dtype=np.int64, endpoint=False)Parameters:
- low: int or array-like of ints – Lowest (signed) integers to be drawn from the distribution (unless
high=None, in which case this parameter is 0 and this value is used for high). - high: int or array-like of ints, optional – If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if
high=None). If array-like, must contain integer values - size: int or tuple of ints, optional – Output shape. If the given shape is, e.g.,
(m, n, k), thenm * n * ksamples are drawn. Default is None, in which case a single value is returned. - dtype: dtype, optional – Desired dtype of the result. Byteorder must be native. The default value is np.int64.
- endpoint: bool, optional – If true, sample from the interval [low, high] instead of the default [low, high) Defaults to False
Returns:
- out: int or ndarray of ints –
size-shaped array of random integers from the appropriate distribution, or a single such random int ifsizenot provided.
Example-1:
rng.integers(low=0, high=10)Output:
3Example-2:
rng.integers(low=0, high=10, size=10)Output:
array([1, 9, 2, 9, 5, 1, 4, 5, 3, 2], dtype=int64)Example-5: Generating Statistical Distribution
- We can use the standard_normal() method to generate the statistical distribution.
Syntax:
random.standard_normal(size=None)Parameters:
- size: int or tuple of ints, optional – Output shape. If the given shape is, e.g.,
(m, n, k), thenm * n * ksamples are drawn. Default is None, in which case a single value is returned.
Returns:
- out: float or ndarray –
A floating-point array of shape
sizeof drawn samples, or a single sample ifsizewas not specified.
Example-1:
import numpy as np
rng = np.random.default_rng()
rng.standard_normal()Output:
-0.8662744069697407Example-2:
import numpy as np
rng = np.random.default_rng()
rng.standard_normal(20)Output:
array([-1.065457 , 1.35928533, 2.0422915 , 0.21322883, 0.1687038 ,
-0.50008546, -0.08658725, -0.50808513, -0.44901999, 0.55842963,
-2.25624767, 1.08228704, 0.57186154, 0.24438807, 1.37391439,
-1.2284484 , 1.3928497 , -1.97238106, 0.11309411, -0.7474105 ])