Numpy Random
In machine learning and data science, at times you would be required to work with randomly generated data. In this post, we will see how we can use different methods of numpy random to generate random integers or a numpy array with random integers. We are going to cover the following –
1) numpy random seed
2) numpy random normal
3) numpy random rand
4) numpy random randn
5) numpy random choice
6) numpy random uniform
7) numpy random binomial
8) numpy random poisson
9) numpy random randint
10) numpy random sample
Before we start with this tutorial, let us first import numpy.
Numpy Import
import numpy as np
1) np.random.seed
Numpy Random generates pseudo-random numbers, which means that the numbers are not entirely random. They only appear random but there are algorithms involved in it.
If we initialize the initial conditions with a particular seed value, then it will always generate the same random numbers for that seed value. This means numpy random is deterministic for a given seed value.
np.random.seed can be used to set the seed value before generating numpy random arrays or random numbers.
Syntax
np.random.seed(seed=None)
seed (optional) – The input is int or 1-d array_like.
Setting the Numpy Seed Value
np.random.seed(5)
2) np.random.normal
np.random.normal returns a random numpy array or scalar whose elements are randomly drawn from a normal distribution
Syntax
np.random.normal(loc=0.0, scale=1.0, size=None)
loc – It represents Mean (“centre”) of the distribution. It is float or array_like of floats
scale – It represents Standard deviation (spread or “width”) of the distribution. It is float or array_like of floats
size (optional) – It represents the shape of the output array. If the given shape is, e.g., (m, n, k), then m n k samples are drawn. If the size is None (default), a single value is returned, if loc and scale are both scalars. Otherwise, np.broadcast(loc, scale).size samples are drawn.
Example – 1: Creating 1-D Numpy Random Array
np.random.normal(1,1,2)
array([ 3.2336465 , -0.40779152])
Example – 2: Creating 2-D Numpy Random Array
np.random.normal(2,1,(3,2))
array([[1.39441865, 1.28409766], [2.74850019, 2.03432562], [1.68098484, 1.94838727]])
Example – 3: Creating 3-D Numpy Random Array
np.random.normal(2,3,(3,2,4))
array([[[ 2.40038206, -1.60592695, 2.99413407, 3.01597174], [ 6.24667659, 0.01566748, -2.45512535, 4.55757841]], [[ 1.98075188, 0.21801424, 4.88015683, 1.08838025], [ 3.38653395, -0.61127652, 3.29363281, 0.95027105]], [[-0.12289567, 4.20374089, 7.8723445 , -1.62212758], [ 7.73655833, 8.64351739, 3.9377977 , 7.42301642]]])
Example 4: A Random Python Float
np.random.normal(2,3)
4.563562413316882
3) np.random.rand
np.random.rand returns a random numpy array or scalar whose element(s) are drawn randomly from the normal distribution over [0,1). (including 0 but excluding 1)
It returns a single python float if no input parameter is specified.
Syntax
np.random.rand(d0,d1,d2,.. dn)
d0,d1,d2,.. dn (optional) – It represents the dimension of the required array given as int. It is optional, if not specified, it will return a single python float.
Example 1: Creating 1-D Numpy Random Array
np.random.rand(3)
array([0.7798154 , 0.45685334, 0.89824928])
Example 2: Creating 2-D Numpy Random Array
np.random.rand(5,3)
array([[0.17963626, 0.46373528, 0.30762711], [0.27334617, 0.45668808, 0.54813439], [0.44506229, 0.32059869, 0.92962626], [0.78297602, 0.20849134, 0.65793903], [0.66985367, 0.45470811, 0.05023272]])
Example 3: Creating 3-D Numpy Random Array
np.random.rand(3,2,4)
array([[[0.82564261, 0.99100227, 0.3498416 , 0.25349147], [0.97474162, 0.8879708 , 0.52644532, 0.48392986]], [[0.24771917, 0.55511987, 0.64328105, 0.26636461], [0.83461679, 0.19501868, 0.51199488, 0.75963094]], [[0.545668 , 0.22256917, 0.51817445, 0.84151684], [0.80153195, 0.42129928, 0.49337318, 0.8382367 ]]])
Example 4: A Random Python Float
np.random.rand()
0.5747916494126569
4) np.random.randn
np.random.randn returns a random numpy array or scalar of sample(s), drawn randomly from the standard normal distribution.
It returns a single python float if no input parameter is specified.
Syntax
np.random.randn(d0,d1,d2,.. dn)
d0,d1,d2,.. dn (optional) – It represents the dimension of the required array given as int. It is optional, if not specified, it will return a single python float.
Example 1: Creating 1-D Random Array
np.random.randn(6)
array([-0.42125684, -0.0421679 , 0.63053175, 0.08204267, -1.08237789, 1.13159155])
Example 2: Creating 2-D Numpy Random Array
np.random.randn(6,4)
array([[-1.20911446, 0.22615005, 2.74344014, -0.47000636], [ 2.45453931, -0.36098073, 0.9761115 , 0.21063749], [ 1.05366423, 0.35103113, -0.16083158, -0.70649343], [ 0.22107229, 0.17888074, -1.13098505, -0.26359566], [ 2.29313593, 1.90569166, 0.71343492, 0.85209564], [ 0.67663365, 0.56029281, 1.11382612, -0.92873211]])
Example 3: Creating 3-D Numpy Random Array
np.random.randn(3,4,2)
array([[[-0.13509054, 1.31253658], [ 0.79514661, -0.15733937], [-0.42428779, 0.07816613], [ 1.27951041, -1.2528357 ]], [[-0.49349802, -0.1929593 ], [-0.51593638, -1.08389571], [-0.72854643, -0.44708392], [-0.01845007, 2.02125787]], [[-1.8826071 , 1.65592025], [-2.18326764, -0.07711314], [-2.9275772 , 2.3173623 ], [ 0.94757097, -0.13646251]]])
Example 4: A Random Python Float
np.random.randn()
-0.36506602839929475
5) np.random.choice
np.random.choice returns a numpy array or a scalar by drawing random samples from a given 1-D array
Syntax
np.random.choice(a, size=None, replace=True, p=None)
a – This represents a 1-D array-like (Tuple/Lists) or int. If it is a ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if a were np.arange(a)
size (optional) – This represents the desired output shape. It is either int or tuple of ints 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.
replace (optional) – This signifies whether the sample is to be drawn with or without replacement. It is given as boolean.
p – It represents the probabilities associated with each entry in the input ‘a’. It is given as 1-D array-like. If it is not provided, then the sample assumes a uniform distribution over all entries in a.
Example 1: Generating one Random Sample from List
np.random.choice([2,4,5,8])
8
Example 2: Generating a Numpy Array of Random Sample from Tuple
np.random.choice((2,4,5,8), size = (2,3))
array([[5, 2, 8], [4, 2, 2]])
Example 3: Generating a Numpy Array of Random Sample by passing an int
When we pass an int instead of an array, it treats the input as np.arange(a)
np.random.choice(4, size = (2,2)) ## 4 is treated as np.arange(4)
array([[0, 1], [3, 3]])
6) np.random.uniform
np.random.uniform returns a random numpy array or scalar whose element(s) are drawn randomly from the uniform distribution over [low,high). (including low but excluding high)
Syntax
np.random.uniform(low=0.0, high=1.0, size=None)
low (optional) – It represents the lower boundary of the output interval. All samples generated are greater than or equal to low. This input is float or array_like of floats. The default value is 0.
high (optional) – It represents the upper boundary of the output interval. All samples generated are less than high. This input is float or array_like of floats. The default value is 1.0.
size (optional) – It represents the output shape. This input is int or tuple of ints. If the given shape is, e.g., (m, n, k), then m n k samples are drawn. If the size is not given, then a single value is returned if low and high are both scalars. Otherwise, np.broadcast(low, high).size samples are drawn.
Example 1: Generating a Random sample
np.random.uniform() ## Default inputs
0.8058568054860041
np.random.uniform(low = 2, high = 10)
8.78205396325222
Example 2: Creating a 1-D Random Numpy Array
np.random.uniform(low = 2, high = 10, size = (3))
array([6.79454712, 3.03754559, 9.95066636])
Example 3: Creating a 2-D Random Numpy Array
np.random.uniform(low = 2, high = 10, size = (3,4))
array([[9.86355621, 8.45282899, 3.83705226, 8.13030929], [7.71767957, 2.05086033, 8.88954274, 4.23133409], [6.64220628, 3.26866265, 2.71334945, 9.34853175]])
7) np.random.binomial
np.random.binomial returns a random numpy array or scalar whose element(s) are drawn randomly from a binomial distribution
Syntax
np.random.binomial(n, p, size=None)
n – It represents the parameter of the distribution, >= 0. The input is int or array_like of ints. Floats are valid, but they will be truncated to integers.
p – It represents the parameter of the binomial distribution, >= 0 and <=1. The input is float or array_like of floats.
size – It represents the output shape. The input is int or tuple of ints, optional. If the given shape is, e.g., (m, n, k), then m n k samples are drawn. If the size is not given, a single value is returned if n and p are both scalars. Otherwise, np.broadcast(n, p).size samples are drawn.
Example 1: Generating a Random Sample
np.random.binomial(n=52, p=0.7)
34
Example 2: Creating a 1-D Random Numpy Array
np.random.binomial(n=52, p=0.7, size = (2))
array([39, 32])
Example 3: Creating a 2-D Random Numpy Array
np.random.binomial(n=52, p=0.7, size = (2,3))
array([[39, 33, 39], [32, 37, 36]])
8) np.random.poisson
np.random.poisson returns a random numpy array or scalar whose element(s) are drawn randomly from a poisson distribution
Syntax
np.random.poisson(lam=1.0, size=None)
lam – It represents the expectation of interval, should be >=0. A sequence of expectation intervals must be broadcastable over the requested size. The input is float or array_like of floats.
*size (optional) – It represents the shape of the output. The input is int or tuple of ints. If the given shape is, e.g., (m, n, k), then m n k samples are drawn. If size is not given, then a single value is returned if lam is a scalar. Otherwise, np.array(lam).size samples are drawn.
Example 1: Generating a Random Sample
np.random.poisson(5)
4
Example 2: Creating a 1-D Random Numpy Array
np.random.poisson(5,size=(4))
array([2, 3, 1, 7])
Example 3: Creating a 2-D Random Numpy Array
np.random.poisson(5,size=(4,3))
array([[ 7, 2, 10], [ 3, 4, 7], [ 5, 7, 5], [ 8, 9, 4]])
9) np.random.randint
np.random.randint returns a random numpy array or scalar, whose element(s) is int, drawn randomly from low (inclusive) to the high (exclusive) range.
Syntax
np.random.randint(low, high=None, size=None, dtype=’l’)
low – It represents the lowest inclusive bound of the distribution from where the sample can be drawn. Unless high=None, in which case this parameter is one above the highest such integer. The input is int
high (optional) – It represents the upper exclusive bound of the distribution from where the sample can be drawn. (see above for behavior if high=None). The input is int.
size (optional) – It represents the shape of the output. The input is int or tuple of ints. 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) – It represents the required dtype of the result. The default value is ‘np.int’.
Example 1: Generating a Random Number
np.random.randint(1,50)
42
Example 2: Creating a 1-D Random Numpy Array
np.random.randint(1,50, size=(4))
array([ 4, 24, 4, 32])
Example 3: Creating a 2-D Random Numpy Array
np.random.randint(1,50, size=(4,3))
array([[48, 8, 1], [36, 38, 2], [31, 28, 32], [30, 7, 8]])
10) np.random.sample
np.random.sample returns a random numpy array or scalar whose element(s) are floats, drawn randomly from the half-open interval [0.0, 1.0) (including 0 and excluding 1)
Syntax
np.random.sample(size=None)
size (optional) – It represents the shape of the output. The input is int or tuple of ints. If the given shape is, e.g., (m, n, k), then m n k samples are drawn. If no size is given, then a single value is returned.
Example 1: Generating a Random Sample
np.random.sample()
0.4263312607851021
Example 2: Creating a 1-D Random Numpy Array
np.random.sample(2)
array([0.96748937, 0.74883372])
Example 3: Creating a 2-D Random Numpy Array
np.random.sample((2,4)) ## Two Dimension
array([[0.52717883, 0.96121114, 0.78901011, 0.49669551], [0.211141 , 0.60398414, 0.74857581, 0.75586383]])
References:
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