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numpy.random() in Python

The random is a module present in the NumPy library. This module contains the functions which are used for generating random numbers. This module contains some simple random data generation methods, some permutation and distribution functions, and random generator functions.

All the functions in a random module are as follows:

Simple random data

There are the following functions of simple random data:

1) p.random.rand(d0, d1, ..., dn)

This function of random module is used to generate random numbers or values in a given shape.

Example:

Output:

array([[0.74710182, 0.13306399],
           [0.01463718, 0.47618842],
           [0.98980426, 0.48390004],
           [0.58661785, 0.62895758],
           [0.38432729, 0.90384119]])

2) np.random.randn(d0, d1, ..., dn)

This function of random module return a sample from the "standard normal" distribution.

Example:

Output:

array([[ 1.43327469, -0.02019121],
       [ 1.54626422,  1.05831067]])
b=np.random.randn()
b
-0.3080190768904835

3) np.random.randint(low[, high, size, dtype])

This function of random module is used to generate random integers from inclusive(low) to exclusive(high).

Example:

Output:

array([1, 1, 1, 2, 0, 0, 0, 0, 0, 0])

4) np.random.random_integers(low[, high, size])

This function of random module is used to generate random integers number of type np.int between low and high.

Example:

Output:

2
<type 'numpy.int32'>
array([[1, 1],
           [2, 5],
           [1, 3]])

5) np.random.random_sample([size])

This function of random module is used to generate random floats number in the half-open interval [0.0, 1.0).

Example:

Output:

0.09250360565571492
<type 'float'>
array([0.34665418, 0.47027209, 0.75944969, 0.37991244, 0.14159746])

6) np.random.random([size])

This function of random module is used to generate random floats number in the half-open interval [0.0, 1.0).

Example:

Output:

0.008786953974334155
<type 'float'>
array([0.05530122, 0.59133394, 0.17258794, 0.6912388 , 0.33412534])

7) np.random.ranf([size])

This function of random module is used to generate random floats number in the half-open interval [0.0, 1.0).

Example:

Output:

0.2907792098474542
<type 'float'>
array([0.34084881, 0.07268237, 0.38161256, 0.46494681, 0.88071377])

8) np.random.sample([size])

This function of random module is used to generate random floats number in the half-open interval [0.0, 1.0).

Example:

Output:

0.012298209913766511
<type 'float'>
array([0.71878544, 0.11486169, 0.38189074, 0.14303308, 0.07217287])

9) np.random.choice(a[, size, replace, p])

This function of random module is used to generate random sample from a given 1-D array.

Example:

Output:

array([0, 3, 4])
array([2, 2, 2], dtype=int64)

10) np.random.bytes(length)

This function of random module is used to generate random bytes.

Example:

Output:

'nQ\x08\x83\xf9\xde\x8a'

Permutations

There are the following functions of permutations:

1) np.random.shuffle()

This function is used for modifying a sequence in-place by shuffling its contents.

Example:

Output:

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
array([10,  3,  2,  4,  5,  8,  0,  9,  1, 11,  7,  6])

2) np.random.permutation()

This function permute a sequence randomly or return a permuted range.

Example:

Output:

array([ 8,  7,  3, 11,  6,  0,  9, 10,  2,  5,  4,  1])

Distributions

There are the following functions of permutations:

1) beta(a, b[, size])

This function is used to draw samples from a Beta distribution.

Example:

2) binomial(n, p[, size])

This function is used to draw sample from a binomial distribution.

Example:

Output:

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

3) chisquare(df[, size])

This function is used to draw sample from a binomial distribution.

Example:

Output:

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

4) dirichlet(alpha[, size])

This function is used to draw a sample from the Dirichlet distribution.

Example:

Output:

numpy.random in Python

5) exponential([scale, size])

This function is used to draw sample from an exponential distribution.

Example:

6) f(dfnum, dfden[, size])

This function is used to draw sample from an F distribution.

Example:

Output:

array([0.00264041, 0.04725478, 0.07140803, 0.19526217, 0.23979   ,
       0.24023478, 0.63141254, 0.95316446, 1.40281789, 1.68327507])

7) gamma(shape[, scale, size])

This function is used to draw sample from a Gamma distribution

Example:

numpy.random in Python

8) geometric(p[, size])

This function is used to draw sample from a geometric distribution.

Example:

Output:

3.

9) gumbel([loc, scale, size])

This function is used to draw sample from a Gumble distribution.

Example:

Output:

numpy.random in Python

10) hypergeometric(ngood, nbad, nsample[, size])

This function is used to draw sample from a Hypergeometric distribution.

Example:

Output:

(array([ 13.,   0.,   0.,   0.,   0., 163.,   0.,   0.,   0., 824.]), array([ 8. ,  8.2,  8.4,  8.6,  8.8,  9. ,  9.2,  9.4,  9.6,  9.8, 10. ]), <a list of 10 Patch objects>)

numpy.random in Python

11) laplace([loc, scale, size])

This function is used to draw sample from the Laplace or double exponential distribution with specified location and scale.

Example:

Output:

array([-2.77127948, -1.46401453, -0.03723516, -1.61223942,  2.29590691,
        1.74297722,  1.49438411,  0.30325513, -0.15948891, -4.99669747])

12) logistic([loc, scale, size])

This function is used to draw sample from logistic distribution.

Example:

Output:

array([1.000e+00, 1.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 1.000e+00,
       1.000e+00, 5.000e+00, 7.000e+00, 1.100e+01, 1.800e+01, 3.500e+01,
       5.300e+01, 6.700e+01, 1.150e+02, 1.780e+02, 2.300e+02, 3.680e+02,
       4.910e+02, 6.400e+02, 8.250e+02, 9.100e+02, 9.750e+02, 1.039e+03,
       9.280e+02, 8.040e+02, 6.530e+02, 5.240e+02, 3.380e+02, 2.470e+02,
       1.650e+02, 1.150e+02, 8.500e+01, 6.400e+01, 3.300e+01, 1.600e+01,
       2.400e+01, 1.400e+01, 4.000e+00, 5.000e+00, 2.000e+00, 2.000e+00,
       1.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00,
       0.000e+00, 1.000e+00])
array([ 0.50643911,  0.91891814,  1.33139717,  1.7438762 ,  2.15635523,
        2.56883427,  2.9813133 ,  3.39379233,  3.80627136,  4.2187504 ,
        4.63122943,  5.04370846,  5.45618749,  5.86866652,  6.28114556,
        6.69362459,  7.10610362,  7.51858265,  7.93106169,  8.34354072,
        8.75601975,  9.16849878,  9.58097781,  9.99345685, 10.40593588,
       10.81841491, 11.23089394, 11.64337298, 12.05585201, 12.46833104,
       12.88081007, 13.2932891 , 13.70576814, 14.11824717, 14.5307262 ,
       14.94320523, 15.35568427, 15.7681633 , 16.18064233, 16.59312136,
       17.00560039, 17.41807943, 17.83055846, 18.24303749, 18.65551652,
       19.06799556, 19.48047459, 19.89295362, 20.30543265, 20.71791168,
       21.13039072])
<a list of 50 Patch objects>

numpy.random in Python

13) lognormal([mean, sigma, size])

This function is used to draw sample from a log-normal distribution.

Example:

Output:

numpy.random in Python

14) logseries(p[, size])

This function is used to draw sample from a logarithmic distribution.

Example:

Output:

numpy.random in Python

15) multinomial(n, pvals[, size])

This function is used to draw sample from a multinomial distribution.

Example:

Output:

array([[4, 2, 5, 5, 3, 1]])

16) multivariate_normal(mean, cov[, size, ...)

This function is used to draw sample from a multivariate normal distribution.

Example:

Output:

numpy.random in Python

17) negative_binomial(n, p[, size])

This function is used to draw sample from a negative binomial distribution.

Example:

Output:

1 wells drilled, probability of one success = 0
2 wells drilled, probability of one success = 0
3 wells drilled, probability of one success = 0
4 wells drilled, probability of one success = 0
5 wells drilled, probability of one success = 0
6 wells drilled, probability of one success = 0
7 wells drilled, probability of one success = 0
8 wells drilled, probability of one success = 0
9 wells drilled, probability of one success = 0
10 wells drilled, probability of one success = 0

18) noncentral_chisquare(df, nonc[, size])

This function is used to draw sample from a noncentral chi-square distribution.

Example:

Output:

numpy.random in Python

19) normal([loc, scale, size])

This function is used to draw sample from a normal distribution.

Example:

Output:

numpy.random in Python

20) pareto(a[, size])

This function is used to draw samples from a Lomax or Pareto II with specified shape.

Example:

Output:

numpy.random in Python

21) power(a[, size])

This function is used to draw samples in [0, 1] from a power distribution with positive exponent a-1.

Example:

Output:

numpy.random in Python

22) rayleigh([scale, size])

This function is used to draw sample from a Rayleigh distribution.

Example:

Output:

0.087300000000000003

numpy.random in Python

23) standard_cauchy([size])

This function is used to draw sample from a standard Cauchy distribution with mode=0.

Example:

Output:

numpy.random in Python

24) standard_exponential([size])

This function is used to draw sample from a standard exponential distribution.

Example:

Output:

array([[0.53857931, 0.181262  , 0.20478701, ..., 3.66232881, 1.83882709,
        1.77963295],
       [0.65163973, 1.40001955, 0.7525986 , ..., 0.76516523, 0.8400617 ,
        0.88551011]])

25) standard_gamma([size])

This function is used to draw sample from a standard Gamma distribution.

Example:

Output:

numpy.random in Python

26) standard_normal([size])

This function is used to draw sample from a standard Normal distribution.

Example:

Output:

array([-3.14907597,  0.95366265, -1.20100026, ...,  3.47180222,
        0.9608679 ,  0.0774319 ])
array([[[ 1.55635461, -1.29541713],
        [-1.50534663, -0.02829194],
        [ 1.03949348, -0.26128132],
        [ 1.51921798,  0.82136178]],

       [[-0.4011052 , -0.52458858],
        [-1.31803814,  0.37415379],
        [-0.67077365,  0.97447018],
        [-0.20212115,  0.67840888]],

       [[ 1.86183474,  0.19946562],
        [-0.07376021,  0.84599701],
        [-0.84341386,  0.32081667],
        [-3.32016062, -1.19029818]]])

27) standard_t(df[, size])

This function is used to draw sample from a standard Student's distribution with df degree of freedom.

Example:

Output:

6677.5
1174.1101831694598
0.00864

numpy.random in Python

28) triangular(left, mode, right[, size])

This function is used to draw sample from a triangular distribution over the interval.

Example:

Output:

numpy.random in Python

29) uniform([low, high, size])

This function is used to draw sample from a uniform distribution.

Example:

Output:

numpy.random in Python

30) vonmises(m1, m2[, size])

This function is used to draw sample from a von Mises distribution.

Example:

Output:

numpy.random in Python

31) wald(mean, scale[, size])

This function is used to draw sample from a Wald, or inverse Gaussian distribution.

Example:

Output:

numpy.random in Python

32) weibull(a[, size])

This function is used to draw sample from a Weibull distribution.

Example:

Output:

numpy.random in Python

33) zipf(a[, size])

This function is used to draw sample from a Zipf distribution.

Example:

Output:

numpy.random in Python
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