# Python中的numpy.random()用法详细图解

## 简单随机数据

1)p.random.rand(d0, d1, …, dn)

``````import numpy as np
a=np.random.rand(5, 2)
a``````

``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)

``````import numpy as np
a=np.random.randn(2, 2)
a``````

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

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

random模块的此功能用于生成从inclusive(低)到exclusive(高)的随机整数。

``````import numpy as np
a=np.random.randint(3, size=10)
a``````

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

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

``````import numpy as np
a=np.random.random_integers(3)
a
b=type(np.random.random_integers(3))
b
c=np.random.random_integers(5, size=(3, 2))
c``````

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

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

``````import numpy as np
a=np.random.random_sample()
a
b=type(np.random.random_sample())
b
c=np.random.random_sample((5, ))
c``````

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

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

``````import numpy as np
a=np.random.random()
a
b=type(np.random.random())
b
c=np.random.random((5, ))
c``````

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

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

``````import numpy as np
a=np.random.ranf()
a
b=type(np.random.ranf())
b
c=np.random.ranf((5, ))
c``````

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

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

``````import numpy as np
a=np.random.sample()
a
b=type(np.random.sample())
b
c=np.random.sample((5, ))
c``````

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

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

``````import numpy as np
a=np.random.choice(5, 3)
a
b=np.random.choice(5, 3, p=[0.2, 0.1, 0.4, 0.2, 0.1])
b``````

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

10)np.random.bytes(长度)

``````import numpy as np
a=np.random.bytes(7)
a``````

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

## 排列

1)np.random.shuffle()

``````import numpy as np
a=np.arange(12)
a
np.random.shuffle(a)
a``````

``````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()

``````import numpy as np
a=np.random.permutation(12)
a``````

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

## 发行版

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

``````def setup(self):
self.dist = dist.beta
self.cargs = []
self.ckwd = dict(alpha=2, beta=3)
self.np_rand_fxn = numpy.random.beta
self.np_args = [2, 3]
self.np_kwds = dict()``````

2)二项式(n, p [, size])

``````import numpy as np
n, p = 10, .6
s1= np.random.binomial(n, p, 10)
s1``````

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

3)chisquare(df [, size])

``````import numpy as np
np.random.chisquare(2, 4)
sum(np.random.binomial(9, 0.1, 20000) == 0)/20000.``````

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

4)dirichlet(alpha [, size])

``````Import numpy as np
import matplotlib.pyplot as plt
s1 = np.random.dirichlet((10, 5, 3), 20).transpose()
plt.barh(range(20), s1[0])
plt.barh(range(20), s1[1], left=s1[0], color='g')
plt.barh(range(20), s1[2], left=s1[0]+s1[1], color='r')
plt.title("Lengths of Strings")
plt.show()``````

5)指数([scale, size])

``````def __init__(self, sourceid, targetid):
self.__type = 'Transaction'
self.id = uuid4()
self.source = sourceid
self.target = targetid
self.date = self._datetime.date(start=2015, end=2019)
self.time = self._datetime.time()

if random() < 0.05:
self.amount = self._numbers.between(100000, 1000000)
self.amount = npr.exponential(10)

if random() < 0.15:
else:
self.currency = None``````

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

``````import numpy as np
dfno= 1.
dfden = 48.
s1 = np.random.f(dfno, dfden, 10)
np.sort(s1)``````

``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])

``````import numpy as np
shape, scale = 2., 2.
s1 = np.random.gamma(shape, scale, 1000)
import matplotlib.pyplot as plt
import scipy.special as spss
count, bins, ignored = plt.hist(s1, 50, density=True)
a = bins**(shape-1)*(np.exp(-bins/scale) /
(spss.gamma(shape)*scale**shape))
plt.plot(bins, a, linewidth=2, color='r')
plt.show()``````

8)geometric(p [, size])

``````import numpy as np
a = np.random.geometric(p=0.35, size=10000)
(a == 1).sum() / 1000``````

``3.``

9)gumbel([位置, 比例, 大小])

``````import numpy as np
lov, scale = 0, 0.2
s1 = np.random.gumbel(loc, scale, 1000)
import matplotlib.pyplot as plt
count, bins, ignored = plt.hist(s1, 30, density=True)
plt.plot(bins, (1/beta)*np.exp(-(bins - loc)/beta)* np.exp( -np.exp( -(bins - loc) /beta) ), linewidth=2, color='r')
plt.show()``````

``````import numpy as np
good, bad, samp = 100, 2, 10
s1 = np.random.hypergeometric(good, bad, samp, 1000)
plt.hist(s1)
plt.show()``````

``(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>)``

11)laplace([位置, 比例, 大小])

``````import numpy as np
location, scale = 0., 2.
s = np.random.laplace(location, scale, 10)
s``````

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

12)后勤([位置, 比例, 大小])

``````import numpy as np
import matplotlib.pyplot as plt
location, scale = 10, 1
s1 = np.random.logistic(location, scale, 10000)
count, bins, ignored = plt.hist(s1, bins=50)
count
bins
ignored
plt.show()``````

``````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>``````

13)对数正态([均值, sigma, 大小])

``````import numpy as np
mu, sigma = 2., 1.
s1 = np.random.lognormal(mu, sigma, 1000)
import matplotlib.pyplot as plt
count, bins, ignored = plt.hist(s1, 100, density=True, align='mid')
a = np.linspace(min(bins), max(bins), 10000)
pdf = (np.exp(-(np.log(a) - mu)**2 / (2 * sigma**2))/ (a * sigma * np.sqrt(2 * np.pi)))
plt.plot(a, pdf, linewidth=2, color='r')
plt.axis('tight')
plt.show()``````

14)logseries(p [, size])

``````import numpy as np
x = .6
s1 = np.random.logseries(x, 10000)
count, bins, ignored = plt.hist(s1)
def logseries(k, p):
return -p**k/(k*log(1-p))
plt.plot(bins, logseries(bins, x)*count.max()/logseries(bins, a).max(), 'r')
plt.show()``````

15)多项式(n, pvals [, size])

``````import numpy as np
np.random.multinomial(20, [1/6.]*6, size=1)``````

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

16)multivariate_normal(平均值, cov [, 大小, …)

``````import numpy as np
mean = (1, 2)
coveriance = [[1, 0], [0, 100]]
import matplotlib.pyplot as plt
a, b = np.random.multivariate_normal(mean, coveriance, 5000).T
plt.plot(a, b, 'x')
plt.axis('equal'023
030
)
plt.show()``````

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

``````import numpy as np
s1 = np.random.negative_binomial(1, 0.1, 100000)
for i in range(1, 11):
probability = sum(s1<i) / 100000.
print i, "wells drilled, probability of one success =", probability``````

``````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])

``````import numpy as np
import matplotlib.pyplot as plt
val = plt.hist(np.random.noncentral_chisquare(3, 25, 100000), bins=200, normed=True)
plt.show()``````

19)正常([位置, 比例, 大小])

``````import numpy as np
import matplotlib.pyplot as plt
mu, sigma = 0, 0.2 # mean and standard deviation
s1 = np.random.normal(mu, sigma, 1000)
abs(mu - np.mean(s1)) < 0.01
abs(sigma - np.std(s1, ddof=1)) < 0.01
count, bins, ignored = plt.hist(s1, 30, density=True)
plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (bins - mu)**2 / (2 * sigma**2) ), linewidth=2, color='r')
plt.show()``````

20)pareto(a [, size])

``````import numpy as np
import matplotlib.pyplot as plt
b, m1 = 3., 2.  # shape and mode
s1 = (np.random.pareto(b, 1000) + 1) * m1
count, bins, _ = plt.hist(s1, 100, density=True)
fit = b*m**b / bins**(b+1)
plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r')
plt.show()``````

21)power(a [, size])

``````import numpy as np
x = 5. # shape
samples = 1000
s1 = np.random.power(x, samples)
import matplotlib.pyplot as plt
count, bins, ignored = plt.hist(s1, bins=30)
a = np.linspace(0, 1, 100)
b = x*a**(x-1.)
density_b = samples*np.diff(bins)[0]*b
plt.plot(a, density_b)
plt.show()``````

22)瑞利([scale, size])

``````val = hist(np.random.rayleigh(3, 100000), bins=200, density=True)
meanval = 1
modeval = np.sqrt(2 / np.pi) * meanval
s1 = np.random.rayleigh(modeval, 1000000)
100.*sum(s1>3)/1000000.``````

``0.087300000000000003``

23)standard_cauchy([size])

``````import numpy as np
import matplotlib.pyplot as plt
s1 = np.random.standard_cauchy(1000000)
s1 = s1[(s1>-25) & (s1<25)]  # truncate distribution so it plots well
plt.hist(s1, bins=100)
plt.show()``````

24)standard_exponential([size])

``````import numpy as np
n = np.random.standard_exponential((2, 7000))``````

``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])

``````import numpy as np
shape, scale = 2., 1.
s1 = np.random.standard_gamma(shape, 1000000)
import matplotlib.pyplot as plt
import scipy.special as sps
count1, bins1, ignored1 = plt.hist(s, 50, density=True)
y = bins1**(shape-1) * ((np.exp(-bins1/scale))/ (sps.gamma(shape) * scale**shape))
plt.plot(bins1, y, linewidth=2, color='r')
plt.show()``````

26)standard_normal([size])

``````import numpy as np
import matplotlib.pyplot as plt
s1= np.random.standard_normal(8000)
s1
q = np.random.standard_normal(size=(3, 4, 2))
q``````

``````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])

``````intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, 8230, 8770])
s1 = np.random.standard_t(10, size=100000)
np.mean(intake)
intake.std(ddof=1)
t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))
h = plt.hist(s1, bins=100, density=True)
np.sum(s1<t) / float(len(s1))
plt.show()``````

``````6677.5
1174.1101831694598
0.00864``````

28)三角形(左, 模式, 右[, 大小])

``````import numpy as np
import matplotlib.pyplot as plt
h = plt.hist(np.random.triangular(-4, 0, 8, 1000000), bins=300, density=True)
plt.show()``````

29)统一([低, 高, 大小])

``````import numpy as np
import matplotlib.pyplot as plt
s1 = np.random.uniform(-1, 0, 1000)
np.all(s1 >= -1)
np.all(s1 < 0)
count, bins, ignored = plt.hist(s1, 15, density=True)
plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
plt.show()``````

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

``````import numpy as np
import matplotlib.pyplot as plt
m1, m2 = 0.0, 4.0
s1 = np.random.vonmises(m1, m2, 1000)
from scipy.special import i0
plt.hist(s1, 50, density=True)
x = np.linspace(-np.pi, np.pi, num=51)
y = np.exp(m2*np.cos(x-m1))/(2*np.pi*i0(m2))
plt.plot(x, y, linewidth=2, color='r')
plt.show()``````

31)wald(平均值, 比例尺[, 大小])

``````import numpy as np
import matplotlib.pyplot as plt
h = plt.hist(np.random.wald(3, 3, 100000), bins=250, density=True)
plt.show()``````

32)weibull(a [, size])

``````import numpy as np
import matplotlib.pyplot as plt
from scipy import special
x=2.0
s=np.random.weibull(x, 1000)
a = np.arange(1, 100.)/50.
def weib(x, n, a):
return (a/n)*(x/n)**np.exp(-(x/n)**a)
count, bins, ignored = plt.hist(np.random.weibull(5., 1000))
a= np.arange(1, 100.)/50.
scale = count.max()/weib(x, 1., 5.).max()
scale = count.max()/weib(a, 1., 5.).max()
plt.plot(x, weib(x, 1., 5.)*scale)
plt.show()``````

33)zipf(a [, size])

``````import numpy as np
import matplotlib.pyplot as plt
from scipy import special
x=2.0
s=np.random.zipf(x, 1000)
count, bins, ignored = plt.hist(s[s<50], 50, density=True)
a = np.arange(1., 50.)
b= a**(-x) / special.zetac(x)
plt.plot(a, b/max(b), linewidth=2, color='r')
plt.show()``````

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