NumPy教程-numpy.random()在Python中的使用

简单随机数据

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]])

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

array([[ 1.43327469, -0.02019121],       [ 1.54626422,  1.05831067]])-0.30801907689048353) np.random.randint(low[, high, size, dtype])

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

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

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

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

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

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

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])

专属福利 
👉点击领取：最全Python资料合集

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])

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)

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

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

排列

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])

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

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

分布

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()

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])

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])

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()

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:              self.currency = self._business.currency_iso_code()          else:              self.currency = None

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])

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()

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

3.

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>)

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])

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>

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()

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()

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

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

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()

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 = 02 wells drilled, probability of one success = 03 wells drilled, probability of one success = 04 wells drilled, probability of one success = 05 wells drilled, probability of one success = 06 wells drilled, probability of one success = 07 wells drilled, probability of one success = 08 wells drilled, probability of one success = 09 wells drilled, probability of one success = 010 wells drilled, probability of one success = 0

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()

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()

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()

import numpy as npx = 5. # 形状参数samples = 1000s1 = np.random.power(x, samples)import matplotlib.pyplot as pltcount, 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] * bplt.plot(a, density_b)plt.show()

val = hist(np.random.rayleigh(3, 100000), bins=200, density=True)meanval = 1modeval = np.sqrt(2 / np.pi) * meanvals1 = np.random.rayleigh(modeval, 1000000)100. * sum(s1 > 3) / 1000000.

0.087300000000000003

import numpy as npimport matplotlib.pyplot as plts1 = np.random.standard_cauchy(1000000)s1 = s1[(s1 > -25) & (s1 < 25)]  # 截断分布以便绘图plt.hist(s1, bins=100)plt.show()

import numpy as npn = 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]])

import numpy as npshape, scale = 2., 1.s1 = np.random.standard_gamma(shape, 1000000)import matplotlib.pyplot as pltimport scipy.special as spscount1, 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()

import numpy as npimport matplotlib.pyplot as plts1 = np.random.standard_normal(8000)s1q = 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]]])

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()

6677.51174.11018316945980.00864

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

import numpy as npimport matplotlib.pyplot as plts1 = 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()

import numpy as npimport matplotlib.pyplot as pltm1, m2 = 0.0, 4.0s1 = np.random.vonmises(m1, m2, 1000)from scipy.special import i0plt.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()

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

import numpy as npimport matplotlib.pyplot as pltfrom scipy import specialx = 2.0s = 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()

import numpy as npimport matplotlib.pyplot as pltfrom scipy import specialx = 2.0s = 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()

 1

热门推荐

• Python 3.12正式发布：性能提升、no-GIL将在3.13提供

• NumPy教程-numpy.sum()在Python中的使用

• 用Python实现实时显示视频下载进度！