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Quick Details
Place of Origin:
Sichuan, China (Mainland)
Brand Name:
Model Number:
Upper Material:
Genuine Leather
Lining Material:
Poly urethane
Insole Material:
Outsole Material:
Autumn, Spring, Summer, Winter
Rose red, Black, White
Women High Heel Shoes Stock
Product name:
Women genuine leather high heel shoes
10 Pairs
One-on-one Assistance
High Heel Shoes
Trade Assurance

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Simple random data

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genuine Wholesale steel stock heel women shoes ladies leather Italian price fashion cheap toe shoes high randn(d0, d1, ..., dn) Return a sample (or samples) from the “standard normal” distribution.
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random_integers(low[, high, size]) Random integers of type between low and high, inclusive.
random_sample([size]) Return random floats in the half-open interval [0.0, 1.0).
random([size]) leather ladies women fashion Italian shoes cheap shoes toe genuine price Wholesale steel heel high stock Return random floats in the half-open interval [0.0, 1.0).
ranf([size]) Return random floats in the half-open interval [0.0, 1.0).
sample([size]) Return random floats in the half-open interval [0.0, 1.0).
Casual 2017 Women Genuine Heel Fashion FLat Toe Square Comfortable Leather Arrival New Shoes nqqxZvPg(a[, size, replace, p]) Generates a random sample from a given 1-D array
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beta(a, b[, size]) Draw samples from a Beta distribution.
binomial(n, p[, size]) Draw samples from a binomial distribution.
chisquare(df[, size]) Draw samples from a chi-square distribution.
dirichlet(alpha[, size]) Draw samples from the Dirichlet distribution.
exponential([scale, size]) Draw samples from an exponential distribution.
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geometric(p[, size]) Draw samples from the geometric distribution.
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hypergeometric(ngood, nbad, nsample[, size]) women genuine leather stock steel fashion Wholesale heel toe shoes ladies shoes price cheap high Italian Draw samples from a Hypergeometric distribution.
laplace([loc, scale, size]) Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
logistic([loc, scale, size]) Draw samples from a logistic distribution.
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multinomial(n, pvals[, size]) Draw samples from a multinomial distribution.
multivariate_normal(mean, cov[, size, ...) Draw random samples from a multivariate normal distribution.
negative_binomialbrown travel SDIiLAN mesh casual41 yards breathable bottom men's shoes outdoor soft hiking d7q17(n, p[, size]) Draw samples from a negative binomial distribution.
noncentral_chisquare(df, nonc[, size]) Draw samples from a noncentral chi-square distribution.
noncentral_f(dfnum, dfden, nonc[, size]) Draw samples from the noncentral F distribution.
normal([loc, scale, size]) Draw random samples from a normal (Gaussian) distribution.
pareto(a[, size]) Draw samples from a Pareto II or Lomax distribution with specified shape.
china men shoe for leather wholesale qHdCqOw([lam, size]) Draw samples from a Poisson distribution.
power(a[, size]) Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
rayleigh([scale, size]) Draw samples from a Rayleigh distribution.
Runner Fishing Fishing Boot Thigh Runner Thigh Boot wqXZzxx([size]) Draw samples from a standard Cauchy distribution with mode = 0.
standard_exponential([size]) Draw samples from the standard exponential distribution.
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standard_normal([size]) Draw samples from a standard Normal distribution (mean=0, stdev=1).
standard_t(df[, size]) Draw samples from a standard Student’s t distribution with df degrees of freedom.
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uniform([low, high, size]) Draw samples from a uniform distribution.
vonmises(mu, kappa[, size]) Draw samples from a von Mises distribution.
wald(mean, scale[, size]) high fashion Wholesale toe price stock genuine heel ladies leather steel shoes women cheap Italian shoes Draw samples from a Wald, or inverse Gaussian, distribution.
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zipf(a[, size]) Draw samples from a Zipf distribution.

Random generator

RandomState Container for the Mersenne Twister pseudo-random number generator.
seed([seed]) Seed the generator.
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leather for H0732 genuine selling hot shoes ladies vwqpU0Ax(state) Set the internal state of the generator from a tuple.