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- Random sampling (
numpy.random) > Random sampling (numpy.random)...NumPy reference NumPy’s module structure Random sampling (numpy.random)...
- numpy.random (Python module, in Random sampling (
numpy.random)) - C API for random
...NumPy reference NumPy’s module structure Random sampling (numpy.random) C API for random...
- NumPy 1.13.0 Release Notes
...can now specify a not-constant spacing for data. In particular np.gradient can now take: A single scalar to specify a sample distance for all dimensions. N scalars to specify a constant sample distance for each dimension. i.e. dx, dy, dz,...
- NumPy 1.16.4 Release Notes
...to the random stream Due to bugs in the application of log to random floating point numbers, the stream may change when sampling from np.random.beta, np.random.binomial, np.random.laplace, np.random.logistic, np.random.logseries or np.rando...
- NumPy 1.17.0 Release Notes
...in old random module Due to bugs in the application of log to random floating point numbers, the stream may change when sampling from beta, binomial, laplace, logistic, logseries or multinomial if a 0 is generated in the underlying MT19937...
- NumPy 1.17.4 Release Notes
...8 pull requests were merged for this release. #14758: BLD: declare support for python 3.8 #14781: BUG: random: biased samples from integers() with 8 or 16 bit… #14851: BUG: Fix _ctypes class circular reference. (#13808) #14852: BLD: add ‘...
- NumPy 1.18.0 Release Notes
...riate stream from numpy.random.Generator.integers There was a bug in numpy.random.Generator.integers that caused biased sampling of 8 and 16 bit integer types. Fixing that bug has changed the output stream from what it was in previous relea...
- NumPy 1.18.3 Release Notes
...ghts Fix for the method=’eigh’ and method=’cholesky’ methods in numpy.random.multivariate_normal. Those were producing samples from the wrong distribution. Contributors A total of 6 people contributed to this release. People with a “+”...
- NumPy 1.19.0 Release Notes
...tivariate_normal Previously, when passing method='eigh' or method='cholesky', numpy.random.multivariate_normal produced samples from the wrong distribution. This is now fixed. (gh-15872) Fixed the jumping implementation in MT19937.jumped...
- NumPy 1.7.0 Release Notes
...ectorize improvements (added ‘excluded’ and ‘cache’ keyword, general cleanup and bug fixes) numpy.random.choice (random sample generating function) Compatibility notes In a future version of numpy, the functions np.diag, np.diagonal, and...
- NumPy for MATLAB users
...andom 3x4 array with default random number generator and seed = 42 linspace(1,3,4) np.linspace(1,3,4) 4 equally spaced samples between 1 and 3, inclusive [x,y]=meshgrid(0:8,0:5) np.mgrid[0:9.,0:6.] or np.meshgrid(r_[0:9.],r_[0:6.]) two 2D...
- NumPy reference
...(numpy.linalg) Logic functions Masked array operations Mathematical functions Miscellaneous routines Polynomials Random sampling (numpy.random) Set routines Sorting, searching, and counting Statistics Test support (numpy.testing) Window fun...
- numpy.histogram
...large enough to hold accumulated values as well. densitybool, optionalIf False, the result will contain the number of samples in each bin. If True, the result is the value of the probability density function at the bin, normalized such th...
- numpy.histogram2d
...ogram2d(x, y, bins=10, range=None, density=None, weights=None)[source] Compute the bi-dimensional histogram of two data samples. Parameters: xarray_like, shape (N,)An array containing the x coordinates of the points to be histogrammed. y...
- numpy.histogramdd
...numpy.histogramdd numpy.histogramdd(sample, bins=10, range=None, density=None, weights=None)[source] Compute the multidimensional histogram of some data. P...
- numpy.ma.masked_array.var
...ntly, e.g.: \[\frac{\sum_i{|a_i - \bar{a}|^2}}{N - 1}\] In statistics, the resulting quantity is sometimes called the “sample variance” because if a is a random sample from a larger population, this calculation provides an unbiased estimat...
- numpy.ma.MaskedArray.var
...ntly, e.g.: \[\frac{\sum_i{|a_i - \bar{a}|^2}}{N - 1}\] In statistics, the resulting quantity is sometimes called the “sample variance” because if a is a random sample from a larger population, this calculation provides an unbiased estimat...
- numpy.ma.var
...ntly, e.g.: \[\frac{\sum_i{|a_i - \bar{a}|^2}}{N - 1}\] In statistics, the resulting quantity is sometimes called the “sample variance” because if a is a random sample from a larger population, this calculation provides an unbiased estimat...
- numpy.matlib.rand
...rce] Return a matrix of random values with given shape. Create a matrix of the given shape and propagate it with random samples from a uniform distribution over [0, 1). Parameters: *argsArgumentsShape of the output. If given as N integers...
- numpy.matlib.randn
...n a random matrix with data from the “standard normal” distribution. randn generates a matrix filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1. Parameters: *argsArgumentsShape o...
- numpy.polynomial.hermite.hermfit
...c_0 + c_1 * H_1(x) + ... + c_n * H_n(x),\] where n is deg. Parameters: xarray_like, shape (M,)x-coordinates of the M sample points (x[i], y[i]). yarray_like, shape (M,) or (M, K)y-coordinates of the sample points. Several data sets of s...
- numpy.polynomial.hermite_e.hermefit
..._0 + c_1 * He_1(x) + ... + c_n * He_n(x),\] where n is deg. Parameters: xarray_like, shape (M,)x-coordinates of the M sample points (x[i], y[i]). yarray_like, shape (M,) or (M, K)y-coordinates of the sample points. Several data sets of s...
- numpy.polynomial.laguerre.lagfit
...c_0 + c_1 * L_1(x) + ... + c_n * L_n(x),\] where n is deg. Parameters: xarray_like, shape (M,)x-coordinates of the M sample points (x[i], y[i]). yarray_like, shape (M,) or (M, K)y-coordinates of the sample points. Several data sets of s...
- numpy.polynomial.polynomial.polyfit
...\[p(x) = c_0 + c_1 * x + ... + c_n * x^n,\] where n is deg. Parameters: xarray_like, shape (M,)x-coordinates of the M sample (data) points (x[i], y[i]). yarray_like, shape (M,) or (M, K)y-coordinates of the sample points. Several sets o...
- numpy.quantile
...ent to quantile, but with q in the range [0, 100]. medianequivalent to quantile(..., 0.5) nanquantile Notes Given a sample a from an underlying distribution, quantile provides a nonparametric estimate of the inverse cumulative distribut...
- numpy.random.beta
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Legacy random generation numpy.random.beta...
- numpy.random.binomial
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Legacy random generation numpy.random.binomial...
- numpy.random.BitGenerator.random_raw
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Bit generators numpy.random.BitGenerator numpy.random.BitGe...
- numpy.random.chisquare
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Legacy random generation numpy.random.chisquare...
- numpy.random.choice
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Legacy random generation numpy.random.choice...
- numpy.random.dirichlet
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Legacy random generation numpy.random.dirichlet...
- numpy.random.exponential
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Legacy random generation numpy.random.exponential...
- numpy.random.f
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Legacy random generation numpy.random.f...
- numpy.random.gamma
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Legacy random generation numpy.random.gamma...
- numpy.random.Generator.beta
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.beta...
- numpy.random.Generator.binomial
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.binomial...
- numpy.random.Generator.chisquare
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.chisquare...
- numpy.random.Generator.choice
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.choice...
- numpy.random.Generator.dirichlet
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.dirichlet...
- numpy.random.Generator.exponential
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.exponential...
- numpy.random.Generator.f
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.f...
- numpy.random.Generator.gamma
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.gamma...
- numpy.random.Generator.geometric
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.geometric...
- numpy.random.Generator.gumbel
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.gumbel...
- numpy.random.Generator.hypergeometric
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.hypergeometric...
- numpy.random.Generator.integers
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.integers...
- numpy.random.Generator.laplace
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.laplace...
- numpy.random.Generator.logistic
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.logistic...
- numpy.random.Generator.lognormal
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.lognormal...
- numpy.random.Generator.logseries
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.logseries...
- numpy.random.Generator.multinomial
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.multinomial...
- numpy.random.Generator.multivariate_hypergeometric
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.multivariate_hypergeometric...
- numpy.random.Generator.multivariate_normal
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Random Generator numpy.random.Generator.multivariate_normal...
- numpy.random.Generator.negative_binomial
- numpy.random.Generator.noncentral_chisquare
- numpy.random.Generator.noncentral_f
- numpy.random.Generator.normal
- numpy.random.Generator.pareto
- numpy.random.Generator.poisson
- numpy.random.Generator.power
- numpy.random.Generator.random
- numpy.random.Generator.rayleigh
- numpy.random.Generator.standard_cauchy
- numpy.random.Generator.standard_exponential
- numpy.random.Generator.standard_gamma
- numpy.random.Generator.standard_normal
- numpy.random.Generator.standard_t
- numpy.random.Generator.triangular
- numpy.random.Generator.uniform
- numpy.random.Generator.vonmises
- numpy.random.Generator.wald
- numpy.random.Generator.weibull
- numpy.random.Generator.zipf
- numpy.random.geometric
- numpy.random.gumbel
- numpy.random.hypergeometric
- numpy.random.laplace
- numpy.random.logistic
- numpy.random.lognormal
- numpy.random.logseries
- numpy.random.multinomial
- numpy.random.multivariate_normal
- numpy.random.negative_binomial
- numpy.random.noncentral_chisquare
- numpy.random.noncentral_f
- numpy.random.normal
- numpy.random.pareto
- numpy.random.poisson
- numpy.random.power
- numpy.random.rand
- numpy.random.randint
- numpy.random.randn
- numpy.random.random_integers
- numpy.random.random_sample
- numpy.random.RandomState.beta
- numpy.random.RandomState.binomial
- numpy.random.RandomState.chisquare
- numpy.random.RandomState.choice
- numpy.random.RandomState.dirichlet
- numpy.random.RandomState.exponential
- numpy.random.RandomState.f
- numpy.random.RandomState.gamma
- numpy.random.RandomState.geometric
- numpy.random.RandomState.gumbel
- numpy.random.RandomState.hypergeometric
- numpy.random.RandomState.laplace
- numpy.random.RandomState.logistic
- numpy.random.RandomState.lognormal
- numpy.random.RandomState.logseries
- numpy.random.RandomState.multinomial
- numpy.random.RandomState.multivariate_normal
- numpy.random.RandomState.negative_binomial
- numpy.random.RandomState.noncentral_chisquare
- numpy.random.RandomState.noncentral_f
- numpy.random.RandomState.normal
- numpy.random.RandomState.pareto
- numpy.random.RandomState.poisson
- numpy.random.RandomState.power
- numpy.random.RandomState.rand
- numpy.random.RandomState.randint
- numpy.random.RandomState.randn
- numpy.random.RandomState.random_integers
- numpy.random.RandomState.random_sample
- numpy.random.RandomState.rayleigh
- numpy.random.RandomState.standard_cauchy
- numpy.random.RandomState.standard_exponential
- numpy.random.RandomState.standard_gamma
- numpy.random.RandomState.standard_normal
- numpy.random.RandomState.standard_t
- numpy.random.RandomState.triangular
- numpy.random.RandomState.uniform
- numpy.random.RandomState.vonmises
- numpy.random.RandomState.wald
- numpy.random.RandomState.weibull
- numpy.random.RandomState.zipf
- numpy.random.rayleigh
- numpy.random.sample
- numpy.random.standard_cauchy
- numpy.random.standard_exponential
- numpy.random.standard_gamma
- numpy.random.standard_normal
- numpy.random.standard_t
- numpy.random.triangular
- numpy.random.uniform
- numpy.random.vonmises
- numpy.random.wald
- numpy.random.weibull
- numpy.random.zipf
- numpy.std
- numpy.var
- Random sampling (
numpy.random) - Extending
- Routines and objects by topic
- What’s new or different