Statistical functions (scipy.stats)

This module contains a large number of probability distributions, summary and frequency statistics, correlation functions and statistical tests, masked statistics, kernel density estimation, quasi-Monte Carlo functionality, and more.

Statistics is a very large area, and there are topics that are out of scope for SciPy and are covered by other packages. Some of the most important ones are:

• statsmodels: regression, linear models, time series analysis, extensions to topics also covered by scipy.stats.

• Pandas: tabular data, time series functionality, interfaces to other statistical languages.

• PyMC: Bayesian statistical modeling, probabilistic machine learning.

• scikit-learn: classification, regression, model selection.

• Seaborn: statistical data visualization.

• rpy2: Python to R bridge.

Probability distributions

Each univariate distribution is an instance of a subclass of rv_continuous (rv_discrete for discrete distributions):

rv_continuous([momtype, a, b, xtol, ...])	A generic continuous random variable class meant for subclassing.
rv_discrete([a, b, name, badvalue, ...])	A generic discrete random variable class meant for subclassing.
rv_histogram(histogram, *args[, density])	Generates a distribution given by a histogram.

Continuous distributions

alpha	An alpha continuous random variable.
anglit	An anglit continuous random variable.
arcsine	An arcsine continuous random variable.
argus	Argus distribution
beta	A beta continuous random variable.
betaprime	A beta prime continuous random variable.
bradford	A Bradford continuous random variable.
burr	A Burr (Type III) continuous random variable.
burr12	A Burr (Type XII) continuous random variable.
cauchy	A Cauchy continuous random variable.
chi	A chi continuous random variable.
chi2	A chi-squared continuous random variable.
cosine	A cosine continuous random variable.
crystalball	Crystalball distribution
dgamma	A double gamma continuous random variable.
dweibull	A double Weibull continuous random variable.
erlang	An Erlang continuous random variable.
expon	An exponential continuous random variable.
exponnorm	An exponentially modified Normal continuous random variable.
exponweib	An exponentiated Weibull continuous random variable.
exponpow	An exponential power continuous random variable.
f	An F continuous random variable.
fatiguelife	A fatigue-life (Birnbaum-Saunders) continuous random variable.
fisk	A Fisk continuous random variable.
foldcauchy	A folded Cauchy continuous random variable.
foldnorm	A folded normal continuous random variable.
genlogistic	A generalized logistic continuous random variable.
gennorm	A generalized normal continuous random variable.
genpareto	A generalized Pareto continuous random variable.
genexpon	A generalized exponential continuous random variable.
genextreme	A generalized extreme value continuous random variable.
gausshyper	A Gauss hypergeometric continuous random variable.
gamma	A gamma continuous random variable.
gengamma	A generalized gamma continuous random variable.
genhalflogistic	A generalized half-logistic continuous random variable.
genhyperbolic	A generalized hyperbolic continuous random variable.
geninvgauss	A Generalized Inverse Gaussian continuous random variable.
gibrat	A Gibrat continuous random variable.
gilbrat	Deprecated since version 1.9.0.
gompertz	A Gompertz (or truncated Gumbel) continuous random variable.
gumbel_r	A right-skewed Gumbel continuous random variable.
gumbel_l	A left-skewed Gumbel continuous random variable.
halfcauchy	A Half-Cauchy continuous random variable.
halflogistic	A half-logistic continuous random variable.
halfnorm	A half-normal continuous random variable.
halfgennorm	The upper half of a generalized normal continuous random variable.
hypsecant	A hyperbolic secant continuous random variable.
invgamma	An inverted gamma continuous random variable.
invgauss	An inverse Gaussian continuous random variable.
invweibull	An inverted Weibull continuous random variable.
johnsonsb	A Johnson SB continuous random variable.
johnsonsu	A Johnson SU continuous random variable.
kappa4	Kappa 4 parameter distribution.
kappa3	Kappa 3 parameter distribution.
ksone	Kolmogorov-Smirnov one-sided test statistic distribution.
kstwo	Kolmogorov-Smirnov two-sided test statistic distribution.
kstwobign	Limiting distribution of scaled Kolmogorov-Smirnov two-sided test statistic.
laplace	A Laplace continuous random variable.
laplace_asymmetric	An asymmetric Laplace continuous random variable.
levy	A Levy continuous random variable.
levy_l	A left-skewed Levy continuous random variable.
levy_stable	A Levy-stable continuous random variable.
logistic	A logistic (or Sech-squared) continuous random variable.
loggamma	A log gamma continuous random variable.
loglaplace	A log-Laplace continuous random variable.
lognorm	A lognormal continuous random variable.
loguniform	A loguniform or reciprocal continuous random variable.
lomax	A Lomax (Pareto of the second kind) continuous random variable.
maxwell	A Maxwell continuous random variable.
mielke	A Mielke Beta-Kappa / Dagum continuous random variable.
moyal	A Moyal continuous random variable.
nakagami	A Nakagami continuous random variable.
ncx2	A non-central chi-squared continuous random variable.
ncf	A non-central F distribution continuous random variable.
nct	A non-central Student's t continuous random variable.
norm	A normal continuous random variable.
norminvgauss	A Normal Inverse Gaussian continuous random variable.
pareto	A Pareto continuous random variable.
pearson3	A pearson type III continuous random variable.
powerlaw	A power-function continuous random variable.
powerlognorm	A power log-normal continuous random variable.
powernorm	A power normal continuous random variable.
rdist	An R-distributed (symmetric beta) continuous random variable.
rayleigh	A Rayleigh continuous random variable.
rice	A Rice continuous random variable.
recipinvgauss	A reciprocal inverse Gaussian continuous random variable.
semicircular	A semicircular continuous random variable.
skewcauchy	A skewed Cauchy random variable.
skewnorm	A skew-normal random variable.
studentized_range	A studentized range continuous random variable.
t	A Student's t continuous random variable.
trapezoid	A trapezoidal continuous random variable.
triang	A triangular continuous random variable.
truncexpon	A truncated exponential continuous random variable.
truncnorm	A truncated normal continuous random variable.
truncpareto	An upper truncated Pareto continuous random variable.
truncweibull_min	A doubly truncated Weibull minimum continuous random variable.
tukeylambda	A Tukey-Lamdba continuous random variable.
uniform	A uniform continuous random variable.
vonmises	A Von Mises continuous random variable.
vonmises_line	A Von Mises continuous random variable.
wald	A Wald continuous random variable.
weibull_min	Weibull minimum continuous random variable.
weibull_max	Weibull maximum continuous random variable.
wrapcauchy	A wrapped Cauchy continuous random variable.

Multivariate distributions

multivariate_normal	A multivariate normal random variable.
matrix_normal	A matrix normal random variable.
dirichlet	A Dirichlet random variable.
wishart	A Wishart random variable.
invwishart	An inverse Wishart random variable.
multinomial	A multinomial random variable.
special_ortho_group	A Special Orthogonal matrix (SO(N)) random variable.
ortho_group	An Orthogonal matrix (O(N)) random variable.
unitary_group	A matrix-valued U(N) random variable.
random_correlation	A random correlation matrix.
multivariate_t	A multivariate t-distributed random variable.
multivariate_hypergeom	A multivariate hypergeometric random variable.
random_table	Contingency tables from independent samples with fixed marginal sums.
uniform_direction	A vector-valued uniform direction.

scipy.stats.multivariate_normal methods accept instances of the following class to represent the covariance.

Covariance()

Representation of a covariance matrix

Discrete distributions

bernoulli	A Bernoulli discrete random variable.
betabinom	A beta-binomial discrete random variable.
binom	A binomial discrete random variable.
boltzmann	A Boltzmann (Truncated Discrete Exponential) random variable.
dlaplace	A Laplacian discrete random variable.
geom	A geometric discrete random variable.
hypergeom	A hypergeometric discrete random variable.
logser	A Logarithmic (Log-Series, Series) discrete random variable.
nbinom	A negative binomial discrete random variable.
nchypergeom_fisher	A Fisher's noncentral hypergeometric discrete random variable.
nchypergeom_wallenius	A Wallenius' noncentral hypergeometric discrete random variable.
nhypergeom	A negative hypergeometric discrete random variable.
planck	A Planck discrete exponential random variable.
poisson	A Poisson discrete random variable.
randint	A uniform discrete random variable.
skellam	A Skellam discrete random variable.
yulesimon	A Yule-Simon discrete random variable.
zipf	A Zipf (Zeta) discrete random variable.
zipfian	A Zipfian discrete random variable.

An overview of statistical functions is given below. Many of these functions have a similar version in scipy.stats.mstats which work for masked arrays.

Summary statistics

describe(a[, axis, ddof, bias, nan_policy])	Compute several descriptive statistics of the passed array.
gmean(a[, axis, dtype, weights, nan_policy, ...])	Compute the weighted geometric mean along the specified axis.
hmean(a[, axis, dtype, weights, nan_policy, ...])	Calculate the weighted harmonic mean along the specified axis.
pmean(a, p, *[, axis, dtype, weights, ...])	Calculate the weighted power mean along the specified axis.
kurtosis(a[, axis, fisher, bias, ...])	Compute the kurtosis (Fisher or Pearson) of a dataset.
mode(a[, axis, nan_policy, keepdims])	Return an array of the modal (most common) value in the passed array.
moment(a[, moment, axis, nan_policy, keepdims])	Calculate the nth moment about the mean for a sample.
expectile(a[, alpha, weights])	Compute the expectile at the specified level.
skew(a[, axis, bias, nan_policy, keepdims])	Compute the sample skewness of a data set.
kstat(data[, n, axis, nan_policy, keepdims])	Return the nth k-statistic (1<=n<=4 so far).
kstatvar(data[, n, axis, nan_policy, keepdims])	Return an unbiased estimator of the variance of the k-statistic.
tmean(a[, limits, inclusive, axis])	Compute the trimmed mean.
tvar(a[, limits, inclusive, axis, ddof])	Compute the trimmed variance.
tmin(a[, lowerlimit, axis, inclusive, ...])	Compute the trimmed minimum.
tmax(a[, upperlimit, axis, inclusive, ...])	Compute the trimmed maximum.
tstd(a[, limits, inclusive, axis, ddof])	Compute the trimmed sample standard deviation.
tsem(a[, limits, inclusive, axis, ddof])	Compute the trimmed standard error of the mean.
variation(a[, axis, nan_policy, ddof, keepdims])	Compute the coefficient of variation.
find_repeats(arr)	Find repeats and repeat counts.
trim_mean(a, proportiontocut[, axis])	Return mean of array after trimming distribution from both tails.
gstd(a[, axis, ddof])	Calculate the geometric standard deviation of an array.
iqr(x[, axis, rng, scale, nan_policy, ...])	Compute the interquartile range of the data along the specified axis.
sem(a[, axis, ddof, nan_policy])	Compute standard error of the mean.
bayes_mvs(data[, alpha])	Bayesian confidence intervals for the mean, var, and std.
mvsdist(data)	'Frozen' distributions for mean, variance, and standard deviation of data.
entropy(pk[, qk, base, axis])	Calculate the Shannon entropy/relative entropy of given distribution(s).
differential_entropy(values, *[, ...])	Given a sample of a distribution, estimate the differential entropy.
median_abs_deviation(x[, axis, center, ...])	Compute the median absolute deviation of the data along the given axis.

Frequency statistics

cumfreq(a[, numbins, defaultreallimits, weights])	Return a cumulative frequency histogram, using the histogram function.
percentileofscore(a, score[, kind, nan_policy])	Compute the percentile rank of a score relative to a list of scores.
scoreatpercentile(a, per[, limit, ...])	Calculate the score at a given percentile of the input sequence.
relfreq(a[, numbins, defaultreallimits, weights])	Return a relative frequency histogram, using the histogram function.

binned_statistic(x, values[, statistic, ...])	Compute a binned statistic for one or more sets of data.
binned_statistic_2d(x, y, values[, ...])	Compute a bidimensional binned statistic for one or more sets of data.
binned_statistic_dd(sample, values[, ...])	Compute a multidimensional binned statistic for a set of data.

Correlation functions

f_oneway(*samples[, axis])	Perform one-way ANOVA.
alexandergovern(*samples[, nan_policy])	Performs the Alexander Govern test.
pearsonr(x, y, *[, alternative])	Pearson correlation coefficient and p-value for testing non-correlation.
spearmanr(a[, b, axis, nan_policy, alternative])	Calculate a Spearman correlation coefficient with associated p-value.
pointbiserialr(x, y)	Calculate a point biserial correlation coefficient and its p-value.
kendalltau(x, y[, initial_lexsort, ...])	Calculate Kendall's tau, a correlation measure for ordinal data.
weightedtau(x, y[, rank, weigher, additive])	Compute a weighted version of Kendall's \(\tau\).
somersd(x[, y, alternative])	Calculates Somers' D, an asymmetric measure of ordinal association.
linregress(x[, y, alternative])	Calculate a linear least-squares regression for two sets of measurements.
siegelslopes(y[, x, method])	Computes the Siegel estimator for a set of points (x, y).
theilslopes(y[, x, alpha, method])	Computes the Theil-Sen estimator for a set of points (x, y).
multiscale_graphcorr(x, y[, ...])	Computes the Multiscale Graph Correlation (MGC) test statistic.

Statistical tests

ttest_1samp(a, popmean[, axis, nan_policy, ...])	Calculate the T-test for the mean of ONE group of scores.
ttest_ind(a, b[, axis, equal_var, ...])	Calculate the T-test for the means of two independent samples of scores.
ttest_ind_from_stats(mean1, std1, nobs1, ...)	T-test for means of two independent samples from descriptive statistics.
ttest_rel(a, b[, axis, nan_policy, ...])	Calculate the t-test on TWO RELATED samples of scores, a and b.
chisquare(f_obs[, f_exp, ddof, axis])	Calculate a one-way chi-square test.
cramervonmises(rvs, cdf[, args])	Perform the one-sample Cramér-von Mises test for goodness of fit.
cramervonmises_2samp(x, y[, method])	Perform the two-sample Cramér-von Mises test for goodness of fit.
power_divergence(f_obs[, f_exp, ddof, axis, ...])	Cressie-Read power divergence statistic and goodness of fit test.
kstest(rvs, cdf[, args, N, alternative, method])	Performs the (one-sample or two-sample) Kolmogorov-Smirnov test for goodness of fit.
ks_1samp(x, cdf[, args, alternative, method])	Performs the one-sample Kolmogorov-Smirnov test for goodness of fit.
ks_2samp(data1, data2[, alternative, method])	Performs the two-sample Kolmogorov-Smirnov test for goodness of fit.
epps_singleton_2samp(x, y[, t])	Compute the Epps-Singleton (ES) test statistic.
mannwhitneyu(x, y[, use_continuity, ...])	Perform the Mann-Whitney U rank test on two independent samples.
tiecorrect(rankvals)	Tie correction factor for Mann-Whitney U and Kruskal-Wallis H tests.
rankdata(a[, method, axis, nan_policy])	Assign ranks to data, dealing with ties appropriately.
ranksums(x, y[, alternative, axis, ...])	Compute the Wilcoxon rank-sum statistic for two samples.
wilcoxon(x[, y, zero_method, correction, ...])	Calculate the Wilcoxon signed-rank test.
kruskal(*samples[, nan_policy, axis, keepdims])	Compute the Kruskal-Wallis H-test for independent samples.
friedmanchisquare(*samples)	Compute the Friedman test for repeated samples.
brunnermunzel(x, y[, alternative, ...])	Compute the Brunner-Munzel test on samples x and y.
combine_pvalues(pvalues[, method, weights])	Combine p-values from independent tests that bear upon the same hypothesis.
jarque_bera(x, *[, axis, nan_policy, keepdims])	Perform the Jarque-Bera goodness of fit test on sample data.
page_trend_test(data[, ranked, ...])	Perform Page's Test, a measure of trend in observations between treatments.
tukey_hsd(*args)	Perform Tukey's HSD test for equality of means over multiple treatments.
poisson_means_test(k1, n1, k2, n2, *[, ...])	Performs the Poisson means test, AKA the "E-test".

ansari(x, y[, alternative])	Perform the Ansari-Bradley test for equal scale parameters.
bartlett(*samples)	Perform Bartlett's test for equal variances.
levene(*samples[, center, proportiontocut])	Perform Levene test for equal variances.
shapiro(x)	Perform the Shapiro-Wilk test for normality.
anderson(x[, dist])	Anderson-Darling test for data coming from a particular distribution.
anderson_ksamp(samples[, midrank])	The Anderson-Darling test for k-samples.
binom_test(x[, n, p, alternative])	Perform a test that the probability of success is p.
binomtest(k, n[, p, alternative])	Perform a test that the probability of success is p.
fligner(*samples[, center, proportiontocut])	Perform Fligner-Killeen test for equality of variance.
median_test(*samples[, ties, correction, ...])	Perform a Mood's median test.
mood(x, y[, axis, alternative])	Perform Mood's test for equal scale parameters.
skewtest(a[, axis, nan_policy, alternative])	Test whether the skew is different from the normal distribution.
kurtosistest(a[, axis, nan_policy, alternative])	Test whether a dataset has normal kurtosis.
normaltest(a[, axis, nan_policy])	Test whether a sample differs from a normal distribution.
goodness_of_fit(dist, data, *[, ...])	Perform a goodness of fit test comparing data to a distribution family.

Quasi-Monte Carlo

• Quasi-Monte Carlo submodule (scipy.stats.qmc)
  • Quasi-Monte Carlo
    • Engines
      • scipy.stats.qmc.QMCEngine
      • scipy.stats.qmc.Sobol
      • scipy.stats.qmc.Halton
      • scipy.stats.qmc.LatinHypercube
      • scipy.stats.qmc.PoissonDisk
      • scipy.stats.qmc.MultinomialQMC
      • scipy.stats.qmc.MultivariateNormalQMC
    • Helpers
      • scipy.stats.qmc.discrepancy
      • scipy.stats.qmc.update_discrepancy
      • scipy.stats.qmc.scale
  • Introduction to Quasi-Monte Carlo
    • References

Resampling Methods

bootstrap(data, statistic, *[, n_resamples, ...])	Compute a two-sided bootstrap confidence interval of a statistic.
permutation_test(data, statistic, *[, ...])	Performs a permutation test of a given statistic on provided data.
monte_carlo_test(sample, rvs, statistic, *)	Monte Carlo test that a sample is drawn from a given distribution.

Masked statistics functions

• Statistical functions for masked arrays (scipy.stats.mstats)
  • Summary statistics
    • scipy.stats.mstats.describe
      • describe
    • scipy.stats.mstats.gmean
      • gmean
    • scipy.stats.mstats.hmean
      • hmean
    • scipy.stats.mstats.kurtosis
      • kurtosis
    • scipy.stats.mstats.mode
      • mode
    • scipy.stats.mstats.mquantiles
      • mquantiles
    • scipy.stats.mstats.hdmedian
      • hdmedian
    • scipy.stats.mstats.hdquantiles
      • hdquantiles
    • scipy.stats.mstats.hdquantiles_sd
      • hdquantiles_sd
    • scipy.stats.mstats.idealfourths
      • idealfourths
    • scipy.stats.mstats.plotting_positions
      • plotting_positions
    • scipy.stats.mstats.meppf
      • meppf
    • scipy.stats.mstats.moment
      • moment
    • scipy.stats.mstats.skew
      • skew
    • scipy.stats.mstats.tmean
      • tmean
    • scipy.stats.mstats.tvar
      • tvar
    • scipy.stats.mstats.tmin
      • tmin
    • scipy.stats.mstats.tmax
      • tmax
    • scipy.stats.mstats.tsem
      • tsem
    • scipy.stats.mstats.variation
      • variation
    • scipy.stats.mstats.find_repeats
      • find_repeats
    • scipy.stats.mstats.sem
      • sem
    • scipy.stats.mstats.trimmed_mean
      • trimmed_mean
    • scipy.stats.mstats.trimmed_mean_ci
      • trimmed_mean_ci
    • scipy.stats.mstats.trimmed_std
      • trimmed_std
    • scipy.stats.mstats.trimmed_var
      • trimmed_var
  • Frequency statistics
    • scipy.stats.mstats.scoreatpercentile
      • scoreatpercentile
  • Correlation functions
    • scipy.stats.mstats.f_oneway
      • f_oneway
    • scipy.stats.mstats.pearsonr
      • pearsonr
    • scipy.stats.mstats.spearmanr
      • spearmanr
    • scipy.stats.mstats.pointbiserialr
      • pointbiserialr
    • scipy.stats.mstats.kendalltau
      • kendalltau
    • scipy.stats.mstats.kendalltau_seasonal
      • kendalltau_seasonal
    • scipy.stats.mstats.linregress
      • linregress
    • scipy.stats.mstats.siegelslopes
      • siegelslopes
    • scipy.stats.mstats.theilslopes
      • theilslopes
    • scipy.stats.mstats.sen_seasonal_slopes
      • sen_seasonal_slopes
  • Statistical tests
    • scipy.stats.mstats.ttest_1samp
      • ttest_1samp
    • scipy.stats.mstats.ttest_onesamp
      • ttest_onesamp
    • scipy.stats.mstats.ttest_ind
      • ttest_ind
    • scipy.stats.mstats.ttest_rel
      • ttest_rel
    • scipy.stats.mstats.chisquare
      • chisquare
    • scipy.stats.mstats.kstest
      • kstest
    • scipy.stats.mstats.ks_2samp
      • ks_2samp
    • scipy.stats.mstats.ks_1samp
      • ks_1samp
    • scipy.stats.mstats.ks_twosamp
      • ks_twosamp
    • scipy.stats.mstats.mannwhitneyu
      • mannwhitneyu
    • scipy.stats.mstats.rankdata
      • rankdata
    • scipy.stats.mstats.kruskal
      • kruskal
    • scipy.stats.mstats.kruskalwallis
      • kruskalwallis
    • scipy.stats.mstats.friedmanchisquare
      • friedmanchisquare
    • scipy.stats.mstats.brunnermunzel
      • brunnermunzel
    • scipy.stats.mstats.skewtest
      • skewtest
    • scipy.stats.mstats.kurtosistest
      • kurtosistest
    • scipy.stats.mstats.normaltest
      • normaltest
  • Transformations
    • scipy.stats.mstats.obrientransform
      • obrientransform
    • scipy.stats.mstats.trim
      • trim
    • scipy.stats.mstats.trima
      • trima
    • scipy.stats.mstats.trimmed_stde
      • trimmed_stde
    • scipy.stats.mstats.trimr
      • trimr
    • scipy.stats.mstats.trimtail
      • trimtail
    • scipy.stats.mstats.trimboth
      • trimboth
    • scipy.stats.mstats.winsorize
      • winsorize
    • scipy.stats.mstats.zmap
      • zmap
    • scipy.stats.mstats.zscore
      • zscore
  • Other
    • scipy.stats.mstats.argstoarray
      • argstoarray
    • scipy.stats.mstats.count_tied_groups
      • count_tied_groups
    • scipy.stats.mstats.msign
      • msign
    • scipy.stats.mstats.compare_medians_ms
      • compare_medians_ms
    • scipy.stats.mstats.median_cihs
      • median_cihs
    • scipy.stats.mstats.mjci
      • mjci
    • scipy.stats.mstats.mquantiles_cimj
      • mquantiles_cimj
    • scipy.stats.mstats.rsh
      • rsh

Other statistical functionality

Transformations

boxcox(x[, lmbda, alpha, optimizer])	Return a dataset transformed by a Box-Cox power transformation.
boxcox_normmax(x[, brack, method, optimizer])	Compute optimal Box-Cox transform parameter for input data.
boxcox_llf(lmb, data)	The boxcox log-likelihood function.
yeojohnson(x[, lmbda])	Return a dataset transformed by a Yeo-Johnson power transformation.
yeojohnson_normmax(x[, brack])	Compute optimal Yeo-Johnson transform parameter.
yeojohnson_llf(lmb, data)	The yeojohnson log-likelihood function.
obrientransform(*samples)	Compute the O'Brien transform on input data (any number of arrays).
sigmaclip(a[, low, high])	Perform iterative sigma-clipping of array elements.
trimboth(a, proportiontocut[, axis])	Slice off a proportion of items from both ends of an array.
trim1(a, proportiontocut[, tail, axis])	Slice off a proportion from ONE end of the passed array distribution.
zmap(scores, compare[, axis, ddof, nan_policy])	Calculate the relative z-scores.
zscore(a[, axis, ddof, nan_policy])	Compute the z score.
gzscore(a, *[, axis, ddof, nan_policy])	Compute the geometric standard score.

Statistical distances

wasserstein_distance(u_values, v_values[, ...])	Compute the first Wasserstein distance between two 1D distributions.
energy_distance(u_values, v_values[, ...])	Compute the energy distance between two 1D distributions.

Sampling

• Random Number Generators (scipy.stats.sampling)
  • Generators Wrapped
    • For continuous distributions
      • scipy.stats.sampling.NumericalInverseHermite
      • scipy.stats.sampling.NumericalInversePolynomial
      • scipy.stats.sampling.TransformedDensityRejection
      • scipy.stats.sampling.SimpleRatioUniforms
    • For discrete distributions
      • scipy.stats.sampling.DiscreteAliasUrn
      • scipy.stats.sampling.DiscreteGuideTable
    • Warnings / Errors used in scipy.stats.sampling
      • scipy.stats.sampling.UNURANError

Random variate generation / CDF Inversion

rvs_ratio_uniforms(pdf, umax, vmin, vmax[, ...])

Generate random samples from a probability density function using the ratio-of-uniforms method.

Distribution Fitting

fit(dist, data[, bounds, guess, method, ...])

Fit a discrete or continuous distribution to data

Directional statistical functions

directional_stats(samples, *[, axis, normalize])	Computes sample statistics for directional data.
circmean(samples[, high, low, axis, nan_policy])	Compute the circular mean for samples in a range.
circvar(samples[, high, low, axis, nan_policy])	Compute the circular variance for samples assumed to be in a range.
circstd(samples[, high, low, axis, ...])	Compute the circular standard deviation for samples assumed to be in the range [low to high].

Contingency table functions

chi2_contingency(observed[, correction, lambda_])	Chi-square test of independence of variables in a contingency table.
contingency.crosstab(*args[, levels, sparse])	Return table of counts for each possible unique combination in *args.
contingency.expected_freq(observed)	Compute the expected frequencies from a contingency table.
contingency.margins(a)	Return a list of the marginal sums of the array a.
contingency.relative_risk(exposed_cases, ...)	Compute the relative risk (also known as the risk ratio).
contingency.association(observed[, method, ...])	Calculates degree of association between two nominal variables.
contingency.odds_ratio(table, *[, kind])	Compute the odds ratio for a 2x2 contingency table.
fisher_exact(table[, alternative])	Perform a Fisher exact test on a 2x2 contingency table.
barnard_exact(table[, alternative, pooled, n])	Perform a Barnard exact test on a 2x2 contingency table.
boschloo_exact(table[, alternative, n])	Perform Boschloo's exact test on a 2x2 contingency table.

Plot-tests

ppcc_max(x[, brack, dist])	Calculate the shape parameter that maximizes the PPCC.
ppcc_plot(x, a, b[, dist, plot, N])	Calculate and optionally plot probability plot correlation coefficient.
probplot(x[, sparams, dist, fit, plot, rvalue])	Calculate quantiles for a probability plot, and optionally show the plot.
boxcox_normplot(x, la, lb[, plot, N])	Compute parameters for a Box-Cox normality plot, optionally show it.
yeojohnson_normplot(x, la, lb[, plot, N])	Compute parameters for a Yeo-Johnson normality plot, optionally show it.

Univariate and multivariate kernel density estimation

gaussian_kde(dataset[, bw_method, weights])

Representation of a kernel-density estimate using Gaussian kernels.

Warnings / Errors used in scipy.stats

DegenerateDataWarning([msg])	Warns when data is degenerate and results may not be reliable.
ConstantInputWarning([msg])	Warns when all values in data are exactly equal.
NearConstantInputWarning([msg])	Warns when all values in data are nearly equal.
FitError([msg])	Represents an error condition when fitting a distribution to data.