SciPy

Statistical functions (scipy.stats)

This module contains a large number of probability distributions as well as a growing library of statistical functions.

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, **kwargs) 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.
frechet_r A frechet_r continuous random variable.
frechet_l A frechet_l 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.
gilbrat A Gilbrat continuous random variable.
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 General Kolmogorov-Smirnov one-sided test.
kstwobign Kolmogorov-Smirnov two-sided test for large N.
laplace A 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.
lomax A Lomax (Pareto of the second kind) continuous random variable.
maxwell A Maxwell continuous random variable.
mielke A Mielke’s Beta-Kappa 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 continuous random variable.
reciprocal A reciprocal 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.
skewnorm A skew-normal random variable.
t A Student’s t continuous random variable.
trapz 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.
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 matrix-valued SO(N) random variable.
ortho_group A matrix-valued O(N) random variable.
unitary_group A matrix-valued U(N) random variable.
random_correlation A random correlation matrix.

Discrete distributions

bernoulli A Bernoulli 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.
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.
zipf A Zipf discrete random variable.
yulesimon A Yule-Simon discrete random variable.

An overview of statistical functions is given below. Several 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]) Compute the geometric mean along the specified axis.
hmean(a[, axis, dtype]) Calculate the harmonic mean along the specified axis.
kurtosis(a[, axis, fisher, bias, nan_policy]) Compute the kurtosis (Fisher or Pearson) of a dataset.
mode(a[, axis, nan_policy]) Return an array of the modal (most common) value in the passed array.
moment(a[, moment, axis, nan_policy]) Calculate the nth moment about the mean for a sample.
skew(a[, axis, bias, nan_policy]) Compute the skewness of a data set.
kstat(data[, n]) Return the nth k-statistic (1<=n<=4 so far).
kstatvar(data[, n]) Returns 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]) Compute the coefficient of variation, the ratio of the biased standard deviation to the mean.
find_repeats(arr) Find repeats and repeat counts.
trim_mean(a, proportiontocut[, axis]) Return mean of array after trimming distribution from both tails.
iqr(x[, axis, rng, scale, nan_policy, …]) Compute the interquartile range of the data along the specified axis.
sem(a[, axis, ddof, nan_policy]) Calculate the standard error of the mean (or standard error of measurement) of the values in the input array.
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]) Calculate the entropy of a distribution for given probability values.

Frequency statistics

cumfreq(a[, numbins, defaultreallimits, weights]) Return a cumulative frequency histogram, using the histogram function.
itemfreq(*args, **kwds) itemfreq is deprecated! itemfreq is deprecated and will be removed in a future version.
percentileofscore(a, score[, kind]) 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(*args) Performs a 1-way ANOVA.
pearsonr(x, y) Calculate a Pearson correlation coefficient and the p-value for testing non-correlation.
spearmanr(a[, b, axis, nan_policy]) Calculate a Spearman rank-order correlation coefficient and the p-value to test for non-correlation.
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 \(\tau\).
linregress(x[, y]) 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]) Computes the Theil-Sen estimator for a set of points (x, y).

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, nan_policy]) 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.
kstest(rvs, cdf[, args, N, alternative, mode]) Perform the Kolmogorov-Smirnov test for goodness of fit.
chisquare(f_obs[, f_exp, ddof, axis]) Calculate a one-way chi square test.
power_divergence(f_obs[, f_exp, ddof, axis, …]) Cressie-Read power divergence statistic and goodness of fit test.
ks_2samp(data1, data2) Compute the Kolmogorov-Smirnov statistic on 2 samples.
mannwhitneyu(x, y[, use_continuity, alternative]) Compute the Mann-Whitney rank test on samples x and y.
tiecorrect(rankvals) Tie correction factor for ties in the Mann-Whitney U and Kruskal-Wallis H tests.
rankdata(a[, method]) Assign ranks to data, dealing with ties appropriately.
ranksums(x, y) Compute the Wilcoxon rank-sum statistic for two samples.
wilcoxon(x[, y, zero_method, correction]) Calculate the Wilcoxon signed-rank test.
kruskal(*args, **kwargs) Compute the Kruskal-Wallis H-test for independent samples
friedmanchisquare(*args) Compute the Friedman test for repeated measurements
brunnermunzel(x, y[, alternative, …]) Computes the Brunner-Munzel test on samples x and y
combine_pvalues(pvalues[, method, weights]) Methods for combining the p-values of independent tests bearing upon the same hypothesis.
jarque_bera(x) Perform the Jarque-Bera goodness of fit test on sample data.
ansari(x, y) Perform the Ansari-Bradley test for equal scale parameters
bartlett(*args) Perform Bartlett’s test for equal variances
levene(*args, **kwds) 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.
fligner(*args, **kwds) Perform Fligner-Killeen test for equality of variance.
median_test(*args, **kwds) Mood’s median test.
mood(x, y[, axis]) Perform Mood’s test for equal scale parameters.
skewtest(a[, axis, nan_policy]) Test whether the skew is different from the normal distribution.
kurtosistest(a[, axis, nan_policy]) Test whether a dataset has normal kurtosis.
normaltest(a[, axis, nan_policy]) Test whether a sample differs from a normal distribution.

Transformations

boxcox(x[, lmbda, alpha]) Return a positive dataset transformed by a Box-Cox power transformation.
boxcox_normmax(x[, brack, method]) 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 for input data, using maximum likelihood estimation.
yeojohnson_llf(lmb, data) The yeojohnson log-likelihood function.
obrientransform(*args) Compute the O’Brien transform on input data (any number of arrays).
sigmaclip(a[, low, high]) Iterative sigma-clipping of array elements.
trimboth(a, proportiontocut[, axis]) Slices off a proportion of items from both ends of an array.
trim1(a, proportiontocut[, tail, axis]) Slices off a proportion from ONE end of the passed array distribution.
zmap(scores, compare[, axis, ddof]) Calculate the relative z-scores.
zscore(a[, axis, ddof]) Calculate the z score of each value in the sample, relative to the sample mean and standard deviation.

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.

Random variate generation

rvs_ratio_uniforms(pdf, umax, vmin, vmax[, …]) Generate random samples from a probability density function using the ratio-of-uniforms method.

Circular statistical functions

circmean(samples[, high, low, axis]) Compute the circular mean for samples in a range.
circvar(samples[, high, low, axis]) 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.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.
fisher_exact(table[, alternative]) Performs a Fisher 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.

Masked statistics functions

Univariate and multivariate kernel density estimation (scipy.stats.kde)

gaussian_kde(dataset[, bw_method, weights]) Representation of a kernel-density estimate using Gaussian kernels.

For many more stat related functions install the software R and the interface package rpy.

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