| alexandergovern(*args, nan_policy='propagate') |
Performs the Alexander Govern test. [extrait de alexandergovern.__doc__] |
| alpha(*args, **kwds) |
An alpha continuous random variable. [extrait de __doc__] |
| anderson(x, dist='norm') |
Anderson-Darling test for data coming from a particular distribution. [extrait de anderson.__doc__] |
| anderson_ksamp(samples, midrank=True) |
The Anderson-Darling test for k-samples. [extrait de anderson_ksamp.__doc__] |
| anglit(*args, **kwds) |
An anglit continuous random variable. [extrait de __doc__] |
| ansari(x, y, alternative='two-sided') |
Perform the Ansari-Bradley test for equal scale parameters. [extrait de ansari.__doc__] |
| arcsine(*args, **kwds) |
An arcsine continuous random variable. [extrait de __doc__] |
| argus(*args, **kwds) |
|
| barnard_exact(table, alternative='two-sided', pooled=True, n=32) |
Perform a Barnard exact test on a 2x2 contingency table. [extrait de barnard_exact.__doc__] |
| bartlett(*args) |
Perform Bartlett's test for equal variances. [extrait de bartlett.__doc__] |
| bayes_mvs(data, alpha=0.9) |
|
| bernoulli(*args, **kwds) |
A Bernoulli discrete random variable. [extrait de __doc__] |
| beta(*args, **kwds) |
A beta continuous random variable. [extrait de __doc__] |
| betabinom(*args, **kwds) |
A beta-binomial discrete random variable. [extrait de __doc__] |
| betaprime(*args, **kwds) |
A beta prime continuous random variable. [extrait de __doc__] |
| binned_statistic(x, values, statistic='mean', bins=10, range=None) |
|
| binned_statistic_2d(x, y, values, statistic='mean', bins=10, range=None, expand_binnumbers=False) |
|
| binned_statistic_dd(sample, values, statistic='mean', bins=10, range=None, expand_binnumbers=False, binned_statistic_result=None) |
|
| binom(*args, **kwds) |
A binomial discrete random variable. [extrait de __doc__] |
| binom_test(x, n=None, p=0.5, alternative='two-sided') |
Perform a test that the probability of success is p. [extrait de binom_test.__doc__] |
| binomtest(k, n, p=0.5, alternative='two-sided') |
|
| boltzmann(*args, **kwds) |
A Boltzmann (Truncated Discrete Exponential) random variable. [extrait de __doc__] |
| bootstrap(data, statistic, *, vectorized=True, paired=False, axis=0, confidence_level=0.95, n_resamples=9999, batch=None, method='BCa', random_state=None) |
|
| boschloo_exact(table, alternative='two-sided', n=32) |
Perform Boschloo's exact test on a 2x2 contingency table. [extrait de boschloo_exact.__doc__] |
| boxcox(x, lmbda=None, alpha=None, optimizer=None) |
Return a dataset transformed by a Box-Cox power transformation. [extrait de boxcox.__doc__] |
| boxcox_llf(lmb, data) |
The boxcox log-likelihood function. [extrait de boxcox_llf.__doc__] |
| boxcox_normmax(x, brack=None, method='pearsonr', optimizer=None) |
Compute optimal Box-Cox transform parameter for input data. [extrait de boxcox_normmax.__doc__] |
| boxcox_normplot(x, la, lb, plot=None, N=80) |
Compute parameters for a Box-Cox normality plot, optionally show it. [extrait de boxcox_normplot.__doc__] |
| bradford(*args, **kwds) |
A Bradford continuous random variable. [extrait de __doc__] |
| brunnermunzel(x, y, alternative='two-sided', distribution='t', nan_policy='propagate') |
Compute the Brunner-Munzel test on samples x and y. [extrait de brunnermunzel.__doc__] |
| burr(*args, **kwds) |
A Burr (Type III) continuous random variable. [extrait de __doc__] |
| burr12(*args, **kwds) |
A Burr (Type XII) continuous random variable. [extrait de __doc__] |
| cauchy(*args, **kwds) |
A Cauchy continuous random variable. [extrait de __doc__] |
| chi(*args, **kwds) |
A chi continuous random variable. [extrait de __doc__] |
| chi2(*args, **kwds) |
A chi-squared continuous random variable. [extrait de __doc__] |
| chi2_contingency(observed, correction=True, lambda_=None) |
Chi-square test of independence of variables in a contingency table. [extrait de chi2_contingency.__doc__] |
| chisquare(f_obs, f_exp=None, ddof=0, axis=0) |
Calculate a one-way chi-square test. [extrait de chisquare.__doc__] |
| circmean(samples, high=6.283185307179586, low=0, axis=None, nan_policy='propagate') |
Compute the circular mean for samples in a range. [extrait de circmean.__doc__] |
| circstd(samples, high=6.283185307179586, low=0, axis=None, nan_policy='propagate') |
|
| circvar(samples, high=6.283185307179586, low=0, axis=None, nan_policy='propagate') |
Compute the circular variance for samples assumed to be in a range. [extrait de circvar.__doc__] |
| combine_pvalues(pvalues, method='fisher', weights=None) |
|
| cosine(*args, **kwds) |
A cosine continuous random variable. [extrait de __doc__] |
| cramervonmises(rvs, cdf, args=()) |
Perform the one-sample Cramér-von Mises test for goodness of fit. [extrait de cramervonmises.__doc__] |
| cramervonmises_2samp(x, y, method='auto') |
Perform the two-sample Cramér-von Mises test for goodness of fit. [extrait de cramervonmises_2samp.__doc__] |
| crystalball(*args, **kwds) |
|
| cumfreq(a, numbins=10, defaultreallimits=None, weights=None) |
Return a cumulative frequency histogram, using the histogram function. [extrait de cumfreq.__doc__] |
| describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate') |
Compute several descriptive statistics of the passed array. [extrait de describe.__doc__] |
| dgamma(*args, **kwds) |
A double gamma continuous random variable. [extrait de __doc__] |
| differential_entropy(values: 'np.typing.ArrayLike', *, window_length: 'Optional[int]' = None, base: 'Optional[float]' = None, axis: 'int' = 0, method: 'str' = 'auto') -> 'Union[np.number, np.ndarray]' |
Given a sample of a distribution, estimate the differential entropy. [extrait de differential_entropy.__doc__] |
| dirichlet(alpha, seed=None) |
A Dirichlet random variable. [extrait de __doc__] |
| dlaplace(*args, **kwds) |
A Laplacian discrete random variable. [extrait de __doc__] |
| dweibull(*args, **kwds) |
A double Weibull continuous random variable. [extrait de __doc__] |
| energy_distance(u_values, v_values, u_weights=None, v_weights=None) |
Compute the energy distance between two 1D distributions. [extrait de energy_distance.__doc__] |
| entropy(pk, qk=None, base=None, axis=0) |
Calculate the entropy of a distribution for given probability values. [extrait de entropy.__doc__] |
| epps_singleton_2samp(x, y, t=(0.4, 0.8)) |
Compute the Epps-Singleton (ES) test statistic. [extrait de epps_singleton_2samp.__doc__] |
| erlang(*args, **kwds) |
An Erlang continuous random variable. [extrait de __doc__] |
| expon(*args, **kwds) |
An exponential continuous random variable. [extrait de __doc__] |
| exponnorm(*args, **kwds) |
An exponentially modified Normal continuous random variable. [extrait de __doc__] |
| exponpow(*args, **kwds) |
An exponential power continuous random variable. [extrait de __doc__] |
| exponweib(*args, **kwds) |
An exponentiated Weibull continuous random variable. [extrait de __doc__] |
| f(*args, **kwds) |
An F continuous random variable. [extrait de __doc__] |
| f_oneway(*args, axis=0) |
Perform one-way ANOVA. [extrait de f_oneway.__doc__] |
| fatiguelife(*args, **kwds) |
A fatigue-life (Birnbaum-Saunders) continuous random variable. [extrait de __doc__] |
| find_repeats(arr) |
Find repeats and repeat counts. [extrait de find_repeats.__doc__] |
| fisher_exact(table, alternative='two-sided') |
Perform a Fisher exact test on a 2x2 contingency table. [extrait de fisher_exact.__doc__] |
| fisk(*args, **kwds) |
A Fisk continuous random variable. [extrait de __doc__] |
| fligner(*args, center='median', proportiontocut=0.05) |
Perform Fligner-Killeen test for equality of variance. [extrait de fligner.__doc__] |
| foldcauchy(*args, **kwds) |
A folded Cauchy continuous random variable. [extrait de __doc__] |
| foldnorm(*args, **kwds) |
A folded normal continuous random variable. [extrait de __doc__] |
| friedmanchisquare(*args) |
Compute the Friedman test for repeated measurements. [extrait de friedmanchisquare.__doc__] |
| gamma(*args, **kwds) |
A gamma continuous random variable. [extrait de __doc__] |
| gausshyper(*args, **kwds) |
A Gauss hypergeometric continuous random variable. [extrait de __doc__] |
| genexpon(*args, **kwds) |
A generalized exponential continuous random variable. [extrait de __doc__] |
| genextreme(*args, **kwds) |
A generalized extreme value continuous random variable. [extrait de __doc__] |
| gengamma(*args, **kwds) |
A generalized gamma continuous random variable. [extrait de __doc__] |
| genhalflogistic(*args, **kwds) |
A generalized half-logistic continuous random variable. [extrait de __doc__] |
| genhyperbolic(*args, **kwds) |
A generalized hyperbolic continuous random variable. [extrait de __doc__] |
| geninvgauss(*args, **kwds) |
A Generalized Inverse Gaussian continuous random variable. [extrait de __doc__] |
| genlogistic(*args, **kwds) |
A generalized logistic continuous random variable. [extrait de __doc__] |
| gennorm(*args, **kwds) |
A generalized normal continuous random variable. [extrait de __doc__] |
| genpareto(*args, **kwds) |
A generalized Pareto continuous random variable. [extrait de __doc__] |
| geom(*args, **kwds) |
A geometric discrete random variable. [extrait de __doc__] |
| gilbrat(*args, **kwds) |
A Gilbrat continuous random variable. [extrait de __doc__] |
| gmean(a, axis=0, dtype=None, weights=None) |
Compute the geometric mean along the specified axis. [extrait de gmean.__doc__] |
| gompertz(*args, **kwds) |
A Gompertz (or truncated Gumbel) continuous random variable. [extrait de __doc__] |
| gstd(a, axis=0, ddof=1) |
|
| gumbel_l(*args, **kwds) |
A left-skewed Gumbel continuous random variable. [extrait de __doc__] |
| gumbel_r(*args, **kwds) |
A right-skewed Gumbel continuous random variable. [extrait de __doc__] |
| halfcauchy(*args, **kwds) |
A Half-Cauchy continuous random variable. [extrait de __doc__] |
| halfgennorm(*args, **kwds) |
The upper half of a generalized normal continuous random variable. [extrait de __doc__] |
| halflogistic(*args, **kwds) |
A half-logistic continuous random variable. [extrait de __doc__] |
| halfnorm(*args, **kwds) |
A half-normal continuous random variable. [extrait de __doc__] |
| hmean(a, axis=0, dtype=None) |
Calculate the harmonic mean along the specified axis. [extrait de hmean.__doc__] |
| hypergeom(*args, **kwds) |
A hypergeometric discrete random variable. [extrait de __doc__] |
| hypsecant(*args, **kwds) |
A hyperbolic secant continuous random variable. [extrait de __doc__] |
| invgamma(*args, **kwds) |
An inverted gamma continuous random variable. [extrait de __doc__] |
| invgauss(*args, **kwds) |
An inverse Gaussian continuous random variable. [extrait de __doc__] |
| invweibull(*args, **kwds) |
An inverted Weibull continuous random variable. [extrait de __doc__] |
| invwishart(df=None, scale=None, seed=None) |
An inverse Wishart random variable. [extrait de __doc__] |
| iqr(x, axis=None, rng=(25, 75), scale=1.0, nan_policy='propagate', interpolation='linear', keepdims=False) |
|
| itemfreq(*args, **kwds) |
`itemfreq` is deprecated! [extrait de itemfreq.__doc__] |
| jarque_bera(x) |
Perform the Jarque-Bera goodness of fit test on sample data. [extrait de jarque_bera.__doc__] |
| johnsonsb(*args, **kwds) |
A Johnson SB continuous random variable. [extrait de __doc__] |
| johnsonsu(*args, **kwds) |
A Johnson SU continuous random variable. [extrait de __doc__] |
| kappa3(*args, **kwds) |
Kappa 3 parameter distribution. [extrait de __doc__] |
| kappa4(*args, **kwds) |
Kappa 4 parameter distribution. [extrait de __doc__] |
| kendalltau(x, y, initial_lexsort=None, nan_policy='propagate', method='auto', variant='b') |
Calculate Kendall's tau, a correlation measure for ordinal data. [extrait de kendalltau.__doc__] |
| kruskal(*args, nan_policy='propagate') |
Compute the Kruskal-Wallis H-test for independent samples. [extrait de kruskal.__doc__] |
| ks_1samp(x, cdf, args=(), alternative='two-sided', mode='auto') |
|
| ks_2samp(data1, data2, alternative='two-sided', mode='auto') |
|
| ksone(*args, **kwds) |
Kolmogorov-Smirnov one-sided test statistic distribution. [extrait de __doc__] |
| kstat(data, n=2) |
|
| kstatvar(data, n=2) |
Return an unbiased estimator of the variance of the k-statistic. [extrait de kstatvar.__doc__] |
| kstest(rvs, cdf, args=(), N=20, alternative='two-sided', mode='auto') |
|
| kstwo(*args, **kwds) |
Kolmogorov-Smirnov two-sided test statistic distribution. [extrait de __doc__] |
| kstwobign(*args, **kwds) |
Limiting distribution of scaled Kolmogorov-Smirnov two-sided test statistic. [extrait de __doc__] |
| kurtosis(a, axis=0, fisher=True, bias=True, nan_policy='propagate') |
Compute the kurtosis (Fisher or Pearson) of a dataset. [extrait de kurtosis.__doc__] |
| kurtosistest(a, axis=0, nan_policy='propagate', alternative='two-sided') |
Test whether a dataset has normal kurtosis. [extrait de kurtosistest.__doc__] |
| laplace(*args, **kwds) |
A Laplace continuous random variable. [extrait de __doc__] |
| laplace_asymmetric(*args, **kwds) |
An asymmetric Laplace continuous random variable. [extrait de __doc__] |
| levene(*args, center='median', proportiontocut=0.05) |
Perform Levene test for equal variances. [extrait de levene.__doc__] |
| levy(*args, **kwds) |
A Levy continuous random variable. [extrait de __doc__] |
| levy_l(*args, **kwds) |
A left-skewed Levy continuous random variable. [extrait de __doc__] |
| levy_stable(*args, **kwds) |
A Levy-stable continuous random variable. [extrait de __doc__] |
| linregress(x, y=None, alternative='two-sided') |
|
| loggamma(*args, **kwds) |
A log gamma continuous random variable. [extrait de __doc__] |
| logistic(*args, **kwds) |
A logistic (or Sech-squared) continuous random variable. [extrait de __doc__] |
| loglaplace(*args, **kwds) |
A log-Laplace continuous random variable. [extrait de __doc__] |
| lognorm(*args, **kwds) |
A lognormal continuous random variable. [extrait de __doc__] |
| logser(*args, **kwds) |
A Logarithmic (Log-Series, Series) discrete random variable. [extrait de __doc__] |
| loguniform(*args, **kwds) |
A loguniform or reciprocal continuous random variable. [extrait de __doc__] |
| lomax(*args, **kwds) |
A Lomax (Pareto of the second kind) continuous random variable. [extrait de __doc__] |
| mannwhitneyu(x, y, use_continuity=True, alternative='two-sided', axis=0, method='auto') |
Perform the Mann-Whitney U rank test on two independent samples. [extrait de mannwhitneyu.__doc__] |
| matrix_normal(mean=None, rowcov=1, colcov=1, seed=None) |
A matrix normal random variable. [extrait de __doc__] |
| maxwell(*args, **kwds) |
A Maxwell continuous random variable. [extrait de __doc__] |
| median_abs_deviation(x, axis=0, center=<function median at 0x7f5076a88f70>, scale=1.0, nan_policy='propagate') |
|
| median_absolute_deviation(*args, **kwds) |
`median_absolute_deviation` is deprecated, use `median_abs_deviation` instead! [extrait de median_absolute_deviation.__doc__] |
| median_test(*args, ties='below', correction=True, lambda_=1, nan_policy='propagate') |
Perform a Mood's median test. [extrait de median_test.__doc__] |
| mielke(*args, **kwds) |
A Mielke Beta-Kappa / Dagum continuous random variable. [extrait de __doc__] |
| mode(a, axis=0, nan_policy='propagate') |
Return an array of the modal (most common) value in the passed array. [extrait de mode.__doc__] |
| moment(a, moment=1, axis=0, nan_policy='propagate') |
Calculate the nth moment about the mean for a sample. [extrait de moment.__doc__] |
| mood(x, y, axis=0, alternative='two-sided') |
Perform Mood's test for equal scale parameters. [extrait de mood.__doc__] |
| moyal(*args, **kwds) |
A Moyal continuous random variable. [extrait de __doc__] |
| multinomial(n, p, seed=None) |
A multinomial random variable. [extrait de __doc__] |
| multiscale_graphcorr(x, y, compute_distance=<function _euclidean_dist at 0x7f505453aa60>, reps=1000, workers=1, is_twosamp=False, random_state=None) |
Computes the Multiscale Graph Correlation (MGC) test statistic. [extrait de multiscale_graphcorr.__doc__] |
| multivariate_hypergeom(m, n, seed=None) |
A multivariate hypergeometric random variable. [extrait de __doc__] |
| multivariate_normal(mean=None, cov=1, allow_singular=False, seed=None) |
A multivariate normal random variable. [extrait de __doc__] |
| multivariate_t(loc=None, shape=1, df=1, allow_singular=False, seed=None) |
A multivariate t-distributed random variable. [extrait de __doc__] |
| mvsdist(data) |
|
| nakagami(*args, **kwds) |
A Nakagami continuous random variable. [extrait de __doc__] |
| nbinom(*args, **kwds) |
A negative binomial discrete random variable. [extrait de __doc__] |
| ncf(*args, **kwds) |
A non-central F distribution continuous random variable. [extrait de __doc__] |
| nchypergeom_fisher(*args, **kwds) |
A Fisher's noncentral hypergeometric discrete random variable. [extrait de __doc__] |
| nchypergeom_wallenius(*args, **kwds) |
A Wallenius' noncentral hypergeometric discrete random variable. [extrait de __doc__] |
| nct(*args, **kwds) |
A non-central Student's t continuous random variable. [extrait de __doc__] |
| ncx2(*args, **kwds) |
A non-central chi-squared continuous random variable. [extrait de __doc__] |
| nhypergeom(*args, **kwds) |
A negative hypergeometric discrete random variable. [extrait de __doc__] |
| norm(*args, **kwds) |
A normal continuous random variable. [extrait de __doc__] |
| normaltest(a, axis=0, nan_policy='propagate') |
Test whether a sample differs from a normal distribution. [extrait de normaltest.__doc__] |
| norminvgauss(*args, **kwds) |
A Normal Inverse Gaussian continuous random variable. [extrait de __doc__] |
| obrientransform(*args) |
Compute the O'Brien transform on input data (any number of arrays). [extrait de obrientransform.__doc__] |
| page_trend_test(data, ranked=False, predicted_ranks=None, method='auto') |
|
| pareto(*args, **kwds) |
A Pareto continuous random variable. [extrait de __doc__] |
| pearson3(*args, **kwds) |
A pearson type III continuous random variable. [extrait de __doc__] |
| pearsonr(x, y) |
|
| percentileofscore(a, score, kind='rank') |
Compute the percentile rank of a score relative to a list of scores. [extrait de percentileofscore.__doc__] |
| planck(*args, **kwds) |
A Planck discrete exponential random variable. [extrait de __doc__] |
| pointbiserialr(x, y) |
Calculate a point biserial correlation coefficient and its p-value. [extrait de pointbiserialr.__doc__] |
| poisson(*args, **kwds) |
A Poisson discrete random variable. [extrait de __doc__] |
| power_divergence(f_obs, f_exp=None, ddof=0, axis=0, lambda_=None) |
Cressie-Read power divergence statistic and goodness of fit test. [extrait de power_divergence.__doc__] |
| powerlaw(*args, **kwds) |
A power-function continuous random variable. [extrait de __doc__] |
| powerlognorm(*args, **kwds) |
A power log-normal continuous random variable. [extrait de __doc__] |
| powernorm(*args, **kwds) |
A power normal continuous random variable. [extrait de __doc__] |
| ppcc_max(x, brack=(0.0, 1.0), dist='tukeylambda') |
Calculate the shape parameter that maximizes the PPCC. [extrait de ppcc_max.__doc__] |
| ppcc_plot(x, a, b, dist='tukeylambda', plot=None, N=80) |
Calculate and optionally plot probability plot correlation coefficient. [extrait de ppcc_plot.__doc__] |
| probplot(x, sparams=(), dist='norm', fit=True, plot=None, rvalue=False) |
|
| randint(*args, **kwds) |
A uniform discrete random variable. [extrait de __doc__] |
| rankdata(a, method='average', *, axis=None) |
Assign ranks to data, dealing with ties appropriately. [extrait de rankdata.__doc__] |
| ranksums(x, y, alternative='two-sided') |
Compute the Wilcoxon rank-sum statistic for two samples. [extrait de ranksums.__doc__] |
| rayleigh(*args, **kwds) |
A Rayleigh continuous random variable. [extrait de __doc__] |
| rdist(*args, **kwds) |
An R-distributed (symmetric beta) continuous random variable. [extrait de __doc__] |
| recipinvgauss(*args, **kwds) |
A reciprocal inverse Gaussian continuous random variable. [extrait de __doc__] |
| reciprocal(*args, **kwds) |
A loguniform or reciprocal continuous random variable. [extrait de __doc__] |
| relfreq(a, numbins=10, defaultreallimits=None, weights=None) |
Return a relative frequency histogram, using the histogram function. [extrait de relfreq.__doc__] |
| rice(*args, **kwds) |
A Rice continuous random variable. [extrait de __doc__] |
| rvs_ratio_uniforms(pdf, umax, vmin, vmax, size=1, c=0, random_state=None) |
|
| scoreatpercentile(a, per, limit=(), interpolation_method='fraction', axis=None) |
Calculate the score at a given percentile of the input sequence. [extrait de scoreatpercentile.__doc__] |
| sem(a, axis=0, ddof=1, nan_policy='propagate') |
Compute standard error of the mean. [extrait de sem.__doc__] |
| semicircular(*args, **kwds) |
A semicircular continuous random variable. [extrait de __doc__] |
| shapiro(x) |
Perform the Shapiro-Wilk test for normality. [extrait de shapiro.__doc__] |
| siegelslopes(y, x=None, method='hierarchical') |
|
| sigmaclip(a, low=4.0, high=4.0) |
Perform iterative sigma-clipping of array elements. [extrait de sigmaclip.__doc__] |
| skellam(*args, **kwds) |
A Skellam discrete random variable. [extrait de __doc__] |
| skew(a, axis=0, bias=True, nan_policy='propagate') |
Compute the sample skewness of a data set. [extrait de skew.__doc__] |
| skewcauchy(*args, **kwds) |
A skewed Cauchy random variable. [extrait de __doc__] |
| skewnorm(*args, **kwds) |
A skew-normal random variable. [extrait de __doc__] |
| skewtest(a, axis=0, nan_policy='propagate', alternative='two-sided') |
Test whether the skew is different from the normal distribution. [extrait de skewtest.__doc__] |
| somersd(x, y=None) |
Calculates Somers' D, an asymmetric measure of ordinal association. [extrait de somersd.__doc__] |
| spearmanr(a, b=None, axis=0, nan_policy='propagate', alternative='two-sided') |
Calculate a Spearman correlation coefficient with associated p-value. [extrait de spearmanr.__doc__] |
| special_ortho_group(dim=None, seed=None) |
A matrix-valued SO(N) random variable. [extrait de __doc__] |
| studentized_range(*args, **kwds) |
A studentized range continuous random variable. [extrait de __doc__] |
| t(*args, **kwds) |
A Student's t continuous random variable. [extrait de __doc__] |
| test(label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, tests=None, parallel=None) |
|
| theilslopes(y, x=None, alpha=0.95) |
|
| tiecorrect(rankvals) |
Tie correction factor for Mann-Whitney U and Kruskal-Wallis H tests. [extrait de tiecorrect.__doc__] |
| tmax(a, upperlimit=None, axis=0, inclusive=True, nan_policy='propagate') |
Compute the trimmed maximum. [extrait de tmax.__doc__] |
| tmean(a, limits=None, inclusive=(True, True), axis=None) |
Compute the trimmed mean. [extrait de tmean.__doc__] |
| tmin(a, lowerlimit=None, axis=0, inclusive=True, nan_policy='propagate') |
Compute the trimmed minimum. [extrait de tmin.__doc__] |
| trapezoid(*args, **kwds) |
A trapezoidal continuous random variable. [extrait de __doc__] |
| trapz(*args, **kwds) |
trapz is an alias for `trapezoid` [extrait de __doc__] |
| triang(*args, **kwds) |
A triangular continuous random variable. [extrait de __doc__] |
| trim1(a, proportiontocut, tail='right', axis=0) |
Slice off a proportion from ONE end of the passed array distribution. [extrait de trim1.__doc__] |
| trim_mean(a, proportiontocut, axis=0) |
Return mean of array after trimming distribution from both tails. [extrait de trim_mean.__doc__] |
| trimboth(a, proportiontocut, axis=0) |
Slice off a proportion of items from both ends of an array. [extrait de trimboth.__doc__] |
| truncexpon(*args, **kwds) |
A truncated exponential continuous random variable. [extrait de __doc__] |
| truncnorm(*args, **kwds) |
A truncated normal continuous random variable. [extrait de __doc__] |
| tsem(a, limits=None, inclusive=(True, True), axis=0, ddof=1) |
Compute the trimmed standard error of the mean. [extrait de tsem.__doc__] |
| tstd(a, limits=None, inclusive=(True, True), axis=0, ddof=1) |
Compute the trimmed sample standard deviation. [extrait de tstd.__doc__] |
| ttest_1samp(a, popmean, axis=0, nan_policy='propagate', alternative='two-sided') |
Calculate the T-test for the mean of ONE group of scores. [extrait de ttest_1samp.__doc__] |
| ttest_ind(a, b, axis=0, equal_var=True, nan_policy='propagate', permutations=None, random_state=None, alternative='two-sided', trim=0) |
|
| ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2, equal_var=True, alternative='two-sided') |
|
| ttest_rel(a, b, axis=0, nan_policy='propagate', alternative='two-sided') |
Calculate the t-test on TWO RELATED samples of scores, a and b. [extrait de ttest_rel.__doc__] |
| tukeylambda(*args, **kwds) |
A Tukey-Lamdba continuous random variable. [extrait de __doc__] |
| tvar(a, limits=None, inclusive=(True, True), axis=0, ddof=1) |
Compute the trimmed variance. [extrait de tvar.__doc__] |
| uniform(*args, **kwds) |
A uniform continuous random variable. [extrait de __doc__] |
| variation(a, axis=0, nan_policy='propagate', ddof=0) |
Compute the coefficient of variation. [extrait de variation.__doc__] |
| vonmises(*args, **kwds) |
A Von Mises continuous random variable. [extrait de __doc__] |
| vonmises_line(*args, **kwds) |
A Von Mises continuous random variable. [extrait de __doc__] |
| wald(*args, **kwds) |
A Wald continuous random variable. [extrait de __doc__] |
| wasserstein_distance(u_values, v_values, u_weights=None, v_weights=None) |
|
| weibull_max(*args, **kwds) |
Weibull maximum continuous random variable. [extrait de __doc__] |
| weibull_min(*args, **kwds) |
Weibull minimum continuous random variable. [extrait de __doc__] |
| weightedtau(x, y, rank=True, weigher=None, additive=True) |
Compute a weighted version of Kendall's :math:`\tau`. [extrait de weightedtau.__doc__] |
| wilcoxon(x, y=None, zero_method='wilcox', correction=False, alternative='two-sided', mode='auto') |
Calculate the Wilcoxon signed-rank test. [extrait de wilcoxon.__doc__] |
| wishart(df=None, scale=None, seed=None) |
A Wishart random variable. [extrait de __doc__] |
| wrapcauchy(*args, **kwds) |
A wrapped Cauchy continuous random variable. [extrait de __doc__] |
| yeojohnson(x, lmbda=None) |
Return a dataset transformed by a Yeo-Johnson power transformation. [extrait de yeojohnson.__doc__] |
| yeojohnson_llf(lmb, data) |
The yeojohnson log-likelihood function. [extrait de yeojohnson_llf.__doc__] |
| yeojohnson_normmax(x, brack=(-2, 2)) |
Compute optimal Yeo-Johnson transform parameter. [extrait de yeojohnson_normmax.__doc__] |
| yeojohnson_normplot(x, la, lb, plot=None, N=80) |
Compute parameters for a Yeo-Johnson normality plot, optionally show it. [extrait de yeojohnson_normplot.__doc__] |
| yulesimon(*args, **kwds) |
A Yule-Simon discrete random variable. [extrait de __doc__] |
| zipf(*args, **kwds) |
A Zipf (Zeta) discrete random variable. [extrait de __doc__] |
| zipfian(*args, **kwds) |
A Zipfian discrete random variable. [extrait de __doc__] |
| zmap(scores, compare, axis=0, ddof=0, nan_policy='propagate') |
|
| zscore(a, axis=0, ddof=0, nan_policy='propagate') |
|
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