| argstoarray(*args) |
|
| brunnermunzel(x, y, alternative='two-sided', distribution='t') |
|
| chisquare(f_obs, f_exp=None, ddof=0, axis=0) |
Calculate a one-way chi-square test. [extrait de chisquare.__doc__] |
| compare_medians_ms(group_1, group_2, axis=None) |
|
| count_tied_groups(x, use_missing=False) |
|
| describe(a, axis=0, ddof=0, bias=True) |
|
| f_oneway(*args) |
|
| find_repeats(arr) |
Find repeats in arr and return a tuple (repeats, repeat_count). [extrait de find_repeats.__doc__] |
| friedmanchisquare(*args) |
Friedman Chi-Square is a non-parametric, one-way within-subjects ANOVA. [extrait de friedmanchisquare.__doc__] |
| gmean(a, axis=0, dtype=None, weights=None) |
Compute the geometric mean along the specified axis. [extrait de gmean.__doc__] |
| hdmedian(data, axis=-1, var=False) |
|
| hdquantiles(data, prob=[0.25, 0.5, 0.75], axis=None, var=False) |
|
| hdquantiles_sd(data, prob=[0.25, 0.5, 0.75], axis=None) |
|
| hmean(a, axis=0, dtype=None) |
Calculate the harmonic mean along the specified axis. [extrait de hmean.__doc__] |
| idealfourths(data, axis=None) |
|
| kendalltau(x, y, use_ties=True, use_missing=False, method='auto') |
|
| kendalltau_seasonal(x) |
|
| kruskal(*args) |
|
| ks_1samp(x, cdf, args=(), alternative='two-sided', mode='auto') |
|
| ks_2samp(data1, data2, alternative='two-sided', mode='auto') |
|
| kstest(data1, data2, args=(), alternative='two-sided', mode='auto') |
|
| kurtosis(a, axis=0, fisher=True, bias=True) |
|
| kurtosistest(a, axis=0) |
|
| linregress(x, y=None) |
|
| mannwhitneyu(x, y, use_continuity=True) |
|
| median_cihs(data, alpha=0.05, axis=None) |
|
| mjci(data, prob=[0.25, 0.5, 0.75], axis=None) |
|
| mode(a, axis=0) |
|
| moment(a, moment=1, axis=0) |
|
| mquantiles(a, prob=[0.25, 0.5, 0.75], alphap=0.4, betap=0.4, axis=None, limit=()) |
|
| mquantiles_cimj(data, prob=[0.25, 0.5, 0.75], alpha=0.05, axis=None) |
|
| msign(x) |
Returns the sign of x, or 0 if x is masked. [extrait de msign.__doc__] |
| normaltest(a, axis=0) |
|
| obrientransform(*args) |
|
| pearsonr(x, y) |
|
| plotting_positions(data, alpha=0.4, beta=0.4) |
|
| pointbiserialr(x, y) |
Calculates a point biserial correlation coefficient and its p-value. [extrait de pointbiserialr.__doc__] |
| rankdata(data, axis=None, use_missing=False) |
Returns the rank (also known as order statistics) of each data point [extrait de rankdata.__doc__] |
| rsh(data, points=None) |
|
| scoreatpercentile(data, per, limit=(), alphap=0.4, betap=0.4) |
Calculate the score at the given 'per' percentile of the [extrait de scoreatpercentile.__doc__] |
| sem(a, axis=0, ddof=1) |
|
| sen_seasonal_slopes(x) |
|
| siegelslopes(y, x=None, method='hierarchical') |
|
| skew(a, axis=0, bias=True) |
|
| skewtest(a, axis=0) |
|
| spearmanr(x, y=None, use_ties=True, axis=None, nan_policy='propagate', alternative='two-sided') |
|
| theilslopes(y, x=None, alpha=0.95) |
|
| tmax(a, upperlimit=None, axis=0, inclusive=True) |
|
| tmean(a, limits=None, inclusive=(True, True), axis=None) |
|
| tmin(a, lowerlimit=None, axis=0, inclusive=True) |
|
| trim(a, limits=None, inclusive=(True, True), relative=False, axis=None) |
|
| trima(a, limits=None, inclusive=(True, True)) |
|
| trimboth(data, proportiontocut=0.2, inclusive=(True, True), axis=None) |
|
| trimmed_mean(a, limits=(0.1, 0.1), inclusive=(1, 1), relative=True, axis=None) |
Returns the trimmed mean of the data along the given axis. [extrait de trimmed_mean.__doc__] |
| trimmed_mean_ci(data, limits=(0.2, 0.2), inclusive=(True, True), alpha=0.05, axis=None) |
|
| trimmed_std(a, limits=(0.1, 0.1), inclusive=(1, 1), relative=True, axis=None, ddof=0) |
Returns the trimmed standard deviation of the data along the given axis. [extrait de trimmed_std.__doc__] |
| trimmed_stde(a, limits=(0.1, 0.1), inclusive=(1, 1), axis=None) |
|
| trimmed_var(a, limits=(0.1, 0.1), inclusive=(1, 1), relative=True, axis=None, ddof=0) |
Returns the trimmed variance of the data along the given axis. [extrait de trimmed_var.__doc__] |
| trimr(a, limits=None, inclusive=(True, True), axis=None) |
|
| trimtail(data, proportiontocut=0.2, tail='left', inclusive=(True, True), axis=None) |
|
| tsem(a, limits=None, inclusive=(True, True), axis=0, ddof=1) |
|
| ttest_1samp(a, popmean, axis=0) |
|
| ttest_ind(a, b, axis=0, equal_var=True) |
|
| ttest_rel(a, b, axis=0) |
|
| tvar(a, limits=None, inclusive=(True, True), axis=0, ddof=1) |
|
| variation(a, axis=0, ddof=0) |
|
| winsorize(a, limits=None, inclusive=(True, True), inplace=False, axis=None, nan_policy='propagate') |
Returns a Winsorized version of the input array. [extrait de winsorize.__doc__] |
| zmap(scores, compare, axis=0, ddof=0, nan_policy='propagate') |
|
| zscore(a, axis=0, ddof=0, nan_policy='propagate') |
|
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