Skip to main content
`Ctrl`+`K`

# Statistical functions for masked arrays (`scipy.stats.mstats`)

This module contains a large number of statistical functions that can be used with masked arrays.

Most of these functions are similar to those in `scipy.stats` but might have small differences in the API or in the algorithm used. Since this is a relatively new package, some API changes are still possible.

## Summary statistics

`describe`(a[, axis, ddof, bias]) | Computes 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.
`kurtosis`(a[, axis, fisher, bias]) | Computes the kurtosis (Fisher or Pearson) of a dataset.
`mode`(a[, axis]) | Returns an array of the modal (most common) value in the passed array.
`mquantiles`(a[, prob, alphap, betap, axis, limit]) | Computes empirical quantiles for a data array.
`hdmedian`(data[, axis, var]) | Returns the Harrell-Davis estimate of the median along the given axis.
`hdquantiles`(data[, prob, axis, var]) | Computes quantile estimates with the Harrell-Davis method.
`hdquantiles_sd`(data[, prob, axis]) | The standard error of the Harrell-Davis quantile estimates by jackknife.
`idealfourths`(data[, axis]) | Returns an estimate of the lower and upper quartiles.
`plotting_positions`(data[, alpha, beta]) | Returns plotting positions (or empirical percentile points) for the data.
`meppf`(data[, alpha, beta]) | Returns plotting positions (or empirical percentile points) for the data.
`moment`(a[, moment, axis]) | Calculates the nth moment about the mean for a sample.
`skew`(a[, axis, bias]) | Computes the skewness of a data set.
`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
`tsem`(a[, limits, inclusive, axis, ddof]) | Compute the trimmed standard error of the mean.
`variation`(a[, axis, ddof]) | Compute the coefficient of variation.
`find_repeats`(arr) | Find repeats in arr and return a tuple (repeats, repeat_count).
`sem`(a[, axis, ddof]) | Calculates the standard error of the mean of the input array.
`trimmed_mean`(a[, limits, inclusive, ...]) | Returns the trimmed mean of the data along the given axis.
`trimmed_mean_ci`(data[, limits, inclusive, ...]) | Selected confidence interval of the trimmed mean along the given axis.
`trimmed_std`(a[, limits, inclusive, ...]) | Returns the trimmed standard deviation of the data along the given axis.
`trimmed_var`(a[, limits, inclusive, ...]) | Returns the trimmed variance of the data along the given axis.

## Frequency statistics

`scoreatpercentile`(data, per[, limit, ...]) | Calculate the score at the given 'per' percentile of the sequence a.

## Correlation functions

`f_oneway`(*args) | Performs a 1-way ANOVA, returning an F-value and probability given any number of groups.
`pearsonr`(x, y) | Pearson correlation coefficient and p-value for testing non-correlation.
`spearmanr`(x[, y, use_ties, axis, ...]) | Calculates a Spearman rank-order correlation coefficient and the p-value to test for non-correlation.
`pointbiserialr`(x, y) | Calculates a point biserial correlation coefficient and its p-value.
`kendalltau`(x, y[, use_ties, use_missing, ...]) | Computes Kendall's rank correlation tau on two variables x and y.
`kendalltau_seasonal`(x) | Computes a multivariate Kendall's rank correlation tau, for seasonal data.
`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, method]) | Computes the Theil-Sen estimator for a set of points (x, y).
`sen_seasonal_slopes`(x) | Computes seasonal Theil-Sen and Kendall slope estimators.

## Statistical tests

`ttest_1samp`(a, popmean[, axis, alternative]) | Calculates the T-test for the mean of ONE group of scores.
`ttest_onesamp`(a, popmean[, axis, alternative]) | Calculates the T-test for the mean of ONE group of scores.
`ttest_ind`(a, b[, axis, equal_var, alternative]) | Calculates the T-test for the means of TWO INDEPENDENT samples of scores.
`ttest_rel`(a, b[, axis, alternative]) | Calculates the T-test on TWO RELATED samples of scores, a and b.
`chisquare`(f_obs[, f_exp, ddof, axis, ...]) | Perform Pearson's chi-squared test.
`kstest`(data1, data2[, args, alternative, method]) | Performs the Kolmogorov-Smirnov test for goodness of fit.
`ks_2samp`(data1, data2[, alternative, method]) | Computes the Kolmogorov-Smirnov test on two samples.
`ks_1samp`(x, cdf[, args, alternative, method]) | Computes the Kolmogorov-Smirnov test on one sample of masked values.
`ks_twosamp`(data1, data2[, alternative, method]) | Computes the Kolmogorov-Smirnov test on two samples.
`mannwhitneyu`(x, y[, use_continuity]) | Computes the Mann-Whitney statistic
`rankdata`(data[, axis, use_missing]) | Returns the rank (also known as order statistics) of each data point along the given axis.
`kruskal`(*samples) | Compute the Kruskal-Wallis H-test for independent samples
`kruskalwallis`(*samples) | Compute the Kruskal-Wallis H-test for independent samples
`friedmanchisquare`(*args) | Friedman Chi-Square is a non-parametric, one-way within-subjects ANOVA.
`brunnermunzel`(x, y[, alternative, distribution]) | Compute the Brunner-Munzel test on samples x and y.
`skewtest`(a[, axis, alternative]) | Tests whether the skew is different from the normal distribution.
`kurtosistest`(a[, axis, alternative]) | Tests whether a dataset has normal kurtosis
`normaltest`(a[, axis]) | Tests whether a sample differs from a normal distribution.

## Transformations

`obrientransform`(*args) | Computes a transform on input data (any number of columns).
`trim`(a[, limits, inclusive, relative, axis]) | Trims an array by masking the data outside some given limits.
`trima`(a[, limits, inclusive]) | Trims an array by masking the data outside some given limits.
`trimmed_stde`(a[, limits, inclusive, axis]) | Returns the standard error of the trimmed mean along the given axis.
`trimr`(a[, limits, inclusive, axis]) | Trims an array by masking some proportion of the data on each end.
`trimtail`(data[, proportiontocut, tail, ...]) | Trims the data by masking values from one tail.
`trimboth`(data[, proportiontocut, inclusive, ...]) | Trims the smallest and largest data values.
`winsorize`(a[, limits, inclusive, inplace, ...]) | Returns a Winsorized version of the input array.
`zmap`(scores, compare[, axis, ddof, nan_policy]) | Calculate the relative z-scores.
`zscore`(a[, axis, ddof, nan_policy]) | Compute the z score.

## Other

`argstoarray`(*args) | Constructs a 2D array from a group of sequences.
`count_tied_groups`(x[, use_missing]) | Counts the number of tied values.
`msign`(x) | Returns the sign of x, or 0 if x is masked.
`compare_medians_ms`(group_1, group_2[, axis]) | Compares the medians from two independent groups along the given axis.
`median_cihs`(data[, alpha, axis]) | Computes the alpha-level confidence interval for the median of the data.
`mjci`(data[, prob, axis]) | Returns the Maritz-Jarrett estimators of the standard error of selected experimental quantiles of the data.
`mquantiles_cimj`(data[, prob, alpha, axis]) | Computes the alpha confidence interval for the selected quantiles of the data, with Maritz-Jarrett estimators.
`rsh`(data[, points]) | Evaluates Rosenblatt's shifted histogram estimators for each data point.