Statistical functions for masked arrays (scipy.stats.mstats)ΒΆ

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.

argstoarray(*args) Constructs a 2D array from a group of sequences.
betai(a, b, x) Returns the incomplete beta function.
chisquare(f_obs[, f_exp, ddof, axis]) Calculates a one-way chi square test.
count_tied_groups(x[, use_missing]) Counts the number of tied values.
describe(a[, axis, ddof]) Computes several descriptive statistics of the passed array.
f_oneway(*args) Performs a 1-way ANOVA, returning an F-value and probability given any number of groups.
f_value_wilks_lambda(ER, EF, dfnum, dfden, a, b) Calculation of Wilks lambda F-statistic for multivariate data, per Maxwell & Delaney p.657.
find_repeats(arr) Find repeats in arr and return a tuple (repeats, repeat_count).
friedmanchisquare(*args) Friedman Chi-Square is a non-parametric, one-way within-subjects ANOVA.
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.
kruskalwallis(*args) Compute the Kruskal-Wallis H-test for independent samples The Kruskal-Wallis H-test tests the null hypothesis that the population median of all of the groups are equal.
ks_twosamp(data1, data2[, alternative]) Computes the Kolmogorov-Smirnov test on two samples.
kurtosis(a[, axis, fisher, bias]) Computes the kurtosis (Fisher or Pearson) of a dataset.
kurtosistest(a[, axis]) Tests whether a dataset has normal kurtosis This function tests the null hypothesis that the kurtosis of the population from which the sample was drawn is that of the normal distribution: kurtosis = 3(n-1)/(n+1).
linregress(*args) Calculate a regression line This computes a least-squares regression for two sets of measurements.
mannwhitneyu(x, y[, use_continuity]) Computes the Mann-Whitney statistic Missing values in x and/or y are discarded.
plotting_positions(data[, alpha, beta]) Returns plotting positions (or empirical percentile points) for the data.
mode(a[, axis]) Returns an array of the modal (most common) value in the passed array.
moment(a[, moment, axis]) Calculates the nth moment about the mean for a sample.
mquantiles(a[, prob, alphap, betap, axis, limit]) Computes empirical quantiles for a data array.
msign(x) Returns the sign of x, or 0 if x is masked.
normaltest(a[, axis]) Tests whether a sample differs from a normal distribution.
obrientransform(*args) Computes a transform on input data (any number of columns).
pearsonr(x, y) Calculates a Pearson correlation coefficient and the p-value for testing non-correlation.
plotting_positions(data[, alpha, beta]) Returns plotting positions (or empirical percentile points) for the data.
pointbiserialr(x, y) Calculates a point biserial correlation coefficient and the associated p-value.
rankdata(data[, axis, use_missing]) Returns the rank (also known as order statistics) of each data point along the given axis.
scoreatpercentile(data, per[, limit, ...]) Calculate the score at the given ‘per’ percentile of the sequence a.
sem(a[, axis, ddof]) Calculates the standard error of the mean of the input array.
signaltonoise(*args, **kwds) signaltonoise is deprecated!
skew(a[, axis, bias]) Computes the skewness of a data set.
skewtest(a[, axis]) Tests whether the skew is different from the normal distribution.
spearmanr(x, y[, use_ties]) Calculates a Spearman rank-order correlation coefficient and the p-value to test for non-correlation.
theilslopes(y[, x, alpha]) Computes the Theil-Sen estimator for a set of points (x, y).
threshold(a[, threshmin, threshmax, newval]) Clip array to a given value.
tmax(a, upperlimit[, axis, inclusive]) Compute the trimmed maximum This function computes the maximum value of an array along a given axis, while ignoring values larger than a specified upper limit.
tmean(a[, limits, inclusive]) Compute the trimmed mean.
tmin(a[, lowerlimit, axis, inclusive]) Compute the trimmed minimum This function finds the miminum value of an array a along the specified axis, but only considering values greater than a specified lower limit.
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.
trimboth(data[, proportiontocut, inclusive, ...]) Trims the smallest and largest data values.
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.
tsem(a[, limits, inclusive]) Compute the trimmed standard error of the mean.
ttest_onesamp(a, popmean[, axis]) Calculates the T-test for the mean of ONE group of scores.
ttest_ind(a, b[, axis]) Calculates the T-test for the means of TWO INDEPENDENT samples of scores.
ttest_onesamp(a, popmean[, axis]) Calculates the T-test for the mean of ONE group of scores.
ttest_rel(a, b[, axis]) Calculates the T-test on TWO RELATED samples of scores, a and b.
tvar(a[, limits, inclusive]) Compute the trimmed variance This function computes the sample variance of an array of values, while ignoring values which are outside of given limits.
variation(a[, axis]) Computes the coefficient of variation, the ratio of the biased standard deviation to the mean.
winsorize(a[, limits, inclusive, inplace, axis]) Returns a Winsorized version of the input array.
zmap(scores, compare[, axis, ddof]) Calculates the relative z-scores.
zscore(a[, axis, ddof]) Calculates the z score of each value in the sample, relative to the sample mean and standard deviation.