| describe(a[, axis, ddof, bias, nan_policy]) |
Computes several descriptive statistics of the passed array. |
| gmean(a[, axis, dtype]) |
Compute the geometric mean along the specified axis. |
| hmean(a[, axis, dtype]) |
Calculates the harmonic mean along the specified axis. |
| kurtosis(a[, axis, fisher, bias, nan_policy]) |
Computes the kurtosis (Fisher or Pearson) of a dataset. |
| kurtosistest(a[, axis, nan_policy]) |
Tests whether a dataset has normal kurtosis |
| mode(a[, axis, nan_policy]) |
Returns an array of the modal (most common) value in the passed array. |
| moment(a[, moment, axis, nan_policy]) |
Calculates the nth moment about the mean for a sample. |
| normaltest(a[, axis, nan_policy]) |
Tests whether a sample differs from a normal distribution. |
| skew(a[, axis, bias, nan_policy]) |
Computes the skewness of a data set. |
| skewtest(a[, axis, nan_policy]) |
Tests whether the skew is different from the normal distribution. |
| 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]) |
Computes 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. |
| cumfreq(a[, numbins, defaultreallimits, weights]) |
Returns a cumulative frequency histogram, using the histogram function. |
| histogram2(*args, **kwds) |
histogram2 is deprecated! |
| histogram(*args, **kwds) |
histogram is deprecated! |
| itemfreq(a) |
Returns a 2-D array of item frequencies. |
| 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]) |
Returns 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. |
| obrientransform(*args) |
Computes the O’Brien transform on input data (any number of arrays). |
| signaltonoise(*args, **kwds) |
signaltonoise is deprecated! |
| 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. |
| sem(a[, axis, ddof, nan_policy]) |
Calculates the standard error of the mean (or standard error of measurement) of the values in 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. |
| iqr(x[, axis, rng, scale, nan_policy, …]) |
Compute the interquartile range of the data along the specified axis. |
| sigmaclip(a[, low, high]) |
Iterative sigma-clipping of array elements. |
| threshold(*args, **kwds) |
threshold is deprecated! |
| 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. |
| f_oneway(*args) |
Performs a 1-way ANOVA. |
| pearsonr(x, y) |
Calculates a Pearson correlation coefficient and the p-value for testing non-correlation. |
| spearmanr(a[, b, axis, nan_policy]) |
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[, initial_lexsort, nan_policy]) |
Calculates Kendall’s tau, a correlation measure for ordinal data. |
| weightedtau(x, y[, rank, weigher, additive]) |
Computes a weighted version of Kendall’s ┱┱. |
| linregress(x[, y]) |
Calculate a linear least-squares regression for two sets of measurements. |
| theilslopes(y[, x, alpha]) |
Computes the Theil-Sen estimator for a set of points (x, y). |
| f_value(*args, **kwds) |
f_value is deprecated! |
| ttest_1samp(a, popmean[, axis, nan_policy]) |
Calculates the T-test for the mean of ONE group of scores. |
| ttest_ind(a, b[, axis, equal_var, nan_policy]) |
Calculates 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]) |
Calculates 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]) |
Calculates 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) |
Computes the Kolmogorov-Smirnov statistic on 2 samples. |
| mannwhitneyu(x, y[, use_continuity, alternative]) |
Computes 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) |
Computes the Friedman test for repeated measurements |
| combine_pvalues(pvalues[, method, weights]) |
Methods for combining the p-values of independent tests bearing upon the same hypothesis. |
| ss(*args, **kwds) |
ss is deprecated! |
| square_of_sums(*args, **kwds) |
square_of_sums is deprecated! |
| 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[, a, reta]) |
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. |
| 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. |
| entropy(pk[, qk, base]) |
Calculate the entropy of a distribution for given probability values. |
| chisqprob(*args, **kwds) |
chisqprob is deprecated! |
| betai(*args, **kwds) |
betai is deprecated! |
