Statistics#

Order statistics#

ptp(a[, axis, out, keepdims])

Range of values (maximum - minimum) along an axis.

percentile(a, q[, axis, out, ...])

Compute the q-th percentile of the data along the specified axis.

nanpercentile(a, q[, axis, out, ...])

Compute the qth percentile of the data along the specified axis, while ignoring nan values.

quantile(a, q[, axis, out, overwrite_input, ...])

Compute the q-th quantile of the data along the specified axis.

nanquantile(a, q[, axis, out, ...])

Compute the qth quantile of the data along the specified axis, while ignoring nan values.

Averages and variances#

median(a[, axis, out, overwrite_input, keepdims])

Compute the median along the specified axis.

average(a[, axis, weights, returned, keepdims])

Compute the weighted average along the specified axis.

mean(a[, axis, dtype, out, keepdims, where])

Compute the arithmetic mean along the specified axis.

std(a[, axis, dtype, out, ddof, keepdims, ...])

Compute the standard deviation along the specified axis.

var(a[, axis, dtype, out, ddof, keepdims, ...])

Compute the variance along the specified axis.

nanmedian(a[, axis, out, overwrite_input, ...])

Compute the median along the specified axis, while ignoring NaNs.

nanmean(a[, axis, dtype, out, keepdims, where])

Compute the arithmetic mean along the specified axis, ignoring NaNs.

nanstd(a[, axis, dtype, out, ddof, ...])

Compute the standard deviation along the specified axis, while ignoring NaNs.

nanvar(a[, axis, dtype, out, ddof, ...])

Compute the variance along the specified axis, while ignoring NaNs.

Correlating#

corrcoef(x[, y, rowvar, dtype])

Return Pearson product-moment correlation coefficients.

correlate(a, v[, mode])

Cross-correlation of two 1-dimensional sequences.

cov(m[, y, rowvar, bias, ddof, fweights, ...])

Estimate a covariance matrix, given data and weights.

Histograms#

histogram(a[, bins, range, density, weights])

Compute the histogram of a dataset.

histogram2d(x, y[, bins, range, density, ...])

Compute the bi-dimensional histogram of two data samples.

histogramdd(sample[, bins, range, density, ...])

Compute the multidimensional histogram of some data.

bincount(x, /[, weights, minlength])

Count number of occurrences of each value in array of non-negative ints.

histogram_bin_edges(a[, bins, range, weights])

Function to calculate only the edges of the bins used by the histogram function.

digitize(x, bins[, right])

Return the indices of the bins to which each value in input array belongs.