This is documentation for an old release of NumPy (version 1.12.0). Read this page Search for this page in the documentation of the latest stable release (version > 1.17).

Statistics

Order statistics

amin(a[, axis, out, keepdims])	Return the minimum of an array or minimum along an axis.
amax(a[, axis, out, keepdims])	Return the maximum of an array or maximum along an axis.
nanmin(a[, axis, out, keepdims])	Return minimum of an array or minimum along an axis, ignoring any NaNs.
nanmax(a[, axis, out, keepdims])	Return the maximum of an array or maximum along an axis, ignoring any NaNs.
ptp(a[, axis, out])	Range of values (maximum - minimum) along an axis.
percentile(a, q[, axis, out, ...])	Compute the qth 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.

Averages and variances

median(a[, axis, out, overwrite_input, keepdims])	Compute the median along the specified axis.
average(a[, axis, weights, returned])	Compute the weighted average along the specified axis.
mean(a[, axis, dtype, out, keepdims])	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])	Compute the arithmetic mean along the specified axis, ignoring NaNs.
nanstd(a[, axis, dtype, out, ddof, keepdims])	Compute the standard deviation along the specified axis, while ignoring NaNs.
nanvar(a[, axis, dtype, out, ddof, keepdims])	Compute the variance along the specified axis, while ignoring NaNs.

Correlating

corrcoef(x[, y, rowvar, bias, ddof])	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, normed, weights, ...])	Compute the histogram of a set of data.
histogram2d(x, y[, bins, range, normed, weights])	Compute the bi-dimensional histogram of two data samples.
histogramdd(sample[, bins, range, normed, ...])	Compute the multidimensional histogram of some data.
bincount(x[, weights, minlength])	Count number of occurrences of each value in array of non-negative ints.
digitize(x, bins[, right])	Return the indices of the bins to which each value in input array belongs.