Skip to main content
`Ctrl`+`K`

# 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, bias, ddof, 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.