### Table Of Contents

- Statistics
  - Order statistics
  - Averages and variances
  - Correlating
  - Histograms

#### Next topic

numpy.amin

# 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.

## 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.
`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 correlation coefficients.
`correlate`(a, v[, mode, old_behavior]) | Cross-correlation of two 1-dimensional sequences.
`cov`(m[, y, rowvar, bias, ddof]) | Estimate a covariance matrix, given data.

## 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.