# Statistics¶

## Extremal values¶

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

## Averages and variances¶

 average(a[, axis, weights, returned]) Compute the weighted average along the specified axis. mean(a[, axis, dtype, out]) Compute the arithmetic mean along the specified axis. median(a[, axis, out, overwrite_input]) Compute the median along the specified axis. std(a[, axis, dtype, out, ddof]) Compute the standard deviation along the specified axis. var(a[, axis, dtype, out, ddof]) Compute the variance along the specified axis.

## 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]) Count number of occurrences of each value in array of non-negative ints. digitize(x, bins) Return the indices of the bins to which each value in input array belongs.

### Table Of Contents

#### Previous topic

numpy.binary_repr

numpy.amin