# 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.

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