# Statistics

The following tables show statistical functions provided by NLCPy.

## Order Statistics

 `nlcpy.amax` Returns the maximum of an array or maximum along an axis. `nlcpy.amin` Returns the minimum of an array or minimum along an axis. `nlcpy.nanmax` Returns maximum of an array or maximum along an axis, ignoring any NaNs. `nlcpy.nanmin` Returns minimum of an array or minimum along an axis, ignoring any NaNs. `nlcpy.ptp` Range of values (maximum - minimum) along an axis. `nlcpy.percentile` Computes the q-th percentile of the data along the specified axis. `nlcpy.nanpercentile` Computes the q-th percentile of the data along the specified axis, while ignoring nan values. `nlcpy.quantile` Computes the q-th quantile of the data along the specified axis. `nlcpy.nanquantile` Computes the q-th quantile of the data along the specified axis, while ignoring nan values.

## Averages and Variances

 `nlcpy.average` Computes the weighted average along the specified axis. `nlcpy.mean` Computes the arithmetic mean along the specified axis. `nlcpy.median` Computes the median along the specified axis. `nlcpy.nanmedian` Computes the median along the specified axis, while ignoring NaNs. `nlcpy.nanmean` Computes the arithmetic mean along the specified axis, ignoring NaNs. `nlcpy.nanstd` Computes the standard deviation along the specified axis, while ignoring NaNs. `nlcpy.nanvar` Computes the variance along the specified axis, while ignoring NaNs. `nlcpy.std` Computes the standard deviation along the specified axis. `nlcpy.var` Computes the variance along the specified axis.

## Correlating

 `nlcpy.correlate` Cross-correlation of two 1-dimensional sequences. `nlcpy.corrcoef` Returns Pearson product-moment correlation coefficients. `nlcpy.cov` Estimates a covariance matrix, given data and weights.

## Histograms

 `nlcpy.histogram` Computes the histogram of a set of data. `nlcpy.histogram2d` Computes the bi-dimensional histogram of two data samples. `nlcpy.histogramdd` Computes the multidimensional histogram of some data. `nlcpy.bincount` Counts number of occurrences of each value in array of non-negative ints. `nlcpy.histogram_bin_edges` Function to calculate only the edges of the bins used by `histogram()` function. `nlcpy.digitize` Return is the indices of the bins to which each value in input array belongs.