Statistics & Binning

Statistical tools and binning containers.

Note

All the containers documented here can be populated in parallel from Dask arrays using the helpers in Dask Integration.

Descriptive Statistics

DescriptiveStatistics(values[, weights, ...])

Univariate descriptive statistics.

Binning Containers

Binning1D(x[, range, dtype])	Create a 1D binning for grouping values into bins on a vector.
Binning1DFloat32(self, x[, range])	Create a 1D binning for grouping values into bins on a vector.
Binning1DFloat64(self, x[, range])	Create a 1D binning for grouping values into bins on a vector.
Binning2D(x, y[, spheroid, dtype])	Create a 2D binning for grouping values into bins on a grid.
Binning2DFloat32(self, x, y[, spheroid])	Create a 2D binning for grouping values into bins on a grid.
Binning2DFloat64(self, x, y[, spheroid])	Create a 2D binning for grouping values into bins on a grid.

Histograms

Histogram2D(x, y[, compression, dtype])	Create a 2D histogram for binning continuous values into a grid using TDigest.
Histogram2DFloat32(self, x, y[, compression])	Create a 2D histogram for binning continuous values into a grid using TDigest.
Histogram2DFloat64(self, x, y[, compression])	Create a 2D histogram for binning continuous values into a grid using TDigest.

Quantile Estimation

TDigest(values[, weights, axis, ...])	T-Digest for incremental quantile estimation.
TDigestFloat32(self, values[, device, device])	T-Digest for incremental quantile estimation.
TDigestFloat64(self, values[, device, device])	T-Digest for incremental quantile estimation.