scikitplot.externals#

External dependencies vendored for stability and reproducibility for Scikit-Plots.

This package contains third-party Python modules that Scikit-Plots depends on for plotting, statistics, array APIs, and numerical utilities. These modules are bundled directly to ensure consistent behavior across different environments, even when the original libraries are missing or version-incompatible.

User guide. See the Externals (experimental) section for further details.

data-apis array_api_compat as submodule.#

NumPy Array API compatibility library

This is a small wrapper around NumPy, CuPy, JAX, sparse and others that are compatible with the Array API standard https://data-apis.org/array-api/latest/. See also NEP 47 https://numpy.org/neps/nep-0047-array-api-standard.html.

Unlike array_api_strict, this is not a strict minimal implementation of the Array API, but rather just an extension of the main NumPy namespace with changes needed to be compliant with the Array API. See https://numpy.org/doc/stable/reference/array_api.html for a full list of changes. In particular, unlike array_api_strict, this package does not use a separate Array object, but rather just uses numpy.ndarray directly.

Library authors using the Array API may wish to test against array_api_strict to ensure they are not using functionality outside of the standard, but prefer this implementation for the default when working with NumPy arrays.

User guide. See the Python Data API Standards: array_api_compat (experimental) section for further details.

array_api_compat

NumPy Array API compatibility library

data-apis array_api_extra as submodule.#

Extra array functions built on top of the array API standard.

User guide. See the Python Data API Standards: array_api_extra (experimental) section for further details.

array_api_extra

Extra array functions built on top of the array API standard.

Real probability scales for matplotlib.#

Real probability scales for matplotlib.

User guide. See the Probability Plots (experimental) section for further details.

`_probscale.ProbScale`	A probability scale for matplotlib Axes.
`_probscale.probplot`	Probability, percentile, and quantile plots.
`_probscale.plot_pos`	Compute the plotting positions for a dataset.
`_probscale.fit_line`	Fits a line to x-y data in various forms (linear, log, prob scales).

Seaborn as submodule.#

User guide. See the Seaborn (experimental) section for further details.

_seaborn

Matplotlib Sphinxext Ext.#

sphinxext.

Notes

matplotlib/matplotlib

User guide. See the Matplotlib Sphinx extensions for embedded plots (experimental) section for further details.

`_sphinxext`	sphinxext.
`_sphinxext.figmpl_directive`	Add a `figure-mpl` directive that is a responsive version of `figure`.
`_sphinxext.mathmpl`	A role and directive to display mathtext in Sphinx.
`_sphinxext.plot_directive`	A directive for including a Matplotlib plot in a Sphinx document.
`_sphinxext.roles`	Custom roles for the Matplotlib documentation.
`_sphinxext.sphinx_tabs_patch`	Sphinx compatibility shim for docutils `backrefs`.

Tweedie Family.#

Tweedie Distribution Module.

This module implements the Tweedie distribution, a member of the exponential dispersion model (EDM) family, using SciPy’s rv_continuous class.

It is especially useful for modeling claim amounts in the insurance industry, where data often exhibit a mixture of zeroes and positive continuous values.

The primary focus of this package is the compound-Poisson behavior of the Tweedie distribution, particularly in the range 1 < p < 2. However, it supports calculations for all valid values of the shape parameter p.

User guide. See the Tweedie Family section for further details.

`_tweedie`	Tweedie Distribution Module.
`_tweedie.tweedie`	An instance of `tweedie_gen`, providing Tweedie distribution functionality.
`_tweedie.tweedie_gen`	A Tweedie continuous random variable inherited `scipy.stats.rv_continuous`.