scikitplot.externals#
External, bundled dependencies.
User guide. See the externals-index section for further details.
Real probability scales for matplotlib.#
Real probability scales for matplotlib.
User guide. See the Probability Plots (experimental) section for further details.
A probability scale for matplotlib Axes. |
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Probability, percentile, and quantile plots. |
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Compute the plotting positions for a dataset. |
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Fits a line to x-y data in various forms (linear, log, prob scales). |
Seaborn as submodule.#
seaborn: statistical data visualization
Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
Documentation is available in the docstrings and online at https://seaborn.pydata.org/.
User guide. See the Seaborn (experimental) section for further details.
seaborn: statistical data visualization |
Matplotlib Sphinxext Ext.#
sphinxext.
References
User guide. See the Matplotlib Sphinx extensions for embedded plots (experimental) section for further details.
sphinxext. |
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 Distribution Module |
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.
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.
Extra array functions built on top of the array API standard. |