pdstools.valuefinder.Plots
==========================

.. py:module:: pdstools.valuefinder.Plots


Attributes
----------

.. autoapisummary::

   pdstools.valuefinder.Plots.logger
   pdstools.valuefinder.Plots.COLORSCALE_TYPES
   pdstools.valuefinder.Plots.Figure
   pdstools.valuefinder.Plots.T
   pdstools.valuefinder.Plots.P


Classes
-------

.. autoapisummary::

   pdstools.valuefinder.Plots.Plots


Module Contents
---------------

.. py:data:: logger

.. py:data:: COLORSCALE_TYPES

.. py:data:: Figure

.. py:data:: T

.. py:data:: P

.. py:class:: Plots(vf: pdstools.valuefinder.ValueFinder.ValueFinder)

   Bases: :py:obj:`pdstools.utils.namespaces.LazyNamespace`


   .. py:attribute:: dependencies
      :value: ['plotly']



   .. py:attribute:: vf


   .. py:method:: funnel_chart(by: str, query: pdstools.utils.types.QUERY | None = None, return_df: Literal[False] = False) -> Figure
                  funnel_chart(by: str, query: pdstools.utils.types.QUERY | None = None, return_df: Literal[True] = True) -> polars.LazyFrame


   .. py:method:: propensity_distribution(sample_size: int = 10000) -> Figure


   .. py:method:: propensity_threshold(sample_size: int = 10000, stage='Eligibility') -> Figure


   .. py:method:: _get_thresholds(thresholds: collections.abc.Iterable[float] | None = None, quantiles: collections.abc.Iterable[float] | None = None, default: collections.abc.Iterable[float] | None = None) -> collections.abc.Iterable[float]


   .. py:method:: pie_charts(*, thresholds: collections.abc.Iterable[float] | None = None, quantiles: collections.abc.Iterable[float] | None = None, rounding: int = 3)


   .. py:method:: distribution_per_threshold(*, thresholds: collections.abc.Iterable[float] | None = None, quantiles: collections.abc.Iterable[float] | None = None, rounding: int = 3)