pdstools.valuefinder.Plots

Attributes

logger

COLORSCALE_TYPES

Figure

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Classes

Plots

Module Contents

logger
COLORSCALE_TYPES
Figure
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class Plots(vf: pdstools.valuefinder.ValueFinder.ValueFinder)

Bases: pdstools.utils.namespaces.LazyNamespace

Parameters:

vf (pdstools.valuefinder.ValueFinder.ValueFinder)

dependencies = ['plotly']
vf
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
propensity_distribution(sample_size: int = 10000) → Figure
Parameters:

sample_size (int)

Return type:

Figure

propensity_threshold(sample_size: int = 10000, stage='Eligibility') → Figure
Parameters:

sample_size (int)

Return type:

Figure

_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]
Parameters:
Return type:

collections.abc.Iterable[float]

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