data_describe.core.distributions¶
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Distribution Plots. |
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class
data_describe.core.distributions.
DistributionWidget
(input_data=None, spike_value=None, skew_value=None, spike_factor=None, skew_factor=None, viz_backend=None)¶ Bases:
data_describe._widget.BaseWidget
Container for distributions.
This class (object) is returned from the
distribution
function. The attributes documented below can be accessed or extracted.-
input_data
¶ The input data
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spike_value
¶ Measure of the “spikey”ness metric, which diagnoses spikey histograms where the tallest bin is
n
times taller than the average bin.
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skew_value
¶ Measure of the skewness metric.
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spike_factor
¶ The threshold factor used to diagnose “spikey”ness.
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skew_factor
¶ The threshold factor used to diagnose skew.
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show
(self, viz_backend=None, **kwargs)¶ The default display for this output.
Displays a summary of diagnostics.
- Parameters
viz_backend (str, optional) – The visualization backend.
**kwargs – Keyword arguments.
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plot_distribution
(self, x: Optional[str] = None, contrast: Optional[str] = None, viz_backend: Optional[str] = None, **kwargs)¶ Generate distribution plot(s).
Numeric features will be visualized using a histogram/violin plot, and any other types will be visualized using a categorical bar plot.
- Parameters
x (str, optional) – The feature name to plot. If None, will plot all features.
contrast (str, optional) – The feature name to compare histograms by contrast.
mode (str) – {‘combo’, ‘violin’, ‘hist’} The type of plot to display. Defaults to a combined histogram/violin plot.
hist_kwargs (dict, optional) – Keyword args for seaborn.histplot.
violin_kwargs (dict, optional) – Keyword args for seaborn.violinplot.
viz_backend (optional) – The visualization backend.
**kwargs – Additional keyword arguments for the visualization backend.
- Returns
Histogram plot(s).
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data_describe.core.distributions.
distribution
(data, diagnostic=True, compute_backend=None, viz_backend=None, **kwargs) → DistributionWidget¶ Distribution Plots.
Visualizes univariate distributions. This feature can be used for generating various types of plots for univariate distributions, including: histograms, violin plots, bar (count) plots.
- Parameters
data – Data Frame
diagnostic – If True, will run diagnostics to select “interesting” plots.
compute_backend – The compute backend.
viz_backend – The visualization backend.
**kwargs – Keyword arguments.
- Raises
ValueError – Invalid input data type.
- Returns
DistributionWidget