Initial configuration commit
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typings/seaborn/distributions.pyi
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typings/seaborn/distributions.pyi
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"""
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This type stub file was generated by pyright.
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"""
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from ._core import VectorPlotter
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from ._decorators import _deprecate_positional_args
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"""Plotting functions for visualizing distributions."""
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__all__ = ["displot", "histplot", "kdeplot", "ecdfplot", "rugplot", "distplot"]
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_dist_params = ...
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_param_docs = ...
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class _DistributionPlotter(VectorPlotter):
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semantics = ...
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wide_structure = ...
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flat_structure = ...
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def __init__(self, data=..., variables=...) -> None:
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...
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@property
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def univariate(self): # -> bool:
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"""Return True if only x or y are used."""
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...
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@property
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def data_variable(self): # -> str:
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"""Return the variable with data for univariate plots."""
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...
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@property
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def has_xy_data(self): # -> bool:
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"""Return True at least one of x or y is defined."""
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...
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def plot_univariate_histogram(self, multiple, element, fill, common_norm, common_bins, shrink, kde, kde_kws, color, legend, line_kws, estimate_kws, **plot_kws):
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...
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def plot_bivariate_histogram(self, common_bins, common_norm, thresh, pthresh, pmax, color, legend, cbar, cbar_ax, cbar_kws, estimate_kws, **plot_kws):
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...
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def plot_univariate_density(self, multiple, common_norm, common_grid, fill, legend, estimate_kws, **plot_kws):
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...
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def plot_bivariate_density(self, common_norm, fill, levels, thresh, color, legend, cbar, cbar_ax, cbar_kws, estimate_kws, **contour_kws):
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...
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def plot_univariate_ecdf(self, estimate_kws, legend, **plot_kws): # -> None:
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...
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def plot_rug(self, height, expand_margins, legend, **kws): # -> None:
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...
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class _DistributionFacetPlotter(_DistributionPlotter):
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semantics = ...
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def histplot(data=..., *, x=..., y=..., hue=..., weights=..., stat=..., bins=..., binwidth=..., binrange=..., discrete=..., cumulative=..., common_bins=..., common_norm=..., multiple=..., element=..., fill=..., shrink=..., kde=..., kde_kws=..., line_kws=..., thresh=..., pthresh=..., pmax=..., cbar=..., cbar_ax=..., cbar_kws=..., palette=..., hue_order=..., hue_norm=..., color=..., log_scale=..., legend=..., ax=..., **kwargs): # -> Axes:
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...
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@_deprecate_positional_args
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def kdeplot(x=..., *, y=..., shade=..., vertical=..., kernel=..., bw=..., gridsize=..., cut=..., clip=..., legend=..., cumulative=..., shade_lowest=..., cbar=..., cbar_ax=..., cbar_kws=..., ax=..., weights=..., hue=..., palette=..., hue_order=..., hue_norm=..., multiple=..., common_norm=..., common_grid=..., levels=..., thresh=..., bw_method=..., bw_adjust=..., log_scale=..., color=..., fill=..., data=..., data2=..., **kwargs): # -> Axes:
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...
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def ecdfplot(data=..., *, x=..., y=..., hue=..., weights=..., stat=..., complementary=..., palette=..., hue_order=..., hue_norm=..., log_scale=..., legend=..., ax=..., **kwargs): # -> Axes:
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...
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@_deprecate_positional_args
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def rugplot(x=..., *, height=..., axis=..., ax=..., data=..., y=..., hue=..., palette=..., hue_order=..., hue_norm=..., expand_margins=..., legend=..., a=..., **kwargs): # -> Axes:
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...
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def displot(data=..., *, x=..., y=..., hue=..., row=..., col=..., weights=..., kind=..., rug=..., rug_kws=..., log_scale=..., legend=..., palette=..., hue_order=..., hue_norm=..., color=..., col_wrap=..., row_order=..., col_order=..., height=..., aspect=..., facet_kws=..., **kwargs): # -> FacetGrid:
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...
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def distplot(a=..., bins=..., hist=..., kde=..., rug=..., fit=..., hist_kws=..., kde_kws=..., rug_kws=..., fit_kws=..., color=..., vertical=..., norm_hist=..., axlabel=..., label=..., ax=..., x=...):
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"""DEPRECATED: Flexibly plot a univariate distribution of observations.
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.. warning::
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This function is deprecated and will be removed in a future version.
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Please adapt your code to use one of two new functions:
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- :func:`displot`, a figure-level function with a similar flexibility
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over the kind of plot to draw
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- :func:`histplot`, an axes-level function for plotting histograms,
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including with kernel density smoothing
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This function combines the matplotlib ``hist`` function (with automatic
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calculation of a good default bin size) with the seaborn :func:`kdeplot`
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and :func:`rugplot` functions. It can also fit ``scipy.stats``
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distributions and plot the estimated PDF over the data.
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Parameters
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----------
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a : Series, 1d-array, or list.
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Observed data. If this is a Series object with a ``name`` attribute,
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the name will be used to label the data axis.
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bins : argument for matplotlib hist(), or None, optional
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Specification of hist bins. If unspecified, as reference rule is used
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that tries to find a useful default.
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hist : bool, optional
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Whether to plot a (normed) histogram.
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kde : bool, optional
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Whether to plot a gaussian kernel density estimate.
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rug : bool, optional
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Whether to draw a rugplot on the support axis.
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fit : random variable object, optional
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An object with `fit` method, returning a tuple that can be passed to a
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`pdf` method a positional arguments following a grid of values to
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evaluate the pdf on.
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hist_kws : dict, optional
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Keyword arguments for :meth:`matplotlib.axes.Axes.hist`.
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kde_kws : dict, optional
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Keyword arguments for :func:`kdeplot`.
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rug_kws : dict, optional
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Keyword arguments for :func:`rugplot`.
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color : matplotlib color, optional
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Color to plot everything but the fitted curve in.
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vertical : bool, optional
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If True, observed values are on y-axis.
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norm_hist : bool, optional
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If True, the histogram height shows a density rather than a count.
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This is implied if a KDE or fitted density is plotted.
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axlabel : string, False, or None, optional
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Name for the support axis label. If None, will try to get it
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from a.name if False, do not set a label.
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label : string, optional
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Legend label for the relevant component of the plot.
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ax : matplotlib axis, optional
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If provided, plot on this axis.
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Returns
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-------
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ax : matplotlib Axes
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Returns the Axes object with the plot for further tweaking.
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See Also
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--------
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kdeplot : Show a univariate or bivariate distribution with a kernel
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density estimate.
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rugplot : Draw small vertical lines to show each observation in a
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distribution.
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Examples
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--------
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Show a default plot with a kernel density estimate and histogram with bin
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size determined automatically with a reference rule:
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.. plot::
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:context: close-figs
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>>> import seaborn as sns, numpy as np
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>>> sns.set_theme(); np.random.seed(0)
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>>> x = np.random.randn(100)
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>>> ax = sns.distplot(x)
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Use Pandas objects to get an informative axis label:
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.. plot::
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:context: close-figs
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>>> import pandas as pd
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>>> x = pd.Series(x, name="x variable")
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>>> ax = sns.distplot(x)
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Plot the distribution with a kernel density estimate and rug plot:
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.. plot::
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:context: close-figs
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>>> ax = sns.distplot(x, rug=True, hist=False)
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Plot the distribution with a histogram and maximum likelihood gaussian
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distribution fit:
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.. plot::
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:context: close-figs
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>>> from scipy.stats import norm
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>>> ax = sns.distplot(x, fit=norm, kde=False)
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Plot the distribution on the vertical axis:
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.. plot::
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:context: close-figs
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>>> ax = sns.distplot(x, vertical=True)
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Change the color of all the plot elements:
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.. plot::
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:context: close-figs
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>>> sns.set_color_codes()
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>>> ax = sns.distplot(x, color="y")
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Pass specific parameters to the underlying plot functions:
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.. plot::
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:context: close-figs
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>>> ax = sns.distplot(x, rug=True, rug_kws={"color": "g"},
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... kde_kws={"color": "k", "lw": 3, "label": "KDE"},
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... hist_kws={"histtype": "step", "linewidth": 3,
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... "alpha": 1, "color": "g"})
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"""
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...
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