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