Distribution Plots

def displot(
    data,
    *,
    x=None,
    y=None,
    hue=None,
    row=None,
    col=None,
    weights=None,
    kind="hist",
    rug=False,
    rug_kws=None,
    log_scale=None,
    legend=True,
    palette=None,
    hue_order=None,
    hue_norm=None,
    color=None,
    col_wrap=None,
    row_order=None,
    col_order=None,
    height=5,
    aspect=1,
    facet_kws=None,
    **kwargs
):
    """
    Figure-level interface for drawing distribution plots onto a FacetGrid.
    
    Parameters:
    - data: DataFrame, dict, or array of data
    - x, y: str, names of variables in data
    - hue: str, grouping variable for color mapping
    - row, col: str, variables for faceting into subplots
    - kind: str, plot type ("hist", "kde", "ecdf")
    - rug: bool, add marginal rug plot
    - weights: str, variable for observation weights
    - height: float, height of each facet in inches
    - aspect: float, aspect ratio of each facet
    
    Returns:
    FacetGrid object
    """

def histplot(
    data=None,
    *,
    x=None,
    y=None,
    hue=None,
    weights=None,
    stat="count",
    bins="auto",
    binwidth=None,
    binrange=None,
    discrete=None,
    cumulative=False,
    common_bins=True,
    common_norm=True,
    multiple="layer",
    element="bars",
    fill=True,
    shrink=1,
    kde=False,
    kde_kws=None,
    line_kws=None,
    thresh=0,
    pthresh=None,
    pmax=None,
    cbar=False,
    cbar_ax=None,
    cbar_kws=None,
    palette=None,
    hue_order=None,
    hue_norm=None,
    color=None,
    log_scale=None,
    legend=True,
    ax=None,
    **kwargs
):
    """
    Plot univariate or bivariate histograms to show distributions of datasets.
    
    Parameters:
    - data: DataFrame, dict, or array of data
    - x, y: str or array-like, input data variables
    - hue: str, grouping variable for color mapping
    - stat: str, statistic to compute ("count", "frequency", "probability", "proportion", "percent", "density")
    - bins: int, str, or sequence, binning specification
    - binwidth: float, width of bins
    - cumulative: bool, compute cumulative statistic
    - multiple: str, approach for multiple hue levels ("layer", "dodge", "stack", "fill")
    - element: str, visual representation ("bars", "step", "poly")
    - kde: bool, overlay kernel density estimate
    
    Returns:
    matplotlib Axes object
    """

def kdeplot(
    data=None,
    *,
    x=None,
    y=None,
    hue=None,
    weights=None,
    palette=None,
    hue_order=None,
    hue_norm=None,
    color=None,
    fill=None,
    multiple="layer",
    common_norm=True,
    common_grid=False,
    cumulative=False,
    bw_method="scott",
    bw_adjust=1,
    warn_singular=True,
    log_scale=None,
    levels=10,
    thresh=0.05,
    gridsize=200,
    cut=3,
    clip=None,
    legend=True,
    cbar=False,
    cbar_ax=None,
    cbar_kws=None,
    ax=None,
    **kwargs
):
    """
    Plot univariate or bivariate distributions using kernel density estimation.
    
    Parameters:
    - data: DataFrame, dict, or array of data
    - x, y: str or array-like, input data variables
    - hue: str, grouping variable for color mapping
    - fill: bool, fill area under univariate curves
    - bw_method: str or scalar, bandwidth estimation method
    - bw_adjust: float, bandwidth scaling factor
    - levels: int or sequence, contour levels for bivariate plots
    - thresh: float, density threshold for contours
    - gridsize: int, evaluation grid size
    - cut: float, distance to extend beyond data extremes
    - clip: tuple, data range to clip density
    
    Returns:
    matplotlib Axes object
    """

def ecdfplot(
    data=None,
    *,
    x=None,
    y=None,
    hue=None,
    weights=None,
    stat="proportion",
    complementary=False,
    palette=None,
    hue_order=None,
    hue_norm=None,
    log_scale=None,
    legend=True,
    ax=None,
    **kwargs
):
    """
    Plot empirical cumulative distribution functions.
    
    Parameters:
    - data: DataFrame, dict, or array of data
    - x, y: str or array-like, input data variables
    - hue: str, grouping variable for color mapping
    - stat: str, statistic to compute ("proportion" or "count")
    - complementary: bool, plot complementary CDF (1 - CDF)
    - weights: str or array-like, observation weights
    
    Returns:
    matplotlib Axes object
    """

def rugplot(
    data=None,
    *,
    x=None,
    y=None,
    hue=None,
    height=0.025,
    expand_margins=True,
    palette=None,
    hue_order=None,
    hue_norm=None,
    legend=True,
    ax=None,
    **kwargs
):
    """
    Plot marginal distributions by drawing ticks along the x and y axes.
    
    Parameters:
    - data: DataFrame, dict, or array of data
    - x, y: str or array-like, input data variables
    - hue: str, grouping variable for color mapping
    - height: float, height of ticks as proportion of total
    - expand_margins: bool, expand axis margins to fit ticks
    
    Returns:
    matplotlib Axes object
    """

def distplot(
    a,
    bins=None,
    hist=True,
    kde=True,
    rug=False,
    fit=None,
    hist_kws=None,
    kde_kws=None,
    rug_kws=None,
    fit_kws=None,
    color=None,
    vertical=False,
    norm_hist=False,
    axlabel=None,
    label=None,
    ax=None,
    x=None
):
    """
    DEPRECATED - Flexibly plot a univariate distribution of observations.
    
    Note: This function is deprecated. Use histplot() and/or kdeplot() instead.
    
    Parameters:
    - a: array-like, observed data
    - bins: int or sequence, histogram bins
    - hist: bool, plot histogram
    - kde: bool, plot kernel density estimate
    - rug: bool, plot rug plot
    - fit: distribution, fit parametric distribution
    
    Returns:
    matplotlib Axes object
    """

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

# Basic histogram
sns.histplot(data=tips, x="total_bill")
plt.show()

# Histogram with hue grouping
sns.histplot(data=tips, x="total_bill", hue="smoker", multiple="stack")
plt.show()

# KDE plot with fill
sns.kdeplot(data=tips, x="total_bill", fill=True)
plt.show()

# 2D histogram
sns.histplot(data=tips, x="total_bill", y="tip")
plt.show()

# 2D KDE with contours
sns.kdeplot(data=tips, x="total_bill", y="tip")
plt.show()

# Empirical cumulative distribution
sns.ecdfplot(data=tips, x="total_bill", hue="smoker")
plt.show()

# Create subplots by time
sns.displot(
    data=tips,
    x="total_bill", hue="smoker",
    col="time", kind="hist"
)
plt.show()

# Histogram with KDE overlay and rug plot
sns.histplot(data=tips, x="total_bill", kde=True)
sns.rugplot(data=tips, x="total_bill")
plt.show()