class_docstr: Univariate and bivariate kernel density estimator.
class_docstr_tok: ["Univariate","and","bivariate","kernel","density","estimator","."]
docstr: Initialize the estimator with its parameters.
  
  Parameters
  ----------
  bw_method : string, scalar, or callable, optional
      Method for determining the smoothing bandwidth to use; passed to
      :class:`scipy.stats.gaussian_kde`.
  bw_adjust : number, optional
      Factor that multiplicatively scales the value chosen using
      ``bw_method``. Increasing will make the curve smoother. See Notes.
  gridsize : int, optional
      Number of points on each dimension of the evaluation grid.
  cut : number, optional
      Factor, multiplied by the smoothing bandwidth, that determines how
      far the evaluation grid extends past the extreme datapoints. When
      set to 0, truncate the curve at the data limits.
  clip : pair of numbers or None, or a pair of such pairs
      Do not evaluate the density outside of these limits.
  cumulative : bool, optional
      If True, estimate a cumulative distribution function. Requires scipy.
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code: def __init__(
      self,
      *,
      bw_method=None,
      bw_adjust=1,
      gridsize=200,
      cut=3,
      clip=None,
      cumulative=False,
  ):
      """Initialize the estimator with its parameters.
  
      Parameters
      ----------
      bw_method : string, scalar, or callable, optional
          Method for determining the smoothing bandwidth to use; passed to
          :class:`scipy.stats.gaussian_kde`.
      bw_adjust : number, optional
          Factor that multiplicatively scales the value chosen using
          ``bw_method``. Increasing will make the curve smoother. See Notes.
      gridsize : int, optional
          Number of points on each dimension of the evaluation grid.
      cut : number, optional
          Factor, multiplied by the smoothing bandwidth, that determines how
          far the evaluation grid extends past the extreme datapoints. When
          set to 0, truncate the curve at the data limits.
      clip : pair of numbers or None, or a pair of such pairs
          Do not evaluate the density outside of these limits.
      cumulative : bool, optional
          If True, estimate a cumulative distribution function. Requires scipy.
  
      """
      if clip is None:
          clip = None, None
  
      self.bw_method = bw_method
      self.bw_adjust = bw_adjust
      self.gridsize = gridsize
      self.cut = cut
      self.clip = clip
      self.cumulative = cumulative
  
      if cumulative and _no_scipy:
          raise RuntimeError("Cumulative KDE evaluation requires scipy")
  
      self.support = None
  
imports: from numbers import Number
  from statistics import NormalDist
  import numpy
  import pandas
  from .algorithms import bootstrap
  from .utils import _check_argument
label_desc: Use image node_id 1 to create a new KDE object  with example: obj = KDE()