class_docstr: Univariate and bivariate kernel density estimator.
class_docstr_tok: ["Univariate","and","bivariate","kernel","density","estimator","."]
docstr: Fit and evaluate a univariate on univariate data.
docstr_tok: ["Fit","and","evaluate","a","univariate","on","univariate","data","."]
code: def _eval_univariate(self, x, weights=None):
      """Fit and evaluate a univariate on univariate data."""
      support = self.support
      if support is None:
          support = self.define_support(x, cache=False)
  
      kde = self._fit(x, weights)
  
      if self.cumulative:
          s_0 = support[0]
          density = np.array(
              [kde.integrate_box_1d(s_0, s_i) for s_i in support]
          )
      else:
          density = kde(support)
  
      return density, support
  
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 7 for calling the KDE obj's underlying member method code with example usage: obj._eval_univariate(x, weights) and returns: density,  support