class_docstr: Univariate and bivariate kernel density estimator.
class_docstr_tok: ["Univariate","and","bivariate","kernel","density","estimator","."]
docstr: Fit the scipy kde while adding bw_adjust logic and version check.
docstr_tok: ["Fit","the","scipy","kde","while","adding","bw_adjust","logic","and","version","check","."]
code: def _fit(self, fit_data, weights=None):
      """Fit the scipy kde while adding bw_adjust logic and version check."""
      fit_kws = {"bw_method": self.bw_method}
      if weights is not None:
          fit_kws["weights"] = weights
  
      kde = gaussian_kde(fit_data, **fit_kws)
      kde.set_bandwidth(kde.factor * self.bw_adjust)
  
      return kde
  
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 6 for calling the KDE obj's underlying member method code with example usage: obj._fit(fit_data, weights) and returns: kde