Use np.nan instead of float for consistency

nannyml/drift/univariate/methods.py

@@ -307,7 +307,7 @@ def _calculate(self, data: pd.Series):
  reference_proba_in_bins = copy(self._reference_proba_in_bins)
  data = _remove_missing_data(data)
  if data.empty:
- return float('nan')
+ return np.nan
  if self._treat_as_type == 'cont':
  len_data = len(data)
  data_proba_in_bins = np.histogram(data, bins=self._bins)[0] / len_data
@@ -391,7 +391,7 @@ def _fit(self, reference_data: pd.Series, timestamps: Optional[pd.Series] = None
  def _calculate(self, data: pd.Series):
  data = _remove_missing_data(data)
  if data.empty:
- return float('nan')
+ return np.nan
  if not self._fitted:
  raise NotFittedException(
  "tried to call 'calculate' on an unfitted method " f"{self.display_name}. Please run 'fit' first"
@@ -451,7 +451,7 @@ def _fit(self, reference_data: pd.Series, timestamps: Optional[pd.Series] = None
  def _calculate(self, data: pd.Series):
  data = _remove_missing_data(data)
  if data.empty:
- return float('nan')
+ return np.nan
  if not self._fitted:
  raise NotFittedException(
  "tried to call 'calculate' on an unfitted method " f"{self.display_name}. Please run 'fit' first"
@@ -518,7 +518,7 @@ def _calculate(self, data: pd.Series):
  )
  data = _remove_missing_data(data)
  if data.empty:
- return float('nan')
+ return np.nan
  data_labels = data.unique()
  data_ratios = {label: (data == label).sum() / len(data) for label in data_labels}
 
@@ -594,7 +594,7 @@ def _calculate(self, data: pd.Series):
  )
  data = _remove_missing_data(data)
  if data.empty:
- return float('nan')
+ return np.nan
  if (
  self.calculation_method == 'auto' and self._reference_size >= 10_000
  ) or self.calculation_method == 'estimated':
@@ -697,7 +697,7 @@ def _fit(self, reference_data: pd.Series, timestamps: Optional[pd.Series] = None
  def _calculate(self, data: pd.Series):
  data = _remove_missing_data(data)
  if data.empty:
- return float('nan')
+ return np.nan
  reference_proba_in_bins = copy(self._reference_proba_in_bins)
  if self._treat_as_type == 'cont':
  len_data = len(data)