make **kw explicit

pandas/core/internals/blocks.py

@@ -1453,7 +1453,6 @@ def quantile(self, qs, interpolation='linear', axis=0):
  -------
  Block
  """
- kw = {'interpolation': interpolation}
  values = self.get_values()
  values, _ = self._try_coerce_args(values, values)
 
@@ -1476,7 +1475,8 @@ def quantile(self, qs, interpolation='linear', axis=0):
  mask = isna(self.values)
  result = _nanpercentile(values, np.array(qs) * 100,
  axis=axis, na_value=self._na_value,
- mask=mask, ndim=self.ndim, **kw)
+ mask=mask, ndim=self.ndim,
+ interpolation=interpolation)
 
  result = np.array(result, copy=False)
  if self.ndim > 1:
@@ -3327,7 +3327,7 @@ def _putmask_preserve(nv, n):
 
 
 # TODO: belongs elsewhere?
-def _nanpercentile1D(values, mask, q, na_value, **kw):
+def _nanpercentile1D(values, mask, q, na_value, interpolation):
  # mask is Union[ExtensionArray, ndarray]
  values = values[~mask]
 
@@ -3338,23 +3338,25 @@ def _nanpercentile1D(values, mask, q, na_value, **kw):
  return np.array([na_value] * len(q),
  dtype=values.dtype)
 
- return np.percentile(values, q, **kw)
+ return np.percentile(values, q, interpolation=interpolation)
 
 
-def _nanpercentile(values, q, axis, na_value, mask, ndim, **kw):
+def _nanpercentile(values, q, axis, na_value, mask, ndim, interpolation):
  if not lib.is_scalar(mask) and mask.any():
  if ndim == 1:
- return _nanpercentile1D(values, mask, q, na_value, **kw)
+ return _nanpercentile1D(values, mask, q, na_value,
+ interpolation=interpolation)
  else:
  # for nonconsolidatable blocks mask is 1D, but values 2D
  if mask.ndim < values.ndim:
  mask = mask.reshape(values.shape)
  if axis == 0:
  values = values.T
  mask = mask.T
- result = [_nanpercentile1D(val, m, q, na_value, **kw) for (val, m)
- in zip(list(values), list(mask))]
+ result = [_nanpercentile1D(val, m, q, na_value,
+ interpolation=interpolation)
+ for (val, m) in zip(list(values), list(mask))]
  result = np.array(result, dtype=values.dtype, copy=False).T
  return result
  else:
- return np.percentile(values, q, axis=axis, **kw)
+ return np.percentile(values, q, axis=axis, interpolation=interpolation)