CLN: Centralised _check_percentile

pandas/core/algorithms.py

@@ -1105,6 +1105,22 @@ def _get_score(at):
  return result
 
 
+def check_percentile(q):
+ """
+ Validate percentiles (used by describe and quantile).
+ """
+
+ msg = "percentiles should all be in the interval [0, 1]. " "Try {0} instead."
+ q = np.asarray(q)
+ if q.ndim == 0:
+ if not 0 <= q <= 1:
+ raise ValueError(msg.format(q / 100.0))
+ else:
+ if not all(0 <= qs <= 1 for qs in q):
+ raise ValueError(msg.format(q / 100.0))
+ return q
+
+
 # --------------- #
 # select n #
 # --------------- #

pandas/core/frame.py

@@ -8206,7 +8206,7 @@ def quantile(self, q=0.5, axis=0, numeric_only=True, interpolation="linear"):
  C 1 days 12:00:00
  Name: 0.5, dtype: object
  """
- self._check_percentile(q)
+ algorithms.check_percentile(q)
 
  data = self._get_numeric_data() if numeric_only else self
  axis = self._get_axis_number(axis)

pandas/core/generic.py

@@ -10157,7 +10157,7 @@ def describe(self, percentiles=None, include=None, exclude=None):
  percentiles = list(percentiles)
 
  # get them all to be in [0, 1]
- self._check_percentile(percentiles)
+ algos.check_percentile(percentiles)
 
  # median should always be included
  if 0.5 not in percentiles:
@@ -10261,21 +10261,6 @@ def describe_1d(data):
  d.columns = data.columns.copy()
  return d
 
- def _check_percentile(self, q):
- """
- Validate percentiles (used by describe and quantile).
- """
-
- msg = "percentiles should all be in the interval [0, 1]. " "Try {0} instead."
- q = np.asarray(q)
- if q.ndim == 0:
- if not 0 <= q <= 1:
- raise ValueError(msg.format(q / 100.0))
- else:
- if not all(0 <= qs <= 1 for qs in q):
- raise ValueError(msg.format(q / 100.0))
- return q
-
  _shared_docs[
  "pct_change"
  ] = """

pandas/core/series.py

@@ -2354,7 +2354,7 @@ def quantile(self, q=0.5, interpolation="linear"):
  dtype: float64
  """
 
- self._check_percentile(q)
+ algorithms.check_percentile(q)
 
  # We dispatch to DataFrame so that core.internals only has to worry
  # about 2D cases.