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.