REF/TYP: extract function/type args in describe.py (#39165)

Author: ivanovmg

pandas/core/describe.py

@@ -4,7 +4,7 @@
 Method NDFrame.describe() delegates actual execution to function describe_ndframe().
 """
 
-from typing import TYPE_CHECKING, List, Optional, Sequence, Union
+from typing import TYPE_CHECKING, List, Optional, Sequence, Union, cast
 import warnings
 
 import numpy as np
@@ -62,32 +62,14 @@ def describe_ndframe(
  if obj.ndim == 2 and obj.columns.size == 0:
  raise ValueError("Cannot describe a DataFrame without columns")
 
- if percentiles is not None:
- # explicit conversion of `percentiles` to list
- percentiles = list(percentiles)
-
- # get them all to be in [0, 1]
- validate_percentile(percentiles)
-
- # median should always be included
- if 0.5 not in percentiles:
- percentiles.append(0.5)
- percentiles = np.asarray(percentiles)
- else:
- percentiles = np.array([0.25, 0.5, 0.75])
-
- # sort and check for duplicates
- unique_pcts = np.unique(percentiles)
- assert percentiles is not None
- if len(unique_pcts) < len(percentiles):
- raise ValueError("percentiles cannot contain duplicates")
- percentiles = unique_pcts
+ percentiles = _refine_percentiles(percentiles)
 
  if obj.ndim == 1:
+ series = cast("Series", obj)
  # Incompatible return value type
  # (got "Series", expected "FrameOrSeries") [return-value]
  return describe_1d(
- obj,
+ series,
  percentiles,
  datetime_is_numeric,
  is_series=True,
@@ -125,14 +107,14 @@ def describe_ndframe(
  return d
 
 
-def describe_numeric_1d(series, percentiles) -> "Series":
+def describe_numeric_1d(series: "Series", percentiles: Sequence[float]) -> "Series":
  """Describe series containing numerical data.
 
  Parameters
  ----------
  series : Series
  Series to be described.
- percentiles : list-like of numbers, optional
+ percentiles : list-like of numbers
  The percentiles to include in the output.
  """
  from pandas import Series
@@ -148,7 +130,7 @@ def describe_numeric_1d(series, percentiles) -> "Series":
  return Series(d, index=stat_index, name=series.name)
 
 
-def describe_categorical_1d(data, is_series) -> "Series":
+def describe_categorical_1d(data: "Series", is_series: bool) -> "Series":
  """Describe series containing categorical data.
 
  Parameters
@@ -210,14 +192,14 @@ def describe_categorical_1d(data, is_series) -> "Series":
  return Series(result, index=names, name=data.name, dtype=dtype)
 
 
-def describe_timestamp_1d(data, percentiles) -> "Series":
+def describe_timestamp_1d(data: "Series", percentiles: Sequence[float]) -> "Series":
  """Describe series containing datetime64 dtype.
 
  Parameters
  ----------
  data : Series
  Series to be described.
- percentiles : list-like of numbers, optional
+ percentiles : list-like of numbers
  The percentiles to include in the output.
  """
  # GH-30164
@@ -234,14 +216,20 @@ def describe_timestamp_1d(data, percentiles) -> "Series":
  return Series(d, index=stat_index, name=data.name)
 
 
-def describe_1d(data, percentiles, datetime_is_numeric, *, is_series) -> "Series":
+def describe_1d(
+ data: "Series",
+ percentiles: Sequence[float],
+ datetime_is_numeric: bool,
+ *,
+ is_series: bool,
+) -> "Series":
  """Describe series.
 
  Parameters
  ----------
  data : Series
  Series to be described.
- percentiles : list-like of numbers, optional
+ percentiles : list-like of numbers
  The percentiles to include in the output.
  datetime_is_numeric : bool, default False
  Whether to treat datetime dtypes as numeric.
@@ -263,3 +251,35 @@ def describe_1d(data, percentiles, datetime_is_numeric, *, is_series) -> "Series
  return describe_numeric_1d(data, percentiles)
  else:
  return describe_categorical_1d(data, is_series)
+
+
+def _refine_percentiles(percentiles: Optional[Sequence[float]]) -> Sequence[float]:
+ """Ensure that percentiles are unique and sorted.
+
+ Parameters
+ ----------
+ percentiles : list-like of numbers, optional
+ The percentiles to include in the output.
+ """
+ if percentiles is None:
+ return np.array([0.25, 0.5, 0.75])
+
+ # explicit conversion of `percentiles` to list
+ percentiles = list(percentiles)
+
+ # get them all to be in [0, 1]
+ validate_percentile(percentiles)
+
+ # median should always be included
+ if 0.5 not in percentiles:
+ percentiles.append(0.5)
+
+ percentiles = np.asarray(percentiles)
+
+ # sort and check for duplicates
+ unique_pcts = np.unique(percentiles)
+ assert percentiles is not None
+ if len(unique_pcts) < len(percentiles):
+ raise ValueError("percentiles cannot contain duplicates")
+
+ return unique_pcts