TYP: Series.quantile (#47304)

* TYP: Series.quantile

* common.py

Author: twoertwein

pandas/_typing.py

@@ -323,3 +323,6 @@ def closed(self) -> bool:
 # sort_index
 SortKind = Literal["quicksort", "mergesort", "heapsort", "stable"]
 NaPosition = Literal["first", "last"]
+
+# quantile interpolation
+QuantileInterpolation = Literal["linear", "lower", "higher", "midpoint", "nearest"]

pandas/core/common.py

@@ -125,11 +125,11 @@ def is_bool_indexer(key: Any) -> bool:
         is_array_like(key) and is_extension_array_dtype(key.dtype)
     ):
         if key.dtype == np.object_:
-            key = np.asarray(key)
+            key_array = np.asarray(key)
 
-            if not lib.is_bool_array(key):
+            if not lib.is_bool_array(key_array):
                 na_msg = "Cannot mask with non-boolean array containing NA / NaN values"
-                if lib.infer_dtype(key) == "boolean" and isna(key).any():
+                if lib.infer_dtype(key_array) == "boolean" and isna(key_array).any():
                     # Don't raise on e.g. ["A", "B", np.nan], see
                     #  test_loc_getitem_list_of_labels_categoricalindex_with_na
                     raise ValueError(na_msg)
@@ -508,18 +508,14 @@ def get_rename_function(mapper):
     Returns a function that will map names/labels, dependent if mapper
     is a dict, Series or just a function.
     """
-    if isinstance(mapper, (abc.Mapping, ABCSeries)):
 
-        def f(x):
-            if x in mapper:
-                return mapper[x]
-            else:
-                return x
-
-    else:
-        f = mapper
+    def f(x):
+        if x in mapper:
+            return mapper[x]
+        else:
+            return x
 
-    return f
+    return f if isinstance(mapper, (abc.Mapping, ABCSeries)) else mapper
 
 
 def convert_to_list_like(

pandas/core/series.py

@@ -43,6 +43,7 @@
     IndexKeyFunc,
     Level,
     NaPosition,
+    QuantileInterpolation,
     Renamer,
     SingleManager,
     SortKind,
@@ -2479,7 +2480,33 @@ def round(self, decimals=0, *args, **kwargs) -> Series:
 
         return result
 
-    def quantile(self, q=0.5, interpolation="linear"):
+    @overload
+    def quantile(
+        self, q: float = ..., interpolation: QuantileInterpolation = ...
+    ) -> float:
+        ...
+
+    @overload
+    def quantile(
+        self,
+        q: Sequence[float] | AnyArrayLike,
+        interpolation: QuantileInterpolation = ...,
+    ) -> Series:
+        ...
+
+    @overload
+    def quantile(
+        self,
+        q: float | Sequence[float] | AnyArrayLike = ...,
+        interpolation: QuantileInterpolation = ...,
+    ) -> float | Series:
+        ...
+
+    def quantile(
+        self,
+        q: float | Sequence[float] | AnyArrayLike = 0.5,
+        interpolation: QuantileInterpolation = "linear",
+    ) -> float | Series:
         """
         Return value at the given quantile.
 

pandas/io/formats/style.py

@@ -25,6 +25,7 @@
     FilePath,
     IndexLabel,
     Level,
+    QuantileInterpolation,
     Scalar,
     WriteBuffer,
 )
@@ -3467,7 +3468,7 @@ def highlight_quantile(
         axis: Axis | None = 0,
         q_left: float = 0.0,
         q_right: float = 1.0,
-        interpolation: str = "linear",
+        interpolation: QuantileInterpolation = "linear",
         inclusive: str = "both",
         props: str | None = None,
     ) -> Styler:
@@ -3539,13 +3540,17 @@ def highlight_quantile(
 
         # after quantile is found along axis, e.g. along rows,
         # applying the calculated quantile to alternate axis, e.g. to each column
-        kwargs = {"q": [q_left, q_right], "interpolation": interpolation}
+        quantiles = [q_left, q_right]
         if axis is None:
-            q = Series(data.to_numpy().ravel()).quantile(**kwargs)
+            q = Series(data.to_numpy().ravel()).quantile(
+                q=quantiles, interpolation=interpolation
+            )
             axis_apply: int | None = None
         else:
             axis = self.data._get_axis_number(axis)
-            q = data.quantile(axis=axis, numeric_only=False, **kwargs)
+            q = data.quantile(
+                axis=axis, numeric_only=False, q=quantiles, interpolation=interpolation
+            )
             axis_apply = 1 - axis
 
         if props is None:

pyright_reportGeneralTypeIssues.json

@@ -35,10 +35,8 @@
         "pandas/core/arrays/string_.py",
         "pandas/core/arrays/string_arrow.py",
         "pandas/core/arrays/timedeltas.py",
-        "pandas/core/common.py",
         "pandas/core/computation/align.py",
         "pandas/core/construction.py",
-        "pandas/core/describe.py",
         "pandas/core/dtypes/cast.py",
         "pandas/core/dtypes/common.py",
         "pandas/core/dtypes/concat.py",