CLN: Styler simplify existing builtin methods

pandas/io/formats/style.py

@@ -1296,33 +1296,6 @@ def hide_columns(self, subset) -> Styler:
     # A collection of "builtin" styles
     # -----------------------------------------------------------------------
 
-    @staticmethod
-    def _highlight_null(v, null_color: str) -> str:
-        return f"background-color: {null_color}" if pd.isna(v) else ""
-
-    def highlight_null(
-        self,
-        null_color: str = "red",
-        subset: Optional[IndexLabel] = None,
-    ) -> Styler:
-        """
-        Shade the background ``null_color`` for missing values.
-
-        Parameters
-        ----------
-        null_color : str, default 'red'
-        subset : label or list of labels, default None
-            A valid slice for ``data`` to limit the style application to.
-
-            .. versionadded:: 1.1.0
-
-        Returns
-        -------
-        self : Styler
-        """
-        self.applymap(self._highlight_null, null_color=null_color, subset=subset)
-        return self
-
     def background_gradient(
         self,
         cmap="PuBu",
@@ -1638,8 +1611,39 @@ def bar(
 
         return self
 
+    def highlight_null(
+        self,
+        null_color: str = "red",
+        subset: Optional[IndexLabel] = None,
+    ) -> Styler:
+        """
+        Shade the background ``null_color`` for missing values.
+
+        Parameters
+        ----------
+        null_color : str, default 'red'
+        subset : label or list of labels, default None
+            A valid slice for ``data`` to limit the style application to.
+
+            .. versionadded:: 1.1.0
+
+        Returns
+        -------
+        self : Styler
+        """
+
+        def f(data: DataFrame, props: str) -> np.ndarray:
+            return np.where(pd.isna(data).values, props, "")
+
+        return self.apply(
+            f, axis=None, subset=subset, props=f"background-color: {null_color};"
+        )
+
     def highlight_max(
-        self, subset=None, color: str = "yellow", axis: Optional[Axis] = 0
+        self,
+        subset: Optional[IndexLabel] = None,
+        color: str = "yellow",
+        axis: Optional[Axis] = 0,
     ) -> Styler:
         """
         Highlight the maximum by shading the background.
@@ -1658,10 +1662,19 @@ def highlight_max(
         -------
         self : Styler
         """
-        return self._highlight_handler(subset=subset, color=color, axis=axis, max_=True)
+
+        def f(data: FrameOrSeries, props: str) -> np.ndarray:
+            return np.where(data == np.nanmax(data.values), props, "")
+
+        return self.apply(
+            f, axis=axis, subset=subset, props=f"background-color: {color};"
+        )
 
     def highlight_min(
-        self, subset=None, color: str = "yellow", axis: Optional[Axis] = 0
+        self,
+        subset: Optional[IndexLabel] = None,
+        color: str = "yellow",
+        axis: Optional[Axis] = 0,
     ) -> Styler:
         """
         Highlight the minimum by shading the background.
@@ -1680,43 +1693,13 @@ def highlight_min(
         -------
         self : Styler
         """
-        return self._highlight_handler(
-            subset=subset, color=color, axis=axis, max_=False
-        )
 
-    def _highlight_handler(
-        self,
-        subset=None,
-        color: str = "yellow",
-        axis: Optional[Axis] = None,
-        max_: bool = True,
-    ) -> Styler:
-        subset = non_reducing_slice(maybe_numeric_slice(self.data, subset))
-        self.apply(
-            self._highlight_extrema, color=color, axis=axis, subset=subset, max_=max_
-        )
-        return self
+        def f(data: FrameOrSeries, props: str) -> np.ndarray:
+            return np.where(data == np.nanmin(data.values), props, "")
 
-    @staticmethod
-    def _highlight_extrema(
-        data: FrameOrSeries, color: str = "yellow", max_: bool = True
-    ):
-        """
-        Highlight the min or max in a Series or DataFrame.
-        """
-        attr = f"background-color: {color}"
-
-        if max_:
-            extrema = data == np.nanmax(data.to_numpy())
-        else:
-            extrema = data == np.nanmin(data.to_numpy())
-
-        if data.ndim == 1:  # Series from .apply
-            return [attr if v else "" for v in extrema]
-        else:  # DataFrame from .tee
-            return pd.DataFrame(
-                np.where(extrema, attr, ""), index=data.index, columns=data.columns
-            )
+        return self.apply(
+            f, axis=axis, subset=subset, props=f"background-color: {color};"
+        )
 
     @classmethod
     def from_custom_template(cls, searchpath, name):