simplify

Author: attack68

pandas/io/formats/style.py

@@ -27,7 +27,7 @@
 from pandas._config import get_option
 
 from pandas._libs import lib
-from pandas._typing import Axis, FrameOrSeries, FrameOrSeriesUnion, IndexLabel
+from pandas._typing import Axis, FrameOrSeries, FrameOrSeriesUnion, IndexLabel, Scalar
 from pandas.compat._optional import import_optional_dependency
 from pandas.util._decorators import doc
 
@@ -1758,16 +1758,26 @@ def highlight_range(
         >>> df = pd.DataFrame([[1,2], [3,4]])
         >>> df.style.highlight_range(start=2, stop=3, props='font-weight:bold;')
         """
+
+        def f(
+            data: DataFrame,
+            props: str,
+            d: Optional[Scalar] = None,
+            u: Optional[Scalar] = None,
+        ) -> np.ndarray:
+            ge_d = data >= d if d is not None else np.full_like(data, True, dtype=bool)
+            le_u = data <= u if u is not None else np.full_like(data, True, dtype=bool)
+            return np.where(ge_d & le_u, props, "")
+
         if props is None:
             props = f"background-color: {color};"
         return self.apply(
-            _Builtins._highlight_func,
+            f,
             axis=None,
             subset=subset,
             props=props,
-            highlight="range",
-            start=start,
-            stop=stop,
+            d=start,
+            u=stop,
         )
 
     def highlight_quantile(
@@ -1819,14 +1829,32 @@ def highlight_quantile(
         This method uses ``pandas.qcut`` to implement the quantile labelling of data
         values.
         """
+
+        def f(
+            data: FrameOrSeries,
+            props: str,
+            q_low: float = 0,
+            q_high: float = 1,
+            axis_: Optional[Axis] = 0,
+        ):
+            if q_low > 0:
+                q, tgt_label = [0, q_low, q_high], 1
+            else:
+                q, tgt_label = [0, q_high], 0
+            if axis_ is None:
+                shape = data.values.shape
+                labels = pd.qcut(data.values.ravel(), q=q, labels=False).reshape(shape)
+            else:
+                labels = pd.qcut(data, q=q, labels=False)
+            return np.where(labels == tgt_label, props, "")
+
         if props is None:
             props = f"background-color: {color};"
         return self.apply(
-            _Builtins._highlight_func,
+            f,
             axis=axis,
             subset=subset,
             props=props,
-            highlight="quantile",
             q_low=q_low,
             q_high=q_high,
             axis_=axis,
@@ -1934,46 +1962,6 @@ def pipe(self, func: Callable, *args, **kwargs):
         return com.pipe(self, func, *args, **kwargs)
 
 
-class _Builtins:
-    @staticmethod
-    def _highlight_func(
-        data: FrameOrSeries,
-        props: str = "background-color: yellow;",
-        highlight: str = "max",
-        **kwargs,
-    ) -> np.ndarray:
-        """
-        Highlight the value in a Series or DataFrame by func with css-properties
-        """
-        if highlight == "max":
-            return np.where(data == np.nanmax(data.values), props, "")
-        elif highlight == "min":
-            return np.where(data == np.nanmin(data.values), props, "")
-        elif highlight == "null":
-            return np.where(pd.isna(data).values, props, "")
-        elif highlight == "range":
-            l, h = kwargs.get("start"), kwargs.get("stop")
-            ge_l = data >= l if l is not None else np.full_like(data, True, dtype=bool)
-            le_h = data <= h if h is not None else np.full_like(data, True, dtype=bool)
-            return np.where(ge_l & le_h, props, "")
-        elif highlight == "quantile":
-            l, h = kwargs.get("q_low"), kwargs.get("q_high")
-            if l > 0:
-                q, tgt_label = [0, l, h], 1
-            else:
-                q, tgt_label = [0, h], 0
-            if kwargs.get("axis_") is None:
-                shape = data.values.shape
-                labels = pd.qcut(data.values.ravel(), q=q, labels=False).reshape(shape)
-            else:
-                labels = pd.qcut(data, q=q, labels=False)
-            return np.where(labels == tgt_label, props, "")
-        else:
-            raise ValueError(
-                "'highlight' must be one of 'max', 'min', 'null', 'range', 'quantile'."
-            )
-
-
 class _Tooltips:
     """
     An extension to ``Styler`` that allows for and manipulates tooltips on hover