Enhancements in numeric.py for 0x,0o,0b

pandas/core/_numba/kernels/min_max_.py

@@ -112,11 +112,9 @@ def grouped_min_max(
             continue
 
         if is_max:
-            if val > output[lab]:
-                output[lab] = val
+            output[lab] = max(val, output[lab])
         else:
-            if val < output[lab]:
-                output[lab] = val
+            output[lab] = min(val, output[lab])
 
     # Set labels that don't satisfy min_periods as np.nan
     for lab, count in enumerate(nobs):

pandas/core/dtypes/cast.py

@@ -683,9 +683,7 @@ def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
 
         elif dtype.kind == "f":
             mst = np.min_scalar_type(fill_value)
-            if mst > dtype:
-                # e.g. mst is np.float64 and dtype is np.float32
-                dtype = mst
+            dtype = max(mst, dtype)
 
         elif dtype.kind == "c":
             mst = np.min_scalar_type(fill_value)
@@ -718,9 +716,7 @@ def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
 
         elif dtype.kind == "c":
             mst = np.min_scalar_type(fill_value)
-            if mst > dtype:
-                # e.g. mst is np.complex128 and dtype is np.complex64
-                dtype = mst
+            dtype = max(mst, dtype)
 
     else:
         dtype = np.dtype(np.object_)

pandas/core/tools/numeric.py

@@ -47,10 +47,10 @@
 
 
 def to_numeric(
-    arg,
-    errors: DateTimeErrorChoices = "raise",
-    downcast: Literal["integer", "signed", "unsigned", "float"] | None = None,
-    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+        arg,
+        errors: DateTimeErrorChoices = "raise",
+        downcast: Literal["integer", "signed", "unsigned", "float"] | None = None,
+        dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
 ):
     """
     Convert argument to a numeric type.
@@ -220,25 +220,32 @@ def to_numeric(
         values = values.view(np.int64)
     else:
         values = ensure_object(values)
-        coerce_numeric = errors != "raise"
-        values, new_mask = lib.maybe_convert_numeric(  # type: ignore[call-overload]
-            values,
-            set(),
-            coerce_numeric=coerce_numeric,
-            convert_to_masked_nullable=dtype_backend is not lib.no_default
-            or isinstance(values_dtype, StringDtype)
-            and values_dtype.na_value is libmissing.NA,
-        )
+        parsed_values = []
+        new_mask = []
+        for idx, x in enumerate(values):
+            parsed_value = parse_numeric(x)
+            if libmissing.checknull(parsed_value):
+                if errors == "raise":
+                    raise ValueError(f"Unable to parse string '{x}' at position {idx}")
+                elif errors == "coerce":
+                    parsed_values.append(libmissing.NA)
+                    new_mask.append(True)
+                    continue
+            else:
+                parsed_values.append(parsed_value)
+                new_mask.append(False)
+        values = np.array(parsed_values, dtype=object)
+        new_mask = np.array(new_mask, dtype=bool)
 
     if new_mask is not None:
         # Remove unnecessary values, is expected later anyway and enables
         # downcasting
         values = values[~new_mask]
     elif (
-        dtype_backend is not lib.no_default
-        and new_mask is None
-        or isinstance(values_dtype, StringDtype)
-        and values_dtype.na_value is libmissing.NA
+            dtype_backend is not lib.no_default
+            and new_mask is None
+            or isinstance(values_dtype, StringDtype)
+            and values_dtype.na_value is libmissing.NA
     ):
         new_mask = np.zeros(values.shape, dtype=np.bool_)
 
@@ -315,3 +322,15 @@ def to_numeric(
         return values[0]
     else:
         return values
+
+
+def parse_numeric(value):
+    if isinstance(value, str):
+        try:
+            return int(value, 0)  # Automatically detect radix
+        except ValueError:
+            try:
+                return float(value)
+            except ValueError:
+                return libmissing.NA
+    return value

pandas/io/excel/_odfreader.py

@@ -142,8 +142,7 @@ def get_sheet_data(
                         empty_cells = 0
                         table_row.extend([value] * column_repeat)
 
-            if max_row_len < len(table_row):
-                max_row_len = len(table_row)
+            max_row_len = max(max_row_len, len(table_row))
 
             row_repeat = self._get_row_repeat(sheet_row)
             if len(table_row) == 0:

pandas/tests/arithmetic/test_numeric.py

@@ -274,9 +274,7 @@ def test_numeric_arr_rdiv_tdscalar(self, three_days, numeric_idx, box_with_array
         expected = TimedeltaIndex(["3 Days", "36 Hours"])
         if isinstance(three_days, np.timedelta64):
             dtype = three_days.dtype
-            if dtype < np.dtype("m8[s]"):
-                # i.e. resolution is lower -> use lowest supported resolution
-                dtype = np.dtype("m8[s]")
+            dtype = max(dtype, np.dtype("m8[s]"))
             expected = expected.astype(dtype)
         elif type(three_days) is timedelta:
             expected = expected.astype("m8[us]")

pandas/tests/frame/test_query_eval.py

@@ -1388,7 +1388,7 @@ def test_expr_with_column_name_with_backtick_and_hash(self):
     def test_expr_with_column_name_with_backtick(self):
         # GH 59285
         df = DataFrame({"a`b": (1, 2, 3), "ab": (4, 5, 6)})
-        result = df.query("`a``b` < 2")  # noqa
+        result = df.query("`a``b` < 2")
         # Note: Formatting checks may wrongly consider the above ``inline code``.
         expected = df[df["a`b"] < 2]
         tm.assert_frame_equal(result, expected)