REF: Use numpy methods instead of np.array

pandas/core/array_algos/quantile.py

@@ -91,7 +91,7 @@ def quantile_with_mask(
     if is_empty:
         # create the array of na_values
         # 2d len(values) * len(qs)
-        flat = np.array([fill_value] * len(qs))
+        flat = np.full(len(qs), fill_value)
         result = np.repeat(flat, len(values)).reshape(len(values), len(qs))
     else:
         result = _nanquantile(

pandas/core/arrays/_ranges.py

@@ -18,6 +18,8 @@
     iNaT,
 )
 
+from pandas.core.construction import range_to_ndarray
+
 if TYPE_CHECKING:
     from pandas._typing import npt
 
@@ -82,17 +84,7 @@ def generate_regular_range(
             "at least 'start' or 'end' should be specified if a 'period' is given."
         )
 
-    with np.errstate(over="raise"):
-        # If the range is sufficiently large, np.arange may overflow
-        #  and incorrectly return an empty array if not caught.
-        try:
-            values = np.arange(b, e, stride, dtype=np.int64)
-        except FloatingPointError:
-            xdr = [b]
-            while xdr[-1] != e:
-                xdr.append(xdr[-1] + stride)
-            values = np.array(xdr[:-1], dtype=np.int64)
-    return values
+    return range_to_ndarray(range(b, e, stride))
 
 
 def _generate_range_overflow_safe(

pandas/core/dtypes/dtypes.py

@@ -513,7 +513,7 @@ def _hash_categories(self) -> int:
                 [cat_array, np.arange(len(cat_array), dtype=cat_array.dtype)]
             )
         else:
-            cat_array = np.array([cat_array])
+            cat_array = cat_array.reshape(1, len(cat_array))
         combined_hashed = combine_hash_arrays(iter(cat_array), num_items=len(cat_array))
         return np.bitwise_xor.reduce(combined_hashed)
 

pandas/core/groupby/groupby.py

@@ -4370,11 +4370,12 @@ def post_processor(
 
             return vals
 
-        qs = np.array(q, dtype=np.float64)
-        pass_qs: np.ndarray | None = qs
         if is_scalar(q):
             qs = np.array([q], dtype=np.float64)
-            pass_qs = None
+            pass_qs: None | np.ndarray = None
+        else:
+            qs = np.asarray(q, dtype=np.float64)
+            pass_qs = qs
 
         ids = self._grouper.ids
         ngroups = self._grouper.ngroups

pandas/core/indexes/multi.py

@@ -2686,9 +2686,9 @@ def _get_codes_for_sorting(self) -> list[Categorical]:
         a valid valid
         """
 
-        def cats(level_codes):
+        def cats(level_codes: np.ndarray) -> np.ndarray:
             return np.arange(
-                np.array(level_codes).max() + 1 if len(level_codes) else 0,
+                level_codes.max() + 1 if len(level_codes) else 0,
                 dtype=level_codes.dtype,
             )
 

pandas/core/tools/datetimes.py

@@ -418,7 +418,7 @@ def _convert_listlike_datetimes(
         arg, _ = maybe_convert_dtype(arg, copy=False, tz=libtimezones.maybe_get_tz(tz))
     except TypeError:
         if errors == "coerce":
-            npvalues = np.array(["NaT"], dtype="datetime64[ns]").repeat(len(arg))
+            npvalues = np.full(len(arg), np.datetime64("NaT", "ns"))
             return DatetimeIndex(npvalues, name=name)
         raise
 

pandas/core/window/rolling.py

@@ -1186,7 +1186,7 @@ def homogeneous_func(values: np.ndarray):
                 return values.copy()
 
             def calc(x):
-                additional_nans = np.array([np.nan] * offset)
+                additional_nans = np.full(offset, np.nan)
                 x = np.concatenate((x, additional_nans))
                 return func(
                     x,