Use asarray where possible

pandas/core/array_algos/quantile.py

@@ -4,8 +4,6 @@
 
 import numpy as np
 
-from pandas.compat.numpy import np_version_gt2
-
 from pandas.core.dtypes.missing import (
     isna,
     na_value_for_dtype,
@@ -104,8 +102,7 @@ def quantile_with_mask(
             interpolation=interpolation,
         )
 
-        copy_false = None if np_version_gt2 else False
-        result = np.array(result, copy=copy_false)
+        result = np.asarray(result)
         result = result.T
 
     return result
@@ -202,12 +199,11 @@ def _nanpercentile(
             _nanpercentile_1d(val, m, qs, na_value, interpolation=interpolation)
             for (val, m) in zip(list(values), list(mask))
         ]
-        copy_false = None if np_version_gt2 else False
         if values.dtype.kind == "f":
             # preserve itemsize
-            result = np.array(result, dtype=values.dtype, copy=copy_false).T
+            result = np.asarray(result, dtype=values.dtype).T
         else:
-            result = np.array(result, copy=copy_false).T
+            result = np.asarray(result).T
             if (
                 result.dtype != values.dtype
                 and not mask.all()

pandas/core/arrays/base.py

@@ -27,10 +27,7 @@
     lib,
 )
 from pandas.compat import set_function_name
-from pandas.compat.numpy import (
-    function as nv,
-    np_version_gt2,
-)
+from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import (
     Appender,
@@ -713,8 +710,6 @@ def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
                 return self
             else:
                 return self.copy()
-        if np_version_gt2 and not copy:
-            copy = None
 
         if isinstance(dtype, ExtensionDtype):
             cls = dtype.construct_array_type()
@@ -730,7 +725,10 @@ def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
 
             return TimedeltaArray._from_sequence(self, dtype=dtype, copy=copy)
 
-        return np.array(self, dtype=dtype, copy=copy)
+        if not copy:
+            return np.asarray(self, dtype=dtype)
+        else:
+            return np.array(self, dtype=dtype, copy=copy)
 
     def isna(self) -> np.ndarray | ExtensionArraySupportsAnyAll:
         """

pandas/core/arrays/datetimes.py

@@ -43,7 +43,6 @@
     tzconversion,
 )
 from pandas._libs.tslibs.dtypes import abbrev_to_npy_unit
-from pandas.compat.numpy import np_version_gt2
 from pandas.errors import PerformanceWarning
 from pandas.util._exceptions import find_stack_level
 from pandas.util._validators import validate_inclusive
@@ -2422,8 +2421,7 @@ def objects_to_datetime64(
     assert errors in ["raise", "coerce"]
 
     # if str-dtype, convert
-    copy_false = None if np_version_gt2 else False
-    data = np.array(data, dtype=np.object_, copy=copy_false)
+    data = np.asarray(data, dtype=np.object_)
 
     result, tz_parsed = tslib.array_to_datetime(
         data,

pandas/core/arrays/numeric.py

@@ -13,7 +13,6 @@
     lib,
     missing as libmissing,
 )
-from pandas.compat.numpy import np_version_gt2
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import cache_readonly
 
@@ -138,12 +137,6 @@ def _coerce_to_data_and_mask(
     values, dtype, copy: bool, dtype_cls: type[NumericDtype], default_dtype: np.dtype
 ):
     checker = dtype_cls._checker
-    if np_version_gt2:
-        copy_false = None
-        if not copy:
-            copy = None
-    else:
-        copy_false = False
 
     mask = None
     inferred_type = None
@@ -167,7 +160,10 @@ def _coerce_to_data_and_mask(
         return values, mask, dtype, inferred_type
 
     original = values
-    values = np.array(values, copy=copy)
+    if not copy:
+        values = np.asarray(values)
+    else:
+        values = np.array(values, copy=copy)
     inferred_type = None
     if values.dtype == object or is_string_dtype(values.dtype):
         inferred_type = lib.infer_dtype(values, skipna=True)
@@ -176,7 +172,10 @@ def _coerce_to_data_and_mask(
             raise TypeError(f"{values.dtype} cannot be converted to {name}")
 
     elif values.dtype.kind == "b" and checker(dtype):
-        values = np.array(values, dtype=default_dtype, copy=copy)
+        if not copy:
+            values = np.asarray(values, dtype=default_dtype)
+        else:
+            values = np.array(values, dtype=default_dtype, copy=copy)
 
     elif values.dtype.kind not in "iuf":
         name = dtype_cls.__name__.strip("_")
@@ -215,9 +214,9 @@ def _coerce_to_data_and_mask(
                     inferred_type not in ["floating", "mixed-integer-float"]
                     and not mask.any()
                 ):
-                    values = np.array(original, dtype=dtype, copy=copy_false)
+                    values = np.asarray(original, dtype=dtype)
                 else:
-                    values = np.array(original, dtype="object", copy=copy_false)
+                    values = np.asarray(original, dtype="object")
 
     # we copy as need to coerce here
     if mask.any():

pandas/core/arrays/period.py

@@ -50,7 +50,6 @@
     get_period_field_arr,
     period_asfreq_arr,
 )
-from pandas.compat.numpy import np_version_gt2
 from pandas.util._decorators import (
     cache_readonly,
     doc,
@@ -244,9 +243,6 @@ def __init__(
             if not isinstance(dtype, PeriodDtype):
                 raise ValueError(f"Invalid dtype {dtype} for PeriodArray")
 
-        if np_version_gt2 and not copy:
-            copy = None
-
         if isinstance(values, ABCSeries):
             values = values._values
             if not isinstance(values, type(self)):
@@ -260,7 +256,10 @@ def __init__(
                 raise raise_on_incompatible(values, dtype.freq)
             values, dtype = values._ndarray, values.dtype
 
-        values = np.array(values, dtype="int64", copy=copy)
+        if not copy:
+            values = np.asarray(values, dtype="int64")
+        else:
+            values = np.array(values, dtype="int64", copy=copy)
         if dtype is None:
             raise ValueError("dtype is not specified and cannot be inferred")
         dtype = cast(PeriodDtype, dtype)

pandas/core/arrays/timedeltas.py

@@ -36,10 +36,7 @@
     parse_timedelta_unit,
     truediv_object_array,
 )
-from pandas.compat.numpy import (
-    function as nv,
-    np_version_gt2,
-)
+from pandas.compat.numpy import function as nv
 from pandas.util._validators import validate_endpoints
 
 from pandas.core.dtypes.common import (
@@ -1075,10 +1072,10 @@ def sequence_to_td64ns(
         # This includes datetime64-dtype, see GH#23539, GH#29794
         raise TypeError(f"dtype {data.dtype} cannot be converted to timedelta64[ns]")
 
-    copy_false = None if np_version_gt2 else False
     if not copy:
-        copy = copy_false
-    data = np.array(data, copy=copy)
+        data = np.asarray(data)
+    else:
+        data = np.array(data, copy=copy)
 
     assert data.dtype.kind == "m"
     assert data.dtype != "m8"  # i.e. not unit-less
@@ -1158,8 +1155,7 @@ def _objects_to_td64ns(
     higher level.
     """
     # coerce Index to np.ndarray, converting string-dtype if necessary
-    copy_false = None if np_version_gt2 else False
-    values = np.array(data, dtype=np.object_, copy=copy_false)
+    values = np.asarray(data, dtype=np.object_)
 
     result = array_to_timedelta64(values, unit=unit, errors=errors)
     return result.view("timedelta64[ns]")

pandas/core/construction.py

@@ -34,7 +34,6 @@
     DtypeObj,
     T,
 )
-from pandas.compat.numpy import np_version_gt2
 from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.base import ExtensionDtype
@@ -627,9 +626,10 @@ def sanitize_array(
 
     elif hasattr(data, "__array__"):
         # e.g. dask array GH#38645
-        if np_version_gt2 and not copy:
-            copy = None
-        data = np.array(data, copy=copy)
+        if not copy:
+            data = np.asarray(data)
+        else:
+            data = np.array(data, copy=copy)
         return sanitize_array(
             data,
             index=index,
@@ -738,9 +738,6 @@ def _sanitize_str_dtypes(
     """
     Ensure we have a dtype that is supported by pandas.
     """
-    copy_false = None if np_version_gt2 else False
-    if not copy:
-        copy = copy_false
 
     # This is to prevent mixed-type Series getting all casted to
     # NumPy string type, e.g. NaN --> '-1#IND'.
@@ -750,8 +747,11 @@ def _sanitize_str_dtypes(
         # GH#19853: If data is a scalar, result has already the result
         if not lib.is_scalar(data):
             if not np.all(isna(data)):
-                data = np.array(data, dtype=dtype, copy=copy_false)
-            result = np.array(data, dtype=object, copy=copy)
+                data = np.asarray(data, dtype=dtype)
+            if not copy:
+                result = np.asarray(data, dtype=object)
+            else:
+                result = np.array(data, dtype=object, copy=copy)
     return result
 
 
@@ -787,8 +787,6 @@ def _try_cast(
     np.ndarray or ExtensionArray
     """
     is_ndarray = isinstance(arr, np.ndarray)
-    if np_version_gt2 and not copy:
-        copy = None
 
     if dtype == object:
         if not is_ndarray:
@@ -818,6 +816,8 @@ def _try_cast(
         # this will raise if we have e.g. floats
 
         subarr = maybe_cast_to_integer_array(arr, dtype)
+    elif not copy:
+        subarr = np.asarray(arr, dtype=dtype)
     else:
         subarr = np.array(arr, dtype=dtype, copy=copy)
 

pandas/core/dtypes/cast.py

@@ -1491,9 +1491,6 @@ def construct_2d_arraylike_from_scalar(
     value: Scalar, length: int, width: int, dtype: np.dtype, copy: bool
 ) -> np.ndarray:
     shape = (length, width)
-    copy_false = None if np_version_gt2 else False
-    if not copy:
-        copy = copy_false
 
     if dtype.kind in "mM":
         value = _maybe_box_and_unbox_datetimelike(value, dtype)
@@ -1506,7 +1503,10 @@ def construct_2d_arraylike_from_scalar(
 
     # Attempt to coerce to a numpy array
     try:
-        arr = np.array(value, dtype=dtype, copy=copy)
+        if not copy:
+            arr = np.asarray(value, dtype=dtype)
+        else:
+            arr = np.array(value, dtype=dtype, copy=copy)
     except (ValueError, TypeError) as err:
         raise TypeError(
             f"DataFrame constructor called with incompatible data and dtype: {err}"
@@ -1655,8 +1655,7 @@ def maybe_cast_to_integer_array(arr: list | np.ndarray, dtype: np.dtype) -> np.n
                         "out-of-bound Python int",
                         DeprecationWarning,
                     )
-                copy_false = None if np_version_gt2 else False
-                casted = np.array(arr, dtype=dtype, copy=copy_false)
+                casted = np.asarray(arr, dtype=dtype)
         else:
             with warnings.catch_warnings():
                 warnings.filterwarnings("ignore", category=RuntimeWarning)

pandas/core/dtypes/missing.py

@@ -18,7 +18,6 @@
     NaT,
     iNaT,
 )
-from pandas.compat.numpy import np_version_gt2
 
 from pandas.core.dtypes.common import (
     DT64NS_DTYPE,
@@ -565,8 +564,7 @@ def infer_fill_value(val):
     """
     if not is_list_like(val):
         val = [val]
-    copy_false = None if np_version_gt2 else False
-    val = np.array(val, copy=copy_false)
+    val = np.asarray(val)
     if val.dtype.kind in "mM":
         return np.array("NaT", dtype=val.dtype)
     elif val.dtype == object:

pandas/core/indexes/multi.py

@@ -44,10 +44,7 @@
     Shape,
     npt,
 )
-from pandas.compat.numpy import (
-    function as nv,
-    np_version_gt2,
-)
+from pandas.compat.numpy import function as nv
 from pandas.errors import (
     InvalidIndexError,
     PerformanceWarning,
@@ -773,8 +770,7 @@ def _values(self) -> np.ndarray:
             ):
                 vals = vals.astype(object)
 
-            copy_false = None if np_version_gt2 else False
-            array_vals = np.array(vals, copy=copy_false)
+            array_vals = np.asarray(vals)
             array_vals = algos.take_nd(array_vals, codes, fill_value=index._na_value)
             values.append(array_vals)
 
@@ -3361,8 +3357,7 @@ def convert_indexer(start, stop, step, indexer=indexer, codes=level_codes):
                     locs = (level_codes >= idx.start) & (level_codes < idx.stop)
                     return locs
 
-                copy_false = None if np_version_gt2 else False
-                locs = np.array(level_codes == idx, dtype=bool, copy=copy_false)
+                locs = np.asarray(level_codes == idx, dtype=bool)
 
                 if not locs.any():
                     # The label is present in self.levels[level] but unused:

pandas/core/internals/managers.py

@@ -30,7 +30,6 @@
     BlockValuesRefs,
 )
 from pandas._libs.tslibs import Timestamp
-from pandas.compat.numpy import np_version_gt2
 from pandas.errors import (
     AbstractMethodError,
     PerformanceWarning,
@@ -1798,9 +1797,6 @@ def as_array(
         arr : ndarray
         """
         passed_nan = lib.is_float(na_value) and isna(na_value)
-        copy_false = None if np_version_gt2 else False
-        if not copy:
-            copy = copy_false
 
         if len(self.blocks) == 0:
             arr = np.empty(self.shape, dtype=float)
@@ -1828,6 +1824,8 @@ def as_array(
                     na_value=na_value,
                     copy=copy,
                 ).reshape(blk.shape)
+            elif not copy:
+                arr = np.asarray(blk.values, dtype=dtype)
             else:
                 arr = np.array(blk.values, dtype=dtype, copy=copy)
 

pandas/io/pytables.py

@@ -40,7 +40,6 @@
 from pandas._libs.lib import is_string_array
 from pandas._libs.tslibs import timezones
 from pandas.compat._optional import import_optional_dependency
-from pandas.compat.numpy import np_version_gt2
 from pandas.compat.pickle_compat import patch_pickle
 from pandas.errors import (
     AttributeConflictWarning,
@@ -4044,8 +4043,7 @@ def _create_axes(
             if isinstance(data_converted.dtype, CategoricalDtype):
                 ordered = data_converted.ordered
                 meta = "category"
-                copy_false = None if np_version_gt2 else False
-                metadata = np.array(data_converted.categories, copy=copy_false).ravel()
+                metadata = np.asarray(data_converted.categories).ravel()
 
             data, dtype_name = _get_data_and_dtype_name(data_converted)