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TYP: Arraylike alias change follow up #40398

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Merged
merged 7 commits into from
Mar 13, 2021
Merged

TYP: Arraylike alias change follow up #40398

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193ff2f
more overloads for DatetimeLikeArrayMixin.view
simonjayhawkins Mar 12, 2021
a6500c7
overload astype_nansafe
simonjayhawkins Mar 12, 2021
1570db3
Literal for numpy order argument
simonjayhawkins Mar 12, 2021
c5374e3
np.string_ -> np.dtype(str)
simonjayhawkins Mar 12, 2021
e151708
Merge remote-tracking branch 'upstream/master' into arraylike-alias-c…
simonjayhawkins Mar 13, 2021
c8740e5
post merge clean
simonjayhawkins Mar 13, 2021
683d441
remove cast
simonjayhawkins Mar 13, 2021
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10 changes: 10 additions & 0 deletions pandas/core/arrays/datetimelike.py
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Original file line number Diff line number Diff line change
Expand Up @@ -123,6 +123,8 @@
from pandas.tseries import frequencies

if TYPE_CHECKING:
from typing import Literal

from pandas.core.arrays import (
DatetimeArray,
TimedeltaArray,
Expand Down Expand Up @@ -458,6 +460,14 @@ def astype(self, dtype, copy=True):
def view(self: DatetimeLikeArrayT) -> DatetimeLikeArrayT:
...

@overload
def view(self, dtype: Literal["M8[ns]"]) -> DatetimeArray:
...

@overload
def view(self, dtype: Literal["m8[ns]"]) -> TimedeltaArray:
...

@overload
def view(self, dtype: Optional[Dtype] = ...) -> ArrayLike:
...
Expand Down
6 changes: 1 addition & 5 deletions pandas/core/arrays/period.py
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Expand Up @@ -643,11 +643,7 @@ def fillna(self, value=None, method=None, limit=None) -> PeriodArray:
if method is not None:
# view as dt64 so we get treated as timelike in core.missing
dta = self.view("M8[ns]")
# error: Item "ndarray" of "Union[ExtensionArray, ndarray]" has no attribute
# "fillna"
result = dta.fillna( # type: ignore[union-attr]
value=value, method=method, limit=limit
)
result = dta.fillna(value=value, method=method, limit=limit)
return result.view(self.dtype)
return super().fillna(value=value, method=method, limit=limit)

Expand Down
9 changes: 2 additions & 7 deletions pandas/core/arrays/sparse/dtype.py
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Expand Up @@ -27,7 +27,6 @@
from pandas.core.dtypes.cast import astype_nansafe
from pandas.core.dtypes.common import (
is_bool_dtype,
is_extension_array_dtype,
is_object_dtype,
is_scalar,
is_string_dtype,
Expand Down Expand Up @@ -339,14 +338,10 @@ def update_dtype(self, dtype):
dtype = pandas_dtype(dtype)

if not isinstance(dtype, cls):
if is_extension_array_dtype(dtype):
if not isinstance(dtype, np.dtype):
raise TypeError("sparse arrays of extension dtypes not supported")

# error: Item "ExtensionArray" of "Union[ExtensionArray, ndarray]" has no
# attribute "item"
fill_value = astype_nansafe( # type: ignore[union-attr]
np.array(self.fill_value), dtype
).item()
fill_value = astype_nansafe(np.array(self.fill_value), dtype).item()
dtype = cls(dtype, fill_value=fill_value)

return dtype
Expand Down
25 changes: 19 additions & 6 deletions pandas/core/dtypes/cast.py
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Expand Up @@ -25,6 +25,7 @@
Type,
Union,
cast,
overload,
)
import warnings

Expand Down Expand Up @@ -107,6 +108,8 @@
)

if TYPE_CHECKING:
from typing import Literal

from pandas import Series
from pandas.core.arrays import (
DatetimeArray,
Expand Down Expand Up @@ -1164,6 +1167,20 @@ def astype_td64_unit_conversion(
return result


@overload
def astype_nansafe(
arr: np.ndarray, dtype: np.dtype, copy: bool = ..., skipna: bool = ...
) -> np.ndarray:
...


@overload
def astype_nansafe(
arr: np.ndarray, dtype: ExtensionDtype, copy: bool = ..., skipna: bool = ...
) -> ExtensionArray:
...


def astype_nansafe(
arr: np.ndarray, dtype: DtypeObj, copy: bool = True, skipna: bool = False
) -> ArrayLike:
Expand All @@ -1190,14 +1207,10 @@ def astype_nansafe(
flags = arr.flags
flat = arr.ravel("K")
result = astype_nansafe(flat, dtype, copy=copy, skipna=skipna)
order = "F" if flags.f_contiguous else "C"
order: Literal["C", "F"] = "F" if flags.f_contiguous else "C"
# error: Item "ExtensionArray" of "Union[ExtensionArray, ndarray]" has no
# attribute "reshape"
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@jbrockmendel this is not necessarily a false positive. I think this maybe assuming that if ndim > 1, we have a numpy array. but a 2d EA will have additional methods such as reshape.

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yah even if we got here with NDArrayBackedExtensionArray (which does have reshape) itd break bc it doesn't have flags. ignoring for the time being seems reasonable

# error: No overload variant of "reshape" of "_ArrayOrScalarCommon" matches
# argument types "Tuple[int, ...]", "str"
return result.reshape( # type: ignore[union-attr,call-overload]
arr.shape, order=order
)
return result.reshape(arr.shape, order=order) # type: ignore[union-attr]

# We get here with 0-dim from sparse
arr = np.atleast_1d(arr)
Expand Down