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REF: share astype code in MaskedArray #38490

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Merged
merged 3 commits into from
Dec 21, 2020
Merged

REF: share astype code in MaskedArray #38490

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1efca14
REF: share astype code in MaskedArray
jbrockmendel Dec 15, 2020
8ad627b
mypy fixup
jbrockmendel Dec 15, 2020
856ec2c
super -> NotImplementedError
jbrockmendel Dec 19, 2020
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18 changes: 3 additions & 15 deletions pandas/core/arrays/boolean.py
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Original file line number Diff line number Diff line change
Expand Up @@ -372,34 +372,22 @@ def astype(self, dtype, copy: bool = True) -> ArrayLike:
if incompatible type with an BooleanDtype, equivalent of same_kind
casting
"""
from pandas.core.arrays.string_ import StringDtype

dtype = pandas_dtype(dtype)

if isinstance(dtype, BooleanDtype):
values, mask = coerce_to_array(self, copy=copy)
if not copy:
return self
else:
return BooleanArray(values, mask, copy=False)
elif isinstance(dtype, StringDtype):
return dtype.construct_array_type()._from_sequence(self, copy=False)
if is_extension_array_dtype(dtype):
return super().astype(dtype, copy)

if is_bool_dtype(dtype):
# astype_nansafe converts np.nan to True
if self._hasna:
raise ValueError("cannot convert float NaN to bool")
else:
return self._data.astype(dtype, copy=copy)
if is_extension_array_dtype(dtype) and is_integer_dtype(dtype):
from pandas.core.arrays import IntegerArray

return IntegerArray(
self._data.astype(dtype.numpy_dtype), self._mask.copy(), copy=False
)
# for integer, error if there are missing values
if is_integer_dtype(dtype) and self._hasna:
raise ValueError("cannot convert NA to integer")

# for float dtype, ensure we use np.nan before casting (numpy cannot
# deal with pd.NA)
na_value = self._na_value
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21 changes: 3 additions & 18 deletions pandas/core/arrays/floating.py
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Expand Up @@ -19,14 +19,13 @@
is_object_dtype,
pandas_dtype,
)
from pandas.core.dtypes.dtypes import register_extension_dtype
from pandas.core.dtypes.dtypes import ExtensionDtype, register_extension_dtype
from pandas.core.dtypes.missing import isna

from pandas.core import ops
from pandas.core.ops import invalid_comparison
from pandas.core.tools.numeric import to_numeric

from .masked import BaseMaskedDtype
from .numeric import NumericArray, NumericDtype


Expand Down Expand Up @@ -332,24 +331,10 @@ def astype(self, dtype, copy: bool = True) -> ArrayLike:
if incompatible type with an FloatingDtype, equivalent of same_kind
casting
"""
from pandas.core.arrays.string_ import StringArray, StringDtype

dtype = pandas_dtype(dtype)

# if the dtype is exactly the same, we can fastpath
if self.dtype == dtype:
# return the same object for copy=False
return self.copy() if copy else self
# if we are astyping to another nullable masked dtype, we can fastpath
if isinstance(dtype, BaseMaskedDtype):
# TODO deal with NaNs
data = self._data.astype(dtype.numpy_dtype, copy=copy)
# mask is copied depending on whether the data was copied, and
# not directly depending on the `copy` keyword
mask = self._mask if data is self._data else self._mask.copy()
return dtype.construct_array_type()(data, mask, copy=False)
elif isinstance(dtype, StringDtype):
return StringArray._from_sequence(self, copy=False)
if isinstance(dtype, ExtensionDtype):
return super().astype(dtype, copy=copy)

# coerce
if is_float_dtype(dtype):
Expand Down
20 changes: 3 additions & 17 deletions pandas/core/arrays/integer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
from pandas.compat.numpy import function as nv
from pandas.util._decorators import cache_readonly

from pandas.core.dtypes.base import register_extension_dtype
from pandas.core.dtypes.base import ExtensionDtype, register_extension_dtype
from pandas.core.dtypes.common import (
is_bool_dtype,
is_datetime64_dtype,
Expand Down Expand Up @@ -390,24 +390,10 @@ def astype(self, dtype, copy: bool = True) -> ArrayLike:
if incompatible type with an IntegerDtype, equivalent of same_kind
casting
"""
from pandas.core.arrays.masked import BaseMaskedDtype
from pandas.core.arrays.string_ import StringDtype

dtype = pandas_dtype(dtype)

# if the dtype is exactly the same, we can fastpath
if self.dtype == dtype:
# return the same object for copy=False
return self.copy() if copy else self
# if we are astyping to another nullable masked dtype, we can fastpath
if isinstance(dtype, BaseMaskedDtype):
data = self._data.astype(dtype.numpy_dtype, copy=copy)
# mask is copied depending on whether the data was copied, and
# not directly depending on the `copy` keyword
mask = self._mask if data is self._data else self._mask.copy()
return dtype.construct_array_type()(data, mask, copy=False)
elif isinstance(dtype, StringDtype):
return dtype.construct_array_type()._from_sequence(self, copy=False)
if isinstance(dtype, ExtensionDtype):
return super().astype(dtype, copy=copy)

# coerce
if is_float_dtype(dtype):
Expand Down
28 changes: 27 additions & 1 deletion pandas/core/arrays/masked.py
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Expand Up @@ -5,16 +5,18 @@
import numpy as np

from pandas._libs import lib, missing as libmissing
from pandas._typing import Scalar
from pandas._typing import ArrayLike, Dtype, Scalar
from pandas.errors import AbstractMethodError
from pandas.util._decorators import cache_readonly, doc

from pandas.core.dtypes.base import ExtensionDtype
from pandas.core.dtypes.common import (
is_dtype_equal,
is_integer,
is_object_dtype,
is_scalar,
is_string_dtype,
pandas_dtype,
)
from pandas.core.dtypes.missing import isna, notna

Expand Down Expand Up @@ -229,6 +231,30 @@ def to_numpy(
data = self._data.astype(dtype, copy=copy)
return data

def astype(self, dtype: Dtype, copy: bool = True) -> ArrayLike:
dtype = pandas_dtype(dtype)
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If we know this method is only called from the subclasses, this line is not needed (each of the subclass methods already does it as well. The type annotation also indicates we already have a dtype object)

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The type annotation also indicates we already have a dtype object)

heads up "Dtype" includes string; "DtypeObj" means a dtype object

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Ah, yes. Can you then update the annotation to DtypeObj? Or actually, to ExtensionDtype? Or will mypy complain about that because the methods in the subclasses have a less strict annotation?

BTW, I would still remove this line


if is_dtype_equal(dtype, self.dtype):
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here could in theory actually use if self.dtype == dtype since we know we have an extension dtype, and those should never raise when being compared (avoiding yet another set of checks, not sure if it would ever be significant, though).

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im fine with it either way. this branch was broken off from one that was trying to push some astype boilerplate into a decorator so used the is_dtype_equal version

if copy:
return self.copy()
return self

# if we are astyping to another nullable masked dtype, we can fastpath
if isinstance(dtype, BaseMaskedDtype):
# TODO deal with NaNs for FloatingArray case
data = self._data.astype(dtype.numpy_dtype, copy=copy)
# mask is copied depending on whether the data was copied, and
# not directly depending on the `copy` keyword
mask = self._mask if data is self._data else self._mask.copy()
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I wrote this code myself in the past, but I am not actually sure now this is needed. AFAIK the self._data.astype (numpy's method) will always return a copy unless dtype.numpy_dtype is the exact same type as the data. But that case should already be covered by the if is_dtype_equal(dtype, self.dtype) above.

(now since this is copying existing code, fine to leave this for another issue)

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makes sense, and i think you're right about the numpy behavior

cls = dtype.construct_array_type()
return cls(data, mask, copy=False)

if isinstance(dtype, ExtensionDtype):
cls = dtype.construct_array_type()
return cls._from_sequence(self, dtype=dtype, copy=copy)

return super().astype(dtype, copy=copy)
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this is in theory not reachable?

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not with the existing subclasses. this still seemed like the idiomatic thing to do

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I would personally remove it then, if it's not reachable at the moment.
Then you can also remove the if isinstance(dtype, ExtensionDtype): from the previous block.

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im not wild about this method relying on the current behavior of the existing subclasses. better to conform to the standard patterns, even if it means a couple of extra checks that turn out to be unnecessary

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@jorisvandenbossche jorisvandenbossche Dec 16, 2020 •
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Could also call it _astype instead of calling super(), to make it clearer it is a shared helper method, and not an actual (full) base implementation
(and then the method could also be properly typed)


__array_priority__ = 1000 # higher than ndarray so ops dispatch to us

def __array__(self, dtype=None) -> np.ndarray:
Expand Down
5 changes: 4 additions & 1 deletion pandas/core/arrays/string_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@
from pandas.core.dtypes.common import (
is_array_like,
is_bool_dtype,
is_dtype_equal,
is_integer_dtype,
is_object_dtype,
is_string_dtype,
Expand Down Expand Up @@ -285,10 +286,12 @@ def __setitem__(self, key, value):

def astype(self, dtype, copy=True):
dtype = pandas_dtype(dtype)
if isinstance(dtype, StringDtype):

if is_dtype_equal(dtype, self.dtype):
if copy:
return self.copy()
return self

elif isinstance(dtype, _IntegerDtype):
arr = self._ndarray.copy()
mask = self.isna()
Expand Down