def test_bounds_check_large(self):
arr = np.array([1, 2])
with pytest.raises(IndexError):
algos.take(arr, [2, 3], allow_fill=True)
with pytest.raises(IndexError):
algos.take(arr, [2, 3], allow_fill=False)
def test_bounds_check_large(self):
arr = np.array([1, 2])
with pytest.raises(IndexError):
algos.take(arr, [2, 3], allow_fill=True)
with pytest.raises(IndexError):
algos.take(arr, [2, 3], allow_fill=False)
def take(self, indices, allow_fill=False, fill_value=None, axis=None, **kwargs):
"""
Take elements from the IntervalArray.
Parameters
----------
indices : sequence of integers
Indices to be taken.
allow_fill : bool, default False
How to handle negative values in `indices`.
* False: negative values in `indices` indicate positional indices
from the right (the default). This is similar to
:func:`numpy.take`.
* True: negative values in `indices` indicate
missing values. These values are set to `fill_value`. Any other
other negative values raise a ``ValueError``.
fill_value : Interval or NA, optional
Fill value to use for NA-indices when `allow_fill` is True.
This may be ``None``, in which case the default NA value for
the type, ``self.dtype.na_value``, is used.
For many ExtensionArrays, there will be two representations of
`fill_value`: a user-facing "boxed" scalar, and a low-level
physical NA value. `fill_value` should be the user-facing version,
and the implementation should handle translating that to the
physical version for processing the take if necessary.
axis : any, default None
Present for compat with IntervalIndex; does nothing.
Returns
-------
IntervalArray
Raises
------
IndexError
When the indices are out of bounds for the array.
ValueError
When `indices` contains negative values other than ``-1``
and `allow_fill` is True.
"""
nv.validate_take(tuple(), kwargs)
fill_left = fill_right = fill_value
if allow_fill:
fill_left, fill_right = self._validate_fill_value(fill_value)
left_take = take(
self._left, indices, allow_fill=allow_fill, fill_value=fill_left
)
right_take = take(
self._right, indices, allow_fill=allow_fill, fill_value=fill_right
)
return self._shallow_copy(left_take, right_take)
def take(
self: BaseMaskedArrayT,
indexer,
*,
allow_fill: bool = False,
fill_value: Scalar | None = None,
) -> BaseMaskedArrayT:
# we always fill with 1 internally
# to avoid upcasting
data_fill_value = self._internal_fill_value if isna(
fill_value) else fill_value
result = take(self._data,
indexer,
fill_value=data_fill_value,
allow_fill=allow_fill)
mask = take(self._mask,
indexer,
fill_value=True,
allow_fill=allow_fill)
# if we are filling
# we only fill where the indexer is null
# not existing missing values
# TODO(jreback) what if we have a non-na float as a fill value?
if allow_fill and notna(fill_value):
fill_mask = np.asarray(indexer) == -1
result[fill_mask] = fill_value
mask = mask ^ fill_mask
return type(self)(result, mask, copy=False)
def test_take_empty(self, allow_fill):
arr = np.array([], dtype=np.int64)
# empty take is ok
result = algos.take(arr, [], allow_fill=allow_fill)
tm.assert_numpy_array_equal(arr, result)
with pytest.raises(IndexError):
algos.take(arr, [0], allow_fill=allow_fill)
def test_take_empty(self, allow_fill):
arr = np.array([], dtype=np.int64)
# empty take is ok
result = algos.take(arr, [], allow_fill=allow_fill)
tm.assert_numpy_array_equal(arr, result)
with pytest.raises(IndexError):
algos.take(arr, [0], allow_fill=allow_fill)
def test_bounds_check_small(self):
arr = np.array([1, 2, 3], dtype=np.int64)
indexer = [0, -1, -2]
with pytest.raises(ValueError):
algos.take(arr, indexer, allow_fill=True)
result = algos.take(arr, indexer)
expected = np.array([1, 3, 2], dtype=np.int64)
tm.assert_numpy_array_equal(result, expected)
def test_bounds_check_small(self):
arr = np.array([1, 2, 3], dtype=np.int64)
indexer = [0, -1, -2]
with pytest.raises(ValueError):
algos.take(arr, indexer, allow_fill=True)
result = algos.take(arr, indexer)
expected = np.array([1, 3, 2], dtype=np.int64)
tm.assert_numpy_array_equal(result, expected)
def test_take_axis_0(self):
arr = np.arange(12).reshape(4, 3)
result = algos.take(arr, [0, -1])
expected = np.array([[0, 1, 2], [9, 10, 11]])
tm.assert_numpy_array_equal(result, expected)
# allow_fill=True
result = algos.take(arr, [0, -1], allow_fill=True, fill_value=0)
expected = np.array([[0, 1, 2], [0, 0, 0]])
tm.assert_numpy_array_equal(result, expected)
def test_take_empty(self, allow_fill):
arr = np.array([], dtype=np.int64)
# empty take is ok
result = algos.take(arr, [], allow_fill=allow_fill)
tm.assert_numpy_array_equal(arr, result)
msg = ("cannot do a non-empty take from an empty axes.|"
"indices are out-of-bounds")
with pytest.raises(IndexError, match=msg):
algos.take(arr, [0], allow_fill=allow_fill)
def test_bounds_check_large(self):
arr = np.array([1, 2])
msg = "indices are out-of-bounds"
with pytest.raises(IndexError, match=msg):
algos.take(arr, [2, 3], allow_fill=True)
msg = "index 2 is out of bounds for( axis 0 with)? size 2"
with pytest.raises(IndexError, match=msg):
algos.take(arr, [2, 3], allow_fill=False)
def test_take_axis_0(self):
arr = np.arange(12).reshape(4, 3)
result = algos.take(arr, [0, -1])
expected = np.array([[0, 1, 2], [9, 10, 11]])
tm.assert_numpy_array_equal(result, expected)
# allow_fill=True
result = algos.take(arr, [0, -1], allow_fill=True, fill_value=0)
expected = np.array([[0, 1, 2], [0, 0, 0]])
tm.assert_numpy_array_equal(result, expected)
def test_bounds_check_small(self):
arr = np.array([1, 2, 3], dtype=np.int64)
indexer = [0, -1, -2]
msg = r"'indices' contains values less than allowed \(-2 < -1\)"
with pytest.raises(ValueError, match=msg):
algos.take(arr, indexer, allow_fill=True)
result = algos.take(arr, indexer)
expected = np.array([1, 3, 2], dtype=np.int64)
tm.assert_numpy_array_equal(result, expected)
def test_take_non_hashable_fill_value(self):
arr = np.array([1, 2, 3])
indexer = np.array([1, -1])
with pytest.raises(ValueError, match="fill_value must be a scalar"):
algos.take(arr, indexer, allow_fill=True, fill_value=[1])
# with object dtype it is allowed
arr = np.array([1, 2, 3], dtype=object)
result = algos.take(arr, indexer, allow_fill=True, fill_value=[1])
expected = np.array([2, [1]], dtype=object)
tm.assert_numpy_array_equal(result, expected)
def test_take_axis_1(self):
arr = np.arange(12).reshape(4, 3)
result = algos.take(arr, [0, -1], axis=1)
expected = np.array([[0, 2], [3, 5], [6, 8], [9, 11]])
tm.assert_numpy_array_equal(result, expected)
# allow_fill=True
result = algos.take(arr, [0, -1], axis=1, allow_fill=True, fill_value=0)
expected = np.array([[0, 0], [3, 0], [6, 0], [9, 0]])
tm.assert_numpy_array_equal(result, expected)
# GH#26976 make sure we validate along the correct axis
with pytest.raises(IndexError, match="indices are out-of-bounds"):
algos.take(arr, [0, 3], axis=1, allow_fill=True, fill_value=0)
def take(self, indices, allow_fill=False, fill_value=None):
"""Take elements from an array.
# type: (Sequence[int], bool, Optional[Any]) -> PintArray
Parameters
----------
indices : sequence of integers
Indices to be taken.
allow_fill : bool, default False
How to handle negative values in `indices`.
* False: negative values in `indices` indicate positional indices
from the right (the default). This is similar to
:func:`numpy.take`.
* True: negative values in `indices` indicate
missing values. These values are set to `fill_value`. Any other
other negative values raise a ``ValueError``.
fill_value : any, optional
Fill value to use for NA-indices when `allow_fill` is True.
This may be ``None``, in which case the default NA value for
the type, ``self.dtype.na_value``, is used.
Returns
-------
PintArray
Raises
------
IndexError
When the indices are out of bounds for the array.
ValueError
When `indices` contains negative values other than ``-1``
and `allow_fill` is True.
Notes
-----
PintArray.take is called by ``Series.__getitem__``, ``.loc``,
``iloc``, when `indices` is a sequence of values. Additionally,
it's called by :meth:`Series.reindex`, or any other method
that causes realignemnt, with a `fill_value`.
See Also
--------
numpy.take
pandas.api.extensions.take
Examples
--------
"""
from pandas.core.algorithms import take
data = self._data
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
if isinstance(fill_value, _Quantity):
fill_value = fill_value.to(self.units).magnitude
result = take(data,
indices,
fill_value=fill_value,
allow_fill=allow_fill)
return PintArray(result, dtype=self.dtype)
def take(self, indices, allow_fill=False, fill_value=None):
if (allow_fill is False or (allow_fill and fill_value is self.dtype.na_value)) \
and len(self) > 0:
return type(self)(self[indices], dtype=self._dtype)
if self._use_arrow:
array = self._arrow_array.to_pandas().to_numpy()
else:
array = self._ndarray
replace = False
if allow_fill and \
(fill_value is None or fill_value == self._dtype.na_value):
fill_value = self.dtype.na_value
replace = True
result = take(array,
indices,
fill_value=fill_value,
allow_fill=allow_fill)
del array
if replace and pa is not None:
# pyarrow cannot recognize pa.NULL
result[result == self.dtype.na_value] = None
return type(self)(result, dtype=self._dtype)
def unstack(self, unstacker, fill_value) -> ArrayManager:
"""
Return a BlockManager with all blocks unstacked..
Parameters
----------
unstacker : reshape._Unstacker
fill_value : Any
fill_value for newly introduced missing values.
Returns
-------
unstacked : BlockManager
"""
indexer, _ = unstacker._indexer_and_to_sort
new_indexer = np.full(unstacker.mask.shape, -1)
new_indexer[unstacker.mask] = indexer
new_indexer2D = new_indexer.reshape(*unstacker.full_shape)
new_arrays = []
for arr in self.arrays:
for i in range(unstacker.full_shape[1]):
new_arr = algos.take(arr,
new_indexer2D[:, i],
allow_fill=True,
fill_value=fill_value)
new_arrays.append(new_arr)
new_index = unstacker.new_index
new_columns = unstacker.get_new_columns(self._axes[1])
new_axes = [new_index, new_columns]
return type(self)(new_arrays, new_axes, do_integrity_check=False)
def take(self, indices, allow_fill=False, fill_value=None):
from pandas.core.algorithms import take
result = take(self._ndarray,
indices,
allow_fill=allow_fill,
fill_value=fill_value)
return type(self)(result)
def take(self, indices, allow_fill=False, fill_value=None):
if fill_value is None:
# Primarily for subclasses
fill_value = self.dtype.na_value
result = take(
self._ndarray, indices, allow_fill=allow_fill, fill_value=fill_value
)
return type(self)(result)
def take(self, indices, allow_fill=False, fill_value=None):
if allow_fill:
fill_value = self._validate_fill_value(fill_value)
new_values = take(self.asi8,
indices,
allow_fill=allow_fill,
fill_value=fill_value)
return type(self)(new_values, dtype=self.dtype)
def take(self, indices, allow_fill=False, fill_value=None):
if allow_fill:
fill_value = self._validate_fill_value(fill_value)
new_values = take(self.asi8,
indices,
allow_fill=allow_fill,
fill_value=fill_value)
return type(self)(new_values, dtype=self.dtype)
def take(
self: _T,
indices: Sequence[int],
allow_fill: bool = False,
fill_value: Any = None,
) -> _T:
if allow_fill:
fill_value = self._validate_fill_value(fill_value)
new_data = take(
self._ndarray, indices, allow_fill=allow_fill, fill_value=fill_value,
)
return self._from_backing_data(new_data)
def take(self, indices, allow_fill=False, fill_value=None):
from pandas.core.algorithms import take
# If the ExtensionArray is backed by an ndarray, then
# just pass that here instead of coercing to object.
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
# fill value should always be translated from the scalar
# type for the array, to the physical storage type for
# the data, before passing to take.
result = take(self.data, indices, fill_value=fill_value,
allow_fill=allow_fill)
return self._from_sequence(result)
def take(self, indices, allow_fill=False, fill_value=None):
from pandas.core.algorithms import take
if allow_fill:
if fill_value is None or pd.isna(fill_value):
fill_value = None
elif not isinstance(fill_value, self.dtype.type):
raise TypeError('Provide geometry or None as fill value')
result = take(self.data,
indices,
allow_fill=allow_fill,
fill_value=fill_value)
if allow_fill and fill_value is None:
result[pd.isna(result)] = None
return self.__class__(result)
def take(
self: NDArrayBackedExtensionArrayT,
indices: TakeIndexer,
*,
allow_fill: bool = False,
fill_value: Any = None,
axis: int = 0,
) -> NDArrayBackedExtensionArrayT:
if allow_fill:
fill_value = self._validate_scalar(fill_value)
new_data = take(
self._ndarray,
indices,
allow_fill=allow_fill,
fill_value=fill_value,
axis=axis,
)
return self._from_backing_data(new_data)
def take(self, indices, allow_fill=False, fill_value=None):
if allow_fill is False:
return ArrowStringArray(self[indices])
string_array = self._arrow_array.to_pandas().to_numpy()
replace = False
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
replace = True
result = take(string_array,
indices,
fill_value=fill_value,
allow_fill=allow_fill)
if replace:
# pyarrow cannot recognize pa.NULL
result[result == self.dtype.na_value] = None
return ArrowStringArray(result)
def take(self, indices, allow_fill=False, fill_value=None):
if allow_fill is False:
return type(self)(self[indices], dtype=self._dtype)
array = self._arrow_array.to_pandas().to_numpy()
replace = False
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
replace = True
result = take(array,
indices,
fill_value=fill_value,
allow_fill=allow_fill)
del array
if replace:
# pyarrow cannot recognize pa.NULL
result[result == self.dtype.na_value] = None
return type(self)(result, dtype=self._dtype)
def take(
self: NDArrayBackedExtensionArrayT,
indices: Sequence[int],
*,
allow_fill: bool = False,
fill_value: Any = None,
axis: int = 0,
) -> NDArrayBackedExtensionArrayT:
if allow_fill:
fill_value = self._validate_fill_value(fill_value)
new_data = take(
self._ndarray,
# error: Argument 2 to "take" has incompatible type "Sequence[int]";
# expected "ndarray"
indices, # type: ignore[arg-type]
allow_fill=allow_fill,
fill_value=fill_value,
axis=axis,
)
return self._from_backing_data(new_data)
def take(self, indices, allow_fill=False, fill_value=None):
if allow_fill:
if isna(fill_value):
fill_value = iNaT
elif isinstance(fill_value, Period):
if self.freq != fill_value.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freq.freqstr,
fill_value.freqstr)
raise IncompatibleFrequency(msg)
fill_value = fill_value.ordinal
else:
msg = "'fill_value' should be a Period. Got '{}'."
raise ValueError(msg.format(fill_value))
new_values = algos.take(self._data,
indices,
allow_fill=allow_fill,
fill_value=fill_value)
return type(self)(new_values, self.freq)
def take(self, indices, allow_fill=False, fill_value=None):
from pandas.core.algorithms import take
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
if fill_value is self.dtype.na_value:
fill_value = self.dtype._record_na_value
# fill value should always be translated from the scalar
# type for the array, to the physical storage type for
# the data, before passing to take.
indices = np.asarray(indices)
if allow_fill:
mask = (indices == -1)
if not len(self):
if not (indices == -1).all():
msg = "Invalid take for empty array. Must be all -1."
raise IndexError(msg)
else:
# all NA take from and empty array
result = np.zeros(len(indices),
dtype=self.dtype._record_type)
result.fill(fill_value)
return self._from_ndarray(result)
if (np.asarray(indices) < -1).any():
msg = ("Invalid value in 'indices'. Must be all >= -1 "
"for 'allow_fill=True'")
raise ValueError(msg)
result = take(self.data, indices, allow_fill=False)
if allow_fill:
result[mask] = fill_value
return self._from_sequence(result,
dtype=self.dtype,
default_money_code=self.default_money_code)
def take(self, indices, allow_fill=False, fill_value=None):
if allow_fill:
if isna(fill_value):
fill_value = iNaT
elif isinstance(fill_value, Period):
if self.freq != fill_value.freq:
msg = DIFFERENT_FREQ_INDEX.format(
self.freq.freqstr,
fill_value.freqstr
)
raise IncompatibleFrequency(msg)
fill_value = fill_value.ordinal
else:
msg = "'fill_value' should be a Period. Got '{}'."
raise ValueError(msg.format(fill_value))
new_values = algos.take(self._data,
indices,
allow_fill=allow_fill,
fill_value=fill_value)
return type(self)(new_values, self.freq)
def test_take_na_empty(self):
result = algos.take(np.array([]), [-1, -1],
allow_fill=True,
fill_value=0.0)
expected = np.array([0., 0.])
tm.assert_numpy_array_equal(result, expected)
def take(self, indices, allow_fill=False, fill_value=None):
from pandas.core.algorithms import take
result = take(self._ndarray, indices, allow_fill=allow_fill,
fill_value=fill_value)
return type(self)(result)
def test_take_coerces_list(self):
arr = [1, 2, 3]
result = algos.take(arr, [0, 0])
expected = np.array([1, 1])
tm.assert_numpy_array_equal(result, expected)
def test_take_na_empty(self):
result = algos.take(np.array([]), [-1, -1], allow_fill=True,
fill_value=0.0)
expected = np.array([0., 0.])
tm.assert_numpy_array_equal(result, expected)
def take(self, indices, allow_fill=False, fill_value=None, axis=None,
**kwargs):
"""
Take elements from the IntervalArray.
Parameters
----------
indices : sequence of integers
Indices to be taken.
allow_fill : bool, default False
How to handle negative values in `indices`.
* False: negative values in `indices` indicate positional indices
from the right (the default). This is similar to
:func:`numpy.take`.
* True: negative values in `indices` indicate
missing values. These values are set to `fill_value`. Any other
other negative values raise a ``ValueError``.
fill_value : Interval or NA, optional
Fill value to use for NA-indices when `allow_fill` is True.
This may be ``None``, in which case the default NA value for
the type, ``self.dtype.na_value``, is used.
For many ExtensionArrays, there will be two representations of
`fill_value`: a user-facing "boxed" scalar, and a low-level
physical NA value. `fill_value` should be the user-facing version,
and the implementation should handle translating that to the
physical version for processing the take if necessary.
axis : any, default None
Present for compat with IntervalIndex; does nothing.
Returns
-------
IntervalArray
Raises
------
IndexError
When the indices are out of bounds for the array.
ValueError
When `indices` contains negative values other than ``-1``
and `allow_fill` is True.
"""
from pandas.core.algorithms import take
nv.validate_take(tuple(), kwargs)
fill_left = fill_right = fill_value
if allow_fill:
if fill_value is None:
fill_left = fill_right = self.left._na_value
elif is_interval(fill_value):
self._check_closed_matches(fill_value, name='fill_value')
fill_left, fill_right = fill_value.left, fill_value.right
elif not is_scalar(fill_value) and notna(fill_value):
msg = ("'IntervalArray.fillna' only supports filling with a "
"'scalar pandas.Interval or NA'. Got a '{}' instead."
.format(type(fill_value).__name__))
raise ValueError(msg)
left_take = take(self.left, indices,
allow_fill=allow_fill, fill_value=fill_left)
right_take = take(self.right, indices,
allow_fill=allow_fill, fill_value=fill_right)
return self._shallow_copy(left_take, right_take)
def take(self, indices, allow_fill=False, fill_value=None):
"""
Take elements from an array.
Parameters
----------
indices : sequence of integers
Indices to be taken.
allow_fill : bool, default False
How to handle negative values in `indices`.
* False: negative values in `indices` indicate position indices
from the right (the default). This is similar to
:func:`numpy.take`.
* True: negative values in `indices` indicate missing values.
These values are set to `fill_value`. Any other negative values
raise a ``ValueError``.
fill_value : any, optional
Fill value to use for NA-indices when `allow_fill` is True.
This may be ``None``, in which case the default NA value for the
type, ``self.dtype.na_value``, is used.
For many ExtensionArrays, there will be two representations of
`fill_value`: a user-facing "boxed" scalar, and
a low-level physical NA value. `fill_value` should be the
user-facing version, and the implementation should
handle translating that to the physical version for
processing the take if necessary.
Returns
-------
ExtensionArray
Raises
------
IndexError
When the indices are out of bounds for the array.
ValueError
When `indices` contain negative values other than ``-1``
and `allow_fill` is True.
Notes
-----
ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``,
``iloc``, when `indices` is a sequence of values. Additionally,
it's called by :meth:`Series.reindex`, or any other method
that causes realignemnt, with a `fill_value`.
See Also
--------
numpy.take
pandas.api.extensions.take
"""
from pandas.core.algorithms import take
data = self.astype(object)
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
# fill value should always be translated from the scalar
# type for the array, to the physical storage type for
# the data, before passing to take.
result = take(data,
indices,
fill_value=fill_value,
allow_fill=allow_fill)
return self._from_sequence(result)
def take(self,
indices,
allow_fill=False,
fill_value=None,
axis=None,
**kwargs):
"""
Take elements from the IntervalArray.
Parameters
----------
indices : sequence of integers
Indices to be taken.
allow_fill : bool, default False
How to handle negative values in `indices`.
* False: negative values in `indices` indicate positional indices
from the right (the default). This is similar to
:func:`numpy.take`.
* True: negative values in `indices` indicate
missing values. These values are set to `fill_value`. Any other
other negative values raise a ``ValueError``.
fill_value : Interval or NA, optional
Fill value to use for NA-indices when `allow_fill` is True.
This may be ``None``, in which case the default NA value for
the type, ``self.dtype.na_value``, is used.
For many ExtensionArrays, there will be two representations of
`fill_value`: a user-facing "boxed" scalar, and a low-level
physical NA value. `fill_value` should be the user-facing version,
and the implementation should handle translating that to the
physical version for processing the take if necessary.
axis : any, default None
Present for compat with IntervalIndex; does nothing.
Returns
-------
IntervalArray
Raises
------
IndexError
When the indices are out of bounds for the array.
ValueError
When `indices` contains negative values other than ``-1``
and `allow_fill` is True.
"""
from pandas.core.algorithms import take
nv.validate_take(tuple(), kwargs)
fill_left = fill_right = fill_value
if allow_fill:
if fill_value is None:
fill_left = fill_right = self.left._na_value
elif is_interval(fill_value):
self._check_closed_matches(fill_value, name="fill_value")
fill_left, fill_right = fill_value.left, fill_value.right
elif not is_scalar(fill_value) and notna(fill_value):
msg = ("'IntervalArray.fillna' only supports filling with a "
"'scalar qq_pandas.Interval or NA'. Got a '{}' instead."
.format(type(fill_value).__name__))
raise ValueError(msg)
left_take = take(self.left,
indices,
allow_fill=allow_fill,
fill_value=fill_left)
right_take = take(self.right,
indices,
allow_fill=allow_fill,
fill_value=fill_right)
return self._shallow_copy(left_take, right_take)
def test_take_coerces_list(self):
arr = [1, 2, 3]
result = algos.take(arr, [0, 0])
expected = np.array([1, 1])
tm.assert_numpy_array_equal(result, expected)
