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
if unstacker.mask.all():
new_indexer = indexer
allow_fill = False
new_mask2D = None
needs_masking = None
else:
new_indexer = np.full(unstacker.mask.shape, -1)
new_indexer[unstacker.mask] = indexer
allow_fill = True
# calculating the full mask once and passing it to take_1d is faster
# than letting take_1d calculate it in each repeated call
new_mask2D = (~unstacker.mask).reshape(*unstacker.full_shape)
needs_masking = new_mask2D.any(axis=0)
new_indexer2D = new_indexer.reshape(*unstacker.full_shape)
new_indexer2D = ensure_platform_int(new_indexer2D)
new_arrays = []
for arr in self.arrays:
for i in range(unstacker.full_shape[1]):
if allow_fill:
# error: Value of type "Optional[Any]" is not indexable [index]
new_arr = take_1d(
arr,
new_indexer2D[:, i],
allow_fill=needs_masking[i], # type: ignore[index]
fill_value=fill_value,
mask=new_mask2D[:, i], # type: ignore[index]
)
else:
new_arr = take_1d(arr,
new_indexer2D[:, i],
allow_fill=False)
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, verify_integrity=False)
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 = take_1d(
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, verify_integrity=False)
def _reindex_indexer(
self: T,
new_axis,
indexer,
axis: int,
fill_value=None,
allow_dups: bool = False,
copy: bool = True,
use_na_proxy: bool = False,
) -> T:
"""
Parameters
----------
new_axis : Index
indexer : ndarray of int64 or None
axis : int
fill_value : object, default None
allow_dups : bool, default False
copy : bool, default True
pandas-indexer with -1's only.
"""
if indexer is None:
if new_axis is self._axes[axis] and not copy:
return self
result = self.copy(deep=copy)
result._axes = list(self._axes)
result._axes[axis] = new_axis
return result
# some axes don't allow reindexing with dups
if not allow_dups:
self._axes[axis]._validate_can_reindex(indexer)
if axis >= self.ndim:
raise IndexError("Requested axis not found in manager")
if axis == 1:
new_arrays = []
for i in indexer:
if i == -1:
arr = self._make_na_array(fill_value=fill_value,
use_na_proxy=use_na_proxy)
else:
arr = self.arrays[i]
new_arrays.append(arr)
else:
validate_indices(indexer, len(self._axes[0]))
indexer = ensure_platform_int(indexer)
if (indexer == -1).any():
allow_fill = True
else:
allow_fill = False
new_arrays = [
take_1d(
arr,
indexer,
allow_fill=allow_fill,
fill_value=fill_value,
# if fill_value is not None else blk.fill_value
) for arr in self.arrays
]
new_axes = list(self._axes)
new_axes[axis] = new_axis
return type(self)(new_arrays, new_axes, verify_integrity=False)
def _reindex_indexer(
self: T,
new_axis,
indexer,
axis: int,
fill_value=None,
allow_dups: bool = False,
copy: bool = True,
) -> T:
"""
Parameters
----------
new_axis : Index
indexer : ndarray of int64 or None
axis : int
fill_value : object, default None
allow_dups : bool, default False
copy : bool, default True
pandas-indexer with -1's only.
"""
if indexer is None:
if new_axis is self._axes[axis] and not copy:
return self
result = self.copy(deep=copy)
result._axes = list(self._axes)
result._axes[axis] = new_axis
return result
# some axes don't allow reindexing with dups
if not allow_dups:
self._axes[axis]._validate_can_reindex(indexer)
if axis >= self.ndim:
raise IndexError("Requested axis not found in manager")
if axis == 1:
new_arrays = []
for i in indexer:
if i == -1:
arr = self._make_na_array(fill_value=fill_value)
else:
arr = self.arrays[i]
new_arrays.append(arr)
else:
validate_indices(indexer, len(self._axes[0]))
new_arrays = [
# error: Value of type variable "ArrayLike" of "take_1d" cannot be
# "Union[ndarray, ExtensionArray]" [type-var]
take_1d( # type: ignore[type-var]
arr,
indexer,
allow_fill=True,
fill_value=fill_value,
# if fill_value is not None else blk.fill_value
) for arr in self.arrays
]
new_axes = list(self._axes)
new_axes[axis] = new_axis
return type(self)(new_arrays, new_axes, verify_integrity=False)