def take(self, indexer, allow_fill=False, fill_value=None):
from pandas.api.extensions import take
# we always fill with 1 internally
# to avoid upcasting
data_fill_value = 1 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 take(self, indices, allow_fill=False, fill_value=None):
from pandas.api.extensions import take
result = take(self.data,
indices,
allow_fill=allow_fill,
fill_value=fill_value)
return MyArry(result)
def take(self, indices, allow_fill=False, fill_value=None):
data = self._data.to_pandas()
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
result = take(data, indices, fill_value=fill_value, allow_fill=allow_fill)
return self._from_sequence(result, dtype=self.dtype)
def take(self, indexer, allow_fill=False, fill_value=None):
if fill_value is None:
fill_value = 0
took = take(self.data,
indexer,
allow_fill=allow_fill,
fill_value=fill_value)
return type(self)(took)
def take(self, indexer, allow_fill=False, fill_value=None):
from pandas.api.extensions import take
data = self._data
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
result = take(data, indexer, fill_value=fill_value, allow_fill=allow_fill)
return self._from_sequence(result)
def take(self, indices, allow_fill=False, fill_value=None):
data = self._data.to_pandas()
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
result = take(data, indices, fill_value=fill_value,
allow_fill=allow_fill)
return self._from_sequence(result, dtype=self.dtype)
def take(self, indexer, allow_fill=False, fill_value=None):
from pandas.api.extensions import take
data = self._data
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
result = take(data, indexer, 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.api.extensions import take
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 StairsArray(result)
def take(self, indices, allow_fill=False, fill_value=None):
from pandas.api.extensions import take
if allow_fill:
if fill_value is None or pd.isna(fill_value):
fill_value = 0
result = take(self.data, indices, allow_fill=allow_fill, fill_value=fill_value)
if fill_value == 0:
result[result == 0] = None
return GeometryArray(result)
def take(self, indexer, allow_fill=False, fill_value=None):
from pandas.api.extensions import take
# we always fill with 1 internally
# to avoid upcasting
data_fill_value = 1 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 take(self, indices, allow_fill=False, fill_value=None):
from pandas.api.extensions import take
if allow_fill:
if fill_value is None or pd.isna(fill_value):
fill_value = None
elif not is_scalar_geometry(fill_value):
raise TypeError("Expected None or geometry 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 GeoArray(result)
def take(self, indexer, allow_fill=False, fill_value=None):
"""Take elements from an array.
Relies on the take method defined in pandas:
https://github.com/pandas-dev/pandas/blob/e246c3b05924ac1fe083565a765ce847fcad3d91/pandas/core/algorithms.py#L1483
"""
from pandas.api.extensions import take
data = self._data
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
result = take(
data, indexer, 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.api.extensions import take
if allow_fill:
if fill_value is None or pd.isna(fill_value):
fill_value = None
elif isinstance(fill_value, BaseGeometry):
fill_value = _shapely_to_geom(fill_value)
elif not _is_scalar_geometry(fill_value):
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 GeometryArray(result, crs=self.crs)
def take(self, indices, allow_fill=False, fill_value=None):
"""
Take elements (=rows) from the TimeArray.
Parameters
----------
indexer : sequence of int
The indices in `self` to take. The meaning of negative values in
`indexer` depends on the value of `allow_fill`.
allow_fill : bool, default False
How to handle negative values in `indexer`.
* False: negative values in `indices` indicate positional indices
from the right. This is similar to
:func:`numpy.take`.
* True: negative values in `indices` indicate missing values
(the default). These values are set to `fill_value`. Any other
other negative values raise a ``ValueError``.
fill_value : object
The value to use for `indices` that are missing (-1), when
``allow_fill=True``.
Returns
-------
TimeArray
See Also
--------
Series.take : Similar method for Series.
numpy.ndarray.take : Similar method for NumPy arrays.
"""
# TODO: revisit and simplify
# Use the take implementation from pandas to get the takes separately
# for the data and the time indices
from pandas.api.extensions import take
if not allow_fill and self.data.shape[0] == 0:
# Quick-fix to raise the correct error when shape (0,0)
# TODO: look into why np.ndarray.take() raises the wrong error when
# it has shape (0,0)
raise IndexError("cannot do a non-empty take from an empty axes.")
data = take(self.data, indices, allow_fill=allow_fill)
time_index = take(self.time_index, indices, allow_fill=allow_fill)
if allow_fill and isinstance(fill_value, TimeBase):
# Fill those indices that were out of range with the fill TimeBase
indices = np.asarray(indices, dtype=np.intp)
out_of_range = (indices < 0) | (indices >= data.shape[0])
data[rows_na(data, axis=1) & out_of_range] = fill_value.data
time_index[rows_na(time_index, axis=1) & out_of_range] = \
fill_value.time_index
elif allow_fill and fill_value is not None:
TypeError(f"Only TimeBase are allowed as fill values, "
f"got {type(fill_value)}")
if data.shape[0] != 0 and np.all(np.isnan(data)):
# If the take resulted in a missing TimeArray (note, this is
# different from a length 0 TimeArray)
# TODO: put in a separate function if needed elsewhere
return self._constructor(empty((data.shape[0], 0)))
return self._constructor(data, time_index)