def _simple_new(cls, left, right, closed=None,
copy=False, dtype=None, verify_integrity=True):
result = IntervalMixin.__new__(cls)
closed = closed or 'right'
left = ensure_index(left, copy=copy)
right = ensure_index(right, copy=copy)
if dtype is not None:
# GH 19262: dtype must be an IntervalDtype to override inferred
dtype = pandas_dtype(dtype)
if not is_interval_dtype(dtype):
msg = 'dtype must be an IntervalDtype, got {dtype}'
raise TypeError(msg.format(dtype=dtype))
elif dtype.subtype is not None:
left = left.astype(dtype.subtype)
right = right.astype(dtype.subtype)
# coerce dtypes to match if needed
if is_float_dtype(left) and is_integer_dtype(right):
right = right.astype(left.dtype)
elif is_float_dtype(right) and is_integer_dtype(left):
left = left.astype(right.dtype)
if type(left) != type(right):
msg = ('must not have differing left [{ltype}] and right '
'[{rtype}] types')
raise ValueError(msg.format(ltype=type(left).__name__,
rtype=type(right).__name__))
elif is_categorical_dtype(left.dtype) or is_string_dtype(left.dtype):
# GH 19016
msg = ('category, object, and string subtypes are not supported '
'for IntervalArray')
raise TypeError(msg)
elif isinstance(left, ABCPeriodIndex):
msg = 'Period dtypes are not supported, use a PeriodIndex instead'
raise ValueError(msg)
elif (isinstance(left, ABCDatetimeIndex) and
str(left.tz) != str(right.tz)):
msg = ("left and right must have the same time zone, got "
"'{left_tz}' and '{right_tz}'")
raise ValueError(msg.format(left_tz=left.tz, right_tz=right.tz))
result._left = left
result._right = right
result._closed = closed
if verify_integrity:
result._validate()
return result
def get_indexer(self, target, method=None, limit=None, tolerance=None):
from pandas.core.arrays.categorical import _recode_for_categories
method = missing.clean_reindex_fill_method(method)
target = ibase.ensure_index(target)
if self.is_unique and self.equals(target):
return np.arange(len(self), dtype='intp')
if method == 'pad' or method == 'backfill':
raise NotImplementedError("method='pad' and method='backfill' not "
"implemented yet for CategoricalIndex")
elif method == 'nearest':
raise NotImplementedError("method='nearest' not implemented yet "
'for CategoricalIndex')
if (isinstance(target, CategoricalIndex) and
self.values.is_dtype_equal(target)):
if self.values.equals(target.values):
# we have the same codes
codes = target.codes
else:
codes = _recode_for_categories(target.codes,
target.categories,
self.values.categories)
else:
if isinstance(target, CategoricalIndex):
code_indexer = self.categories.get_indexer(target.categories)
codes = take_1d(code_indexer, target.codes, fill_value=-1)
else:
codes = self.categories.get_indexer(target)
indexer, _ = self._engine.get_indexer_non_unique(codes)
return ensure_platform_int(indexer)
def get_indexer(self, target, method=None, limit=None, tolerance=None):
self._check_method(method)
target = ensure_index(target)
target = self._maybe_cast_indexed(target)
if self.equals(target):
return np.arange(len(self), dtype='intp')
if self.is_non_overlapping_monotonic:
start, stop = self._find_non_overlapping_monotonic_bounds(target)
start_plus_one = start + 1
if not ((start_plus_one < stop).any()):
return np.where(start_plus_one == stop, start, -1)
if not self.is_unique:
raise ValueError("cannot handle non-unique indices")
# IntervalIndex
if isinstance(target, IntervalIndex):
indexer = self._get_reindexer(target)
# non IntervalIndex
else:
indexer = np.concatenate([self.get_loc(i) for i in target])
return ensure_platform_int(indexer)
def func(self, other, sort=sort):
self._assert_can_do_setop(other)
other = ensure_index(other)
if not isinstance(other, IntervalIndex):
result = getattr(self.astype(object), op_name)(other)
if op_name in ('difference',):
result = result.astype(self.dtype)
return result
elif self.closed != other.closed:
msg = ('can only do set operations between two IntervalIndex '
'objects that are closed on the same side')
raise ValueError(msg)
# GH 19016: ensure set op will not return a prohibited dtype
subtypes = [self.dtype.subtype, other.dtype.subtype]
common_subtype = find_common_type(subtypes)
if is_object_dtype(common_subtype):
msg = ('can only do {op} between two IntervalIndex '
'objects that have compatible dtypes')
raise TypeError(msg.format(op=op_name))
result = getattr(self._multiindex, op_name)(other._multiindex,
sort=sort)
result_name = get_op_result_name(self, other)
# GH 19101: ensure empty results have correct dtype
if result.empty:
result = result.values.astype(self.dtype.subtype)
else:
result = result.values
return type(self).from_tuples(result, closed=self.closed,
name=result_name)
def _as_like_interval_index(self, other):
self._assert_can_do_setop(other)
other = ensure_index(other)
if not isinstance(other, IntervalIndex):
msg = ('the other index needs to be an IntervalIndex too, but '
'was type {}').format(other.__class__.__name__)
raise TypeError(msg)
elif self.closed != other.closed:
msg = ('can only do set operations between two IntervalIndex '
'objects that are closed on the same side')
raise ValueError(msg)
return other
def _maybe_convert_i8(self, key):
"""
Maybe convert a given key to it's equivalent i8 value(s). Used as a
preprocessing step prior to IntervalTree queries (self._engine), which
expects numeric data.
Parameters
----------
key : scalar or list-like
The key that should maybe be converted to i8.
Returns
-------
key: scalar or list-like
The original key if no conversion occured, int if converted scalar,
Int64Index if converted list-like.
"""
original = key
if is_list_like(key):
key = ensure_index(key)
if not self._needs_i8_conversion(key):
return original
scalar = is_scalar(key)
if is_interval_dtype(key) or isinstance(key, Interval):
# convert left/right and reconstruct
left = self._maybe_convert_i8(key.left)
right = self._maybe_convert_i8(key.right)
constructor = Interval if scalar else IntervalIndex.from_arrays
return constructor(left, right, closed=self.closed)
if scalar:
# Timestamp/Timedelta
key_dtype, key_i8 = infer_dtype_from_scalar(key, pandas_dtype=True)
else:
# DatetimeIndex/TimedeltaIndex
key_dtype, key_i8 = key.dtype, Index(key.asi8)
if key.hasnans:
# convert NaT from it's i8 value to np.nan so it's not viewed
# as a valid value, maybe causing errors (e.g. is_overlapping)
key_i8 = key_i8.where(~key._isnan)
# ensure consistency with IntervalIndex subtype
subtype = self.dtype.subtype
msg = ('Cannot index an IntervalIndex of subtype {subtype} with '
'values of dtype {other}')
if not is_dtype_equal(subtype, key_dtype):
raise ValueError(msg.format(subtype=subtype, other=key_dtype))
return key_i8
def get_indexer_non_unique(self, target):
target = ibase.ensure_index(target)
if isinstance(target, CategoricalIndex):
# Indexing on codes is more efficient if categories are the same:
if target.categories is self.categories:
target = target.codes
indexer, missing = self._engine.get_indexer_non_unique(target)
return ensure_platform_int(indexer), missing
target = target.values
codes = self.categories.get_indexer(target)
indexer, missing = self._engine.get_indexer_non_unique(codes)
return ensure_platform_int(indexer), missing
def get_indexer(self, target, method=None, limit=None, tolerance=None):
target = ensure_index(target)
if hasattr(target, 'freq') and target.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, target.freqstr)
raise IncompatibleFrequency(msg)
if isinstance(target, PeriodIndex):
target = target.asi8
if tolerance is not None:
tolerance = self._convert_tolerance(tolerance, target)
return Index.get_indexer(self._int64index, target, method,
limit, tolerance)
def _get_combined_index(indexes, intersect=False, sort=False):
# TODO: handle index names!
indexes = _get_distinct_objs(indexes)
if len(indexes) == 0:
index = Index([])
elif len(indexes) == 1:
index = indexes[0]
elif intersect:
index = indexes[0]
for other in indexes[1:]:
index = index.intersection(other)
else:
index = _union_indexes(indexes, sort=sort)
index = ensure_index(index)
if sort:
try:
index = index.sort_values()
except TypeError:
pass
return index
def get_indexer_non_unique(
self, target: AnyArrayLike) -> Tuple[np.ndarray, np.ndarray]:
target_as_index = ensure_index(target)
# check that target_as_index IntervalIndex is compatible
if isinstance(target_as_index, IntervalIndex):
common_subtype = find_common_type(
[self.dtype.subtype, target_as_index.dtype.subtype])
if self.closed != target_as_index.closed or is_object_dtype(
common_subtype):
# different closed or incompatible subtype -> no matches
return (
np.repeat(-1, len(target_as_index)),
np.arange(len(target_as_index)),
)
if is_object_dtype(target_as_index) or isinstance(
target_as_index, IntervalIndex):
# target_as_index might contain intervals: defer elementwise to get_loc
indexer, missing = [], []
for i, key in enumerate(target_as_index):
try:
locs = self.get_loc(key)
if isinstance(locs, slice):
locs = np.arange(locs.start,
locs.stop,
locs.step,
dtype="intp")
locs = np.array(locs, ndmin=1)
except KeyError:
missing.append(i)
locs = np.array([-1])
indexer.append(locs)
indexer = np.concatenate(indexer)
else:
target_as_index = self._maybe_convert_i8(target_as_index)
indexer, missing = self._engine.get_indexer_non_unique(
target_as_index.values)
return ensure_platform_int(indexer), ensure_platform_int(missing)
def difference(self, other, sort=None):
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
res_name = get_op_result_name(self, other)
other = ensure_index(other)
if self.equals(other):
# pass an empty PeriodArray with the appropriate dtype
return self._shallow_copy(self._data[:0])
if is_object_dtype(other):
return self.astype(object).difference(other).astype(self.dtype)
elif not is_dtype_equal(self.dtype, other.dtype):
return self
i8self = Int64Index._simple_new(self.asi8)
i8other = Int64Index._simple_new(other.asi8)
i8result = i8self.difference(i8other, sort=sort)
result = self._shallow_copy(np.asarray(i8result, dtype=np.int64), name=res_name)
return result
def get_indexer_non_unique(self,
target: Index) -> Tuple[np.ndarray, np.ndarray]:
target = ensure_index(target)
if isinstance(target,
IntervalIndex) and not self._should_compare(target):
# different closed or incompatible subtype -> no matches
return self._get_indexer_non_comparable(target, None, unique=False)
elif is_object_dtype(target.dtype) or isinstance(
target, IntervalIndex):
# target might contain intervals: defer elementwise to get_loc
return self._get_indexer_pointwise(target)
else:
# Note: this case behaves differently from other Index subclasses
# because IntervalIndex does partial-int indexing
target = self._maybe_convert_i8(target)
indexer, missing = self._engine.get_indexer_non_unique(
target.values)
return ensure_platform_int(indexer), ensure_platform_int(missing)
def wrapped(self, other, sort=False):
self._assert_can_do_setop(other)
other = ensure_index(other)
if not isinstance(other, IntervalIndex):
result = getattr(self.astype(object), op_name)(other)
if op_name in ("difference", ):
result = result.astype(self.dtype)
return result
elif self.closed != other.closed:
raise ValueError(
"can only do set operations between two IntervalIndex "
"objects that are closed on the same side")
# GH 19016: ensure set op will not return a prohibited dtype
subtypes = [self.dtype.subtype, other.dtype.subtype]
common_subtype = find_common_type(subtypes)
if is_object_dtype(common_subtype):
raise TypeError(f"can only do {op_name} between two IntervalIndex "
"objects that have compatible dtypes")
return method(self, other, sort)
def intersection(self, other, sort=False):
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
res_name = get_op_result_name(self, other)
other = ensure_index(other)
if self.equals(other):
return self._get_reconciled_name_object(other)
if not is_dtype_equal(self.dtype, other.dtype):
# TODO: fastpath for if we have a different PeriodDtype
this = self.astype("O")
other = other.astype("O")
return this.intersection(other, sort=sort)
i8self = Int64Index._simple_new(self.asi8)
i8other = Int64Index._simple_new(other.asi8)
i8result = i8self.intersection(i8other, sort=sort)
result = self._shallow_copy(np.asarray(i8result, dtype=np.int64),
name=res_name)
return result
def get_indexer(self, target, method=None, limit=None, tolerance=None):
target = ensure_index(target)
if isinstance(target, PeriodIndex):
if not self._is_comparable_dtype(target.dtype):
# i.e. target.freq != self.freq
# No matches
no_matches = -1 * np.ones(self.shape, dtype=np.intp)
return no_matches
target = target._get_engine_target() # i.e. target.asi8
self_index = self._int64index
else:
self_index = self
if tolerance is not None:
tolerance = self._convert_tolerance(tolerance, target)
if self_index is not self:
# convert tolerance to i8
tolerance = self._maybe_convert_timedelta(tolerance)
return Index.get_indexer(self_index, target, method, limit, tolerance)
def _get_combined_index(indexes, intersect=False, sort=False):
"""
Return the union or intersection of indexes.
Parameters
----------
indexes : list of Index or list objects
When intersect=True, do not accept list of lists.
intersect : bool, default False
If True, calculate the intersection between indexes. Otherwise,
calculate the union.
sort : bool, default False
Whether the result index should come out sorted or not.
Returns
-------
Index
"""
# TODO: handle index names!
indexes = _get_distinct_objs(indexes)
if len(indexes) == 0:
index = Index([])
elif len(indexes) == 1:
index = indexes[0]
elif intersect:
index = indexes[0]
for other in indexes[1:]:
index = index.intersection(other)
else:
index = _union_indexes(indexes, sort=sort)
index = ensure_index(index)
if sort:
try:
index = index.sort_values()
except TypeError:
pass
return index
def _get_combined_index(indexes, intersect=False, sort=False):
"""
Return the union or intersection of indexes.
Parameters
----------
indexes : list of Index or list objects
When intersect=True, do not accept list of lists.
intersect : bool, default False
If True, calculate the intersection between indexes. Otherwise,
calculate the union.
sort : bool, default False
Whether the result index should come out sorted or not.
Returns
-------
Index
"""
# TODO: handle index names!
indexes = _get_distinct_objs(indexes)
if len(indexes) == 0:
index = Index([])
elif len(indexes) == 1:
index = indexes[0]
elif intersect:
index = indexes[0]
for other in indexes[1:]:
index = index.intersection(other)
else:
index = union_indexes(indexes, sort=sort)
index = ensure_index(index)
if sort:
try:
index = index.sort_values()
except TypeError:
pass
return index
def func(intvidx_self, other, sort=False):
intvidx_self._assert_can_do_setop(other)
other = ensure_index(other)
if not isinstance(other, IntervalIndex):
result = getattr(intvidx_self.astype(object),
self.op_name)(other)
if self.op_name in ('difference',):
result = result.astype(intvidx_self.dtype)
return result
elif intvidx_self.closed != other.closed:
msg = ('can only do set operations between two IntervalIndex '
'objects that are closed on the same side')
raise ValueError(msg)
# GH 19016: ensure set op will not return a prohibited dtype
subtypes = [intvidx_self.dtype.subtype, other.dtype.subtype]
common_subtype = find_common_type(subtypes)
if is_object_dtype(common_subtype):
msg = ('can only do {op} between two IntervalIndex '
'objects that have compatible dtypes')
raise TypeError(msg.format(op=self.op_name))
return setop(intvidx_self, other, sort)
def func(intvidx_self, other, sort=False):
intvidx_self._assert_can_do_setop(other)
other = ensure_index(other)
if not isinstance(other, IntervalIndex):
result = getattr(intvidx_self.astype(object),
self.op_name)(other)
if self.op_name in ('difference', ):
result = result.astype(intvidx_self.dtype)
return result
elif intvidx_self.closed != other.closed:
msg = ('can only do set operations between two IntervalIndex '
'objects that are closed on the same side')
raise ValueError(msg)
# GH 19016: ensure set op will not return a prohibited dtype
subtypes = [intvidx_self.dtype.subtype, other.dtype.subtype]
common_subtype = find_common_type(subtypes)
if is_object_dtype(common_subtype):
msg = ('can only do {op} between two IntervalIndex '
'objects that have compatible dtypes')
raise TypeError(msg.format(op=self.op_name))
return setop(intvidx_self, other, sort)
def _maybe_convert_i8(self, key):
"""
Maybe convert a given key to its equivalent i8 value(s). Used as a
preprocessing step prior to IntervalTree queries (self._engine), which
expects numeric data.
Parameters
----------
key : scalar or list-like
The key that should maybe be converted to i8.
Returns
-------
scalar or list-like
The original key if no conversion occurred, int if converted scalar,
Int64Index if converted list-like.
"""
original = key
if is_list_like(key):
key = ensure_index(key)
if not self._needs_i8_conversion(key):
return original
scalar = is_scalar(key)
if is_interval_dtype(key) or isinstance(key, Interval):
# convert left/right and reconstruct
left = self._maybe_convert_i8(key.left)
right = self._maybe_convert_i8(key.right)
constructor = Interval if scalar else IntervalIndex.from_arrays
# error: "object" not callable
return constructor(left, right,
closed=self.closed) # type: ignore[operator]
if scalar:
# Timestamp/Timedelta
key_dtype, key_i8 = infer_dtype_from_scalar(key, pandas_dtype=True)
if lib.is_period(key):
key_i8 = key.ordinal
elif isinstance(key_i8, Timestamp):
key_i8 = key_i8.value
elif isinstance(key_i8, (np.datetime64, np.timedelta64)):
key_i8 = key_i8.view("i8")
else:
# DatetimeIndex/TimedeltaIndex
key_dtype, key_i8 = key.dtype, Index(key.asi8)
if key.hasnans:
# convert NaT from its i8 value to np.nan so it's not viewed
# as a valid value, maybe causing errors (e.g. is_overlapping)
key_i8 = key_i8.where(~key._isnan)
# ensure consistency with IntervalIndex subtype
# error: Item "ExtensionDtype"/"dtype[Any]" of "Union[dtype[Any],
# ExtensionDtype]" has no attribute "subtype"
subtype = self.dtype.subtype # type: ignore[union-attr]
if not is_dtype_equal(subtype, key_dtype):
raise ValueError(
f"Cannot index an IntervalIndex of subtype {subtype} with "
f"values of dtype {key_dtype}")
return key_i8
def get_indexer_non_unique(self, target):
target = self._maybe_cast_indexed(ensure_index(target))
return super(IntervalIndex, self).get_indexer_non_unique(target)
def reindex(
self, target, method=None, level=None, limit=None, tolerance=None
) -> tuple[Index, npt.NDArray[np.intp] | None]:
"""
Create index with target's values (move/add/delete values as necessary)
Returns
-------
new_index : pd.Index
Resulting index
indexer : np.ndarray[np.intp] or None
Indices of output values in original index
"""
if method is not None:
raise NotImplementedError(
"argument method is not implemented for CategoricalIndex.reindex"
)
if level is not None:
raise NotImplementedError(
"argument level is not implemented for CategoricalIndex.reindex"
)
if limit is not None:
raise NotImplementedError(
"argument limit is not implemented for CategoricalIndex.reindex"
)
target = ibase.ensure_index(target)
if self.equals(target):
indexer = None
missing = np.array([], dtype=np.intp)
else:
indexer, missing = self.get_indexer_non_unique(target)
if not self.is_unique:
# GH#42568
warnings.warn(
"reindexing with a non-unique Index is deprecated and will "
"raise in a future version.",
FutureWarning,
stacklevel=find_stack_level(),
)
if len(self) and indexer is not None:
new_target = self.take(indexer)
else:
new_target = target
# filling in missing if needed
if len(missing):
cats = self.categories.get_indexer(target)
if not isinstance(target, CategoricalIndex) or (cats == -1).any():
new_target, indexer, _ = super()._reindex_non_unique(target)
else:
codes = new_target.codes.copy()
codes[indexer == -1] = cats[missing]
cat = self._data._from_backing_data(codes)
new_target = type(self)._simple_new(cat, name=self.name)
# we always want to return an Index type here
# to be consistent with .reindex for other index types (e.g. they don't
# coerce based on the actual values, only on the dtype)
# unless we had an initial Categorical to begin with
# in which case we are going to conform to the passed Categorical
if is_categorical_dtype(target):
cat = Categorical(new_target, dtype=target.dtype)
new_target = type(self)._simple_new(cat, name=self.name)
else:
# e.g. test_reindex_with_categoricalindex, test_reindex_duplicate_target
new_target = np.asarray(new_target)
new_target = Index._with_infer(new_target, name=self.name)
return new_target, indexer
def reindex(self,
target,
method=None,
level=None,
limit=None,
tolerance=None):
"""
Create index with target's values (move/add/delete values as necessary)
Returns
-------
new_index : pd.Index
Resulting index
indexer : np.ndarray or None
Indices of output values in original index
"""
if method is not None:
raise NotImplementedError("argument method is not implemented for "
"CategoricalIndex.reindex")
if level is not None:
raise NotImplementedError("argument level is not implemented for "
"CategoricalIndex.reindex")
if limit is not None:
raise NotImplementedError("argument limit is not implemented for "
"CategoricalIndex.reindex")
target = ibase.ensure_index(target)
if not is_categorical_dtype(target) and not target.is_unique:
raise ValueError("cannot reindex with a non-unique indexer")
indexer, missing = self.get_indexer_non_unique(np.array(target))
if len(self.codes):
new_target = self.take(indexer)
else:
new_target = target
# filling in missing if needed
if len(missing):
cats = self.categories.get_indexer(target)
if (cats == -1).any():
# coerce to a regular index here!
result = Index(np.array(self), name=self.name)
new_target, indexer, _ = result._reindex_non_unique(
np.array(target))
else:
codes = new_target.codes.copy()
codes[indexer == -1] = cats[missing]
new_target = self._create_from_codes(codes)
# we always want to return an Index type here
# to be consistent with .reindex for other index types (e.g. they don't
# coerce based on the actual values, only on the dtype)
# unless we had an initial Categorical to begin with
# in which case we are going to conform to the passed Categorical
new_target = np.asarray(new_target)
if is_categorical_dtype(target):
new_target = target._shallow_copy(new_target, name=self.name)
else:
new_target = Index(new_target, name=self.name)
return new_target, indexer
def get_indexer_non_unique(self, target):
target = ibase.ensure_index(target)
return self._get_indexer_non_unique(target._values)
def get_indexer_non_unique(self, target):
target = self._maybe_cast_indexed(ensure_index(target))
return super().get_indexer_non_unique(target)
def _simple_new(
cls, left, right, closed=None, copy=False, dtype=None, verify_integrity=True
):
result = IntervalMixin.__new__(cls)
closed = closed or "right"
left = ensure_index(left, copy=copy)
right = ensure_index(right, copy=copy)
if dtype is not None:
# GH 19262: dtype must be an IntervalDtype to override inferred
dtype = pandas_dtype(dtype)
if not is_interval_dtype(dtype):
msg = f"dtype must be an IntervalDtype, got {dtype}"
raise TypeError(msg)
elif dtype.subtype is not None:
left = left.astype(dtype.subtype)
right = right.astype(dtype.subtype)
# coerce dtypes to match if needed
if is_float_dtype(left) and is_integer_dtype(right):
right = right.astype(left.dtype)
elif is_float_dtype(right) and is_integer_dtype(left):
left = left.astype(right.dtype)
if type(left) != type(right):
msg = (
f"must not have differing left [{type(left).__name__}] and "
f"right [{type(right).__name__}] types"
)
raise ValueError(msg)
elif is_categorical_dtype(left.dtype) or is_string_dtype(left.dtype):
# GH 19016
msg = (
"category, object, and string subtypes are not supported "
"for IntervalArray"
)
raise TypeError(msg)
elif isinstance(left, ABCPeriodIndex):
msg = "Period dtypes are not supported, use a PeriodIndex instead"
raise ValueError(msg)
elif isinstance(left, ABCDatetimeIndex) and str(left.tz) != str(right.tz):
msg = (
"left and right must have the same time zone, got "
f"'{left.tz}' and '{right.tz}'"
)
raise ValueError(msg)
# For dt64/td64 we want DatetimeArray/TimedeltaArray instead of ndarray
from pandas.core.ops.array_ops import maybe_upcast_datetimelike_array
left = maybe_upcast_datetimelike_array(left)
left = extract_array(left, extract_numpy=True)
right = maybe_upcast_datetimelike_array(right)
right = extract_array(right, extract_numpy=True)
lbase = getattr(left, "_ndarray", left).base
rbase = getattr(right, "_ndarray", right).base
if lbase is not None and lbase is rbase:
# If these share data, then setitem could corrupt our IA
right = right.copy()
result._left = left
result._right = right
result._closed = closed
if verify_integrity:
result._validate()
return result
def get_indexer_non_unique(self, target):
target = ibase.ensure_index(target)
codes = self._values._validate_listlike(target._values)
indexer, missing = self._engine.get_indexer_non_unique(codes)
return ensure_platform_int(indexer), missing
def get_indexer_non_unique(self, target) -> tuple[np.ndarray, np.ndarray]:
# both returned ndarrays are np.intp
target = ibase.ensure_index(target)
return self._get_indexer_non_unique(target._values)
def reindex(self, target, method=None, level=None, limit=None,
tolerance=None):
"""
Create index with target's values (move/add/delete values as necessary)
Returns
-------
new_index : pd.Index
Resulting index
indexer : np.ndarray or None
Indices of output values in original index
"""
if method is not None:
raise NotImplementedError("argument method is not implemented for "
"CategoricalIndex.reindex")
if level is not None:
raise NotImplementedError("argument level is not implemented for "
"CategoricalIndex.reindex")
if limit is not None:
raise NotImplementedError("argument limit is not implemented for "
"CategoricalIndex.reindex")
target = ibase.ensure_index(target)
if self.equals(target):
indexer = None
missing = []
else:
if not target.is_unique:
raise ValueError("cannot reindex with a non-unique indexer")
indexer, missing = self.get_indexer_non_unique(np.array(target))
if len(self.codes) and indexer is not None:
new_target = self.take(indexer)
else:
new_target = target
# filling in missing if needed
if len(missing):
cats = self.categories.get_indexer(target)
if (cats == -1).any():
# coerce to a regular index here!
result = Index(np.array(self), name=self.name)
new_target, indexer, _ = result._reindex_non_unique(
np.array(target))
else:
codes = new_target.codes.copy()
codes[indexer == -1] = cats[missing]
new_target = self._create_from_codes(codes)
# we always want to return an Index type here
# to be consistent with .reindex for other index types (e.g. they don't
# coerce based on the actual values, only on the dtype)
# unless we had an initial Categorical to begin with
# in which case we are going to conform to the passed Categorical
new_target = np.asarray(new_target)
if is_categorical_dtype(target):
new_target = target._shallow_copy(new_target, name=self.name)
else:
new_target = Index(new_target, name=self.name)
return new_target, indexer
