GUETBME-WearableDeviceLab/Feature-Extraction
3
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
67a817fd01cdf8a2cc326df4b76241de38b50a34
Feature-Extraction/dist/client/pandas/core/indexes/base.py

7219 lines
237 KiB
Python
Raw Blame History

from __future__ import annotations
from datetime import datetime
import functools
from itertools import zip_longest
import operator
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Literal,
Sequence,
TypeVar,
cast,
final,
overload,
)
import warnings
import numpy as np
from pandas._config import get_option
from pandas._libs import (
NaT,
algos as libalgos,
index as libindex,
lib,
)
import pandas._libs.join as libjoin
from pandas._libs.lib import (
is_datetime_array,
no_default,
)
from pandas._libs.missing import is_float_nan
from pandas._libs.tslibs import (
IncompatibleFrequency,
OutOfBoundsDatetime,
Timestamp,
tz_compare,
)
from pandas._typing import (
AnyArrayLike,
ArrayLike,
Dtype,
DtypeObj,
F,
Shape,
npt,
)
from pandas.compat.numpy import function as nv
from pandas.errors import (
DuplicateLabelError,
InvalidIndexError,
)
from pandas.util._decorators import (
Appender,
cache_readonly,
deprecate_nonkeyword_arguments,
doc,
)
from pandas.util._exceptions import (
find_stack_level,
rewrite_exception,
)
from pandas.core.dtypes.cast import (
can_hold_element,
find_common_type,
infer_dtype_from,
maybe_cast_pointwise_result,
)
from pandas.core.dtypes.common import (
ensure_int64,
ensure_object,
ensure_platform_int,
is_bool_dtype,
is_categorical_dtype,
is_dtype_equal,
is_ea_or_datetimelike_dtype,
is_extension_array_dtype,
is_float,
is_float_dtype,
is_hashable,
is_integer,
is_interval_dtype,
is_iterator,
is_list_like,
is_numeric_dtype,
is_object_dtype,
is_scalar,
is_signed_integer_dtype,
is_unsigned_integer_dtype,
needs_i8_conversion,
pandas_dtype,
validate_all_hashable,
)
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
DatetimeTZDtype,
ExtensionDtype,
IntervalDtype,
PandasDtype,
PeriodDtype,
)
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCDatetimeIndex,
ABCMultiIndex,
ABCPeriodIndex,
ABCSeries,
ABCTimedeltaIndex,
)
from pandas.core.dtypes.inference import is_dict_like
from pandas.core.dtypes.missing import (
array_equivalent,
is_valid_na_for_dtype,
isna,
)
from pandas.core import (
arraylike,
missing,
ops,
)
from pandas.core.accessor import CachedAccessor
import pandas.core.algorithms as algos
from pandas.core.array_algos.putmask import (
setitem_datetimelike_compat,
validate_putmask,
)
from pandas.core.arrays import (
Categorical,
ExtensionArray,
)
from pandas.core.arrays.datetimes import (
tz_to_dtype,
validate_tz_from_dtype,
)
from pandas.core.arrays.masked import BaseMaskedArray
from pandas.core.arrays.sparse import SparseDtype
from pandas.core.base import (
IndexOpsMixin,
PandasObject,
)
import pandas.core.common as com
from pandas.core.construction import (
ensure_wrapped_if_datetimelike,
extract_array,
sanitize_array,
)
from pandas.core.indexers import deprecate_ndim_indexing
from pandas.core.indexes.frozen import FrozenList
from pandas.core.ops import get_op_result_name
from pandas.core.ops.invalid import make_invalid_op
from pandas.core.sorting import (
ensure_key_mapped,
get_group_index_sorter,
nargsort,
)
from pandas.core.strings import StringMethods
from pandas.io.formats.printing import (
PrettyDict,
default_pprint,
format_object_summary,
pprint_thing,
)
if TYPE_CHECKING:
from pandas import (
CategoricalIndex,
DataFrame,
IntervalIndex,
MultiIndex,
Series,
)
from pandas.core.arrays import PeriodArray
__all__ = ["Index"]
_unsortable_types = frozenset(("mixed", "mixed-integer"))
_index_doc_kwargs: dict[str, str] = {
"klass": "Index",
"inplace": "",
"target_klass": "Index",
"raises_section": "",
"unique": "Index",
"duplicated": "np.ndarray",
}
_index_shared_docs: dict[str, str] = {}
str_t = str
_dtype_obj = np.dtype("object")
def _maybe_return_indexers(meth: F) -> F:
"""
Decorator to simplify 'return_indexers' checks in Index.join.
"""
@functools.wraps(meth)
def join(
self,
other,
how: str_t = "left",
level=None,
return_indexers: bool = False,
sort: bool = False,
):
join_index, lidx, ridx = meth(self, other, how=how, level=level, sort=sort)
if not return_indexers:
return join_index
if lidx is not None:
lidx = ensure_platform_int(lidx)
if ridx is not None:
ridx = ensure_platform_int(ridx)
return join_index, lidx, ridx
return cast(F, join)
def disallow_kwargs(kwargs: dict[str, Any]) -> None:
if kwargs:
raise TypeError(f"Unexpected keyword arguments {repr(set(kwargs))}")
def _new_Index(cls, d):
"""
This is called upon unpickling, rather than the default which doesn't
have arguments and breaks __new__.
"""
# required for backward compat, because PI can't be instantiated with
# ordinals through __new__ GH #13277
if issubclass(cls, ABCPeriodIndex):
from pandas.core.indexes.period import _new_PeriodIndex
return _new_PeriodIndex(cls, **d)
if issubclass(cls, ABCMultiIndex):
if "labels" in d and "codes" not in d:
# GH#23752 "labels" kwarg has been replaced with "codes"
d["codes"] = d.pop("labels")
elif "dtype" not in d and "data" in d:
# Prevent Index.__new__ from conducting inference;
# "data" key not in RangeIndex
d["dtype"] = d["data"].dtype
return cls.__new__(cls, **d)
_IndexT = TypeVar("_IndexT", bound="Index")
class Index(IndexOpsMixin, PandasObject):
"""
Immutable sequence used for indexing and alignment. The basic object
storing axis labels for all pandas objects.
Parameters
----------
data : array-like (1-dimensional)
dtype : NumPy dtype (default: object)
If dtype is None, we find the dtype that best fits the data.
If an actual dtype is provided, we coerce to that dtype if it's safe.
Otherwise, an error will be raised.
copy : bool
Make a copy of input ndarray.
name : object
Name to be stored in the index.
tupleize_cols : bool (default: True)
When True, attempt to create a MultiIndex if possible.
See Also
--------
RangeIndex : Index implementing a monotonic integer range.
CategoricalIndex : Index of :class:`Categorical` s.
MultiIndex : A multi-level, or hierarchical Index.
IntervalIndex : An Index of :class:`Interval` s.
DatetimeIndex : Index of datetime64 data.
TimedeltaIndex : Index of timedelta64 data.
PeriodIndex : Index of Period data.
NumericIndex : Index of numpy int/uint/float data.
Int64Index : Index of purely int64 labels (deprecated).
UInt64Index : Index of purely uint64 labels (deprecated).
Float64Index : Index of purely float64 labels (deprecated).
Notes
-----
An Index instance can **only** contain hashable objects
Examples
--------
>>> pd.Index([1, 2, 3])
Int64Index([1, 2, 3], dtype='int64')
>>> pd.Index(list('abc'))
Index(['a', 'b', 'c'], dtype='object')
"""
# tolist is not actually deprecated, just suppressed in the __dir__
_hidden_attrs: frozenset[str] = (
PandasObject._hidden_attrs
| IndexOpsMixin._hidden_attrs
| frozenset(["contains", "set_value"])
)
# To hand over control to subclasses
_join_precedence = 1
# Cython methods; see github.com/cython/cython/issues/2647
# for why we need to wrap these instead of making them class attributes
# Moreover, cython will choose the appropriate-dtyped sub-function
# given the dtypes of the passed arguments
@final
def _left_indexer_unique(self: _IndexT, other: _IndexT) -> npt.NDArray[np.intp]:
# Caller is responsible for ensuring other.dtype == self.dtype
sv = self._get_engine_target()
ov = other._get_engine_target()
return libjoin.left_join_indexer_unique(sv, ov)
@final
def _left_indexer(
self: _IndexT, other: _IndexT
) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
# Caller is responsible for ensuring other.dtype == self.dtype
sv = self._get_engine_target()
ov = other._get_engine_target()
joined_ndarray, lidx, ridx = libjoin.left_join_indexer(sv, ov)
joined = self._from_join_target(joined_ndarray)
return joined, lidx, ridx
@final
def _inner_indexer(
self: _IndexT, other: _IndexT
) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
# Caller is responsible for ensuring other.dtype == self.dtype
sv = self._get_engine_target()
ov = other._get_engine_target()
joined_ndarray, lidx, ridx = libjoin.inner_join_indexer(sv, ov)
joined = self._from_join_target(joined_ndarray)
return joined, lidx, ridx
@final
def _outer_indexer(
self: _IndexT, other: _IndexT
) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
# Caller is responsible for ensuring other.dtype == self.dtype
sv = self._get_engine_target()
ov = other._get_engine_target()
joined_ndarray, lidx, ridx = libjoin.outer_join_indexer(sv, ov)
joined = self._from_join_target(joined_ndarray)
return joined, lidx, ridx
_typ: str = "index"
_data: ExtensionArray | np.ndarray
_data_cls: type[ExtensionArray] | tuple[type[np.ndarray], type[ExtensionArray]] = (
np.ndarray,
ExtensionArray,
)
_id: object | None = None
_name: Hashable = None
# MultiIndex.levels previously allowed setting the index name. We
# don't allow this anymore, and raise if it happens rather than
# failing silently.
_no_setting_name: bool = False
_comparables: list[str] = ["name"]
_attributes: list[str] = ["name"]
_is_numeric_dtype: bool = False
_can_hold_na: bool = True
_can_hold_strings: bool = True
# Whether this index is a NumericIndex, but not a Int64Index, Float64Index,
# UInt64Index or RangeIndex. Needed for backwards compat. Remove this attribute and
# associated code in pandas 2.0.
_is_backward_compat_public_numeric_index: bool = False
_engine_type: type[libindex.IndexEngine] = libindex.ObjectEngine
# whether we support partial string indexing. Overridden
# in DatetimeIndex and PeriodIndex
_supports_partial_string_indexing = False
_accessors = {"str"}
str = CachedAccessor("str", StringMethods)
# --------------------------------------------------------------------
# Constructors
def __new__(
cls, data=None, dtype=None, copy=False, name=None, tupleize_cols=True, **kwargs
) -> Index:
if kwargs:
warnings.warn(
"Passing keywords other than 'data', 'dtype', 'copy', 'name', "
"'tupleize_cols' is deprecated and will raise TypeError in a "
"future version. Use the specific Index subclass directly instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
from pandas.core.arrays import PandasArray
from pandas.core.indexes.range import RangeIndex
name = maybe_extract_name(name, data, cls)
if dtype is not None:
dtype = pandas_dtype(dtype)
if "tz" in kwargs:
tz = kwargs.pop("tz")
validate_tz_from_dtype(dtype, tz)
dtype = tz_to_dtype(tz)
if type(data) is PandasArray:
# ensure users don't accidentally put a PandasArray in an index,
# but don't unpack StringArray
data = data.to_numpy()
if isinstance(dtype, PandasDtype):
dtype = dtype.numpy_dtype
data_dtype = getattr(data, "dtype", None)
# range
if isinstance(data, (range, RangeIndex)):
result = RangeIndex(start=data, copy=copy, name=name)
if dtype is not None:
return result.astype(dtype, copy=False)
return result
elif is_ea_or_datetimelike_dtype(dtype):
# non-EA dtype indexes have special casting logic, so we punt here
klass = cls._dtype_to_subclass(dtype)
if klass is not Index:
return klass(data, dtype=dtype, copy=copy, name=name, **kwargs)
ea_cls = dtype.construct_array_type()
data = ea_cls._from_sequence(data, dtype=dtype, copy=copy)
disallow_kwargs(kwargs)
return Index._simple_new(data, name=name)
elif is_ea_or_datetimelike_dtype(data_dtype):
data_dtype = cast(DtypeObj, data_dtype)
klass = cls._dtype_to_subclass(data_dtype)
if klass is not Index:
result = klass(data, copy=copy, name=name, **kwargs)
if dtype is not None:
return result.astype(dtype, copy=False)
return result
elif dtype is not None:
# GH#45206
data = data.astype(dtype, copy=False)
disallow_kwargs(kwargs)
data = extract_array(data, extract_numpy=True)
return Index._simple_new(data, name=name)
# index-like
elif (
isinstance(data, Index)
and data._is_backward_compat_public_numeric_index
and dtype is None
):
return data._constructor(data, name=name, copy=copy)
elif isinstance(data, (np.ndarray, Index, ABCSeries)):
if isinstance(data, ABCMultiIndex):
data = data._values
if dtype is not None:
# we need to avoid having numpy coerce
# things that look like ints/floats to ints unless
# they are actually ints, e.g. '0' and 0.0
# should not be coerced
# GH 11836
data = sanitize_array(data, None, dtype=dtype, copy=copy)
dtype = data.dtype
if data.dtype.kind in ["i", "u", "f"]:
# maybe coerce to a sub-class
arr = data
else:
arr = com.asarray_tuplesafe(data, dtype=_dtype_obj)
if dtype is None:
arr = _maybe_cast_data_without_dtype(
arr, cast_numeric_deprecated=True
)
dtype = arr.dtype
if kwargs:
return cls(arr, dtype, copy=copy, name=name, **kwargs)
klass = cls._dtype_to_subclass(arr.dtype)
arr = klass._ensure_array(arr, dtype, copy)
disallow_kwargs(kwargs)
return klass._simple_new(arr, name)
elif is_scalar(data):
raise cls._scalar_data_error(data)
elif hasattr(data, "__array__"):
return Index(np.asarray(data), dtype=dtype, copy=copy, name=name, **kwargs)
else:
if tupleize_cols and is_list_like(data):
# GH21470: convert iterable to list before determining if empty
if is_iterator(data):
data = list(data)
if data and all(isinstance(e, tuple) for e in data):
# we must be all tuples, otherwise don't construct
# 10697
from pandas.core.indexes.multi import MultiIndex
return MultiIndex.from_tuples(
data, names=name or kwargs.get("names")
)
# other iterable of some kind
subarr = com.asarray_tuplesafe(data, dtype=_dtype_obj)
if dtype is None:
# with e.g. a list [1, 2, 3] casting to numeric is _not_ deprecated
# error: Incompatible types in assignment (expression has type
# "Union[ExtensionArray, ndarray[Any, Any]]", variable has type
# "ndarray[Any, Any]")
subarr = _maybe_cast_data_without_dtype( # type: ignore[assignment]
subarr, cast_numeric_deprecated=False
)
dtype = subarr.dtype
return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs)
@classmethod
def _ensure_array(cls, data, dtype, copy: bool):
"""
Ensure we have a valid array to pass to _simple_new.
"""
if data.ndim > 1:
# GH#13601, GH#20285, GH#27125
raise ValueError("Index data must be 1-dimensional")
if copy:
# asarray_tuplesafe does not always copy underlying data,
# so need to make sure that this happens
data = data.copy()
return data
@final
@classmethod
def _dtype_to_subclass(cls, dtype: DtypeObj):
# Delay import for perf. https://github.com/pandas-dev/pandas/pull/31423
if isinstance(dtype, ExtensionDtype):
if isinstance(dtype, DatetimeTZDtype):
from pandas import DatetimeIndex
return DatetimeIndex
elif isinstance(dtype, CategoricalDtype):
from pandas import CategoricalIndex
return CategoricalIndex
elif isinstance(dtype, IntervalDtype):
from pandas import IntervalIndex
return IntervalIndex
elif isinstance(dtype, PeriodDtype):
from pandas import PeriodIndex
return PeriodIndex
elif isinstance(dtype, SparseDtype):
warnings.warn(
"In a future version, passing a SparseArray to pd.Index "
"will store that array directly instead of converting to a "
"dense numpy ndarray. To retain the old behavior, use "
"pd.Index(arr.to_numpy()) instead",
FutureWarning,
stacklevel=find_stack_level(),
)
return cls._dtype_to_subclass(dtype.subtype)
return Index
if dtype.kind == "M":
from pandas import DatetimeIndex
return DatetimeIndex
elif dtype.kind == "m":
from pandas import TimedeltaIndex
return TimedeltaIndex
elif is_float_dtype(dtype):
from pandas.core.api import Float64Index
return Float64Index
elif is_unsigned_integer_dtype(dtype):
from pandas.core.api import UInt64Index
return UInt64Index
elif is_signed_integer_dtype(dtype):
from pandas.core.api import Int64Index
return Int64Index
elif dtype == _dtype_obj:
# NB: assuming away MultiIndex
return Index
elif issubclass(dtype.type, (str, bool, np.bool_)):
return Index
raise NotImplementedError(dtype)
"""
NOTE for new Index creation:
- _simple_new: It returns new Index with the same type as the caller.
All metadata (such as name) must be provided by caller's responsibility.
Using _shallow_copy is recommended because it fills these metadata
otherwise specified.
- _shallow_copy: It returns new Index with the same type (using
_simple_new), but fills caller's metadata otherwise specified. Passed
kwargs will overwrite corresponding metadata.
See each method's docstring.
"""
@property
def asi8(self):
"""
Integer representation of the values.
Returns
-------
ndarray
An ndarray with int64 dtype.
"""
warnings.warn(
"Index.asi8 is deprecated and will be removed in a future version.",
FutureWarning,
stacklevel=find_stack_level(),
)
return None
@classmethod
def _simple_new(cls: type[_IndexT], values, name: Hashable = None) -> _IndexT:
"""
We require that we have a dtype compat for the values. If we are passed
a non-dtype compat, then coerce using the constructor.
Must be careful not to recurse.
"""
assert isinstance(values, cls._data_cls), type(values)
result = object.__new__(cls)
result._data = values
result._name = name
result._cache = {}
result._reset_identity()
return result
@classmethod
def _with_infer(cls, *args, **kwargs):
"""
Constructor that uses the 1.0.x behavior inferring numeric dtypes
for ndarray[object] inputs.
"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore", ".*the Index constructor", FutureWarning)
result = cls(*args, **kwargs)
if result.dtype == _dtype_obj and not result._is_multi:
# error: Argument 1 to "maybe_convert_objects" has incompatible type
# "Union[ExtensionArray, ndarray[Any, Any]]"; expected
# "ndarray[Any, Any]"
values = lib.maybe_convert_objects(result._values) # type: ignore[arg-type]
if values.dtype.kind in ["i", "u", "f"]:
return Index(values, name=result.name)
return result
@cache_readonly
def _constructor(self: _IndexT) -> type[_IndexT]:
return type(self)
@final
def _maybe_check_unique(self) -> None:
"""
Check that an Index has no duplicates.
This is typically only called via
`NDFrame.flags.allows_duplicate_labels.setter` when it's set to
True (duplicates aren't allowed).
Raises
------
DuplicateLabelError
When the index is not unique.
"""
if not self.is_unique:
msg = """Index has duplicates."""
duplicates = self._format_duplicate_message()
msg += f"\n{duplicates}"
raise DuplicateLabelError(msg)
@final
def _format_duplicate_message(self) -> DataFrame:
"""
Construct the DataFrame for a DuplicateLabelError.
This returns a DataFrame indicating the labels and positions
of duplicates in an index. This should only be called when it's
already known that duplicates are present.
Examples
--------
>>> idx = pd.Index(['a', 'b', 'a'])
>>> idx._format_duplicate_message()
positions
label
a [0, 2]
"""
from pandas import Series
duplicates = self[self.duplicated(keep="first")].unique()
assert len(duplicates)
out = Series(np.arange(len(self))).groupby(self).agg(list)[duplicates]
if self._is_multi:
# test_format_duplicate_labels_message_multi
# error: "Type[Index]" has no attribute "from_tuples" [attr-defined]
out.index = type(self).from_tuples(out.index) # type: ignore[attr-defined]
if self.nlevels == 1:
out = out.rename_axis("label")
return out.to_frame(name="positions")
# --------------------------------------------------------------------
# Index Internals Methods
@final
def _get_attributes_dict(self) -> dict[str_t, Any]:
"""
Return an attributes dict for my class.
Temporarily added back for compatibility issue in dask, see
https://github.com/pandas-dev/pandas/pull/43895
"""
warnings.warn(
"The Index._get_attributes_dict method is deprecated, and will be "
"removed in a future version",
DeprecationWarning,
stacklevel=find_stack_level(),
)
return {k: getattr(self, k, None) for k in self._attributes}
def _shallow_copy(self: _IndexT, values, name: Hashable = no_default) -> _IndexT:
"""
Create a new Index with the same class as the caller, don't copy the
data, use the same object attributes with passed in attributes taking
precedence.
*this is an internal non-public method*
Parameters
----------
values : the values to create the new Index, optional
name : Label, defaults to self.name
"""
name = self._name if name is no_default else name
return self._simple_new(values, name=name)
def _view(self: _IndexT) -> _IndexT:
"""
fastpath to make a shallow copy, i.e. new object with same data.
"""
result = self._simple_new(self._values, name=self._name)
result._cache = self._cache
return result
@final
def _rename(self: _IndexT, name: Hashable) -> _IndexT:
"""
fastpath for rename if new name is already validated.
"""
result = self._view()
result._name = name
return result
@final
def is_(self, other) -> bool:
"""
More flexible, faster check like ``is`` but that works through views.
Note: this is *not* the same as ``Index.identical()``, which checks
that metadata is also the same.
Parameters
----------
other : object
Other object to compare against.
Returns
-------
bool
True if both have same underlying data, False otherwise.
See Also
--------
Index.identical : Works like ``Index.is_`` but also checks metadata.
"""
if self is other:
return True
elif not hasattr(other, "_id"):
return False
elif self._id is None or other._id is None:
return False
else:
return self._id is other._id
@final
def _reset_identity(self) -> None:
"""
Initializes or resets ``_id`` attribute with new object.
"""
self._id = object()
@final
def _cleanup(self) -> None:
self._engine.clear_mapping()
@cache_readonly
def _engine(
self,
) -> libindex.IndexEngine:
# For base class (object dtype) we get ObjectEngine
if isinstance(self._values, BaseMaskedArray):
# TODO(ExtensionIndex): use libindex.NullableEngine(self._values)
return libindex.ObjectEngine(self._get_engine_target())
elif (
isinstance(self._values, ExtensionArray)
and self._engine_type is libindex.ObjectEngine
):
# TODO(ExtensionIndex): use libindex.ExtensionEngine(self._values)
return libindex.ObjectEngine(self._get_engine_target())
# to avoid a reference cycle, bind `target_values` to a local variable, so
# `self` is not passed into the lambda.
target_values = self._get_engine_target()
return self._engine_type(target_values)
@final
@cache_readonly
def _dir_additions_for_owner(self) -> set[str_t]:
"""
Add the string-like labels to the owner dataframe/series dir output.
If this is a MultiIndex, it's first level values are used.
"""
return {
c
for c in self.unique(level=0)[: get_option("display.max_dir_items")]
if isinstance(c, str) and c.isidentifier()
}
# --------------------------------------------------------------------
# Array-Like Methods
# ndarray compat
def __len__(self) -> int:
"""
Return the length of the Index.
"""
return len(self._data)
def __array__(self, dtype=None) -> np.ndarray:
"""
The array interface, return my values.
"""
return np.asarray(self._data, dtype=dtype)
def __array_ufunc__(self, ufunc: np.ufunc, method: str_t, *inputs, **kwargs):
if any(isinstance(other, (ABCSeries, ABCDataFrame)) for other in inputs):
return NotImplemented
# TODO(2.0) the 'and', 'or' and 'xor' dunder methods are currently set
# operations and not logical operations, so don't dispatch
# This is deprecated, so this full 'if' clause can be removed once
# deprecation is enforced in 2.0
if not (
method == "__call__"
and ufunc in (np.bitwise_and, np.bitwise_or, np.bitwise_xor)
):
result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
self, ufunc, method, *inputs, **kwargs
)
if result is not NotImplemented:
return result
if "out" in kwargs:
# e.g. test_dti_isub_tdi
return arraylike.dispatch_ufunc_with_out(
self, ufunc, method, *inputs, **kwargs
)
if method == "reduce":
result = arraylike.dispatch_reduction_ufunc(
self, ufunc, method, *inputs, **kwargs
)
if result is not NotImplemented:
return result
new_inputs = [x if x is not self else x._values for x in inputs]
result = getattr(ufunc, method)(*new_inputs, **kwargs)
if ufunc.nout == 2:
# i.e. np.divmod, np.modf, np.frexp
return tuple(self.__array_wrap__(x) for x in result)
return self.__array_wrap__(result)
def __array_wrap__(self, result, context=None):
"""
Gets called after a ufunc and other functions e.g. np.split.
"""
result = lib.item_from_zerodim(result)
if is_bool_dtype(result) or lib.is_scalar(result) or np.ndim(result) > 1:
return result
return Index(result, name=self.name)
@cache_readonly
def dtype(self) -> DtypeObj:
"""
Return the dtype object of the underlying data.
"""
return self._data.dtype
@final
def ravel(self, order="C"):
"""
Return an ndarray of the flattened values of the underlying data.
Returns
-------
numpy.ndarray
Flattened array.
See Also
--------
numpy.ndarray.ravel : Return a flattened array.
"""
warnings.warn(
"Index.ravel returning ndarray is deprecated; in a future version "
"this will return a view on self.",
FutureWarning,
stacklevel=find_stack_level(),
)
if needs_i8_conversion(self.dtype):
# Item "ndarray[Any, Any]" of "Union[ExtensionArray, ndarray[Any, Any]]"
# has no attribute "_ndarray"
values = self._data._ndarray # type: ignore[union-attr]
elif is_interval_dtype(self.dtype):
values = np.asarray(self._data)
else:
values = self._get_engine_target()
return values.ravel(order=order)
def view(self, cls=None):
# we need to see if we are subclassing an
# index type here
if cls is not None and not hasattr(cls, "_typ"):
dtype = cls
if isinstance(cls, str):
dtype = pandas_dtype(cls)
if isinstance(dtype, (np.dtype, ExtensionDtype)) and needs_i8_conversion(
dtype
):
if dtype.kind == "m" and dtype != "m8[ns]":
# e.g. m8[s]
return self._data.view(cls)
idx_cls = self._dtype_to_subclass(dtype)
# NB: we only get here for subclasses that override
# _data_cls such that it is a type and not a tuple
# of types.
arr_cls = idx_cls._data_cls
arr = arr_cls(self._data.view("i8"), dtype=dtype)
return idx_cls._simple_new(arr, name=self.name)
result = self._data.view(cls)
else:
result = self._view()
if isinstance(result, Index):
result._id = self._id
return result
def astype(self, dtype, copy: bool = True):
"""
Create an Index with values cast to dtypes.
The class of a new Index is determined by dtype. When conversion is
impossible, a TypeError exception is raised.
Parameters
----------
dtype : numpy dtype or pandas type
Note that any signed integer `dtype` is treated as ``'int64'``,
and any unsigned integer `dtype` is treated as ``'uint64'``,
regardless of the size.
copy : bool, default True
By default, astype always returns a newly allocated object.
If copy is set to False and internal requirements on dtype are
satisfied, the original data is used to create a new Index
or the original Index is returned.
Returns
-------
Index
Index with values cast to specified dtype.
"""
if dtype is not None:
dtype = pandas_dtype(dtype)
if is_dtype_equal(self.dtype, dtype):
# Ensure that self.astype(self.dtype) is self
return self.copy() if copy else self
if (
self.dtype == np.dtype("M8[ns]")
and isinstance(dtype, np.dtype)
and dtype.kind == "M"
and dtype != np.dtype("M8[ns]")
):
# For now DatetimeArray supports this by unwrapping ndarray,
# but DatetimeIndex doesn't
raise TypeError(f"Cannot cast {type(self).__name__} to dtype")
values = self._data
if isinstance(values, ExtensionArray):
with rewrite_exception(type(values).__name__, type(self).__name__):
new_values = values.astype(dtype, copy=copy)
elif isinstance(dtype, ExtensionDtype):
cls = dtype.construct_array_type()
# Note: for RangeIndex and CategoricalDtype self vs self._values
# behaves differently here.
new_values = cls._from_sequence(self, dtype=dtype, copy=copy)
else:
try:
new_values = values.astype(dtype, copy=copy)
except (TypeError, ValueError) as err:
raise TypeError(
f"Cannot cast {type(self).__name__} to dtype {dtype}"
) from err
# pass copy=False because any copying will be done in the astype above
return Index(new_values, name=self.name, dtype=new_values.dtype, copy=False)
_index_shared_docs[
"take"
] = """
Return a new %(klass)s of the values selected by the indices.
For internal compatibility with numpy arrays.
Parameters
----------
indices : array-like
Indices to be taken.
axis : int, optional
The axis over which to select values, always 0.
allow_fill : bool, default True
fill_value : scalar, default None
If allow_fill=True and fill_value is not None, indices specified by
-1 are regarded as NA. If Index doesn't hold NA, raise ValueError.
Returns
-------
Index
An index formed of elements at the given indices. Will be the same
type as self, except for RangeIndex.
See Also
--------
numpy.ndarray.take: Return an array formed from the
elements of a at the given indices.
"""
@Appender(_index_shared_docs["take"] % _index_doc_kwargs)
def take(
self, indices, axis: int = 0, allow_fill: bool = True, fill_value=None, **kwargs
):
if kwargs:
nv.validate_take((), kwargs)
if is_scalar(indices):
raise TypeError("Expected indices to be array-like")
indices = ensure_platform_int(indices)
allow_fill = self._maybe_disallow_fill(allow_fill, fill_value, indices)
# Note: we discard fill_value and use self._na_value, only relevant
# in the case where allow_fill is True and fill_value is not None
values = self._values
if isinstance(values, np.ndarray):
taken = algos.take(
values, indices, allow_fill=allow_fill, fill_value=self._na_value
)
else:
# algos.take passes 'axis' keyword which not all EAs accept
taken = values.take(
indices, allow_fill=allow_fill, fill_value=self._na_value
)
# _constructor so RangeIndex->Int64Index
return self._constructor._simple_new(taken, name=self.name)
@final
def _maybe_disallow_fill(self, allow_fill: bool, fill_value, indices) -> bool:
"""
We only use pandas-style take when allow_fill is True _and_
fill_value is not None.
"""
if allow_fill and fill_value is not None:
# only fill if we are passing a non-None fill_value
if self._can_hold_na:
if (indices < -1).any():
raise ValueError(
"When allow_fill=True and fill_value is not None, "
"all indices must be >= -1"
)
else:
cls_name = type(self).__name__
raise ValueError(
f"Unable to fill values because {cls_name} cannot contain NA"
)
else:
allow_fill = False
return allow_fill
_index_shared_docs[
"repeat"
] = """
Repeat elements of a %(klass)s.
Returns a new %(klass)s where each element of the current %(klass)s
is repeated consecutively a given number of times.
Parameters
----------
repeats : int or array of ints
The number of repetitions for each element. This should be a
non-negative integer. Repeating 0 times will return an empty
%(klass)s.
axis : None
Must be ``None``. Has no effect but is accepted for compatibility
with numpy.
Returns
-------
repeated_index : %(klass)s
Newly created %(klass)s with repeated elements.
See Also
--------
Series.repeat : Equivalent function for Series.
numpy.repeat : Similar method for :class:`numpy.ndarray`.
Examples
--------
>>> idx = pd.Index(['a', 'b', 'c'])
>>> idx
Index(['a', 'b', 'c'], dtype='object')
>>> idx.repeat(2)
Index(['a', 'a', 'b', 'b', 'c', 'c'], dtype='object')
>>> idx.repeat([1, 2, 3])
Index(['a', 'b', 'b', 'c', 'c', 'c'], dtype='object')
"""
@Appender(_index_shared_docs["repeat"] % _index_doc_kwargs)
def repeat(self, repeats, axis=None):
repeats = ensure_platform_int(repeats)
nv.validate_repeat((), {"axis": axis})
res_values = self._values.repeat(repeats)
# _constructor so RangeIndex->Int64Index
return self._constructor._simple_new(res_values, name=self.name)
# --------------------------------------------------------------------
# Copying Methods
def copy(
self: _IndexT,
name: Hashable | None = None,
deep: bool = False,
dtype: Dtype | None = None,
names: Sequence[Hashable] | None = None,
) -> _IndexT:
"""
Make a copy of this object.
Name and dtype sets those attributes on the new object.
Parameters
----------
name : Label, optional
Set name for new object.
deep : bool, default False
dtype : numpy dtype or pandas type, optional
Set dtype for new object.
.. deprecated:: 1.2.0
use ``astype`` method instead.
names : list-like, optional
Kept for compatibility with MultiIndex. Should not be used.
.. deprecated:: 1.4.0
use ``name`` instead.
Returns
-------
Index
Index refer to new object which is a copy of this object.
Notes
-----
In most cases, there should be no functional difference from using
``deep``, but if ``deep`` is passed it will attempt to deepcopy.
"""
if names is not None:
warnings.warn(
"parameter names is deprecated and will be removed in a future "
"version. Use the name parameter instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
name = self._validate_names(name=name, names=names, deep=deep)[0]
if deep:
new_data = self._data.copy()
new_index = type(self)._simple_new(new_data, name=name)
else:
new_index = self._rename(name=name)
if dtype:
warnings.warn(
"parameter dtype is deprecated and will be removed in a future "
"version. Use the astype method instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
new_index = new_index.astype(dtype)
return new_index
@final
def __copy__(self: _IndexT, **kwargs) -> _IndexT:
return self.copy(**kwargs)
@final
def __deepcopy__(self: _IndexT, memo=None) -> _IndexT:
"""
Parameters
----------
memo, default None
Standard signature. Unused
"""
return self.copy(deep=True)
# --------------------------------------------------------------------
# Rendering Methods
@final
def __repr__(self) -> str_t:
"""
Return a string representation for this object.
"""
klass_name = type(self).__name__
data = self._format_data()
attrs = self._format_attrs()
space = self._format_space()
attrs_str = [f"{k}={v}" for k, v in attrs]
prepr = f",{space}".join(attrs_str)
# no data provided, just attributes
if data is None:
data = ""
return f"{klass_name}({data}{prepr})"
def _format_space(self) -> str_t:
# using space here controls if the attributes
# are line separated or not (the default)
# max_seq_items = get_option('display.max_seq_items')
# if len(self) > max_seq_items:
# space = "\n%s" % (' ' * (len(klass) + 1))
return " "
@property
def _formatter_func(self):
"""
Return the formatter function.
"""
return default_pprint
def _format_data(self, name=None) -> str_t:
"""
Return the formatted data as a unicode string.
"""
# do we want to justify (only do so for non-objects)
is_justify = True
if self.inferred_type == "string":
is_justify = False
elif self.inferred_type == "categorical":
self = cast("CategoricalIndex", self)
if is_object_dtype(self.categories):
is_justify = False
return format_object_summary(
self,
self._formatter_func,
is_justify=is_justify,
name=name,
line_break_each_value=self._is_multi,
)
def _format_attrs(self) -> list[tuple[str_t, str_t | int | bool | None]]:
"""
Return a list of tuples of the (attr,formatted_value).
"""
attrs: list[tuple[str_t, str_t | int | bool | None]] = []
if not self._is_multi:
attrs.append(("dtype", f"'{self.dtype}'"))
if self.name is not None:
attrs.append(("name", default_pprint(self.name)))
elif self._is_multi and any(x is not None for x in self.names):
attrs.append(("names", default_pprint(self.names)))
max_seq_items = get_option("display.max_seq_items") or len(self)
if len(self) > max_seq_items:
attrs.append(("length", len(self)))
return attrs
@final
def _mpl_repr(self) -> np.ndarray:
# how to represent ourselves to matplotlib
if isinstance(self.dtype, np.dtype) and self.dtype.kind != "M":
return cast(np.ndarray, self.values)
return self.astype(object, copy=False)._values
def format(
self,
name: bool = False,
formatter: Callable | None = None,
na_rep: str_t = "NaN",
) -> list[str_t]:
"""
Render a string representation of the Index.
"""
header = []
if name:
header.append(
pprint_thing(self.name, escape_chars=("\t", "\r", "\n"))
if self.name is not None
else ""
)
if formatter is not None:
return header + list(self.map(formatter))
return self._format_with_header(header, na_rep=na_rep)
def _format_with_header(self, header: list[str_t], na_rep: str_t) -> list[str_t]:
from pandas.io.formats.format import format_array
values = self._values
if is_object_dtype(values.dtype):
values = cast(np.ndarray, values)
values = lib.maybe_convert_objects(values, safe=True)
result = [pprint_thing(x, escape_chars=("\t", "\r", "\n")) for x in values]
# could have nans
mask = is_float_nan(values)
if mask.any():
result_arr = np.array(result)
result_arr[mask] = na_rep
result = result_arr.tolist()
else:
result = trim_front(format_array(values, None, justify="left"))
return header + result
@final
def to_native_types(self, slicer=None, **kwargs) -> np.ndarray:
"""
Format specified values of `self` and return them.
.. deprecated:: 1.2.0
Parameters
----------
slicer : int, array-like
An indexer into `self` that specifies which values
are used in the formatting process.
kwargs : dict
Options for specifying how the values should be formatted.
These options include the following:
1) na_rep : str
The value that serves as a placeholder for NULL values
2) quoting : bool or None
Whether or not there are quoted values in `self`
3) date_format : str
The format used to represent date-like values.
Returns
-------
numpy.ndarray
Formatted values.
"""
warnings.warn(
"The 'to_native_types' method is deprecated and will be removed in "
"a future version. Use 'astype(str)' instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
values = self
if slicer is not None:
values = values[slicer]
return values._format_native_types(**kwargs)
def _format_native_types(self, *, na_rep="", quoting=None, **kwargs):
"""
Actually format specific types of the index.
"""
mask = isna(self)
if not self.is_object() and not quoting:
values = np.asarray(self).astype(str)
else:
values = np.array(self, dtype=object, copy=True)
values[mask] = na_rep
return values
def _summary(self, name=None) -> str_t:
"""
Return a summarized representation.
Parameters
----------
name : str
name to use in the summary representation
Returns
-------
String with a summarized representation of the index
"""
if len(self) > 0:
head = self[0]
if hasattr(head, "format") and not isinstance(head, str):
head = head.format()
elif needs_i8_conversion(self.dtype):
# e.g. Timedelta, display as values, not quoted
head = self._formatter_func(head).replace("'", "")
tail = self[-1]
if hasattr(tail, "format") and not isinstance(tail, str):
tail = tail.format()
elif needs_i8_conversion(self.dtype):
# e.g. Timedelta, display as values, not quoted
tail = self._formatter_func(tail).replace("'", "")
index_summary = f", {head} to {tail}"
else:
index_summary = ""
if name is None:
name = type(self).__name__
return f"{name}: {len(self)} entries{index_summary}"
# --------------------------------------------------------------------
# Conversion Methods
def to_flat_index(self):
"""
Identity method.
This is implemented for compatibility with subclass implementations
when chaining.
Returns
-------
pd.Index
Caller.
See Also
--------
MultiIndex.to_flat_index : Subclass implementation.
"""
return self
def to_series(self, index=None, name: Hashable = None) -> Series:
"""
Create a Series with both index and values equal to the index keys.
Useful with map for returning an indexer based on an index.
Parameters
----------
index : Index, optional
Index of resulting Series. If None, defaults to original index.
name : str, optional
Name of resulting Series. If None, defaults to name of original
index.
Returns
-------
Series
The dtype will be based on the type of the Index values.
See Also
--------
Index.to_frame : Convert an Index to a DataFrame.
Series.to_frame : Convert Series to DataFrame.
Examples
--------
>>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')
By default, the original Index and original name is reused.
>>> idx.to_series()
animal
Ant Ant
Bear Bear
Cow Cow
Name: animal, dtype: object
To enforce a new Index, specify new labels to ``index``:
>>> idx.to_series(index=[0, 1, 2])
0 Ant
1 Bear
2 Cow
Name: animal, dtype: object
To override the name of the resulting column, specify `name`:
>>> idx.to_series(name='zoo')
animal
Ant Ant
Bear Bear
Cow Cow
Name: zoo, dtype: object
"""
from pandas import Series
if index is None:
index = self._view()
if name is None:
name = self.name
return Series(self._values.copy(), index=index, name=name)
def to_frame(
self, index: bool = True, name: Hashable = lib.no_default
) -> DataFrame:
"""
Create a DataFrame with a column containing the Index.
Parameters
----------
index : bool, default True
Set the index of the returned DataFrame as the original Index.
name : object, default None
The passed name should substitute for the index name (if it has
one).
Returns
-------
DataFrame
DataFrame containing the original Index data.
See Also
--------
Index.to_series : Convert an Index to a Series.
Series.to_frame : Convert Series to DataFrame.
Examples
--------
>>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')
>>> idx.to_frame()
animal
animal
Ant Ant
Bear Bear
Cow Cow
By default, the original Index is reused. To enforce a new Index:
>>> idx.to_frame(index=False)
animal
0 Ant
1 Bear
2 Cow
To override the name of the resulting column, specify `name`:
>>> idx.to_frame(index=False, name='zoo')
zoo
0 Ant
1 Bear
2 Cow
"""
from pandas import DataFrame
if name is None:
warnings.warn(
"Explicitly passing `name=None` currently preserves the Index's name "
"or uses a default name of 0. This behaviour is deprecated, and in "
"the future `None` will be used as the name of the resulting "
"DataFrame column.",
FutureWarning,
stacklevel=find_stack_level(),
)
name = lib.no_default
if name is lib.no_default:
name = self.name or 0
result = DataFrame({name: self._values.copy()})
if index:
result.index = self
return result
# --------------------------------------------------------------------
# Name-Centric Methods
@property
def name(self):
"""
Return Index or MultiIndex name.
"""
return self._name
@name.setter
def name(self, value: Hashable):
if self._no_setting_name:
# Used in MultiIndex.levels to avoid silently ignoring name updates.
raise RuntimeError(
"Cannot set name on a level of a MultiIndex. Use "
"'MultiIndex.set_names' instead."
)
maybe_extract_name(value, None, type(self))
self._name = value
@final
def _validate_names(
self, name=None, names=None, deep: bool = False
) -> list[Hashable]:
"""
Handles the quirks of having a singular 'name' parameter for general
Index and plural 'names' parameter for MultiIndex.
"""
from copy import deepcopy
if names is not None and name is not None:
raise TypeError("Can only provide one of `names` and `name`")
elif names is None and name is None:
new_names = deepcopy(self.names) if deep else self.names
elif names is not None:
if not is_list_like(names):
raise TypeError("Must pass list-like as `names`.")
new_names = names
elif not is_list_like(name):
new_names = [name]
else:
new_names = name
if len(new_names) != len(self.names):
raise ValueError(
f"Length of new names must be {len(self.names)}, got {len(new_names)}"
)
# All items in 'new_names' need to be hashable
validate_all_hashable(*new_names, error_name=f"{type(self).__name__}.name")
return new_names
def _get_names(self) -> FrozenList:
return FrozenList((self.name,))
def _set_names(self, values, *, level=None) -> None:
"""
Set new names on index. Each name has to be a hashable type.
Parameters
----------
values : str or sequence
name(s) to set
level : int, level name, or sequence of int/level names (default None)
If the index is a MultiIndex (hierarchical), level(s) to set (None
for all levels). Otherwise level must be None
Raises
------
TypeError if each name is not hashable.
"""
if not is_list_like(values):
raise ValueError("Names must be a list-like")
if len(values) != 1:
raise ValueError(f"Length of new names must be 1, got {len(values)}")
# GH 20527
# All items in 'name' need to be hashable:
validate_all_hashable(*values, error_name=f"{type(self).__name__}.name")
self._name = values[0]
names = property(fset=_set_names, fget=_get_names)
@deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "names"])
def set_names(self, names, level=None, inplace: bool = False):
"""
Set Index or MultiIndex name.
Able to set new names partially and by level.
Parameters
----------
names : label or list of label or dict-like for MultiIndex
Name(s) to set.
.. versionchanged:: 1.3.0
level : int, label or list of int or label, optional
If the index is a MultiIndex and names is not dict-like, level(s) to set
(None for all levels). Otherwise level must be None.
.. versionchanged:: 1.3.0
inplace : bool, default False
Modifies the object directly, instead of creating a new Index or
MultiIndex.
Returns
-------
Index or None
The same type as the caller or None if ``inplace=True``.
See Also
--------
Index.rename : Able to set new names without level.
Examples
--------
>>> idx = pd.Index([1, 2, 3, 4])
>>> idx
Int64Index([1, 2, 3, 4], dtype='int64')
>>> idx.set_names('quarter')
Int64Index([1, 2, 3, 4], dtype='int64', name='quarter')
>>> idx = pd.MultiIndex.from_product([['python', 'cobra'],
... [2018, 2019]])
>>> idx
MultiIndex([('python', 2018),
('python', 2019),
( 'cobra', 2018),
( 'cobra', 2019)],
)
>>> idx.set_names(['kind', 'year'], inplace=True)
>>> idx
MultiIndex([('python', 2018),
('python', 2019),
( 'cobra', 2018),
( 'cobra', 2019)],
names=['kind', 'year'])
>>> idx.set_names('species', level=0)
MultiIndex([('python', 2018),
('python', 2019),
( 'cobra', 2018),
( 'cobra', 2019)],
names=['species', 'year'])
When renaming levels with a dict, levels can not be passed.
>>> idx.set_names({'kind': 'snake'})
MultiIndex([('python', 2018),
('python', 2019),
( 'cobra', 2018),
( 'cobra', 2019)],
names=['snake', 'year'])
"""
if level is not None and not isinstance(self, ABCMultiIndex):
raise ValueError("Level must be None for non-MultiIndex")
elif level is not None and not is_list_like(level) and is_list_like(names):
raise TypeError("Names must be a string when a single level is provided.")
elif not is_list_like(names) and level is None and self.nlevels > 1:
raise TypeError("Must pass list-like as `names`.")
elif is_dict_like(names) and not isinstance(self, ABCMultiIndex):
raise TypeError("Can only pass dict-like as `names` for MultiIndex.")
elif is_dict_like(names) and level is not None:
raise TypeError("Can not pass level for dictlike `names`.")
if isinstance(self, ABCMultiIndex) and is_dict_like(names) and level is None:
# Transform dict to list of new names and corresponding levels
level, names_adjusted = [], []
for i, name in enumerate(self.names):
if name in names.keys():
level.append(i)
names_adjusted.append(names[name])
names = names_adjusted
if not is_list_like(names):
names = [names]
if level is not None and not is_list_like(level):
level = [level]
if inplace:
idx = self
else:
idx = self._view()
idx._set_names(names, level=level)
if not inplace:
return idx
def rename(self, name, inplace=False):
"""
Alter Index or MultiIndex name.
Able to set new names without level. Defaults to returning new index.
Length of names must match number of levels in MultiIndex.
Parameters
----------
name : label or list of labels
Name(s) to set.
inplace : bool, default False
Modifies the object directly, instead of creating a new Index or
MultiIndex.
Returns
-------
Index or None
The same type as the caller or None if ``inplace=True``.
See Also
--------
Index.set_names : Able to set new names partially and by level.
Examples
--------
>>> idx = pd.Index(['A', 'C', 'A', 'B'], name='score')
>>> idx.rename('grade')
Index(['A', 'C', 'A', 'B'], dtype='object', name='grade')
>>> idx = pd.MultiIndex.from_product([['python', 'cobra'],
... [2018, 2019]],
... names=['kind', 'year'])
>>> idx
MultiIndex([('python', 2018),
('python', 2019),
( 'cobra', 2018),
( 'cobra', 2019)],
names=['kind', 'year'])
>>> idx.rename(['species', 'year'])
MultiIndex([('python', 2018),
('python', 2019),
( 'cobra', 2018),
( 'cobra', 2019)],
names=['species', 'year'])
>>> idx.rename('species')
Traceback (most recent call last):
TypeError: Must pass list-like as `names`.
"""
return self.set_names([name], inplace=inplace)
# --------------------------------------------------------------------
# Level-Centric Methods
@property
def nlevels(self) -> int:
"""
Number of levels.
"""
return 1
def _sort_levels_monotonic(self: _IndexT) -> _IndexT:
"""
Compat with MultiIndex.
"""
return self
@final
def _validate_index_level(self, level) -> None:
"""
Validate index level.
For single-level Index getting level number is a no-op, but some
verification must be done like in MultiIndex.
"""
if isinstance(level, int):
if level < 0 and level != -1:
raise IndexError(
"Too many levels: Index has only 1 level, "
f"{level} is not a valid level number"
)
elif level > 0:
raise IndexError(
f"Too many levels: Index has only 1 level, not {level + 1}"
)
elif level != self.name:
raise KeyError(
f"Requested level ({level}) does not match index name ({self.name})"
)
def _get_level_number(self, level) -> int:
self._validate_index_level(level)
return 0
def sortlevel(self, level=None, ascending=True, sort_remaining=None):
"""
For internal compatibility with the Index API.
Sort the Index. This is for compat with MultiIndex
Parameters
----------
ascending : bool, default True
False to sort in descending order
level, sort_remaining are compat parameters
Returns
-------
Index
"""
if not isinstance(ascending, (list, bool)):
raise TypeError(
"ascending must be a single bool value or"
"a list of bool values of length 1"
)
if isinstance(ascending, list):
if len(ascending) != 1:
raise TypeError("ascending must be a list of bool values of length 1")
ascending = ascending[0]
if not isinstance(ascending, bool):
raise TypeError("ascending must be a bool value")
return self.sort_values(return_indexer=True, ascending=ascending)
def _get_level_values(self, level) -> Index:
"""
Return an Index of values for requested level.
This is primarily useful to get an individual level of values from a
MultiIndex, but is provided on Index as well for compatibility.
Parameters
----------
level : int or str
It is either the integer position or the name of the level.
Returns
-------
Index
Calling object, as there is only one level in the Index.
See Also
--------
MultiIndex.get_level_values : Get values for a level of a MultiIndex.
Notes
-----
For Index, level should be 0, since there are no multiple levels.
Examples
--------
>>> idx = pd.Index(list('abc'))
>>> idx
Index(['a', 'b', 'c'], dtype='object')
Get level values by supplying `level` as integer:
>>> idx.get_level_values(0)
Index(['a', 'b', 'c'], dtype='object')
"""
self._validate_index_level(level)
return self
get_level_values = _get_level_values
@final
def droplevel(self, level=0):
"""
Return index with requested level(s) removed.
If resulting index has only 1 level left, the result will be
of Index type, not MultiIndex.
Parameters
----------
level : int, str, or list-like, default 0
If a string is given, must be the name of a level
If list-like, elements must be names or indexes of levels.
Returns
-------
Index or MultiIndex
Examples
--------
>>> mi = pd.MultiIndex.from_arrays(
... [[1, 2], [3, 4], [5, 6]], names=['x', 'y', 'z'])
>>> mi
MultiIndex([(1, 3, 5),
(2, 4, 6)],
names=['x', 'y', 'z'])
>>> mi.droplevel()
MultiIndex([(3, 5),
(4, 6)],
names=['y', 'z'])
>>> mi.droplevel(2)
MultiIndex([(1, 3),
(2, 4)],
names=['x', 'y'])
>>> mi.droplevel('z')
MultiIndex([(1, 3),
(2, 4)],
names=['x', 'y'])
>>> mi.droplevel(['x', 'y'])
Int64Index([5, 6], dtype='int64', name='z')
"""
if not isinstance(level, (tuple, list)):
level = [level]
levnums = sorted(self._get_level_number(lev) for lev in level)[::-1]
return self._drop_level_numbers(levnums)
@final
def _drop_level_numbers(self, levnums: list[int]):
"""
Drop MultiIndex levels by level _number_, not name.
"""
if not levnums and not isinstance(self, ABCMultiIndex):
return self
if len(levnums) >= self.nlevels:
raise ValueError(
f"Cannot remove {len(levnums)} levels from an index with "
f"{self.nlevels} levels: at least one level must be left."
)
# The two checks above guarantee that here self is a MultiIndex
self = cast("MultiIndex", self)
new_levels = list(self.levels)
new_codes = list(self.codes)
new_names = list(self.names)
for i in levnums:
new_levels.pop(i)
new_codes.pop(i)
new_names.pop(i)
if len(new_levels) == 1:
lev = new_levels[0]
if len(lev) == 0:
# If lev is empty, lev.take will fail GH#42055
if len(new_codes[0]) == 0:
# GH#45230 preserve RangeIndex here
# see test_reset_index_empty_rangeindex
result = lev[:0]
else:
res_values = algos.take(lev._values, new_codes[0], allow_fill=True)
# _constructor instead of type(lev) for RangeIndex compat GH#35230
result = lev._constructor._simple_new(res_values, name=new_names[0])
else:
# set nan if needed
mask = new_codes[0] == -1
result = new_levels[0].take(new_codes[0])
if mask.any():
result = result.putmask(mask, np.nan)
result._name = new_names[0]
return result
else:
from pandas.core.indexes.multi import MultiIndex
return MultiIndex(
levels=new_levels,
codes=new_codes,
names=new_names,
verify_integrity=False,
)
def _get_grouper_for_level(self, mapper, *, level=None):
"""
Get index grouper corresponding to an index level
Parameters
----------
mapper: Group mapping function or None
Function mapping index values to groups
level : int or None
Index level, positional
Returns
-------
grouper : Index
Index of values to group on.
labels : ndarray of int or None
Array of locations in level_index.
uniques : Index or None
Index of unique values for level.
"""
assert level is None or level == 0
if mapper is None:
grouper = self
else:
grouper = self.map(mapper)
return grouper, None, None
# --------------------------------------------------------------------
# Introspection Methods
@final
@property
def is_monotonic(self) -> bool:
"""
Alias for is_monotonic_increasing.
"""
return self.is_monotonic_increasing
@property
def is_monotonic_increasing(self) -> bool:
"""
Return if the index is monotonic increasing (only equal or
increasing) values.
Examples
--------
>>> Index([1, 2, 3]).is_monotonic_increasing
True
>>> Index([1, 2, 2]).is_monotonic_increasing
True
>>> Index([1, 3, 2]).is_monotonic_increasing
False
"""
return self._engine.is_monotonic_increasing
@property
def is_monotonic_decreasing(self) -> bool:
"""
Return if the index is monotonic decreasing (only equal or
decreasing) values.
Examples
--------
>>> Index([3, 2, 1]).is_monotonic_decreasing
True
>>> Index([3, 2, 2]).is_monotonic_decreasing
True
>>> Index([3, 1, 2]).is_monotonic_decreasing
False
"""
return self._engine.is_monotonic_decreasing
@final
@property
def _is_strictly_monotonic_increasing(self) -> bool:
"""
Return if the index is strictly monotonic increasing
(only increasing) values.
Examples
--------
>>> Index([1, 2, 3])._is_strictly_monotonic_increasing
True
>>> Index([1, 2, 2])._is_strictly_monotonic_increasing
False
>>> Index([1, 3, 2])._is_strictly_monotonic_increasing
False
"""
return self.is_unique and self.is_monotonic_increasing
@final
@property
def _is_strictly_monotonic_decreasing(self) -> bool:
"""
Return if the index is strictly monotonic decreasing
(only decreasing) values.
Examples
--------
>>> Index([3, 2, 1])._is_strictly_monotonic_decreasing
True
>>> Index([3, 2, 2])._is_strictly_monotonic_decreasing
False
>>> Index([3, 1, 2])._is_strictly_monotonic_decreasing
False
"""
return self.is_unique and self.is_monotonic_decreasing
@cache_readonly
def is_unique(self) -> bool:
"""
Return if the index has unique values.
"""
return self._engine.is_unique
@final
@property
def has_duplicates(self) -> bool:
"""
Check if the Index has duplicate values.
Returns
-------
bool
Whether or not the Index has duplicate values.
Examples
--------
>>> idx = pd.Index([1, 5, 7, 7])
>>> idx.has_duplicates
True
>>> idx = pd.Index([1, 5, 7])
>>> idx.has_duplicates
False
>>> idx = pd.Index(["Watermelon", "Orange", "Apple",
... "Watermelon"]).astype("category")
>>> idx.has_duplicates
True
>>> idx = pd.Index(["Orange", "Apple",
... "Watermelon"]).astype("category")
>>> idx.has_duplicates
False
"""
return not self.is_unique
@final
def is_boolean(self) -> bool:
"""
Check if the Index only consists of booleans.
Returns
-------
bool
Whether or not the Index only consists of booleans.
See Also
--------
is_integer : Check if the Index only consists of integers.
is_floating : Check if the Index is a floating type.
is_numeric : Check if the Index only consists of numeric data.
is_object : Check if the Index is of the object dtype.
is_categorical : Check if the Index holds categorical data.
is_interval : Check if the Index holds Interval objects.
is_mixed : Check if the Index holds data with mixed data types.
Examples
--------
>>> idx = pd.Index([True, False, True])
>>> idx.is_boolean()
True
>>> idx = pd.Index(["True", "False", "True"])
>>> idx.is_boolean()
False
>>> idx = pd.Index([True, False, "True"])
>>> idx.is_boolean()
False
"""
return self.inferred_type in ["boolean"]
@final
def is_integer(self) -> bool:
"""
Check if the Index only consists of integers.
Returns
-------
bool
Whether or not the Index only consists of integers.
See Also
--------
is_boolean : Check if the Index only consists of booleans.
is_floating : Check if the Index is a floating type.
is_numeric : Check if the Index only consists of numeric data.
is_object : Check if the Index is of the object dtype.
is_categorical : Check if the Index holds categorical data.
is_interval : Check if the Index holds Interval objects.
is_mixed : Check if the Index holds data with mixed data types.
Examples
--------
>>> idx = pd.Index([1, 2, 3, 4])
>>> idx.is_integer()
True
>>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
>>> idx.is_integer()
False
>>> idx = pd.Index(["Apple", "Mango", "Watermelon"])
>>> idx.is_integer()
False
"""
return self.inferred_type in ["integer"]
@final
def is_floating(self) -> bool:
"""
Check if the Index is a floating type.
The Index may consist of only floats, NaNs, or a mix of floats,
integers, or NaNs.
Returns
-------
bool
Whether or not the Index only consists of only consists of floats, NaNs, or
a mix of floats, integers, or NaNs.
See Also
--------
is_boolean : Check if the Index only consists of booleans.
is_integer : Check if the Index only consists of integers.
is_numeric : Check if the Index only consists of numeric data.
is_object : Check if the Index is of the object dtype.
is_categorical : Check if the Index holds categorical data.
is_interval : Check if the Index holds Interval objects.
is_mixed : Check if the Index holds data with mixed data types.
Examples
--------
>>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
>>> idx.is_floating()
True
>>> idx = pd.Index([1.0, 2.0, np.nan, 4.0])
>>> idx.is_floating()
True
>>> idx = pd.Index([1, 2, 3, 4, np.nan])
>>> idx.is_floating()
True
>>> idx = pd.Index([1, 2, 3, 4])
>>> idx.is_floating()
False
"""
return self.inferred_type in ["floating", "mixed-integer-float", "integer-na"]
@final
def is_numeric(self) -> bool:
"""
Check if the Index only consists of numeric data.
Returns
-------
bool
Whether or not the Index only consists of numeric data.
See Also
--------
is_boolean : Check if the Index only consists of booleans.
is_integer : Check if the Index only consists of integers.
is_floating : Check if the Index is a floating type.
is_object : Check if the Index is of the object dtype.
is_categorical : Check if the Index holds categorical data.
is_interval : Check if the Index holds Interval objects.
is_mixed : Check if the Index holds data with mixed data types.
Examples
--------
>>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
>>> idx.is_numeric()
True
>>> idx = pd.Index([1, 2, 3, 4.0])
>>> idx.is_numeric()
True
>>> idx = pd.Index([1, 2, 3, 4])
>>> idx.is_numeric()
True
>>> idx = pd.Index([1, 2, 3, 4.0, np.nan])
>>> idx.is_numeric()
True
>>> idx = pd.Index([1, 2, 3, 4.0, np.nan, "Apple"])
>>> idx.is_numeric()
False
"""
return self.inferred_type in ["integer", "floating"]
@final
def is_object(self) -> bool:
"""
Check if the Index is of the object dtype.
Returns
-------
bool
Whether or not the Index is of the object dtype.
See Also
--------
is_boolean : Check if the Index only consists of booleans.
is_integer : Check if the Index only consists of integers.
is_floating : Check if the Index is a floating type.
is_numeric : Check if the Index only consists of numeric data.
is_categorical : Check if the Index holds categorical data.
is_interval : Check if the Index holds Interval objects.
is_mixed : Check if the Index holds data with mixed data types.
Examples
--------
>>> idx = pd.Index(["Apple", "Mango", "Watermelon"])
>>> idx.is_object()
True
>>> idx = pd.Index(["Apple", "Mango", 2.0])
>>> idx.is_object()
True
>>> idx = pd.Index(["Watermelon", "Orange", "Apple",
... "Watermelon"]).astype("category")
>>> idx.is_object()
False
>>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
>>> idx.is_object()
False
"""
return is_object_dtype(self.dtype)
@final
def is_categorical(self) -> bool:
"""
Check if the Index holds categorical data.
Returns
-------
bool
True if the Index is categorical.
See Also
--------
CategoricalIndex : Index for categorical data.
is_boolean : Check if the Index only consists of booleans.
is_integer : Check if the Index only consists of integers.
is_floating : Check if the Index is a floating type.
is_numeric : Check if the Index only consists of numeric data.
is_object : Check if the Index is of the object dtype.
is_interval : Check if the Index holds Interval objects.
is_mixed : Check if the Index holds data with mixed data types.
Examples
--------
>>> idx = pd.Index(["Watermelon", "Orange", "Apple",
... "Watermelon"]).astype("category")
>>> idx.is_categorical()
True
>>> idx = pd.Index([1, 3, 5, 7])
>>> idx.is_categorical()
False
>>> s = pd.Series(["Peter", "Victor", "Elisabeth", "Mar"])
>>> s
0 Peter
1 Victor
2 Elisabeth
3 Mar
dtype: object
>>> s.index.is_categorical()
False
"""
return self.inferred_type in ["categorical"]
@final
def is_interval(self) -> bool:
"""
Check if the Index holds Interval objects.
Returns
-------
bool
Whether or not the Index holds Interval objects.
See Also
--------
IntervalIndex : Index for Interval objects.
is_boolean : Check if the Index only consists of booleans.
is_integer : Check if the Index only consists of integers.
is_floating : Check if the Index is a floating type.
is_numeric : Check if the Index only consists of numeric data.
is_object : Check if the Index is of the object dtype.
is_categorical : Check if the Index holds categorical data.
is_mixed : Check if the Index holds data with mixed data types.
Examples
--------
>>> idx = pd.Index([pd.Interval(left=0, right=5),
... pd.Interval(left=5, right=10)])
>>> idx.is_interval()
True
>>> idx = pd.Index([1, 3, 5, 7])
>>> idx.is_interval()
False
"""
return self.inferred_type in ["interval"]
@final
def is_mixed(self) -> bool:
"""
Check if the Index holds data with mixed data types.
Returns
-------
bool
Whether or not the Index holds data with mixed data types.
See Also
--------
is_boolean : Check if the Index only consists of booleans.
is_integer : Check if the Index only consists of integers.
is_floating : Check if the Index is a floating type.
is_numeric : Check if the Index only consists of numeric data.
is_object : Check if the Index is of the object dtype.
is_categorical : Check if the Index holds categorical data.
is_interval : Check if the Index holds Interval objects.
Examples
--------
>>> idx = pd.Index(['a', np.nan, 'b'])
>>> idx.is_mixed()
True
>>> idx = pd.Index([1.0, 2.0, 3.0, 5.0])
>>> idx.is_mixed()
False
"""
warnings.warn(
"Index.is_mixed is deprecated and will be removed in a future version. "
"Check index.inferred_type directly instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return self.inferred_type in ["mixed"]
@final
def holds_integer(self) -> bool:
"""
Whether the type is an integer type.
"""
return self.inferred_type in ["integer", "mixed-integer"]
@cache_readonly
def inferred_type(self) -> str_t:
"""
Return a string of the type inferred from the values.
"""
return lib.infer_dtype(self._values, skipna=False)
@cache_readonly
def _is_all_dates(self) -> bool:
"""
Whether or not the index values only consist of dates.
"""
return is_datetime_array(ensure_object(self._values))
@cache_readonly
@final
def is_all_dates(self) -> bool:
"""
Whether or not the index values only consist of dates.
"""
warnings.warn(
"Index.is_all_dates is deprecated, will be removed in a future version. "
"check index.inferred_type instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return self._is_all_dates
@final
@cache_readonly
def _is_multi(self) -> bool:
"""
Cached check equivalent to isinstance(self, MultiIndex)
"""
return isinstance(self, ABCMultiIndex)
# --------------------------------------------------------------------
# Pickle Methods
def __reduce__(self):
d = {"data": self._data, "name": self.name}
return _new_Index, (type(self), d), None
# --------------------------------------------------------------------
# Null Handling Methods
@cache_readonly
def _na_value(self):
"""The expected NA value to use with this index."""
dtype = self.dtype
if isinstance(dtype, np.dtype):
if dtype.kind in ["m", "M"]:
return NaT
return np.nan
return dtype.na_value
@cache_readonly
def _isnan(self) -> npt.NDArray[np.bool_]:
"""
Return if each value is NaN.
"""
if self._can_hold_na:
return isna(self)
else:
# shouldn't reach to this condition by checking hasnans beforehand
values = np.empty(len(self), dtype=np.bool_)
values.fill(False)
return values
@cache_readonly
def hasnans(self) -> bool:
"""
Return True if there are any NaNs.
Enables various performance speedups.
"""
if self._can_hold_na:
return bool(self._isnan.any())
else:
return False
@final
def isna(self) -> npt.NDArray[np.bool_]:
"""
Detect missing values.
Return a boolean same-sized object indicating if the values are NA.
NA values, such as ``None``, :attr:`numpy.NaN` or :attr:`pd.NaT`, get
mapped to ``True`` values.
Everything else get mapped to ``False`` values. Characters such as
empty strings `''` or :attr:`numpy.inf` are not considered NA values
(unless you set ``pandas.options.mode.use_inf_as_na = True``).
Returns
-------
numpy.ndarray[bool]
A boolean array of whether my values are NA.
See Also
--------
Index.notna : Boolean inverse of isna.
Index.dropna : Omit entries with missing values.
isna : Top-level isna.
Series.isna : Detect missing values in Series object.
Examples
--------
Show which entries in a pandas.Index are NA. The result is an
array.
>>> idx = pd.Index([5.2, 6.0, np.NaN])
>>> idx
Float64Index([5.2, 6.0, nan], dtype='float64')
>>> idx.isna()
array([False, False, True])
Empty strings are not considered NA values. None is considered an NA
value.
>>> idx = pd.Index(['black', '', 'red', None])
>>> idx
Index(['black', '', 'red', None], dtype='object')
>>> idx.isna()
array([False, False, False, True])
For datetimes, `NaT` (Not a Time) is considered as an NA value.
>>> idx = pd.DatetimeIndex([pd.Timestamp('1940-04-25'),
... pd.Timestamp(''), None, pd.NaT])
>>> idx
DatetimeIndex(['1940-04-25', 'NaT', 'NaT', 'NaT'],
dtype='datetime64[ns]', freq=None)
>>> idx.isna()
array([False, True, True, True])
"""
return self._isnan
isnull = isna
@final
def notna(self) -> npt.NDArray[np.bool_]:
"""
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA.
Non-missing values get mapped to ``True``. Characters such as empty
strings ``''`` or :attr:`numpy.inf` are not considered NA values
(unless you set ``pandas.options.mode.use_inf_as_na = True``).
NA values, such as None or :attr:`numpy.NaN`, get mapped to ``False``
values.
Returns
-------
numpy.ndarray[bool]
Boolean array to indicate which entries are not NA.
See Also
--------
Index.notnull : Alias of notna.
Index.isna: Inverse of notna.
notna : Top-level notna.
Examples
--------
Show which entries in an Index are not NA. The result is an
array.
>>> idx = pd.Index([5.2, 6.0, np.NaN])
>>> idx
Float64Index([5.2, 6.0, nan], dtype='float64')
>>> idx.notna()
array([ True, True, False])
Empty strings are not considered NA values. None is considered a NA
value.
>>> idx = pd.Index(['black', '', 'red', None])
>>> idx
Index(['black', '', 'red', None], dtype='object')
>>> idx.notna()
array([ True, True, True, False])
"""
return ~self.isna()
notnull = notna
def fillna(self, value=None, downcast=None):
"""
Fill NA/NaN values with the specified value.
Parameters
----------
value : scalar
Scalar value to use to fill holes (e.g. 0).
This value cannot be a list-likes.
downcast : dict, default is None
A dict of item->dtype of what to downcast if possible,
or the string 'infer' which will try to downcast to an appropriate
equal type (e.g. float64 to int64 if possible).
Returns
-------
Index
See Also
--------
DataFrame.fillna : Fill NaN values of a DataFrame.
Series.fillna : Fill NaN Values of a Series.
"""
value = self._require_scalar(value)
if self.hasnans:
result = self.putmask(self._isnan, value)
if downcast is None:
# no need to care metadata other than name
# because it can't have freq if it has NaTs
return Index._with_infer(result, name=self.name)
raise NotImplementedError(
f"{type(self).__name__}.fillna does not support 'downcast' "
"argument values other than 'None'."
)
return self._view()
def dropna(self: _IndexT, how: str_t = "any") -> _IndexT:
"""
Return Index without NA/NaN values.
Parameters
----------
how : {'any', 'all'}, default 'any'
If the Index is a MultiIndex, drop the value when any or all levels
are NaN.
Returns
-------
Index
"""
if how not in ("any", "all"):
raise ValueError(f"invalid how option: {how}")
if self.hasnans:
res_values = self._values[~self._isnan]
return type(self)._simple_new(res_values, name=self.name)
return self._view()
# --------------------------------------------------------------------
# Uniqueness Methods
def unique(self: _IndexT, level: Hashable | None = None) -> _IndexT:
"""
Return unique values in the index.
Unique values are returned in order of appearance, this does NOT sort.
Parameters
----------
level : int or hashable, optional
Only return values from specified level (for MultiIndex).
If int, gets the level by integer position, else by level name.
Returns
-------
Index
See Also
--------
unique : Numpy array of unique values in that column.
Series.unique : Return unique values of Series object.
"""
if level is not None:
self._validate_index_level(level)
if self.is_unique:
return self._view()
result = super().unique()
return self._shallow_copy(result)
@deprecate_nonkeyword_arguments(version=None, allowed_args=["self"])
def drop_duplicates(self: _IndexT, keep: str_t | bool = "first") -> _IndexT:
"""
Return Index with duplicate values removed.
Parameters
----------
keep : {'first', 'last', ``False``}, default 'first'
- 'first' : Drop duplicates except for the first occurrence.
- 'last' : Drop duplicates except for the last occurrence.
- ``False`` : Drop all duplicates.
Returns
-------
deduplicated : Index
See Also
--------
Series.drop_duplicates : Equivalent method on Series.
DataFrame.drop_duplicates : Equivalent method on DataFrame.
Index.duplicated : Related method on Index, indicating duplicate
Index values.
Examples
--------
Generate an pandas.Index with duplicate values.
>>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'])
The `keep` parameter controls which duplicate values are removed.
The value 'first' keeps the first occurrence for each
set of duplicated entries. The default value of keep is 'first'.
>>> idx.drop_duplicates(keep='first')
Index(['lama', 'cow', 'beetle', 'hippo'], dtype='object')
The value 'last' keeps the last occurrence for each set of duplicated
entries.
>>> idx.drop_duplicates(keep='last')
Index(['cow', 'beetle', 'lama', 'hippo'], dtype='object')
The value ``False`` discards all sets of duplicated entries.
>>> idx.drop_duplicates(keep=False)
Index(['cow', 'beetle', 'hippo'], dtype='object')
"""
if self.is_unique:
return self._view()
return super().drop_duplicates(keep=keep)
def duplicated(
self, keep: Literal["first", "last", False] = "first"
) -> npt.NDArray[np.bool_]:
"""
Indicate duplicate index values.
Duplicated values are indicated as ``True`` values in the resulting
array. Either all duplicates, all except the first, or all except the
last occurrence of duplicates can be indicated.
Parameters
----------
keep : {'first', 'last', False}, default 'first'
The value or values in a set of duplicates to mark as missing.
- 'first' : Mark duplicates as ``True`` except for the first
occurrence.
- 'last' : Mark duplicates as ``True`` except for the last
occurrence.
- ``False`` : Mark all duplicates as ``True``.
Returns
-------
np.ndarray[bool]
See Also
--------
Series.duplicated : Equivalent method on pandas.Series.
DataFrame.duplicated : Equivalent method on pandas.DataFrame.
Index.drop_duplicates : Remove duplicate values from Index.
Examples
--------
By default, for each set of duplicated values, the first occurrence is
set to False and all others to True:
>>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama'])
>>> idx.duplicated()
array([False, False, True, False, True])
which is equivalent to
>>> idx.duplicated(keep='first')
array([False, False, True, False, True])
By using 'last', the last occurrence of each set of duplicated values
is set on False and all others on True:
>>> idx.duplicated(keep='last')
array([ True, False, True, False, False])
By setting keep on ``False``, all duplicates are True:
>>> idx.duplicated(keep=False)
array([ True, False, True, False, True])
"""
if self.is_unique:
# fastpath available bc we are immutable
return np.zeros(len(self), dtype=bool)
return self._duplicated(keep=keep)
# --------------------------------------------------------------------
# Arithmetic & Logical Methods
def __iadd__(self, other):
# alias for __add__
return self + other
@final
def __and__(self, other):
warnings.warn(
"Index.__and__ operating as a set operation is deprecated, "
"in the future this will be a logical operation matching "
"Series.__and__. Use index.intersection(other) instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return self.intersection(other)
@final
def __or__(self, other):
warnings.warn(
"Index.__or__ operating as a set operation is deprecated, "
"in the future this will be a logical operation matching "
"Series.__or__. Use index.union(other) instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return self.union(other)
@final
def __xor__(self, other):
warnings.warn(
"Index.__xor__ operating as a set operation is deprecated, "
"in the future this will be a logical operation matching "
"Series.__xor__. Use index.symmetric_difference(other) instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return self.symmetric_difference(other)
@final
def __nonzero__(self):
raise ValueError(
f"The truth value of a {type(self).__name__} is ambiguous. "
"Use a.empty, a.bool(), a.item(), a.any() or a.all()."
)
__bool__ = __nonzero__
# --------------------------------------------------------------------
# Set Operation Methods
def _get_reconciled_name_object(self, other):
"""
If the result of a set operation will be self,
return self, unless the name changes, in which
case make a shallow copy of self.
"""
name = get_op_result_name(self, other)
if self.name is not name:
return self.rename(name)
return self
@final
def _validate_sort_keyword(self, sort):
if sort not in [None, False]:
raise ValueError(
"The 'sort' keyword only takes the values of "
f"None or False; {sort} was passed."
)
@final
def union(self, other, sort=None):
"""
Form the union of two Index objects.
If the Index objects are incompatible, both Index objects will be
cast to dtype('object') first.
.. versionchanged:: 0.25.0
Parameters
----------
other : Index or array-like
sort : bool or None, default None
Whether to sort the resulting Index.
* None : Sort the result, except when
1. `self` and `other` are equal.
2. `self` or `other` has length 0.
3. Some values in `self` or `other` cannot be compared.
A RuntimeWarning is issued in this case.
* False : do not sort the result.
Returns
-------
union : Index
Examples
--------
Union matching dtypes
>>> idx1 = pd.Index([1, 2, 3, 4])
>>> idx2 = pd.Index([3, 4, 5, 6])
>>> idx1.union(idx2)
Int64Index([1, 2, 3, 4, 5, 6], dtype='int64')
Union mismatched dtypes
>>> idx1 = pd.Index(['a', 'b', 'c', 'd'])
>>> idx2 = pd.Index([1, 2, 3, 4])
>>> idx1.union(idx2)
Index(['a', 'b', 'c', 'd', 1, 2, 3, 4], dtype='object')
MultiIndex case
>>> idx1 = pd.MultiIndex.from_arrays(
... [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]
... )
>>> idx1
MultiIndex([(1, 'Red'),
(1, 'Blue'),
(2, 'Red'),
(2, 'Blue')],
)
>>> idx2 = pd.MultiIndex.from_arrays(
... [[3, 3, 2, 2], ["Red", "Green", "Red", "Green"]]
... )
>>> idx2
MultiIndex([(3, 'Red'),
(3, 'Green'),
(2, 'Red'),
(2, 'Green')],
)
>>> idx1.union(idx2)
MultiIndex([(1, 'Blue'),
(1, 'Red'),
(2, 'Blue'),
(2, 'Green'),
(2, 'Red'),
(3, 'Green'),
(3, 'Red')],
)
>>> idx1.union(idx2, sort=False)
MultiIndex([(1, 'Red'),
(1, 'Blue'),
(2, 'Red'),
(2, 'Blue'),
(3, 'Red'),
(3, 'Green'),
(2, 'Green')],
)
"""
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
other, result_name = self._convert_can_do_setop(other)
if not is_dtype_equal(self.dtype, other.dtype):
if (
isinstance(self, ABCMultiIndex)
and not is_object_dtype(unpack_nested_dtype(other))
and len(other) > 0
):
raise NotImplementedError(
"Can only union MultiIndex with MultiIndex or Index of tuples, "
"try mi.to_flat_index().union(other) instead."
)
if (
isinstance(self, ABCDatetimeIndex)
and isinstance(other, ABCDatetimeIndex)
and self.tz is not None
and other.tz is not None
):
# GH#39328
warnings.warn(
"In a future version, the union of DatetimeIndex objects "
"with mismatched timezones will cast both to UTC instead of "
"object dtype. To retain the old behavior, "
"use `index.astype(object).union(other)`",
FutureWarning,
stacklevel=find_stack_level(),
)
dtype = self._find_common_type_compat(other)
left = self.astype(dtype, copy=False)
right = other.astype(dtype, copy=False)
return left.union(right, sort=sort)
elif not len(other) or self.equals(other):
# NB: whether this (and the `if not len(self)` check below) come before
# or after the is_dtype_equal check above affects the returned dtype
return self._get_reconciled_name_object(other)
elif not len(self):
return other._get_reconciled_name_object(self)
result = self._union(other, sort=sort)
return self._wrap_setop_result(other, result)
def _union(self, other: Index, sort):
"""
Specific union logic should go here. In subclasses, union behavior
should be overwritten here rather than in `self.union`.
Parameters
----------
other : Index or array-like
sort : False or None, default False
Whether to sort the resulting index.
* False : do not sort the result.
* None : sort the result, except when `self` and `other` are equal
or when the values cannot be compared.
Returns
-------
Index
"""
lvals = self._values
rvals = other._values
if (
sort is None
and self.is_monotonic
and other.is_monotonic
and not (self.has_duplicates and other.has_duplicates)
and self._can_use_libjoin
):
# Both are monotonic and at least one is unique, so can use outer join
# (actually don't need either unique, but without this restriction
# test_union_same_value_duplicated_in_both fails)
try:
return self._outer_indexer(other)[0]
except (TypeError, IncompatibleFrequency):
# incomparable objects; should only be for object dtype
value_list = list(lvals)
# worth making this faster? a very unusual case
value_set = set(lvals)
value_list.extend([x for x in rvals if x not in value_set])
# If objects are unorderable, we must have object dtype.
return np.array(value_list, dtype=object)
elif not other.is_unique:
# other has duplicates
result = algos.union_with_duplicates(lvals, rvals)
return _maybe_try_sort(result, sort)
# Self may have duplicates; other already checked as unique
# find indexes of things in "other" that are not in "self"
if self._index_as_unique:
indexer = self.get_indexer(other)
missing = (indexer == -1).nonzero()[0]
else:
missing = algos.unique1d(self.get_indexer_non_unique(other)[1])
if len(missing) > 0:
other_diff = rvals.take(missing)
result = concat_compat((lvals, other_diff))
else:
result = lvals
if not self.is_monotonic or not other.is_monotonic:
# if both are monotonic then result should already be sorted
result = _maybe_try_sort(result, sort)
return result
@final
def _wrap_setop_result(self, other: Index, result) -> Index:
name = get_op_result_name(self, other)
if isinstance(result, Index):
if result.name != name:
result = result.rename(name)
else:
result = self._shallow_copy(result, name=name)
if type(self) is Index and self.dtype != _dtype_obj:
# i.e. ExtensionArray-backed
# TODO(ExtensionIndex): revert this astype; it is a kludge to make
# it possible to split ExtensionEngine from ExtensionIndex PR.
return result.astype(self.dtype, copy=False)
return result
@final
def intersection(self, other, sort=False):
"""
Form the intersection of two Index objects.
This returns a new Index with elements common to the index and `other`.
Parameters
----------
other : Index or array-like
sort : False or None, default False
Whether to sort the resulting index.
* False : do not sort the result.
* None : sort the result, except when `self` and `other` are equal
or when the values cannot be compared.
Returns
-------
intersection : Index
Examples
--------
>>> idx1 = pd.Index([1, 2, 3, 4])
>>> idx2 = pd.Index([3, 4, 5, 6])
>>> idx1.intersection(idx2)
Int64Index([3, 4], dtype='int64')
"""
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
other, result_name = self._convert_can_do_setop(other)
if self.equals(other):
if self.has_duplicates:
return self.unique()._get_reconciled_name_object(other)
return self._get_reconciled_name_object(other)
if len(self) == 0 or len(other) == 0:
# fastpath; we need to be careful about having commutativity
if self._is_multi or other._is_multi:
# _convert_can_do_setop ensures that we have both or neither
# We retain self.levels
return self[:0].rename(result_name)
dtype = self._find_common_type_compat(other)
if is_dtype_equal(self.dtype, dtype):
# Slicing allows us to retain DTI/TDI.freq, RangeIndex
# Note: self[:0] vs other[:0] affects
# 1) which index's `freq` we get in DTI/TDI cases
# This may be a historical artifact, i.e. no documented
# reason for this choice.
# 2) The `step` we get in RangeIndex cases
if len(self) == 0:
return self[:0].rename(result_name)
else:
return other[:0].rename(result_name)
return Index([], dtype=dtype, name=result_name)
elif not self._should_compare(other):
# We can infer that the intersection is empty.
if isinstance(self, ABCMultiIndex):
return self[:0].rename(result_name)
return Index([], name=result_name)
elif not is_dtype_equal(self.dtype, other.dtype):
dtype = self._find_common_type_compat(other)
this = self.astype(dtype, copy=False)
other = other.astype(dtype, copy=False)
return this.intersection(other, sort=sort)
result = self._intersection(other, sort=sort)
return self._wrap_intersection_result(other, result)
def _intersection(self, other: Index, sort=False):
"""
intersection specialized to the case with matching dtypes.
"""
if self.is_monotonic and other.is_monotonic and self._can_use_libjoin:
try:
result = self._inner_indexer(other)[0]
except TypeError:
# non-comparable; should only be for object dtype
pass
else:
# TODO: algos.unique1d should preserve DTA/TDA
res = algos.unique1d(result)
return ensure_wrapped_if_datetimelike(res)
res_values = self._intersection_via_get_indexer(other, sort=sort)
res_values = _maybe_try_sort(res_values, sort)
return res_values
def _wrap_intersection_result(self, other, result):
# We will override for MultiIndex to handle empty results
return self._wrap_setop_result(other, result)
@final
def _intersection_via_get_indexer(self, other: Index, sort) -> ArrayLike:
"""
Find the intersection of two Indexes using get_indexer.
Returns
-------
np.ndarray or ExtensionArray
The returned array will be unique.
"""
left_unique = self.unique()
right_unique = other.unique()
# even though we are unique, we need get_indexer_for for IntervalIndex
indexer = left_unique.get_indexer_for(right_unique)
mask = indexer != -1
taker = indexer.take(mask.nonzero()[0])
if sort is False:
# sort bc we want the elements in the same order they are in self
# unnecessary in the case with sort=None bc we will sort later
taker = np.sort(taker)
result = left_unique.take(taker)._values
return result
@final
def difference(self, other, sort=None):
"""
Return a new Index with elements of index not in `other`.
This is the set difference of two Index objects.
Parameters
----------
other : Index or array-like
sort : False or None, default None
Whether to sort the resulting index. By default, the
values are attempted to be sorted, but any TypeError from
incomparable elements is caught by pandas.
* None : Attempt to sort the result, but catch any TypeErrors
from comparing incomparable elements.
* False : Do not sort the result.
Returns
-------
difference : Index
Examples
--------
>>> idx1 = pd.Index([2, 1, 3, 4])
>>> idx2 = pd.Index([3, 4, 5, 6])
>>> idx1.difference(idx2)
Int64Index([1, 2], dtype='int64')
>>> idx1.difference(idx2, sort=False)
Int64Index([2, 1], dtype='int64')
"""
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
other, result_name = self._convert_can_do_setop(other)
if self.equals(other):
# Note: we do not (yet) sort even if sort=None GH#24959
return self[:0].rename(result_name)
if len(other) == 0:
# Note: we do not (yet) sort even if sort=None GH#24959
return self.rename(result_name)
if not self._should_compare(other):
# Nothing matches -> difference is everything
return self.rename(result_name)
result = self._difference(other, sort=sort)
return self._wrap_difference_result(other, result)
def _difference(self, other, sort):
# overridden by RangeIndex
this = self.unique()
indexer = this.get_indexer_for(other)
indexer = indexer.take((indexer != -1).nonzero()[0])
label_diff = np.setdiff1d(np.arange(this.size), indexer, assume_unique=True)
the_diff = this._values.take(label_diff)
the_diff = _maybe_try_sort(the_diff, sort)
return the_diff
def _wrap_difference_result(self, other, result):
# We will override for MultiIndex to handle empty results
return self._wrap_setop_result(other, result)
def symmetric_difference(self, other, result_name=None, sort=None):
"""
Compute the symmetric difference of two Index objects.
Parameters
----------
other : Index or array-like
result_name : str
sort : False or None, default None
Whether to sort the resulting index. By default, the
values are attempted to be sorted, but any TypeError from
incomparable elements is caught by pandas.
* None : Attempt to sort the result, but catch any TypeErrors
from comparing incomparable elements.
* False : Do not sort the result.
Returns
-------
symmetric_difference : Index
Notes
-----
``symmetric_difference`` contains elements that appear in either
``idx1`` or ``idx2`` but not both. Equivalent to the Index created by
``idx1.difference(idx2) | idx2.difference(idx1)`` with duplicates
dropped.
Examples
--------
>>> idx1 = pd.Index([1, 2, 3, 4])
>>> idx2 = pd.Index([2, 3, 4, 5])
>>> idx1.symmetric_difference(idx2)
Int64Index([1, 5], dtype='int64')
"""
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
other, result_name_update = self._convert_can_do_setop(other)
if result_name is None:
result_name = result_name_update
if not self._should_compare(other):
return self.union(other, sort=sort).rename(result_name)
elif not is_dtype_equal(self.dtype, other.dtype):
dtype = self._find_common_type_compat(other)
this = self.astype(dtype, copy=False)
that = other.astype(dtype, copy=False)
return this.symmetric_difference(that, sort=sort).rename(result_name)
this = self.unique()
other = other.unique()
indexer = this.get_indexer_for(other)
# {this} minus {other}
common_indexer = indexer.take((indexer != -1).nonzero()[0])
left_indexer = np.setdiff1d(
np.arange(this.size), common_indexer, assume_unique=True
)
left_diff = this._values.take(left_indexer)
# {other} minus {this}
right_indexer = (indexer == -1).nonzero()[0]
right_diff = other._values.take(right_indexer)
res_values = concat_compat([left_diff, right_diff])
res_values = _maybe_try_sort(res_values, sort)
# pass dtype so we retain object dtype
result = Index(res_values, name=result_name, dtype=res_values.dtype)
if self._is_multi:
self = cast("MultiIndex", self)
if len(result) == 0:
# On equal symmetric_difference MultiIndexes the difference is empty.
# Therefore, an empty MultiIndex is returned GH#13490
return type(self)(
levels=[[] for _ in range(self.nlevels)],
codes=[[] for _ in range(self.nlevels)],
names=result.name,
)
return type(self).from_tuples(result, names=result.name)
return result
@final
def _assert_can_do_setop(self, other) -> bool:
if not is_list_like(other):
raise TypeError("Input must be Index or array-like")
return True
def _convert_can_do_setop(self, other) -> tuple[Index, Hashable]:
if not isinstance(other, Index):
# TODO(2.0): no need to special-case here once _with_infer
# deprecation is enforced
if hasattr(other, "dtype"):
other = Index(other, name=self.name, dtype=other.dtype)
else:
# e.g. list
other = Index(other, name=self.name)
result_name = self.name
else:
result_name = get_op_result_name(self, other)
return other, result_name
# --------------------------------------------------------------------
# Indexing Methods
def get_loc(self, key, method=None, tolerance=None):
"""
Get integer location, slice or boolean mask for requested label.
Parameters
----------
key : label
method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
* default: exact matches only.
* pad / ffill: find the PREVIOUS index value if no exact match.
* backfill / bfill: use NEXT index value if no exact match
* nearest: use the NEAREST index value if no exact match. Tied
distances are broken by preferring the larger index value.
tolerance : int or float, optional
Maximum distance from index value for inexact matches. The value of
the index at the matching location must satisfy the equation
``abs(index[loc] - key) <= tolerance``.
Returns
-------
loc : int if unique index, slice if monotonic index, else mask
Examples
--------
>>> unique_index = pd.Index(list('abc'))
>>> unique_index.get_loc('b')
1
>>> monotonic_index = pd.Index(list('abbc'))
>>> monotonic_index.get_loc('b')
slice(1, 3, None)
>>> non_monotonic_index = pd.Index(list('abcb'))
>>> non_monotonic_index.get_loc('b')
array([False, True, False, True])
"""
if method is None:
if tolerance is not None:
raise ValueError(
"tolerance argument only valid if using pad, "
"backfill or nearest lookups"
)
casted_key = self._maybe_cast_indexer(key)
try:
return self._engine.get_loc(casted_key)
except KeyError as err:
raise KeyError(key) from err
except TypeError:
# If we have a listlike key, _check_indexing_error will raise
# InvalidIndexError. Otherwise we fall through and re-raise
# the TypeError.
self._check_indexing_error(key)
raise
# GH#42269
warnings.warn(
f"Passing method to {type(self).__name__}.get_loc is deprecated "
"and will raise in a future version. Use "
"index.get_indexer([item], method=...) instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
if is_scalar(key) and isna(key) and not self.hasnans:
raise KeyError(key)
if tolerance is not None:
tolerance = self._convert_tolerance(tolerance, np.asarray(key))
indexer = self.get_indexer([key], method=method, tolerance=tolerance)
if indexer.ndim > 1 or indexer.size > 1:
raise TypeError("get_loc requires scalar valued input")
loc = indexer.item()
if loc == -1:
raise KeyError(key)
return loc
_index_shared_docs[
"get_indexer"
] = """
Compute indexer and mask for new index given the current index. The
indexer should be then used as an input to ndarray.take to align the
current data to the new index.
Parameters
----------
target : %(target_klass)s
method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
* default: exact matches only.
* pad / ffill: find the PREVIOUS index value if no exact match.
* backfill / bfill: use NEXT index value if no exact match
* nearest: use the NEAREST index value if no exact match. Tied
distances are broken by preferring the larger index value.
limit : int, optional
Maximum number of consecutive labels in ``target`` to match for
inexact matches.
tolerance : optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations must
satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
Tolerance may be a scalar value, which applies the same tolerance
to all values, or list-like, which applies variable tolerance per
element. List-like includes list, tuple, array, Series, and must be
the same size as the index and its dtype must exactly match the
index's type.
Returns
-------
indexer : np.ndarray[np.intp]
Integers from 0 to n - 1 indicating that the index at these
positions matches the corresponding target values. Missing values
in the target are marked by -1.
%(raises_section)s
Notes
-----
Returns -1 for unmatched values, for further explanation see the
example below.
Examples
--------
>>> index = pd.Index(['c', 'a', 'b'])
>>> index.get_indexer(['a', 'b', 'x'])
array([ 1, 2, -1])
Notice that the return value is an array of locations in ``index``
and ``x`` is marked by -1, as it is not in ``index``.
"""
@Appender(_index_shared_docs["get_indexer"] % _index_doc_kwargs)
@final
def get_indexer(
self,
target,
method: str_t | None = None,
limit: int | None = None,
tolerance=None,
) -> npt.NDArray[np.intp]:
method = missing.clean_reindex_fill_method(method)
target = self._maybe_cast_listlike_indexer(target)
self._check_indexing_method(method, limit, tolerance)
if not self._index_as_unique:
raise InvalidIndexError(self._requires_unique_msg)
if len(target) == 0:
return np.array([], dtype=np.intp)
if not self._should_compare(target) and not self._should_partial_index(target):
# IntervalIndex get special treatment bc numeric scalars can be
# matched to Interval scalars
return self._get_indexer_non_comparable(target, method=method, unique=True)
if is_categorical_dtype(self.dtype):
# _maybe_cast_listlike_indexer ensures target has our dtype
# (could improve perf by doing _should_compare check earlier?)
assert is_dtype_equal(self.dtype, target.dtype)
indexer = self._engine.get_indexer(target.codes)
if self.hasnans and target.hasnans:
loc = self.get_loc(np.nan)
mask = target.isna()
indexer[mask] = loc
return indexer
if is_categorical_dtype(target.dtype):
# potential fastpath
# get an indexer for unique categories then propagate to codes via take_nd
# get_indexer instead of _get_indexer needed for MultiIndex cases
# e.g. test_append_different_columns_types
categories_indexer = self.get_indexer(target.categories)
indexer = algos.take_nd(categories_indexer, target.codes, fill_value=-1)
if (not self._is_multi and self.hasnans) and target.hasnans:
# Exclude MultiIndex because hasnans raises NotImplementedError
# we should only get here if we are unique, so loc is an integer
# GH#41934
loc = self.get_loc(np.nan)
mask = target.isna()
indexer[mask] = loc
return ensure_platform_int(indexer)
pself, ptarget = self._maybe_promote(target)
if pself is not self or ptarget is not target:
return pself.get_indexer(
ptarget, method=method, limit=limit, tolerance=tolerance
)
if is_dtype_equal(self.dtype, target.dtype) and self.equals(target):
# Only call equals if we have same dtype to avoid inference/casting
return np.arange(len(target), dtype=np.intp)
if not is_dtype_equal(self.dtype, target.dtype) and not is_interval_dtype(
self.dtype
):
# IntervalIndex gets special treatment for partial-indexing
dtype = self._find_common_type_compat(target)
this = self.astype(dtype, copy=False)
target = target.astype(dtype, copy=False)
return this._get_indexer(
target, method=method, limit=limit, tolerance=tolerance
)
return self._get_indexer(target, method, limit, tolerance)
def _get_indexer(
self,
target: Index,
method: str_t | None = None,
limit: int | None = None,
tolerance=None,
) -> npt.NDArray[np.intp]:
if tolerance is not None:
tolerance = self._convert_tolerance(tolerance, target)
if method in ["pad", "backfill"]:
indexer = self._get_fill_indexer(target, method, limit, tolerance)
elif method == "nearest":
indexer = self._get_nearest_indexer(target, limit, tolerance)
else:
tgt_values = target._get_engine_target()
if target._is_multi and self._is_multi:
engine = self._engine
# error: "IndexEngine" has no attribute "_extract_level_codes"
tgt_values = engine._extract_level_codes( # type: ignore[attr-defined]
target
)
indexer = self._engine.get_indexer(tgt_values)
return ensure_platform_int(indexer)
@final
def _should_partial_index(self, target: Index) -> bool:
"""
Should we attempt partial-matching indexing?
"""
if is_interval_dtype(self.dtype):
if is_interval_dtype(target.dtype):
return False
# See https://github.com/pandas-dev/pandas/issues/47772 the commented
# out code can be restored (instead of hardcoding `return True`)
# once that issue if fixed
# "Index" has no attribute "left"
# return self.left._should_compare(target) # type: ignore[attr-defined]
return True
return False
@final
def _check_indexing_method(
self,
method: str_t | None,
limit: int | None = None,
tolerance=None,
) -> None:
"""
Raise if we have a get_indexer `method` that is not supported or valid.
"""
if method not in [None, "bfill", "backfill", "pad", "ffill", "nearest"]:
# in practice the clean_reindex_fill_method call would raise
# before we get here
raise ValueError("Invalid fill method") # pragma: no cover
if self._is_multi:
if method == "nearest":
raise NotImplementedError(
"method='nearest' not implemented yet "
"for MultiIndex; see GitHub issue 9365"
)
elif method == "pad" or method == "backfill":
if tolerance is not None:
raise NotImplementedError(
"tolerance not implemented yet for MultiIndex"
)
if is_interval_dtype(self.dtype) or is_categorical_dtype(self.dtype):
# GH#37871 for now this is only for IntervalIndex and CategoricalIndex
if method is not None:
raise NotImplementedError(
f"method {method} not yet implemented for {type(self).__name__}"
)
if method is None:
if tolerance is not None:
raise ValueError(
"tolerance argument only valid if doing pad, "
"backfill or nearest reindexing"
)
if limit is not None:
raise ValueError(
"limit argument only valid if doing pad, "
"backfill or nearest reindexing"
)
def _convert_tolerance(self, tolerance, target: np.ndarray | Index) -> np.ndarray:
# override this method on subclasses
tolerance = np.asarray(tolerance)
if target.size != tolerance.size and tolerance.size > 1:
raise ValueError("list-like tolerance size must match target index size")
return tolerance
@final
def _get_fill_indexer(
self, target: Index, method: str_t, limit: int | None = None, tolerance=None
) -> npt.NDArray[np.intp]:
if self._is_multi:
# TODO: get_indexer_with_fill docstring says values must be _sorted_
# but that doesn't appear to be enforced
# error: "IndexEngine" has no attribute "get_indexer_with_fill"
return self._engine.get_indexer_with_fill( # type: ignore[attr-defined]
target=target._values, values=self._values, method=method, limit=limit
)
if self.is_monotonic_increasing and target.is_monotonic_increasing:
target_values = target._get_engine_target()
own_values = self._get_engine_target()
if method == "pad":
indexer = libalgos.pad(own_values, target_values, limit=limit)
else:
# i.e. "backfill"
indexer = libalgos.backfill(own_values, target_values, limit=limit)
else:
indexer = self._get_fill_indexer_searchsorted(target, method, limit)
if tolerance is not None and len(self):
indexer = self._filter_indexer_tolerance(target, indexer, tolerance)
return indexer
@final
def _get_fill_indexer_searchsorted(
self, target: Index, method: str_t, limit: int | None = None
) -> npt.NDArray[np.intp]:
"""
Fallback pad/backfill get_indexer that works for monotonic decreasing
indexes and non-monotonic targets.
"""
if limit is not None:
raise ValueError(
f"limit argument for {repr(method)} method only well-defined "
"if index and target are monotonic"
)
side: Literal["left", "right"] = "left" if method == "pad" else "right"
# find exact matches first (this simplifies the algorithm)
indexer = self.get_indexer(target)
nonexact = indexer == -1
indexer[nonexact] = self._searchsorted_monotonic(target[nonexact], side)
if side == "left":
# searchsorted returns "indices into a sorted array such that,
# if the corresponding elements in v were inserted before the
# indices, the order of a would be preserved".
# Thus, we need to subtract 1 to find values to the left.
indexer[nonexact] -= 1
# This also mapped not found values (values of 0 from
# np.searchsorted) to -1, which conveniently is also our
# sentinel for missing values
else:
# Mark indices to the right of the largest value as not found
indexer[indexer == len(self)] = -1
return indexer
@final
def _get_nearest_indexer(
self, target: Index, limit: int | None, tolerance
) -> npt.NDArray[np.intp]:
"""
Get the indexer for the nearest index labels; requires an index with
values that can be subtracted from each other (e.g., not strings or
tuples).
"""
if not len(self):
return self._get_fill_indexer(target, "pad")
left_indexer = self.get_indexer(target, "pad", limit=limit)
right_indexer = self.get_indexer(target, "backfill", limit=limit)
left_distances = self._difference_compat(target, left_indexer)
right_distances = self._difference_compat(target, right_indexer)
op = operator.lt if self.is_monotonic_increasing else operator.le
indexer = np.where(
op(left_distances, right_distances) | (right_indexer == -1),
left_indexer,
right_indexer,
)
if tolerance is not None:
indexer = self._filter_indexer_tolerance(target, indexer, tolerance)
return indexer
@final
def _filter_indexer_tolerance(
self,
target: Index,
indexer: npt.NDArray[np.intp],
tolerance,
) -> npt.NDArray[np.intp]:
distance = self._difference_compat(target, indexer)
return np.where(distance <= tolerance, indexer, -1)
@final
def _difference_compat(
self, target: Index, indexer: npt.NDArray[np.intp]
) -> ArrayLike:
# Compatibility for PeriodArray, for which __sub__ returns an ndarray[object]
# of DateOffset objects, which do not support __abs__ (and would be slow
# if they did)
if isinstance(self.dtype, PeriodDtype):
# Note: we only get here with matching dtypes
own_values = cast("PeriodArray", self._data)._ndarray
target_values = cast("PeriodArray", target._data)._ndarray
diff = own_values[indexer] - target_values
else:
# error: Unsupported left operand type for - ("ExtensionArray")
diff = self._values[indexer] - target._values # type: ignore[operator]
return abs(diff)
# --------------------------------------------------------------------
# Indexer Conversion Methods
@final
def _validate_positional_slice(self, key: slice) -> None:
"""
For positional indexing, a slice must have either int or None
for each of start, stop, and step.
"""
self._validate_indexer("positional", key.start, "iloc")
self._validate_indexer("positional", key.stop, "iloc")
self._validate_indexer("positional", key.step, "iloc")
def _convert_slice_indexer(self, key: slice, kind: str_t):
"""
Convert a slice indexer.
By definition, these are labels unless 'iloc' is passed in.
Floats are not allowed as the start, step, or stop of the slice.
Parameters
----------
key : label of the slice bound
kind : {'loc', 'getitem'}
"""
assert kind in ["loc", "getitem"], kind
# potentially cast the bounds to integers
start, stop, step = key.start, key.stop, key.step
# figure out if this is a positional indexer
def is_int(v):
return v is None or is_integer(v)
is_index_slice = is_int(start) and is_int(stop) and is_int(step)
is_positional = is_index_slice and not (
self.is_integer() or self.is_categorical()
)
if kind == "getitem":
"""
called from the getitem slicers, validate that we are in fact
integers
"""
if self.is_integer() or is_index_slice:
self._validate_indexer("slice", key.start, "getitem")
self._validate_indexer("slice", key.stop, "getitem")
self._validate_indexer("slice", key.step, "getitem")
return key
# convert the slice to an indexer here
# if we are mixed and have integers
if is_positional:
try:
# Validate start & stop
if start is not None:
self.get_loc(start)
if stop is not None:
self.get_loc(stop)
is_positional = False
except KeyError:
pass
if com.is_null_slice(key):
# It doesn't matter if we are positional or label based
indexer = key
elif is_positional:
if kind == "loc":
# GH#16121, GH#24612, GH#31810
warnings.warn(
"Slicing a positional slice with .loc is not supported, "
"and will raise TypeError in a future version. "
"Use .loc with labels or .iloc with positions instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
indexer = key
else:
indexer = self.slice_indexer(start, stop, step)
return indexer
@final
def _invalid_indexer(self, form: str_t, key) -> TypeError:
"""
Consistent invalid indexer message.
"""
return TypeError(
f"cannot do {form} indexing on {type(self).__name__} with these "
f"indexers [{key}] of type {type(key).__name__}"
)
# --------------------------------------------------------------------
# Reindex Methods
@final
def _validate_can_reindex(self, indexer: np.ndarray) -> None:
"""
Check if we are allowing reindexing with this particular indexer.
Parameters
----------
indexer : an integer ndarray
Raises
------
ValueError if its a duplicate axis
"""
# trying to reindex on an axis with duplicates
if not self._index_as_unique and len(indexer):
raise ValueError("cannot reindex on an axis with duplicate labels")
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.
Parameters
----------
target : an iterable
method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
* default: exact matches only.
* pad / ffill: find the PREVIOUS index value if no exact match.
* backfill / bfill: use NEXT index value if no exact match
* nearest: use the NEAREST index value if no exact match. Tied
distances are broken by preferring the larger index value.
level : int, optional
Level of multiindex.
limit : int, optional
Maximum number of consecutive labels in ``target`` to match for
inexact matches.
tolerance : int or float, optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations must
satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
Tolerance may be a scalar value, which applies the same tolerance
to all values, or list-like, which applies variable tolerance per
element. List-like includes list, tuple, array, Series, and must be
the same size as the index and its dtype must exactly match the
index's type.
Returns
-------
new_index : pd.Index
Resulting index.
indexer : np.ndarray[np.intp] or None
Indices of output values in original index.
Raises
------
TypeError
If ``method`` passed along with ``level``.
ValueError
If non-unique multi-index
ValueError
If non-unique index and ``method`` or ``limit`` passed.
See Also
--------
Series.reindex
DataFrame.reindex
Examples
--------
>>> idx = pd.Index(['car', 'bike', 'train', 'tractor'])
>>> idx
Index(['car', 'bike', 'train', 'tractor'], dtype='object')
>>> idx.reindex(['car', 'bike'])
(Index(['car', 'bike'], dtype='object'), array([0, 1]))
"""
# GH6552: preserve names when reindexing to non-named target
# (i.e. neither Index nor Series).
preserve_names = not hasattr(target, "name")
# GH7774: preserve dtype/tz if target is empty and not an Index.
target = ensure_has_len(target) # target may be an iterator
if not isinstance(target, Index) and len(target) == 0:
if level is not None and self._is_multi:
# "Index" has no attribute "levels"; maybe "nlevels"?
idx = self.levels[level] # type: ignore[attr-defined]
else:
idx = self
target = idx[:0]
else:
target = ensure_index(target)
if level is not None:
if method is not None:
raise TypeError("Fill method not supported if level passed")
# TODO: tests where passing `keep_order=not self._is_multi`
# makes a difference for non-MultiIndex case
target, indexer, _ = self._join_level(
target, level, how="right", keep_order=not self._is_multi
)
else:
if self.equals(target):
indexer = None
else:
if self._index_as_unique:
indexer = self.get_indexer(
target, method=method, limit=limit, tolerance=tolerance
)
elif self._is_multi:
raise ValueError("cannot handle a non-unique multi-index!")
else:
if method is not None or limit is not None:
raise ValueError(
"cannot reindex a non-unique index "
"with a method or limit"
)
indexer, _ = 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(),
)
target = self._wrap_reindex_result(target, indexer, preserve_names)
return target, indexer
def _wrap_reindex_result(self, target, indexer, preserve_names: bool):
target = self._maybe_preserve_names(target, preserve_names)
return target
def _maybe_preserve_names(self, target: Index, preserve_names: bool):
if preserve_names and target.nlevels == 1 and target.name != self.name:
target = target.copy(deep=False)
target.name = self.name
return target
@final
def _reindex_non_unique(
self, target: Index
) -> tuple[Index, npt.NDArray[np.intp], npt.NDArray[np.intp] | None]:
"""
Create a new index with target's values (move/add/delete values as
necessary) use with non-unique Index and a possibly non-unique target.
Parameters
----------
target : an iterable
Returns
-------
new_index : pd.Index
Resulting index.
indexer : np.ndarray[np.intp]
Indices of output values in original index.
new_indexer : np.ndarray[np.intp] or None
"""
target = ensure_index(target)
if len(target) == 0:
# GH#13691
return self[:0], np.array([], dtype=np.intp), None
indexer, missing = self.get_indexer_non_unique(target)
check = indexer != -1
new_labels = self.take(indexer[check])
new_indexer = None
if len(missing):
length = np.arange(len(indexer), dtype=np.intp)
missing = ensure_platform_int(missing)
missing_labels = target.take(missing)
missing_indexer = length[~check]
cur_labels = self.take(indexer[check]).values
cur_indexer = length[check]
# Index constructor below will do inference
new_labels = np.empty((len(indexer),), dtype=object)
new_labels[cur_indexer] = cur_labels
new_labels[missing_indexer] = missing_labels
# GH#38906
if not len(self):
new_indexer = np.arange(0, dtype=np.intp)
# a unique indexer
elif target.is_unique:
# see GH5553, make sure we use the right indexer
new_indexer = np.arange(len(indexer), dtype=np.intp)
new_indexer[cur_indexer] = np.arange(len(cur_labels))
new_indexer[missing_indexer] = -1
# we have a non_unique selector, need to use the original
# indexer here
else:
# need to retake to have the same size as the indexer
indexer[~check] = -1
# reset the new indexer to account for the new size
new_indexer = np.arange(len(self.take(indexer)), dtype=np.intp)
new_indexer[~check] = -1
if isinstance(self, ABCMultiIndex):
new_index = type(self).from_tuples(new_labels, names=self.names)
else:
new_index = Index._with_infer(new_labels, name=self.name)
return new_index, indexer, new_indexer
# --------------------------------------------------------------------
# Join Methods
@final
@_maybe_return_indexers
def join(
self,
other,
how: str_t = "left",
level=None,
return_indexers: bool = False,
sort: bool = False,
):
"""
Compute join_index and indexers to conform data
structures to the new index.
Parameters
----------
other : Index
how : {'left', 'right', 'inner', 'outer'}
level : int or level name, default None
return_indexers : bool, default False
sort : bool, default False
Sort the join keys lexicographically in the result Index. If False,
the order of the join keys depends on the join type (how keyword).
Returns
-------
join_index, (left_indexer, right_indexer)
"""
other = ensure_index(other)
if isinstance(self, ABCDatetimeIndex) and isinstance(other, ABCDatetimeIndex):
if (self.tz is None) ^ (other.tz is None):
# Raise instead of casting to object below.
raise TypeError("Cannot join tz-naive with tz-aware DatetimeIndex")
if not self._is_multi and not other._is_multi:
# We have specific handling for MultiIndex below
pself, pother = self._maybe_promote(other)
if pself is not self or pother is not other:
return pself.join(
pother, how=how, level=level, return_indexers=True, sort=sort
)
lindexer: np.ndarray | None
rindexer: np.ndarray | None
# try to figure out the join level
# GH3662
if level is None and (self._is_multi or other._is_multi):
# have the same levels/names so a simple join
if self.names == other.names:
pass
else:
return self._join_multi(other, how=how)
# join on the level
if level is not None and (self._is_multi or other._is_multi):
return self._join_level(other, level, how=how)
if len(other) == 0 and how in ("left", "outer"):
join_index = self._view()
rindexer = np.repeat(np.intp(-1), len(join_index))
return join_index, None, rindexer
if len(self) == 0 and how in ("right", "outer"):
join_index = other._view()
lindexer = np.repeat(np.intp(-1), len(join_index))
return join_index, lindexer, None
if self._join_precedence < other._join_precedence:
how = {"right": "left", "left": "right"}.get(how, how)
join_index, lidx, ridx = other.join(
self, how=how, level=level, return_indexers=True
)
lidx, ridx = ridx, lidx
return join_index, lidx, ridx
if not is_dtype_equal(self.dtype, other.dtype):
dtype = self._find_common_type_compat(other)
this = self.astype(dtype, copy=False)
other = other.astype(dtype, copy=False)
return this.join(other, how=how, return_indexers=True)
_validate_join_method(how)
if not self.is_unique and not other.is_unique:
return self._join_non_unique(other, how=how)
elif not self.is_unique or not other.is_unique:
if self.is_monotonic and other.is_monotonic:
if self._can_use_libjoin:
# otherwise we will fall through to _join_via_get_indexer
return self._join_monotonic(other, how=how)
else:
return self._join_non_unique(other, how=how)
elif (
self.is_monotonic
and other.is_monotonic
and self._can_use_libjoin
and (
not isinstance(self, ABCMultiIndex)
or not any(is_categorical_dtype(dtype) for dtype in self.dtypes)
)
):
# Categorical is monotonic if data are ordered as categories, but join can
# not handle this in case of not lexicographically monotonic GH#38502
try:
return self._join_monotonic(other, how=how)
except TypeError:
# object dtype; non-comparable objects
pass
return self._join_via_get_indexer(other, how, sort)
@final
def _join_via_get_indexer(
self, other: Index, how: str_t, sort: bool
) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
# Fallback if we do not have any fastpaths available based on
# uniqueness/monotonicity
# Note: at this point we have checked matching dtypes
if how == "left":
join_index = self
elif how == "right":
join_index = other
elif how == "inner":
# TODO: sort=False here for backwards compat. It may
# be better to use the sort parameter passed into join
join_index = self.intersection(other, sort=False)
elif how == "outer":
# TODO: sort=True here for backwards compat. It may
# be better to use the sort parameter passed into join
join_index = self.union(other)
if sort:
join_index = join_index.sort_values()
if join_index is self:
lindexer = None
else:
lindexer = self.get_indexer_for(join_index)
if join_index is other:
rindexer = None
else:
rindexer = other.get_indexer_for(join_index)
return join_index, lindexer, rindexer
@final
def _join_multi(self, other: Index, how: str_t):
from pandas.core.indexes.multi import MultiIndex
from pandas.core.reshape.merge import restore_dropped_levels_multijoin
# figure out join names
self_names_list = list(com.not_none(*self.names))
other_names_list = list(com.not_none(*other.names))
self_names_order = self_names_list.index
other_names_order = other_names_list.index
self_names = set(self_names_list)
other_names = set(other_names_list)
overlap = self_names & other_names
# need at least 1 in common
if not overlap:
raise ValueError("cannot join with no overlapping index names")
if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
# Drop the non-matching levels from left and right respectively
ldrop_names = sorted(self_names - overlap, key=self_names_order)
rdrop_names = sorted(other_names - overlap, key=other_names_order)
# if only the order differs
if not len(ldrop_names + rdrop_names):
self_jnlevels = self
other_jnlevels = other.reorder_levels(self.names)
else:
self_jnlevels = self.droplevel(ldrop_names)
other_jnlevels = other.droplevel(rdrop_names)
# Join left and right
# Join on same leveled multi-index frames is supported
join_idx, lidx, ridx = self_jnlevels.join(
other_jnlevels, how, return_indexers=True
)
# Restore the dropped levels
# Returned index level order is
# common levels, ldrop_names, rdrop_names
dropped_names = ldrop_names + rdrop_names
levels, codes, names = restore_dropped_levels_multijoin(
self, other, dropped_names, join_idx, lidx, ridx
)
# Re-create the multi-index
multi_join_idx = MultiIndex(
levels=levels, codes=codes, names=names, verify_integrity=False
)
multi_join_idx = multi_join_idx.remove_unused_levels()
return multi_join_idx, lidx, ridx
jl = list(overlap)[0]
# Case where only one index is multi
# make the indices into mi's that match
flip_order = False
if isinstance(self, MultiIndex):
self, other = other, self
flip_order = True
# flip if join method is right or left
how = {"right": "left", "left": "right"}.get(how, how)
level = other.names.index(jl)
result = self._join_level(other, level, how=how)
if flip_order:
return result[0], result[2], result[1]
return result
@final
def _join_non_unique(
self, other: Index, how: str_t = "left"
) -> tuple[Index, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
from pandas.core.reshape.merge import get_join_indexers
# We only get here if dtypes match
assert self.dtype == other.dtype
left_idx, right_idx = get_join_indexers(
[self._values], [other._values], how=how, sort=True
)
mask = left_idx == -1
join_array = self._values.take(left_idx)
right = other._values.take(right_idx)
if isinstance(join_array, np.ndarray):
# Argument 3 to "putmask" has incompatible type "Union[ExtensionArray,
# ndarray[Any, Any]]"; expected "Union[_SupportsArray[dtype[Any]],
# _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, f
# loat, complex, str, bytes, _NestedSequence[Union[bool, int, float,
# complex, str, bytes]]]" [arg-type]
np.putmask(join_array, mask, right) # type: ignore[arg-type]
else:
join_array._putmask(mask, right)
join_index = self._wrap_joined_index(join_array, other)
return join_index, left_idx, right_idx
@final
def _join_level(
self, other: Index, level, how: str_t = "left", keep_order: bool = True
) -> tuple[MultiIndex, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
"""
The join method *only* affects the level of the resulting
MultiIndex. Otherwise it just exactly aligns the Index data to the
labels of the level in the MultiIndex.
If ```keep_order == True```, the order of the data indexed by the
MultiIndex will not be changed; otherwise, it will tie out
with `other`.
"""
from pandas.core.indexes.multi import MultiIndex
def _get_leaf_sorter(labels: list[np.ndarray]) -> npt.NDArray[np.intp]:
"""
Returns sorter for the inner most level while preserving the
order of higher levels.
Parameters
----------
labels : list[np.ndarray]
Each ndarray has signed integer dtype, not necessarily identical.
Returns
-------
np.ndarray[np.intp]
"""
if labels[0].size == 0:
return np.empty(0, dtype=np.intp)
if len(labels) == 1:
return get_group_index_sorter(ensure_platform_int(labels[0]))
# find indexers of beginning of each set of
# same-key labels w.r.t all but last level
tic = labels[0][:-1] != labels[0][1:]
for lab in labels[1:-1]:
tic |= lab[:-1] != lab[1:]
starts = np.hstack(([True], tic, [True])).nonzero()[0]
lab = ensure_int64(labels[-1])
return lib.get_level_sorter(lab, ensure_platform_int(starts))
if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
raise TypeError("Join on level between two MultiIndex objects is ambiguous")
left, right = self, other
flip_order = not isinstance(self, MultiIndex)
if flip_order:
left, right = right, left
how = {"right": "left", "left": "right"}.get(how, how)
assert isinstance(left, MultiIndex)
level = left._get_level_number(level)
old_level = left.levels[level]
if not right.is_unique:
raise NotImplementedError(
"Index._join_level on non-unique index is not implemented"
)
new_level, left_lev_indexer, right_lev_indexer = old_level.join(
right, how=how, return_indexers=True
)
if left_lev_indexer is None:
if keep_order or len(left) == 0:
left_indexer = None
join_index = left
else: # sort the leaves
left_indexer = _get_leaf_sorter(left.codes[: level + 1])
join_index = left[left_indexer]
else:
left_lev_indexer = ensure_platform_int(left_lev_indexer)
rev_indexer = lib.get_reverse_indexer(left_lev_indexer, len(old_level))
old_codes = left.codes[level]
taker = old_codes[old_codes != -1]
new_lev_codes = rev_indexer.take(taker)
new_codes = list(left.codes)
new_codes[level] = new_lev_codes
new_levels = list(left.levels)
new_levels[level] = new_level
if keep_order: # just drop missing values. o.w. keep order
left_indexer = np.arange(len(left), dtype=np.intp)
left_indexer = cast(np.ndarray, left_indexer)
mask = new_lev_codes != -1
if not mask.all():
new_codes = [lab[mask] for lab in new_codes]
left_indexer = left_indexer[mask]
else: # tie out the order with other
if level == 0: # outer most level, take the fast route
max_new_lev = 0 if len(new_lev_codes) == 0 else new_lev_codes.max()
ngroups = 1 + max_new_lev
left_indexer, counts = libalgos.groupsort_indexer(
new_lev_codes, ngroups
)
# missing values are placed first; drop them!
left_indexer = left_indexer[counts[0] :]
new_codes = [lab[left_indexer] for lab in new_codes]
else: # sort the leaves
mask = new_lev_codes != -1
mask_all = mask.all()
if not mask_all:
new_codes = [lab[mask] for lab in new_codes]
left_indexer = _get_leaf_sorter(new_codes[: level + 1])
new_codes = [lab[left_indexer] for lab in new_codes]
# left_indexers are w.r.t masked frame.
# reverse to original frame!
if not mask_all:
left_indexer = mask.nonzero()[0][left_indexer]
join_index = MultiIndex(
levels=new_levels,
codes=new_codes,
names=left.names,
verify_integrity=False,
)
if right_lev_indexer is not None:
right_indexer = right_lev_indexer.take(join_index.codes[level])
else:
right_indexer = join_index.codes[level]
if flip_order:
left_indexer, right_indexer = right_indexer, left_indexer
left_indexer = (
None if left_indexer is None else ensure_platform_int(left_indexer)
)
right_indexer = (
None if right_indexer is None else ensure_platform_int(right_indexer)
)
return join_index, left_indexer, right_indexer
@final
def _join_monotonic(
self, other: Index, how: str_t = "left"
) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
# We only get here with matching dtypes and both monotonic increasing
assert other.dtype == self.dtype
if self.equals(other):
ret_index = other if how == "right" else self
return ret_index, None, None
ridx: np.ndarray | None
lidx: np.ndarray | None
if self.is_unique and other.is_unique:
# We can perform much better than the general case
if how == "left":
join_index = self
lidx = None
ridx = self._left_indexer_unique(other)
elif how == "right":
join_index = other
lidx = other._left_indexer_unique(self)
ridx = None
elif how == "inner":
join_array, lidx, ridx = self._inner_indexer(other)
join_index = self._wrap_joined_index(join_array, other)
elif how == "outer":
join_array, lidx, ridx = self._outer_indexer(other)
join_index = self._wrap_joined_index(join_array, other)
else:
if how == "left":
join_array, lidx, ridx = self._left_indexer(other)
elif how == "right":
join_array, ridx, lidx = other._left_indexer(self)
elif how == "inner":
join_array, lidx, ridx = self._inner_indexer(other)
elif how == "outer":
join_array, lidx, ridx = self._outer_indexer(other)
join_index = self._wrap_joined_index(join_array, other)
lidx = None if lidx is None else ensure_platform_int(lidx)
ridx = None if ridx is None else ensure_platform_int(ridx)
return join_index, lidx, ridx
def _wrap_joined_index(self: _IndexT, joined: ArrayLike, other: _IndexT) -> _IndexT:
assert other.dtype == self.dtype
if isinstance(self, ABCMultiIndex):
name = self.names if self.names == other.names else None
# error: Incompatible return value type (got "MultiIndex",
# expected "_IndexT")
return self._constructor(joined, name=name) # type: ignore[return-value]
else:
name = get_op_result_name(self, other)
return self._constructor._with_infer(joined, name=name)
@cache_readonly
def _can_use_libjoin(self) -> bool:
"""
Whether we can use the fastpaths implement in _libs.join
"""
# Note: this will need to be updated when e.g. Nullable dtypes
# are supported in Indexes.
return not is_interval_dtype(self.dtype)
# --------------------------------------------------------------------
# Uncategorized Methods
@property
def values(self) -> ArrayLike:
"""
Return an array representing the data in the Index.
.. warning::
We recommend using :attr:`Index.array` or
:meth:`Index.to_numpy`, depending on whether you need
a reference to the underlying data or a NumPy array.
Returns
-------
array: numpy.ndarray or ExtensionArray
See Also
--------
Index.array : Reference to the underlying data.
Index.to_numpy : A NumPy array representing the underlying data.
"""
return self._data
# error: Decorated property not supported
# https://github.com/python/mypy/issues/1362
@cache_readonly # type: ignore[misc]
@doc(IndexOpsMixin.array)
def array(self) -> ExtensionArray:
array = self._data
if isinstance(array, np.ndarray):
from pandas.core.arrays.numpy_ import PandasArray
array = PandasArray(array)
return array
@property
def _values(self) -> ExtensionArray | np.ndarray:
"""
The best array representation.
This is an ndarray or ExtensionArray.
``_values`` are consistent between ``Series`` and ``Index``.
It may differ from the public '.values' method.
index | values | _values |
----------------- | --------------- | ------------- |
Index | ndarray | ndarray |
CategoricalIndex | Categorical | Categorical |
DatetimeIndex | ndarray[M8ns] | DatetimeArray |
DatetimeIndex[tz] | ndarray[M8ns] | DatetimeArray |
PeriodIndex | ndarray[object] | PeriodArray |
IntervalIndex | IntervalArray | IntervalArray |
See Also
--------
values : Values
"""
return self._data
def _get_engine_target(self) -> np.ndarray:
"""
Get the ndarray that we can pass to the IndexEngine constructor.
"""
# error: Incompatible return value type (got "Union[ExtensionArray,
# ndarray]", expected "ndarray")
if type(self) is Index and isinstance(self._values, ExtensionArray):
# TODO(ExtensionIndex): remove special-case, just use self._values
return self._values.astype(object)
return self._values # type: ignore[return-value]
def _from_join_target(self, result: np.ndarray) -> ArrayLike:
"""
Cast the ndarray returned from one of the libjoin.foo_indexer functions
back to type(self)._data.
"""
return result
@doc(IndexOpsMixin._memory_usage)
def memory_usage(self, deep: bool = False) -> int:
result = self._memory_usage(deep=deep)
# include our engine hashtable
result += self._engine.sizeof(deep=deep)
return result
@final
def where(self, cond, other=None) -> Index:
"""
Replace values where the condition is False.
The replacement is taken from other.
Parameters
----------
cond : bool array-like with the same length as self
Condition to select the values on.
other : scalar, or array-like, default None
Replacement if the condition is False.
Returns
-------
pandas.Index
A copy of self with values replaced from other
where the condition is False.
See Also
--------
Series.where : Same method for Series.
DataFrame.where : Same method for DataFrame.
Examples
--------
>>> idx = pd.Index(['car', 'bike', 'train', 'tractor'])
>>> idx
Index(['car', 'bike', 'train', 'tractor'], dtype='object')
>>> idx.where(idx.isin(['car', 'train']), 'other')
Index(['car', 'other', 'train', 'other'], dtype='object')
"""
if isinstance(self, ABCMultiIndex):
raise NotImplementedError(
".where is not supported for MultiIndex operations"
)
cond = np.asarray(cond, dtype=bool)
return self.putmask(~cond, other)
# construction helpers
@final
@classmethod
def _scalar_data_error(cls, data):
# We return the TypeError so that we can raise it from the constructor
# in order to keep mypy happy
return TypeError(
f"{cls.__name__}(...) must be called with a collection of some "
f"kind, {repr(data)} was passed"
)
@final
@classmethod
def _string_data_error(cls, data):
raise TypeError(
"String dtype not supported, you may need "
"to explicitly cast to a numeric type"
)
def _validate_fill_value(self, value):
"""
Check if the value can be inserted into our array without casting,
and convert it to an appropriate native type if necessary.
Raises
------
TypeError
If the value cannot be inserted into an array of this dtype.
"""
if not can_hold_element(self._values, value):
raise TypeError
return value
@final
def _require_scalar(self, value):
"""
Check that this is a scalar value that we can use for setitem-like
operations without changing dtype.
"""
if not is_scalar(value):
raise TypeError(f"'value' must be a scalar, passed: {type(value).__name__}")
return value
def _is_memory_usage_qualified(self) -> bool:
"""
Return a boolean if we need a qualified .info display.
"""
return self.is_object()
def is_type_compatible(self, kind: str_t) -> bool:
"""
Whether the index type is compatible with the provided type.
"""
warnings.warn(
"Index.is_type_compatible is deprecated and will be removed in a "
"future version.",
FutureWarning,
stacklevel=find_stack_level(),
)
return kind == self.inferred_type
def __contains__(self, key: Any) -> bool:
"""
Return a boolean indicating whether the provided key is in the index.
Parameters
----------
key : label
The key to check if it is present in the index.
Returns
-------
bool
Whether the key search is in the index.
Raises
------
TypeError
If the key is not hashable.
See Also
--------
Index.isin : Returns an ndarray of boolean dtype indicating whether the
list-like key is in the index.
Examples
--------
>>> idx = pd.Index([1, 2, 3, 4])
>>> idx
Int64Index([1, 2, 3, 4], dtype='int64')
>>> 2 in idx
True
>>> 6 in idx
False
"""
hash(key)
try:
return key in self._engine
except (OverflowError, TypeError, ValueError):
return False
# https://github.com/python/typeshed/issues/2148#issuecomment-520783318
# Incompatible types in assignment (expression has type "None", base class
# "object" defined the type as "Callable[[object], int]")
__hash__: None # type: ignore[assignment]
@final
def __setitem__(self, key, value):
raise TypeError("Index does not support mutable operations")
def __getitem__(self, key):
"""
Override numpy.ndarray's __getitem__ method to work as desired.
This function adds lists and Series as valid boolean indexers
(ndarrays only supports ndarray with dtype=bool).
If resulting ndim != 1, plain ndarray is returned instead of
corresponding `Index` subclass.
"""
getitem = self._data.__getitem__
if is_integer(key) or is_float(key):
# GH#44051 exclude bool, which would return a 2d ndarray
key = com.cast_scalar_indexer(key, warn_float=True)
return getitem(key)
if isinstance(key, slice):
# This case is separated from the conditional above to avoid
# pessimization com.is_bool_indexer and ndim checks.
result = getitem(key)
# Going through simple_new for performance.
return type(self)._simple_new(result, name=self._name)
if com.is_bool_indexer(key):
# if we have list[bools, length=1e5] then doing this check+convert
# takes 166 µs + 2.1 ms and cuts the ndarray.__getitem__
# time below from 3.8 ms to 496 µs
# if we already have ndarray[bool], the overhead is 1.4 µs or .25%
key = np.asarray(key, dtype=bool)
result = getitem(key)
# Because we ruled out integer above, we always get an arraylike here
if result.ndim > 1:
deprecate_ndim_indexing(result)
if hasattr(result, "_ndarray"):
# error: Item "ndarray[Any, Any]" of "Union[ExtensionArray,
# ndarray[Any, Any]]" has no attribute "_ndarray" [union-attr]
# i.e. NDArrayBackedExtensionArray
# Unpack to ndarray for MPL compat
return result._ndarray # type: ignore[union-attr]
return result
# NB: Using _constructor._simple_new would break if MultiIndex
# didn't override __getitem__
return self._constructor._simple_new(result, name=self._name)
def _getitem_slice(self: _IndexT, slobj: slice) -> _IndexT:
"""
Fastpath for __getitem__ when we know we have a slice.
"""
res = self._data[slobj]
return type(self)._simple_new(res, name=self._name)
@final
def _can_hold_identifiers_and_holds_name(self, name) -> bool:
"""
Faster check for ``name in self`` when we know `name` is a Python
identifier (e.g. in NDFrame.__getattr__, which hits this to support
. key lookup). For indexes that can't hold identifiers (everything
but object & categorical) we just return False.
https://github.com/pandas-dev/pandas/issues/19764
"""
if self.is_object() or self.is_categorical():
return name in self
return False
def append(self, other: Index | Sequence[Index]) -> Index:
"""
Append a collection of Index options together.
Parameters
----------
other : Index or list/tuple of indices
Returns
-------
Index
"""
to_concat = [self]
if isinstance(other, (list, tuple)):
to_concat += list(other)
else:
# error: Argument 1 to "append" of "list" has incompatible type
# "Union[Index, Sequence[Index]]"; expected "Index"
to_concat.append(other) # type: ignore[arg-type]
for obj in to_concat:
if not isinstance(obj, Index):
raise TypeError("all inputs must be Index")
names = {obj.name for obj in to_concat}
name = None if len(names) > 1 else self.name
return self._concat(to_concat, name)
def _concat(self, to_concat: list[Index], name: Hashable) -> Index:
"""
Concatenate multiple Index objects.
"""
to_concat_vals = [x._values for x in to_concat]
result = concat_compat(to_concat_vals)
is_numeric = result.dtype.kind in ["i", "u", "f"]
if self._is_backward_compat_public_numeric_index and is_numeric:
return type(self)._simple_new(result, name=name)
return Index._with_infer(result, name=name)
@final
def putmask(self, mask, value) -> Index:
"""
Return a new Index of the values set with the mask.
Returns
-------
Index
See Also
--------
numpy.ndarray.putmask : Changes elements of an array
based on conditional and input values.
"""
mask, noop = validate_putmask(self._values, mask)
if noop:
return self.copy()
if value is None and (self._is_numeric_dtype or self.dtype == object):
value = self._na_value
try:
converted = self._validate_fill_value(value)
except (ValueError, TypeError) as err:
if is_object_dtype(self): # pragma: no cover
raise err
dtype = self._find_common_type_compat(value)
return self.astype(dtype).putmask(mask, value)
values = self._values.copy()
if isinstance(values, np.ndarray):
converted = setitem_datetimelike_compat(values, mask.sum(), converted)
np.putmask(values, mask, converted)
else:
# Note: we use the original value here, not converted, as
# _validate_fill_value is not idempotent
values._putmask(mask, value)
return self._shallow_copy(values)
def equals(self, other: Any) -> bool:
"""
Determine if two Index object are equal.
The things that are being compared are:
* The elements inside the Index object.
* The order of the elements inside the Index object.
Parameters
----------
other : Any
The other object to compare against.
Returns
-------
bool
True if "other" is an Index and it has the same elements and order
as the calling index; False otherwise.
Examples
--------
>>> idx1 = pd.Index([1, 2, 3])
>>> idx1
Int64Index([1, 2, 3], dtype='int64')
>>> idx1.equals(pd.Index([1, 2, 3]))
True
The elements inside are compared
>>> idx2 = pd.Index(["1", "2", "3"])
>>> idx2
Index(['1', '2', '3'], dtype='object')
>>> idx1.equals(idx2)
False
The order is compared
>>> ascending_idx = pd.Index([1, 2, 3])
>>> ascending_idx
Int64Index([1, 2, 3], dtype='int64')
>>> descending_idx = pd.Index([3, 2, 1])
>>> descending_idx
Int64Index([3, 2, 1], dtype='int64')
>>> ascending_idx.equals(descending_idx)
False
The dtype is *not* compared
>>> int64_idx = pd.Int64Index([1, 2, 3])
>>> int64_idx
Int64Index([1, 2, 3], dtype='int64')
>>> uint64_idx = pd.UInt64Index([1, 2, 3])
>>> uint64_idx
UInt64Index([1, 2, 3], dtype='uint64')
>>> int64_idx.equals(uint64_idx)
True
"""
if self.is_(other):
return True
if not isinstance(other, Index):
return False
if is_object_dtype(self.dtype) and not is_object_dtype(other.dtype):
# if other is not object, use other's logic for coercion
return other.equals(self)
if isinstance(other, ABCMultiIndex):
# d-level MultiIndex can equal d-tuple Index
return other.equals(self)
if isinstance(self._values, ExtensionArray):
# Dispatch to the ExtensionArray's .equals method.
if not isinstance(other, type(self)):
return False
earr = cast(ExtensionArray, self._data)
return earr.equals(other._data)
if is_extension_array_dtype(other.dtype):
# All EA-backed Index subclasses override equals
return other.equals(self)
return array_equivalent(self._values, other._values)
@final
def identical(self, other) -> bool:
"""
Similar to equals, but checks that object attributes and types are also equal.
Returns
-------
bool
If two Index objects have equal elements and same type True,
otherwise False.
"""
return (
self.equals(other)
and all(
getattr(self, c, None) == getattr(other, c, None)
for c in self._comparables
)
and type(self) == type(other)
)
@final
def asof(self, label):
"""
Return the label from the index, or, if not present, the previous one.
Assuming that the index is sorted, return the passed index label if it
is in the index, or return the previous index label if the passed one
is not in the index.
Parameters
----------
label : object
The label up to which the method returns the latest index label.
Returns
-------
object
The passed label if it is in the index. The previous label if the
passed label is not in the sorted index or `NaN` if there is no
such label.
See Also
--------
Series.asof : Return the latest value in a Series up to the
passed index.
merge_asof : Perform an asof merge (similar to left join but it
matches on nearest key rather than equal key).
Index.get_loc : An `asof` is a thin wrapper around `get_loc`
with method='pad'.
Examples
--------
`Index.asof` returns the latest index label up to the passed label.
>>> idx = pd.Index(['2013-12-31', '2014-01-02', '2014-01-03'])
>>> idx.asof('2014-01-01')
'2013-12-31'
If the label is in the index, the method returns the passed label.
>>> idx.asof('2014-01-02')
'2014-01-02'
If all of the labels in the index are later than the passed label,
NaN is returned.
>>> idx.asof('1999-01-02')
nan
If the index is not sorted, an error is raised.
>>> idx_not_sorted = pd.Index(['2013-12-31', '2015-01-02',
... '2014-01-03'])
>>> idx_not_sorted.asof('2013-12-31')
Traceback (most recent call last):
ValueError: index must be monotonic increasing or decreasing
"""
self._searchsorted_monotonic(label) # validate sortedness
try:
loc = self.get_loc(label)
except (KeyError, TypeError):
# KeyError -> No exact match, try for padded
# TypeError -> passed e.g. non-hashable, fall through to get
# the tested exception message
indexer = self.get_indexer([label], method="pad")
if indexer.ndim > 1 or indexer.size > 1:
raise TypeError("asof requires scalar valued input")
loc = indexer.item()
if loc == -1:
return self._na_value
else:
if isinstance(loc, slice):
loc = loc.indices(len(self))[-1]
return self[loc]
def asof_locs(self, where: Index, mask: np.ndarray) -> npt.NDArray[np.intp]:
"""
Return the locations (indices) of labels in the index.
As in the `asof` function, if the label (a particular entry in
`where`) is not in the index, the latest index label up to the
passed label is chosen and its index returned.
If all of the labels in the index are later than a label in `where`,
-1 is returned.
`mask` is used to ignore NA values in the index during calculation.
Parameters
----------
where : Index
An Index consisting of an array of timestamps.
mask : np.ndarray[bool]
Array of booleans denoting where values in the original
data are not NA.
Returns
-------
np.ndarray[np.intp]
An array of locations (indices) of the labels from the Index
which correspond to the return values of the `asof` function
for every element in `where`.
"""
# error: No overload variant of "searchsorted" of "ndarray" matches argument
# types "Union[ExtensionArray, ndarray[Any, Any]]", "str"
# TODO: will be fixed when ExtensionArray.searchsorted() is fixed
locs = self._values[mask].searchsorted(
where._values, side="right" # type: ignore[call-overload]
)
locs = np.where(locs > 0, locs - 1, 0)
result = np.arange(len(self), dtype=np.intp)[mask].take(locs)
first_value = self._values[mask.argmax()]
result[(locs == 0) & (where._values < first_value)] = -1
return result
def sort_values(
self,
return_indexer: bool = False,
ascending: bool = True,
na_position: str_t = "last",
key: Callable | None = None,
):
"""
Return a sorted copy of the index.
Return a sorted copy of the index, and optionally return the indices
that sorted the index itself.
Parameters
----------
return_indexer : bool, default False
Should the indices that would sort the index be returned.
ascending : bool, default True
Should the index values be sorted in an ascending order.
na_position : {'first' or 'last'}, default 'last'
Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
the end.
.. versionadded:: 1.2.0
key : callable, optional
If not None, apply the key function to the index values
before sorting. This is similar to the `key` argument in the
builtin :meth:`sorted` function, with the notable difference that
this `key` function should be *vectorized*. It should expect an
``Index`` and return an ``Index`` of the same shape.
.. versionadded:: 1.1.0
Returns
-------
sorted_index : pandas.Index
Sorted copy of the index.
indexer : numpy.ndarray, optional
The indices that the index itself was sorted by.
See Also
--------
Series.sort_values : Sort values of a Series.
DataFrame.sort_values : Sort values in a DataFrame.
Examples
--------
>>> idx = pd.Index([10, 100, 1, 1000])
>>> idx
Int64Index([10, 100, 1, 1000], dtype='int64')
Sort values in ascending order (default behavior).
>>> idx.sort_values()
Int64Index([1, 10, 100, 1000], dtype='int64')
Sort values in descending order, and also get the indices `idx` was
sorted by.
>>> idx.sort_values(ascending=False, return_indexer=True)
(Int64Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2]))
"""
idx = ensure_key_mapped(self, key)
# GH 35584. Sort missing values according to na_position kwarg
# ignore na_position for MultiIndex
if not isinstance(self, ABCMultiIndex):
_as = nargsort(
items=idx, ascending=ascending, na_position=na_position, key=key
)
else:
_as = idx.argsort()
if not ascending:
_as = _as[::-1]
sorted_index = self.take(_as)
if return_indexer:
return sorted_index, _as
else:
return sorted_index
@final
def sort(self, *args, **kwargs):
"""
Use sort_values instead.
"""
raise TypeError("cannot sort an Index object in-place, use sort_values instead")
def shift(self, periods=1, freq=None):
"""
Shift index by desired number of time frequency increments.
This method is for shifting the values of datetime-like indexes
by a specified time increment a given number of times.
Parameters
----------
periods : int, default 1
Number of periods (or increments) to shift by,
can be positive or negative.
freq : pandas.DateOffset, pandas.Timedelta or str, optional
Frequency increment to shift by.
If None, the index is shifted by its own `freq` attribute.
Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.
Returns
-------
pandas.Index
Shifted index.
See Also
--------
Series.shift : Shift values of Series.
Notes
-----
This method is only implemented for datetime-like index classes,
i.e., DatetimeIndex, PeriodIndex and TimedeltaIndex.
Examples
--------
Put the first 5 month starts of 2011 into an index.
>>> month_starts = pd.date_range('1/1/2011', periods=5, freq='MS')
>>> month_starts
DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01', '2011-04-01',
'2011-05-01'],
dtype='datetime64[ns]', freq='MS')
Shift the index by 10 days.
>>> month_starts.shift(10, freq='D')
DatetimeIndex(['2011-01-11', '2011-02-11', '2011-03-11', '2011-04-11',
'2011-05-11'],
dtype='datetime64[ns]', freq=None)
The default value of `freq` is the `freq` attribute of the index,
which is 'MS' (month start) in this example.
>>> month_starts.shift(10)
DatetimeIndex(['2011-11-01', '2011-12-01', '2012-01-01', '2012-02-01',
'2012-03-01'],
dtype='datetime64[ns]', freq='MS')
"""
raise NotImplementedError(
f"This method is only implemented for DatetimeIndex, PeriodIndex and "
f"TimedeltaIndex; Got type {type(self).__name__}"
)
def argsort(self, *args, **kwargs) -> npt.NDArray[np.intp]:
"""
Return the integer indices that would sort the index.
Parameters
----------
*args
Passed to `numpy.ndarray.argsort`.
**kwargs
Passed to `numpy.ndarray.argsort`.
Returns
-------
np.ndarray[np.intp]
Integer indices that would sort the index if used as
an indexer.
See Also
--------
numpy.argsort : Similar method for NumPy arrays.
Index.sort_values : Return sorted copy of Index.
Examples
--------
>>> idx = pd.Index(['b', 'a', 'd', 'c'])
>>> idx
Index(['b', 'a', 'd', 'c'], dtype='object')
>>> order = idx.argsort()
>>> order
array([1, 0, 3, 2])
>>> idx[order]
Index(['a', 'b', 'c', 'd'], dtype='object')
"""
# This works for either ndarray or EA, is overridden
# by RangeIndex, MultIIndex
return self._data.argsort(*args, **kwargs)
@final
def get_value(self, series: Series, key):
"""
Fast lookup of value from 1-dimensional ndarray.
Only use this if you know what you're doing.
Returns
-------
scalar or Series
"""
warnings.warn(
"get_value is deprecated and will be removed in a future version. "
"Use Series[key] instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
self._check_indexing_error(key)
try:
# GH 20882, 21257
# First try to convert the key to a location
# If that fails, raise a KeyError if an integer
# index, otherwise, see if key is an integer, and
# try that
loc = self.get_loc(key)
except KeyError:
if not self._should_fallback_to_positional:
raise
elif is_integer(key):
# If the Index cannot hold integer, then this is unambiguously
# a locational lookup.
loc = key
else:
raise
return self._get_values_for_loc(series, loc, key)
def _check_indexing_error(self, key):
if not is_scalar(key):
# if key is not a scalar, directly raise an error (the code below
# would convert to numpy arrays and raise later any way) - GH29926
raise InvalidIndexError(key)
@cache_readonly
def _should_fallback_to_positional(self) -> bool:
"""
Should an integer key be treated as positional?
"""
return not self.holds_integer() and not self.is_boolean()
def _get_values_for_loc(self, series: Series, loc, key):
"""
Do a positional lookup on the given Series, returning either a scalar
or a Series.
Assumes that `series.index is self`
key is included for MultiIndex compat.
"""
if is_integer(loc):
return series._values[loc]
return series.iloc[loc]
@final
def set_value(self, arr, key, value):
"""
Fast lookup of value from 1-dimensional ndarray.
.. deprecated:: 1.0
Notes
-----
Only use this if you know what you're doing.
"""
warnings.warn(
(
"The 'set_value' method is deprecated, and "
"will be removed in a future version."
),
FutureWarning,
stacklevel=find_stack_level(),
)
loc = self._engine.get_loc(key)
if not can_hold_element(arr, value):
raise ValueError
arr[loc] = value
_index_shared_docs[
"get_indexer_non_unique"
] = """
Compute indexer and mask for new index given the current index. The
indexer should be then used as an input to ndarray.take to align the
current data to the new index.
Parameters
----------
target : %(target_klass)s
Returns
-------
indexer : np.ndarray[np.intp]
Integers from 0 to n - 1 indicating that the index at these
positions matches the corresponding target values. Missing values
in the target are marked by -1.
missing : np.ndarray[np.intp]
An indexer into the target of the values not found.
These correspond to the -1 in the indexer array.
"""
@Appender(_index_shared_docs["get_indexer_non_unique"] % _index_doc_kwargs)
def get_indexer_non_unique(
self, target
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
target = ensure_index(target)
target = self._maybe_cast_listlike_indexer(target)
if not self._should_compare(target) and not is_interval_dtype(self.dtype):
# IntervalIndex get special treatment bc numeric scalars can be
# matched to Interval scalars
return self._get_indexer_non_comparable(target, method=None, unique=False)
pself, ptarget = self._maybe_promote(target)
if pself is not self or ptarget is not target:
return pself.get_indexer_non_unique(ptarget)
if not is_dtype_equal(self.dtype, target.dtype):
# TODO: if object, could use infer_dtype to preempt costly
# conversion if still non-comparable?
dtype = self._find_common_type_compat(target)
this = self.astype(dtype, copy=False)
that = target.astype(dtype, copy=False)
return this.get_indexer_non_unique(that)
# Note: _maybe_promote ensures we never get here with MultiIndex
# self and non-Multi target
tgt_values = target._get_engine_target()
if self._is_multi and target._is_multi:
engine = self._engine
# error: "IndexEngine" has no attribute "_extract_level_codes"
tgt_values = engine._extract_level_codes( # type: ignore[attr-defined]
target
)
indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
return ensure_platform_int(indexer), ensure_platform_int(missing)
@final
def get_indexer_for(self, target) -> npt.NDArray[np.intp]:
"""
Guaranteed return of an indexer even when non-unique.
This dispatches to get_indexer or get_indexer_non_unique
as appropriate.
Returns
-------
np.ndarray[np.intp]
List of indices.
Examples
--------
>>> idx = pd.Index([np.nan, 'var1', np.nan])
>>> idx.get_indexer_for([np.nan])
array([0, 2])
"""
if self._index_as_unique:
return self.get_indexer(target)
indexer, _ = self.get_indexer_non_unique(target)
return indexer
def _get_indexer_strict(self, key, axis_name: str_t) -> tuple[Index, np.ndarray]:
"""
Analogue to get_indexer that raises if any elements are missing.
"""
keyarr = key
if not isinstance(keyarr, Index):
keyarr = com.asarray_tuplesafe(keyarr)
if self._index_as_unique:
indexer = self.get_indexer_for(keyarr)
keyarr = self.reindex(keyarr)[0]
else:
keyarr, indexer, new_indexer = self._reindex_non_unique(keyarr)
self._raise_if_missing(keyarr, indexer, axis_name)
keyarr = self.take(indexer)
if isinstance(key, Index):
# GH 42790 - Preserve name from an Index
keyarr.name = key.name
if keyarr.dtype.kind in ["m", "M"]:
# DTI/TDI.take can infer a freq in some cases when we dont want one
if isinstance(key, list) or (
isinstance(key, type(self))
# "Index" has no attribute "freq"
and key.freq is None # type: ignore[attr-defined]
):
keyarr = keyarr._with_freq(None)
return keyarr, indexer
def _raise_if_missing(self, key, indexer, axis_name: str_t) -> None:
"""
Check that indexer can be used to return a result.
e.g. at least one element was found,
unless the list of keys was actually empty.
Parameters
----------
key : list-like
Targeted labels (only used to show correct error message).
indexer: array-like of booleans
Indices corresponding to the key,
(with -1 indicating not found).
axis_name : str
Raises
------
KeyError
If at least one key was requested but none was found.
"""
if len(key) == 0:
return
# Count missing values
missing_mask = indexer < 0
nmissing = missing_mask.sum()
if nmissing:
# TODO: remove special-case; this is just to keep exception
# message tests from raising while debugging
use_interval_msg = is_interval_dtype(self.dtype) or (
is_categorical_dtype(self.dtype)
# "Index" has no attribute "categories" [attr-defined]
and is_interval_dtype(
self.categories.dtype # type: ignore[attr-defined]
)
)
if nmissing == len(indexer):
if use_interval_msg:
key = list(key)
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
not_found = list(ensure_index(key)[missing_mask.nonzero()[0]].unique())
raise KeyError(f"{not_found} not in index")
@overload
def _get_indexer_non_comparable(
self, target: Index, method, unique: Literal[True] = ...
) -> npt.NDArray[np.intp]:
...
@overload
def _get_indexer_non_comparable(
self, target: Index, method, unique: Literal[False]
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
...
@overload
def _get_indexer_non_comparable(
self, target: Index, method, unique: bool = True
) -> npt.NDArray[np.intp] | tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
...
@final
def _get_indexer_non_comparable(
self, target: Index, method, unique: bool = True
) -> npt.NDArray[np.intp] | tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
"""
Called from get_indexer or get_indexer_non_unique when the target
is of a non-comparable dtype.
For get_indexer lookups with method=None, get_indexer is an _equality_
check, so non-comparable dtypes mean we will always have no matches.
For get_indexer lookups with a method, get_indexer is an _inequality_
check, so non-comparable dtypes mean we will always raise TypeError.
Parameters
----------
target : Index
method : str or None
unique : bool, default True
* True if called from get_indexer.
* False if called from get_indexer_non_unique.
Raises
------
TypeError
If doing an inequality check, i.e. method is not None.
"""
if method is not None:
other = unpack_nested_dtype(target)
raise TypeError(f"Cannot compare dtypes {self.dtype} and {other.dtype}")
no_matches = -1 * np.ones(target.shape, dtype=np.intp)
if unique:
# This is for get_indexer
return no_matches
else:
# This is for get_indexer_non_unique
missing = np.arange(len(target), dtype=np.intp)
return no_matches, missing
@property
def _index_as_unique(self) -> bool:
"""
Whether we should treat this as unique for the sake of
get_indexer vs get_indexer_non_unique.
For IntervalIndex compat.
"""
return self.is_unique
_requires_unique_msg = "Reindexing only valid with uniquely valued Index objects"
@final
def _maybe_promote(self, other: Index) -> tuple[Index, Index]:
"""
When dealing with an object-dtype Index and a non-object Index, see
if we can upcast the object-dtype one to improve performance.
"""
if isinstance(self, ABCDatetimeIndex) and isinstance(other, ABCDatetimeIndex):
if (
self.tz is not None
and other.tz is not None
and not tz_compare(self.tz, other.tz)
):
# standardize on UTC
return self.tz_convert("UTC"), other.tz_convert("UTC")
elif self.inferred_type == "date" and isinstance(other, ABCDatetimeIndex):
try:
return type(other)(self), other
except OutOfBoundsDatetime:
return self, other
elif self.inferred_type == "timedelta" and isinstance(other, ABCTimedeltaIndex):
# TODO: we dont have tests that get here
return type(other)(self), other
elif self.dtype.kind == "u" and other.dtype.kind == "i":
# GH#41873
if other.min() >= 0:
# lookup min as it may be cached
# TODO: may need itemsize check if we have non-64-bit Indexes
return self, other.astype(self.dtype)
elif self._is_multi and not other._is_multi:
try:
# "Type[Index]" has no attribute "from_tuples"
other = type(self).from_tuples(other) # type: ignore[attr-defined]
except (TypeError, ValueError):
# let's instead try with a straight Index
self = Index(self._values)
if not is_object_dtype(self.dtype) and is_object_dtype(other.dtype):
# Reverse op so we dont need to re-implement on the subclasses
other, self = other._maybe_promote(self)
return self, other
@final
def _find_common_type_compat(self, target) -> DtypeObj:
"""
Implementation of find_common_type that adjusts for Index-specific
special cases.
"""
if is_interval_dtype(self.dtype) and is_valid_na_for_dtype(target, self.dtype):
# e.g. setting NA value into IntervalArray[int64]
self = cast("IntervalIndex", self)
return IntervalDtype(np.float64, closed=self.closed)
target_dtype, _ = infer_dtype_from(target, pandas_dtype=True)
# special case: if one dtype is uint64 and the other a signed int, return object
# See https://github.com/pandas-dev/pandas/issues/26778 for discussion
# Now it's:
# * float | [u]int -> float
# * uint64 | signed int -> object
# We may change union(float | [u]int) to go to object.
if self.dtype == "uint64" or target_dtype == "uint64":
if is_signed_integer_dtype(self.dtype) or is_signed_integer_dtype(
target_dtype
):
return _dtype_obj
dtype = find_common_type([self.dtype, target_dtype])
if dtype.kind in ["i", "u"]:
# TODO: what about reversed with self being categorical?
if (
isinstance(target, Index)
and is_categorical_dtype(target.dtype)
and target.hasnans
):
# FIXME: find_common_type incorrect with Categorical GH#38240
# FIXME: some cases where float64 cast can be lossy?
dtype = np.dtype(np.float64)
if dtype.kind == "c":
dtype = _dtype_obj
return dtype
@final
def _should_compare(self, other: Index) -> bool:
"""
Check if `self == other` can ever have non-False entries.
"""
if (other.is_boolean() and self.is_numeric()) or (
self.is_boolean() and other.is_numeric()
):
# GH#16877 Treat boolean labels passed to a numeric index as not
# found. Without this fix False and True would be treated as 0 and 1
# respectively.
return False
other = unpack_nested_dtype(other)
dtype = other.dtype
return self._is_comparable_dtype(dtype) or is_object_dtype(dtype)
def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
"""
Can we compare values of the given dtype to our own?
"""
return True
@final
def groupby(self, values) -> PrettyDict[Hashable, np.ndarray]:
"""
Group the index labels by a given array of values.
Parameters
----------
values : array
Values used to determine the groups.
Returns
-------
dict
{group name -> group labels}
"""
# TODO: if we are a MultiIndex, we can do better
# that converting to tuples
if isinstance(values, ABCMultiIndex):
values = values._values
values = Categorical(values)
result = values._reverse_indexer()
# map to the label
result = {k: self.take(v) for k, v in result.items()}
return PrettyDict(result)
def map(self, mapper, na_action=None):
"""
Map values using an input mapping or function.
Parameters
----------
mapper : function, dict, or Series
Mapping correspondence.
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping correspondence.
Returns
-------
applied : Union[Index, MultiIndex], inferred
The output of the mapping function applied to the index.
If the function returns a tuple with more than one element
a MultiIndex will be returned.
"""
from pandas.core.indexes.multi import MultiIndex
new_values = self._map_values(mapper, na_action=na_action)
# we can return a MultiIndex
if new_values.size and isinstance(new_values[0], tuple):
if isinstance(self, MultiIndex):
names = self.names
elif self.name:
names = [self.name] * len(new_values[0])
else:
names = None
return MultiIndex.from_tuples(new_values, names=names)
dtype = None
if not new_values.size:
# empty
dtype = self.dtype
# e.g. if we are floating and new_values is all ints, then we
# don't want to cast back to floating. But if we are UInt64
# and new_values is all ints, we want to try.
same_dtype = lib.infer_dtype(new_values, skipna=False) == self.inferred_type
if same_dtype:
new_values = maybe_cast_pointwise_result(
new_values, self.dtype, same_dtype=same_dtype
)
if self._is_backward_compat_public_numeric_index and is_numeric_dtype(
new_values.dtype
):
return self._constructor(
new_values, dtype=dtype, copy=False, name=self.name
)
return Index._with_infer(new_values, dtype=dtype, copy=False, name=self.name)
# TODO: De-duplicate with map, xref GH#32349
@final
def _transform_index(self, func, *, level=None) -> Index:
"""
Apply function to all values found in index.
This includes transforming multiindex entries separately.
Only apply function to one level of the MultiIndex if level is specified.
"""
if isinstance(self, ABCMultiIndex):
if level is not None:
# Caller is responsible for ensuring level is positional.
items = [
tuple(func(y) if i == level else y for i, y in enumerate(x))
for x in self
]
else:
items = [tuple(func(y) for y in x) for x in self]
return type(self).from_tuples(items, names=self.names)
else:
items = [func(x) for x in self]
return Index(items, name=self.name, tupleize_cols=False)
def isin(self, values, level=None) -> np.ndarray:
"""
Return a boolean array where the index values are in `values`.
Compute boolean array of whether each index value is found in the
passed set of values. The length of the returned boolean array matches
the length of the index.
Parameters
----------
values : set or list-like
Sought values.
level : str or int, optional
Name or position of the index level to use (if the index is a
`MultiIndex`).
Returns
-------
np.ndarray[bool]
NumPy array of boolean values.
See Also
--------
Series.isin : Same for Series.
DataFrame.isin : Same method for DataFrames.
Notes
-----
In the case of `MultiIndex` you must either specify `values` as a
list-like object containing tuples that are the same length as the
number of levels, or specify `level`. Otherwise it will raise a
``ValueError``.
If `level` is specified:
- if it is the name of one *and only one* index level, use that level;
- otherwise it should be a number indicating level position.
Examples
--------
>>> idx = pd.Index([1,2,3])
>>> idx
Int64Index([1, 2, 3], dtype='int64')
Check whether each index value in a list of values.
>>> idx.isin([1, 4])
array([ True, False, False])
>>> midx = pd.MultiIndex.from_arrays([[1,2,3],
... ['red', 'blue', 'green']],
... names=('number', 'color'))
>>> midx
MultiIndex([(1, 'red'),
(2, 'blue'),
(3, 'green')],
names=['number', 'color'])
Check whether the strings in the 'color' level of the MultiIndex
are in a list of colors.
>>> midx.isin(['red', 'orange', 'yellow'], level='color')
array([ True, False, False])
To check across the levels of a MultiIndex, pass a list of tuples:
>>> midx.isin([(1, 'red'), (3, 'red')])
array([ True, False, False])
For a DatetimeIndex, string values in `values` are converted to
Timestamps.
>>> dates = ['2000-03-11', '2000-03-12', '2000-03-13']
>>> dti = pd.to_datetime(dates)
>>> dti
DatetimeIndex(['2000-03-11', '2000-03-12', '2000-03-13'],
dtype='datetime64[ns]', freq=None)
>>> dti.isin(['2000-03-11'])
array([ True, False, False])
"""
if level is not None:
self._validate_index_level(level)
return algos.isin(self._values, values)
def _get_string_slice(self, key: str_t):
# this is for partial string indexing,
# overridden in DatetimeIndex, TimedeltaIndex and PeriodIndex
raise NotImplementedError
def slice_indexer(
self,
start: Hashable | None = None,
end: Hashable | None = None,
step: int | None = None,
kind=no_default,
) -> slice:
"""
Compute the slice indexer for input labels and step.
Index needs to be ordered and unique.
Parameters
----------
start : label, default None
If None, defaults to the beginning.
end : label, default None
If None, defaults to the end.
step : int, default None
kind : str, default None
.. deprecated:: 1.4.0
Returns
-------
indexer : slice
Raises
------
KeyError : If key does not exist, or key is not unique and index is
not ordered.
Notes
-----
This function assumes that the data is sorted, so use at your own peril
Examples
--------
This is a method on all index types. For example you can do:
>>> idx = pd.Index(list('abcd'))
>>> idx.slice_indexer(start='b', end='c')
slice(1, 3, None)
>>> idx = pd.MultiIndex.from_arrays([list('abcd'), list('efgh')])
>>> idx.slice_indexer(start='b', end=('c', 'g'))
slice(1, 3, None)
"""
self._deprecated_arg(kind, "kind", "slice_indexer")
start_slice, end_slice = self.slice_locs(start, end, step=step)
# return a slice
if not is_scalar(start_slice):
raise AssertionError("Start slice bound is non-scalar")
if not is_scalar(end_slice):
raise AssertionError("End slice bound is non-scalar")
return slice(start_slice, end_slice, step)
def _maybe_cast_indexer(self, key):
"""
If we have a float key and are not a floating index, then try to cast
to an int if equivalent.
"""
if not self.is_floating():
return com.cast_scalar_indexer(key)
return key
def _maybe_cast_listlike_indexer(self, target) -> Index:
"""
Analogue to maybe_cast_indexer for get_indexer instead of get_loc.
"""
return ensure_index(target)
@final
def _validate_indexer(self, form: str_t, key, kind: str_t):
"""
If we are positional indexer, validate that we have appropriate
typed bounds must be an integer.
"""
assert kind in ["getitem", "iloc"]
if key is not None and not is_integer(key):
raise self._invalid_indexer(form, key)
def _maybe_cast_slice_bound(self, label, side: str_t, kind=no_default):
"""
This function should be overloaded in subclasses that allow non-trivial
casting on label-slice bounds, e.g. datetime-like indices allowing
strings containing formatted datetimes.
Parameters
----------
label : object
side : {'left', 'right'}
kind : {'loc', 'getitem'} or None
.. deprecated:: 1.3.0
Returns
-------
label : object
Notes
-----
Value of `side` parameter should be validated in caller.
"""
assert kind in ["loc", "getitem", None, no_default]
self._deprecated_arg(kind, "kind", "_maybe_cast_slice_bound")
# We are a plain index here (sub-class override this method if they
# wish to have special treatment for floats/ints, e.g. Float64Index and
# datetimelike Indexes
# reject them, if index does not contain label
if (is_float(label) or is_integer(label)) and label not in self:
raise self._invalid_indexer("slice", label)
return label
def _searchsorted_monotonic(self, label, side: Literal["left", "right"] = "left"):
if self.is_monotonic_increasing:
return self.searchsorted(label, side=side)
elif self.is_monotonic_decreasing:
# np.searchsorted expects ascending sort order, have to reverse
# everything for it to work (element ordering, search side and
# resulting value).
pos = self[::-1].searchsorted(
label, side="right" if side == "left" else "left"
)
return len(self) - pos
raise ValueError("index must be monotonic increasing or decreasing")
def get_slice_bound(
self, label, side: Literal["left", "right"], kind=no_default
) -> int:
"""
Calculate slice bound that corresponds to given label.
Returns leftmost (one-past-the-rightmost if ``side=='right'``) position
of given label.
Parameters
----------
label : object
side : {'left', 'right'}
kind : {'loc', 'getitem'} or None
.. deprecated:: 1.4.0
Returns
-------
int
Index of label.
"""
assert kind in ["loc", "getitem", None, no_default]
self._deprecated_arg(kind, "kind", "get_slice_bound")
if side not in ("left", "right"):
raise ValueError(
"Invalid value for side kwarg, must be either "
f"'left' or 'right': {side}"
)
original_label = label
# For datetime indices label may be a string that has to be converted
# to datetime boundary according to its resolution.
label = self._maybe_cast_slice_bound(label, side)
# we need to look up the label
try:
slc = self.get_loc(label)
except KeyError as err:
try:
return self._searchsorted_monotonic(label, side)
except ValueError:
# raise the original KeyError
raise err
if isinstance(slc, np.ndarray):
# get_loc may return a boolean array, which
# is OK as long as they are representable by a slice.
assert is_bool_dtype(slc.dtype)
slc = lib.maybe_booleans_to_slice(slc.view("u1"))
if isinstance(slc, np.ndarray):
raise KeyError(
f"Cannot get {side} slice bound for non-unique "
f"label: {repr(original_label)}"
)
if isinstance(slc, slice):
if side == "left":
return slc.start
else:
return slc.stop
else:
if side == "right":
return slc + 1
else:
return slc
def slice_locs(
self, start=None, end=None, step=None, kind=no_default
) -> tuple[int, int]:
"""
Compute slice locations for input labels.
Parameters
----------
start : label, default None
If None, defaults to the beginning.
end : label, default None
If None, defaults to the end.
step : int, defaults None
If None, defaults to 1.
kind : {'loc', 'getitem'} or None
.. deprecated:: 1.4.0
Returns
-------
start, end : int
See Also
--------
Index.get_loc : Get location for a single label.
Notes
-----
This method only works if the index is monotonic or unique.
Examples
--------
>>> idx = pd.Index(list('abcd'))
>>> idx.slice_locs(start='b', end='c')
(1, 3)
"""
self._deprecated_arg(kind, "kind", "slice_locs")
inc = step is None or step >= 0
if not inc:
# If it's a reverse slice, temporarily swap bounds.
start, end = end, start
# GH 16785: If start and end happen to be date strings with UTC offsets
# attempt to parse and check that the offsets are the same
if isinstance(start, (str, datetime)) and isinstance(end, (str, datetime)):
try:
ts_start = Timestamp(start)
ts_end = Timestamp(end)
except (ValueError, TypeError):
pass
else:
if not tz_compare(ts_start.tzinfo, ts_end.tzinfo):
raise ValueError("Both dates must have the same UTC offset")
start_slice = None
if start is not None:
start_slice = self.get_slice_bound(start, "left")
if start_slice is None:
start_slice = 0
end_slice = None
if end is not None:
end_slice = self.get_slice_bound(end, "right")
if end_slice is None:
end_slice = len(self)
if not inc:
# Bounds at this moment are swapped, swap them back and shift by 1.
#
# slice_locs('B', 'A', step=-1): s='B', e='A'
#
# s='A' e='B'
# AFTER SWAP: | |
# v ------------------> V
# -----------------------------------
# | | |A|A|A|A| | | | | |B|B| | | | |
# -----------------------------------
# ^ <------------------ ^
# SHOULD BE: | |
# end=s-1 start=e-1
#
end_slice, start_slice = start_slice - 1, end_slice - 1
# i == -1 triggers ``len(self) + i`` selection that points to the
# last element, not before-the-first one, subtracting len(self)
# compensates that.
if end_slice == -1:
end_slice -= len(self)
if start_slice == -1:
start_slice -= len(self)
return start_slice, end_slice
def delete(self: _IndexT, loc) -> _IndexT:
"""
Make new Index with passed location(-s) deleted.
Parameters
----------
loc : int or list of int
Location of item(-s) which will be deleted.
Use a list of locations to delete more than one value at the same time.
Returns
-------
Index
Will be same type as self, except for RangeIndex.
See Also
--------
numpy.delete : Delete any rows and column from NumPy array (ndarray).
Examples
--------
>>> idx = pd.Index(['a', 'b', 'c'])
>>> idx.delete(1)
Index(['a', 'c'], dtype='object')
>>> idx = pd.Index(['a', 'b', 'c'])
>>> idx.delete([0, 2])
Index(['b'], dtype='object')
"""
values = self._values
res_values: ArrayLike
if isinstance(values, np.ndarray):
# TODO(__array_function__): special casing will be unnecessary
res_values = np.delete(values, loc)
else:
res_values = values.delete(loc)
# _constructor so RangeIndex->Int64Index
return self._constructor._simple_new(res_values, name=self.name)
def insert(self, loc: int, item) -> Index:
"""
Make new Index inserting new item at location.
Follows Python numpy.insert semantics for negative values.
Parameters
----------
loc : int
item : object
Returns
-------
new_index : Index
"""
item = lib.item_from_zerodim(item)
if is_valid_na_for_dtype(item, self.dtype) and self.dtype != object:
item = self._na_value
arr = self._values
try:
if isinstance(arr, ExtensionArray):
res_values = arr.insert(loc, item)
return type(self)._simple_new(res_values, name=self.name)
else:
item = self._validate_fill_value(item)
except (TypeError, ValueError):
# e.g. trying to insert an integer into a DatetimeIndex
# We cannot keep the same dtype, so cast to the (often object)
# minimal shared dtype before doing the insert.
dtype = self._find_common_type_compat(item)
return self.astype(dtype).insert(loc, item)
if arr.dtype != object or not isinstance(
item, (tuple, np.datetime64, np.timedelta64)
):
# with object-dtype we need to worry about numpy incorrectly casting
# dt64/td64 to integer, also about treating tuples as sequences
# special-casing dt64/td64 https://github.com/numpy/numpy/issues/12550
casted = arr.dtype.type(item)
new_values = np.insert(arr, loc, casted)
else:
# No overload variant of "insert" matches argument types
# "ndarray[Any, Any]", "int", "None" [call-overload]
new_values = np.insert(arr, loc, None) # type: ignore[call-overload]
loc = loc if loc >= 0 else loc - 1
new_values[loc] = item
if self._typ == "numericindex":
# Use self._constructor instead of Index to retain NumericIndex GH#43921
# TODO(2.0) can use Index instead of self._constructor
return self._constructor._with_infer(new_values, name=self.name)
else:
return Index._with_infer(new_values, name=self.name)
def drop(self, labels, errors: str_t = "raise") -> Index:
"""
Make new Index with passed list of labels deleted.
Parameters
----------
labels : array-like or scalar
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
Returns
-------
dropped : Index
Will be same type as self, except for RangeIndex.
Raises
------
KeyError
If not all of the labels are found in the selected axis
"""
if not isinstance(labels, Index):
# avoid materializing e.g. RangeIndex
arr_dtype = "object" if self.dtype == "object" else None
labels = com.index_labels_to_array(labels, dtype=arr_dtype)
indexer = self.get_indexer_for(labels)
mask = indexer == -1
if mask.any():
if errors != "ignore":
raise KeyError(f"{list(labels[mask])} not found in axis")
indexer = indexer[~mask]
return self.delete(indexer)
# --------------------------------------------------------------------
# Generated Arithmetic, Comparison, and Unary Methods
def _cmp_method(self, other, op):
"""
Wrapper used to dispatch comparison operations.
"""
if self.is_(other):
# fastpath
if op in {operator.eq, operator.le, operator.ge}:
arr = np.ones(len(self), dtype=bool)
if self._can_hold_na and not isinstance(self, ABCMultiIndex):
# TODO: should set MultiIndex._can_hold_na = False?
arr[self.isna()] = False
return arr
elif op is operator.ne:
arr = np.zeros(len(self), dtype=bool)
if self._can_hold_na and not isinstance(self, ABCMultiIndex):
arr[self.isna()] = True
return arr
if isinstance(other, (np.ndarray, Index, ABCSeries, ExtensionArray)) and len(
self
) != len(other):
raise ValueError("Lengths must match to compare")
if not isinstance(other, ABCMultiIndex):
other = extract_array(other, extract_numpy=True)
else:
other = np.asarray(other)
if is_object_dtype(self.dtype) and isinstance(other, ExtensionArray):
# e.g. PeriodArray, Categorical
with np.errstate(all="ignore"):
result = op(self._values, other)
elif isinstance(self._values, ExtensionArray):
result = op(self._values, other)
elif is_object_dtype(self.dtype) and not isinstance(self, ABCMultiIndex):
# don't pass MultiIndex
with np.errstate(all="ignore"):
result = ops.comp_method_OBJECT_ARRAY(op, self._values, other)
else:
with np.errstate(all="ignore"):
result = ops.comparison_op(self._values, other, op)
return result
def _construct_result(self, result, name):
if isinstance(result, tuple):
return (
Index._with_infer(result[0], name=name),
Index._with_infer(result[1], name=name),
)
return Index._with_infer(result, name=name)
def _arith_method(self, other, op):
if (
isinstance(other, Index)
and is_object_dtype(other.dtype)
and type(other) is not Index
):
# We return NotImplemented for object-dtype index *subclasses* so they have
# a chance to implement ops before we unwrap them.
# See https://github.com/pandas-dev/pandas/issues/31109
return NotImplemented
return super()._arith_method(other, op)
@final
def _unary_method(self, op):
result = op(self._values)
return Index(result, name=self.name)
def __abs__(self):
return self._unary_method(operator.abs)
def __neg__(self):
return self._unary_method(operator.neg)
def __pos__(self):
return self._unary_method(operator.pos)
def __invert__(self):
# GH#8875
return self._unary_method(operator.inv)
# --------------------------------------------------------------------
# Reductions
def any(self, *args, **kwargs):
"""
Return whether any element is Truthy.
Parameters
----------
*args
Required for compatibility with numpy.
**kwargs
Required for compatibility with numpy.
Returns
-------
any : bool or array-like (if axis is specified)
A single element array-like may be converted to bool.
See Also
--------
Index.all : Return whether all elements are True.
Series.all : Return whether all elements are True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity
evaluate to True because these are not equal to zero.
Examples
--------
>>> index = pd.Index([0, 1, 2])
>>> index.any()
True
>>> index = pd.Index([0, 0, 0])
>>> index.any()
False
"""
nv.validate_any(args, kwargs)
self._maybe_disable_logical_methods("any")
# error: Argument 1 to "any" has incompatible type "ArrayLike"; expected
# "Union[Union[int, float, complex, str, bytes, generic], Sequence[Union[int,
# float, complex, str, bytes, generic]], Sequence[Sequence[Any]],
# _SupportsArray]"
return np.any(self.values) # type: ignore[arg-type]
def all(self, *args, **kwargs):
"""
Return whether all elements are Truthy.
Parameters
----------
*args
Required for compatibility with numpy.
**kwargs
Required for compatibility with numpy.
Returns
-------
all : bool or array-like (if axis is specified)
A single element array-like may be converted to bool.
See Also
--------
Index.any : Return whether any element in an Index is True.
Series.any : Return whether any element in a Series is True.
Series.all : Return whether all elements in a Series are True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity
evaluate to True because these are not equal to zero.
Examples
--------
True, because nonzero integers are considered True.
>>> pd.Index([1, 2, 3]).all()
True
False, because ``0`` is considered False.
>>> pd.Index([0, 1, 2]).all()
False
"""
nv.validate_all(args, kwargs)
self._maybe_disable_logical_methods("all")
# error: Argument 1 to "all" has incompatible type "ArrayLike"; expected
# "Union[Union[int, float, complex, str, bytes, generic], Sequence[Union[int,
# float, complex, str, bytes, generic]], Sequence[Sequence[Any]],
# _SupportsArray]"
return np.all(self.values) # type: ignore[arg-type]
@final
def _maybe_disable_logical_methods(self, opname: str_t) -> None:
"""
raise if this Index subclass does not support any or all.
"""
if (
isinstance(self, ABCMultiIndex)
or needs_i8_conversion(self.dtype)
or is_interval_dtype(self.dtype)
or is_categorical_dtype(self.dtype)
or is_float_dtype(self.dtype)
):
# This call will raise
make_invalid_op(opname)(self)
@Appender(IndexOpsMixin.argmin.__doc__)
def argmin(self, axis=None, skipna=True, *args, **kwargs):
nv.validate_argmin(args, kwargs)
nv.validate_minmax_axis(axis)
if not self._is_multi and self.hasnans:
# Take advantage of cache
mask = self._isnan
if not skipna or mask.all():
return -1
return super().argmin(skipna=skipna)
@Appender(IndexOpsMixin.argmax.__doc__)
def argmax(self, axis=None, skipna=True, *args, **kwargs):
nv.validate_argmax(args, kwargs)
nv.validate_minmax_axis(axis)
if not self._is_multi and self.hasnans:
# Take advantage of cache
mask = self._isnan
if not skipna or mask.all():
return -1
return super().argmax(skipna=skipna)
@doc(IndexOpsMixin.min)
def min(self, axis=None, skipna=True, *args, **kwargs):
nv.validate_min(args, kwargs)
nv.validate_minmax_axis(axis)
if not len(self):
return self._na_value
if len(self) and self.is_monotonic_increasing:
# quick check
first = self[0]
if not isna(first):
return first
if not self._is_multi and self.hasnans:
# Take advantage of cache
mask = self._isnan
if not skipna or mask.all():
return self._na_value
if not self._is_multi and not isinstance(self._values, np.ndarray):
# "ExtensionArray" has no attribute "min"
return self._values.min(skipna=skipna) # type: ignore[attr-defined]
return super().min(skipna=skipna)
@doc(IndexOpsMixin.max)
def max(self, axis=None, skipna=True, *args, **kwargs):
nv.validate_max(args, kwargs)
nv.validate_minmax_axis(axis)
if not len(self):
return self._na_value
if len(self) and self.is_monotonic_increasing:
# quick check
last = self[-1]
if not isna(last):
return last
if not self._is_multi and self.hasnans:
# Take advantage of cache
mask = self._isnan
if not skipna or mask.all():
return self._na_value
if not self._is_multi and not isinstance(self._values, np.ndarray):
# "ExtensionArray" has no attribute "max"
return self._values.max(skipna=skipna) # type: ignore[attr-defined]
return super().max(skipna=skipna)
# --------------------------------------------------------------------
@final
@property
def shape(self) -> Shape:
"""
Return a tuple of the shape of the underlying data.
"""
# See GH#27775, GH#27384 for history/reasoning in how this is defined.
return (len(self),)
@final
def _deprecated_arg(self, value, name: str_t, methodname: str_t) -> None:
"""
Issue a FutureWarning if the arg/kwarg is not no_default.
"""
if value is not no_default:
warnings.warn(
f"'{name}' argument in {methodname} is deprecated "
"and will be removed in a future version. Do not pass it.",
FutureWarning,
stacklevel=find_stack_level(),
)
def ensure_index_from_sequences(sequences, names=None) -> Index:
"""
Construct an index from sequences of data.
A single sequence returns an Index. Many sequences returns a
MultiIndex.
Parameters
----------
sequences : sequence of sequences
names : sequence of str
Returns
-------
index : Index or MultiIndex
Examples
--------
>>> ensure_index_from_sequences([[1, 2, 3]], names=["name"])
Int64Index([1, 2, 3], dtype='int64', name='name')
>>> ensure_index_from_sequences([["a", "a"], ["a", "b"]], names=["L1", "L2"])
MultiIndex([('a', 'a'),
('a', 'b')],
names=['L1', 'L2'])
See Also
--------
ensure_index
"""
from pandas.core.indexes.multi import MultiIndex
if len(sequences) == 1:
if names is not None:
names = names[0]
return Index(sequences[0], name=names)
else:
return MultiIndex.from_arrays(sequences, names=names)
def ensure_index(index_like: AnyArrayLike | Sequence, copy: bool = False) -> Index:
"""
Ensure that we have an index from some index-like object.
Parameters
----------
index_like : sequence
An Index or other sequence
copy : bool, default False
Returns
-------
index : Index or MultiIndex
See Also
--------
ensure_index_from_sequences
Examples
--------
>>> ensure_index(['a', 'b'])
Index(['a', 'b'], dtype='object')
>>> ensure_index([('a', 'a'), ('b', 'c')])
Index([('a', 'a'), ('b', 'c')], dtype='object')
>>> ensure_index([['a', 'a'], ['b', 'c']])
MultiIndex([('a', 'b'),
('a', 'c')],
)
"""
if isinstance(index_like, Index):
if copy:
index_like = index_like.copy()
return index_like
if isinstance(index_like, ABCSeries):
name = index_like.name
return Index._with_infer(index_like, name=name, copy=copy)
if is_iterator(index_like):
index_like = list(index_like)
if isinstance(index_like, list):
if type(index_like) is not list:
# must check for exactly list here because of strict type
# check in clean_index_list
index_like = list(index_like)
if len(index_like) and lib.is_all_arraylike(index_like):
from pandas.core.indexes.multi import MultiIndex
return MultiIndex.from_arrays(index_like)
else:
return Index._with_infer(index_like, copy=copy, tupleize_cols=False)
else:
return Index._with_infer(index_like, copy=copy)
def ensure_has_len(seq):
"""
If seq is an iterator, put its values into a list.
"""
try:
len(seq)
except TypeError:
return list(seq)
else:
return seq
def trim_front(strings: list[str]) -> list[str]:
"""
Trims zeros and decimal points.
Examples
--------
>>> trim_front([" a", " b"])
['a', 'b']
>>> trim_front([" a", " "])
['a', '']
"""
if not strings:
return strings
while all(strings) and all(x[0] == " " for x in strings):
strings = [x[1:] for x in strings]
return strings
def _validate_join_method(method: str) -> None:
if method not in ["left", "right", "inner", "outer"]:
raise ValueError(f"do not recognize join method {method}")
def maybe_extract_name(name, obj, cls) -> Hashable:
"""
If no name is passed, then extract it from data, validating hashability.
"""
if name is None and isinstance(obj, (Index, ABCSeries)):
# Note we don't just check for "name" attribute since that would
# pick up e.g. dtype.name
name = obj.name
# GH#29069
if not is_hashable(name):
raise TypeError(f"{cls.__name__}.name must be a hashable type")
return name
_cast_depr_msg = (
"In a future version, passing an object-dtype arraylike to pd.Index will "
"not infer numeric values to numeric dtype (matching the Series behavior). "
"To retain the old behavior, explicitly pass the desired dtype or use the "
"desired Index subclass"
)
def _maybe_cast_data_without_dtype(
subarr: np.ndarray, cast_numeric_deprecated: bool = True
) -> ArrayLike:
"""
If we have an arraylike input but no passed dtype, try to infer
a supported dtype.
Parameters
----------
subarr : np.ndarray[object]
cast_numeric_deprecated : bool, default True
Whether to issue a FutureWarning when inferring numeric dtypes.
Returns
-------
np.ndarray or ExtensionArray
"""
result = lib.maybe_convert_objects(
subarr,
convert_datetime=True,
convert_timedelta=True,
convert_period=True,
convert_interval=True,
dtype_if_all_nat=np.dtype("datetime64[ns]"),
)
if result.dtype.kind in ["i", "u", "f"]:
if not cast_numeric_deprecated:
# i.e. we started with a list, not an ndarray[object]
return result
warnings.warn(
"In a future version, the Index constructor will not infer numeric "
"dtypes when passed object-dtype sequences (matching Series behavior)",
FutureWarning,
stacklevel=3,
)
if result.dtype.kind in ["b", "c"]:
return subarr
result = ensure_wrapped_if_datetimelike(result)
return result
def get_unanimous_names(*indexes: Index) -> tuple[Hashable, ...]:
"""
Return common name if all indices agree, otherwise None (level-by-level).
Parameters
----------
indexes : list of Index objects
Returns
-------
list
A list representing the unanimous 'names' found.
"""
name_tups = [tuple(i.names) for i in indexes]
name_sets = [{*ns} for ns in zip_longest(*name_tups)]
names = tuple(ns.pop() if len(ns) == 1 else None for ns in name_sets)
return names
def unpack_nested_dtype(other: _IndexT) -> _IndexT:
"""
When checking if our dtype is comparable with another, we need
to unpack CategoricalDtype to look at its categories.dtype.
Parameters
----------
other : Index
Returns
-------
Index
"""
dtype = other.dtype
if is_categorical_dtype(dtype):
# If there is ever a SparseIndex, this could get dispatched
# here too.
# error: Item "dtype[Any]"/"ExtensionDtype" of "Union[dtype[Any],
# ExtensionDtype]" has no attribute "categories"
return dtype.categories # type: ignore[union-attr]
return other
def _maybe_try_sort(result, sort):
if sort is None:
try:
result = algos.safe_sort(result)
except TypeError as err:
warnings.warn(
f"{err}, sort order is undefined for incomparable objects.",
RuntimeWarning,
stacklevel=find_stack_level(),
)
return result
Reference in New Issue View Git Blame Copy Permalink