| Current File : //usr/local/lib64/python3.6/site-packages/pandas/core/indexes/base.py |
from copy import copy as copy_func
from datetime import datetime
import operator
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Any,
Callable,
FrozenSet,
Hashable,
List,
Optional,
Union,
)
import warnings
import numpy as np
from pandas._libs import 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.tslibs import OutOfBoundsDatetime, Timestamp
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas._libs.tslibs.timezones import tz_compare
from pandas._typing import DtypeObj, Label
from pandas.compat import set_function_name
from pandas.compat.numpy import function as nv
from pandas.errors import InvalidIndexError
from pandas.util._decorators import Appender, Substitution, cache_readonly, doc
from pandas.core.dtypes import concat as _concat
from pandas.core.dtypes.cast import (
maybe_cast_to_integer_array,
validate_numeric_casting,
)
from pandas.core.dtypes.common import (
ensure_int64,
ensure_object,
ensure_platform_int,
is_bool,
is_bool_dtype,
is_categorical_dtype,
is_datetime64_any_dtype,
is_dtype_equal,
is_extension_array_dtype,
is_float,
is_float_dtype,
is_hashable,
is_integer,
is_integer_dtype,
is_interval_dtype,
is_iterator,
is_list_like,
is_object_dtype,
is_period_dtype,
is_scalar,
is_signed_integer_dtype,
is_timedelta64_dtype,
is_unsigned_integer_dtype,
pandas_dtype,
)
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.generic import (
ABCCategorical,
ABCDataFrame,
ABCDatetimeIndex,
ABCMultiIndex,
ABCPandasArray,
ABCPeriodIndex,
ABCRangeIndex,
ABCSeries,
ABCTimedeltaIndex,
)
from pandas.core.dtypes.missing import array_equivalent, isna
from pandas.core import ops
from pandas.core.accessor import CachedAccessor
import pandas.core.algorithms as algos
from pandas.core.arrays import Categorical, ExtensionArray
from pandas.core.arrays.datetimes import tz_to_dtype, validate_tz_from_dtype
from pandas.core.base import IndexOpsMixin, PandasObject
import pandas.core.common as com
from pandas.core.indexers import deprecate_ndim_indexing
from pandas.core.indexes.frozen import FrozenList
import pandas.core.missing as missing
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
from pandas.core.strings import StringMethods
from pandas.io.formats.printing import (
PrettyDict,
default_pprint,
format_object_attrs,
format_object_summary,
pprint_thing,
)
if TYPE_CHECKING:
from pandas import Series
__all__ = ["Index"]
_unsortable_types = frozenset(("mixed", "mixed-integer"))
_index_doc_kwargs = dict(
klass="Index",
inplace="",
target_klass="Index",
raises_section="",
unique="Index",
duplicated="np.ndarray",
)
_index_shared_docs = dict()
str_t = str
def _make_comparison_op(op, cls):
def cmp_method(self, other):
if isinstance(other, (np.ndarray, Index, ABCSeries, ExtensionArray)):
if other.ndim > 0 and len(self) != len(other):
raise ValueError("Lengths must match to compare")
if is_object_dtype(self.dtype) and isinstance(other, ABCCategorical):
left = type(other)(self._values, dtype=other.dtype)
return op(left, other)
elif is_object_dtype(self.dtype) and isinstance(other, ExtensionArray):
# e.g. PeriodArray
with np.errstate(all="ignore"):
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)
elif is_interval_dtype(self.dtype):
with np.errstate(all="ignore"):
result = op(self._values, np.asarray(other))
else:
with np.errstate(all="ignore"):
result = ops.comparison_op(self._values, np.asarray(other), op)
if is_bool_dtype(result):
return result
return ops.invalid_comparison(self, other, op)
name = f"__{op.__name__}__"
return set_function_name(cmp_method, name, cls)
def _make_arithmetic_op(op, cls):
def index_arithmetic_method(self, other):
if isinstance(other, (ABCSeries, ABCDataFrame, ABCTimedeltaIndex)):
return NotImplemented
from pandas import Series
result = op(Series(self), other)
if isinstance(result, tuple):
return (Index(result[0]), Index(result[1]))
return Index(result)
name = f"__{op.__name__}__"
return set_function_name(index_arithmetic_method, name, cls)
_o_dtype = np.dtype(object)
_Identity = object
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")
return cls.__new__(cls, **d)
class Index(IndexOpsMixin, PandasObject):
"""
Immutable ndarray implementing an ordered, sliceable set. 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.
Int64Index : A special case of :class:`Index` with purely integer labels.
UInt64Index : A special case of :class:`Index` with purely unsigned integer labels.
Float64Index : A special case of :class:`Index` with purely float labels.
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__
_deprecations: FrozenSet[str] = (
PandasObject._deprecations
| IndexOpsMixin._deprecations
| 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
def _left_indexer_unique(self, left, right):
return libjoin.left_join_indexer_unique(left, right)
def _left_indexer(self, left, right):
return libjoin.left_join_indexer(left, right)
def _inner_indexer(self, left, right):
return libjoin.inner_join_indexer(left, right)
def _outer_indexer(self, left, right):
return libjoin.outer_join_indexer(left, right)
_typ = "index"
_data: Union[ExtensionArray, np.ndarray]
_id = None
_name: Label = 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 = ["name"]
_attributes = ["name"]
_is_numeric_dtype = False
_can_hold_na = True
# would we like our indexing holder to defer to us
_defer_to_indexing = False
_engine_type = 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":
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 isinstance(data, ABCPandasArray):
# ensure users don't accidentally put a PandasArray in an index.
data = data.to_numpy()
data_dtype = getattr(data, "dtype", None)
# range
if isinstance(data, RangeIndex):
return RangeIndex(start=data, copy=copy, dtype=dtype, name=name)
elif isinstance(data, range):
return RangeIndex.from_range(data, dtype=dtype, name=name)
# categorical
elif is_categorical_dtype(data_dtype) or is_categorical_dtype(dtype):
# Delay import for perf. https://github.com/pandas-dev/pandas/pull/31423
from pandas.core.indexes.category import CategoricalIndex
return _maybe_asobject(dtype, CategoricalIndex, data, copy, name, **kwargs)
# interval
elif is_interval_dtype(data_dtype) or is_interval_dtype(dtype):
# Delay import for perf. https://github.com/pandas-dev/pandas/pull/31423
from pandas.core.indexes.interval import IntervalIndex
return _maybe_asobject(dtype, IntervalIndex, data, copy, name, **kwargs)
elif is_datetime64_any_dtype(data_dtype) or is_datetime64_any_dtype(dtype):
# Delay import for perf. https://github.com/pandas-dev/pandas/pull/31423
from pandas import DatetimeIndex
return _maybe_asobject(dtype, DatetimeIndex, data, copy, name, **kwargs)
elif is_timedelta64_dtype(data_dtype) or is_timedelta64_dtype(dtype):
# Delay import for perf. https://github.com/pandas-dev/pandas/pull/31423
from pandas import TimedeltaIndex
return _maybe_asobject(dtype, TimedeltaIndex, data, copy, name, **kwargs)
elif is_period_dtype(data_dtype) or is_period_dtype(dtype):
# Delay import for perf. https://github.com/pandas-dev/pandas/pull/31423
from pandas import PeriodIndex
return _maybe_asobject(dtype, PeriodIndex, data, copy, name, **kwargs)
# extension dtype
elif is_extension_array_dtype(data_dtype) or is_extension_array_dtype(dtype):
if not (dtype is None or is_object_dtype(dtype)):
# coerce to the provided dtype
ea_cls = dtype.construct_array_type()
data = ea_cls._from_sequence(data, dtype=dtype, copy=False)
else:
data = np.asarray(data, dtype=object)
# coerce to the object dtype
data = data.astype(object)
return Index(data, dtype=object, copy=copy, name=name, **kwargs)
# index-like
elif isinstance(data, (np.ndarray, Index, ABCSeries)):
# Delay import for perf. https://github.com/pandas-dev/pandas/pull/31423
from pandas.core.indexes.numeric import (
Float64Index,
Int64Index,
UInt64Index,
)
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 = _maybe_cast_with_dtype(data, dtype, copy)
dtype = data.dtype # TODO: maybe not for object?
# maybe coerce to a sub-class
if is_signed_integer_dtype(data.dtype):
return Int64Index(data, copy=copy, dtype=dtype, name=name)
elif is_unsigned_integer_dtype(data.dtype):
return UInt64Index(data, copy=copy, dtype=dtype, name=name)
elif is_float_dtype(data.dtype):
return Float64Index(data, copy=copy, dtype=dtype, name=name)
elif issubclass(data.dtype.type, bool) or is_bool_dtype(data):
subarr = data.astype("object")
else:
subarr = com.asarray_tuplesafe(data, dtype=object)
# asarray_tuplesafe does not always copy underlying data,
# so need to make sure that this happens
if copy:
subarr = subarr.copy()
if dtype is None:
new_data, new_dtype = _maybe_cast_data_without_dtype(subarr)
if new_dtype is not None:
return cls(
new_data, dtype=new_dtype, copy=False, name=name, **kwargs
)
if kwargs:
raise TypeError(f"Unexpected keyword arguments {repr(set(kwargs))}")
if subarr.ndim > 1:
# GH#13601, GH#20285, GH#27125
raise ValueError("Index data must be 1-dimensional")
return cls._simple_new(subarr, name)
elif data is None or 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=object)
return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs)
"""
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.
"""
return None
@classmethod
def _simple_new(cls, values, name: Label = None):
"""
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, np.ndarray), type(values)
result = object.__new__(cls)
result._data = values
# _index_data is a (temporary?) fix to ensure that the direct data
# manipulation we do in `_libs/reduction.pyx` continues to work.
# We need access to the actual ndarray, since we're messing with
# data buffers and strides.
result._index_data = values
result._name = name
result._cache = {}
return result._reset_identity()
@cache_readonly
def _constructor(self):
return type(self)
# --------------------------------------------------------------------
# Index Internals Methods
def _get_attributes_dict(self):
"""
Return an attributes dict for my class.
"""
return {k: getattr(self, k, None) for k in self._attributes}
def _shallow_copy(self, values=None, name: Label = no_default):
"""
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
cache = self._cache.copy() if values is None else {}
if values is None:
values = self._values
result = self._simple_new(values, name=name)
result._cache = cache
return result
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.
"""
# use something other than None to be clearer
return self._id is getattr(other, "_id", Ellipsis) and self._id is not None
def _reset_identity(self):
"""
Initializes or resets ``_id`` attribute with new object.
"""
self._id = _Identity()
return self
def _cleanup(self):
self._engine.clear_mapping()
@cache_readonly
def _engine(self):
# property, for now, slow to look up
# 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(lambda: target_values, len(self))
# --------------------------------------------------------------------
# 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_wrap__(self, result, context=None):
"""
Gets called after a ufunc.
"""
result = lib.item_from_zerodim(result)
if is_bool_dtype(result) or lib.is_scalar(result) or np.ndim(result) > 1:
return result
attrs = self._get_attributes_dict()
return Index(result, **attrs)
@cache_readonly
def dtype(self):
"""
Return the dtype object of the underlying data.
"""
return self._data.dtype
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
"""
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"):
result = self._data.view(cls)
else:
result = self._shallow_copy()
if isinstance(result, Index):
result._id = self._id
return result
def astype(self, dtype, copy=True):
"""
Create an Index with values cast to dtypes.
The class of a new Index is determined by dtype. When conversion is
impossible, a ValueError 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):
return self.copy() if copy else self
elif is_categorical_dtype(dtype):
from pandas.core.indexes.category import CategoricalIndex
return CategoricalIndex(
self._values, name=self.name, dtype=dtype, copy=copy
)
elif is_extension_array_dtype(dtype):
return Index(np.asarray(self), name=self.name, dtype=dtype, copy=copy)
try:
casted = self._values.astype(dtype, copy=copy)
except (TypeError, ValueError) as err:
raise TypeError(
f"Cannot cast {type(self).__name__} to dtype {dtype}"
) from err
return Index(casted, name=self.name, dtype=dtype)
_index_shared_docs[
"take"
] = """
Return a new %(klass)s of the values selected by the indices.
For internal compatibility with numpy arrays.
Parameters
----------
indices : list
Indices to be taken.
axis : int, optional
The axis over which to select values, always 0.
allow_fill : bool, default True
fill_value : bool, default None
If allow_fill=True and fill_value is not None, indices specified by
-1 is regarded as NA. If Index doesn't hold NA, raise ValueError.
Returns
-------
numpy.ndarray
Elements of given indices.
See Also
--------
numpy.ndarray.take
"""
@Appender(_index_shared_docs["take"] % _index_doc_kwargs)
def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs):
if kwargs:
nv.validate_take(tuple(), kwargs)
indices = ensure_platform_int(indices)
if self._can_hold_na:
taken = self._assert_take_fillable(
self._values,
indices,
allow_fill=allow_fill,
fill_value=fill_value,
na_value=self._na_value,
)
else:
if allow_fill and fill_value is not None:
cls_name = type(self).__name__
raise ValueError(
f"Unable to fill values because {cls_name} cannot contain NA"
)
taken = self._values.take(indices)
return self._shallow_copy(taken)
def _assert_take_fillable(
self, values, indices, allow_fill=True, fill_value=None, na_value=np.nan
):
"""
Internal method to handle NA filling of take.
"""
indices = ensure_platform_int(indices)
# only fill if we are passing a non-None fill_value
if allow_fill and fill_value is not None:
if (indices < -1).any():
raise ValueError(
"When allow_fill=True and fill_value is not None, "
"all indices must be >= -1"
)
taken = algos.take(
values, indices, allow_fill=allow_fill, fill_value=na_value
)
else:
taken = values.take(indices)
return taken
_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(tuple(), dict(axis=axis))
return self._shallow_copy(self._values.repeat(repeats))
# --------------------------------------------------------------------
# Copying Methods
def copy(self, name=None, deep=False, dtype=None, names=None):
"""
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.
names : list-like, optional
Kept for compatibility with MultiIndex. Should not be used.
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 deep:
new_index = self._shallow_copy(self._data.copy())
else:
new_index = self._shallow_copy()
names = self._validate_names(name=name, names=names, deep=deep)
new_index = new_index.set_names(names)
if dtype:
new_index = new_index.astype(dtype)
return new_index
def __copy__(self, **kwargs):
return self.copy(**kwargs)
def __deepcopy__(self, memo=None):
"""
Parameters
----------
memo, default None
Standard signature. Unused
"""
return self.copy(deep=True)
# --------------------------------------------------------------------
# Rendering Methods
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 = ""
res = f"{klass_name}({data}{prepr})"
return res
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":
if is_object_dtype(self.categories): # type: ignore
is_justify = False
return format_object_summary(
self, self._formatter_func, is_justify=is_justify, name=name
)
def _format_attrs(self):
"""
Return a list of tuples of the (attr,formatted_value).
"""
return format_object_attrs(self)
def _mpl_repr(self):
# how to represent ourselves to matplotlib
return self.values
def format(
self,
name: bool = False,
formatter: Optional[Callable] = 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 = "NaN"
) -> List[str_t]:
from pandas.io.formats.format import format_array
values = self._values
if is_object_dtype(values.dtype):
values = lib.maybe_convert_objects(values, safe=1)
if is_object_dtype(values.dtype):
result = [pprint_thing(x, escape_chars=("\t", "\r", "\n")) for x in values]
# could have nans
mask = isna(values)
if mask.any():
result = np.array(result)
result[mask] = na_rep
result = result.tolist() # type: ignore
else:
result = trim_front(format_array(values, None, justify="left"))
return header + result
def to_native_types(self, slicer=None, **kwargs):
"""
Format specified values of `self` and return them.
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.
"""
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()
tail = self[-1]
if hasattr(tail, "format") and not isinstance(tail, str):
tail = tail.format()
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.
.. versionadded:: 0.24.0
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=None):
"""
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._shallow_copy()
if name is None:
name = self.name
return Series(self.values.copy(), index=index, name=name)
def to_frame(self, index: bool = True, name=None):
"""
Create a DataFrame with a column containing the Index.
.. versionadded:: 0.24.0
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:
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):
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
def _validate_names(self, name=None, names=None, deep: bool = False):
"""
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:
return 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`.")
return names
else:
if not is_list_like(name):
return [name]
return name
def _get_names(self):
return FrozenList((self.name,))
def _set_names(self, values, level=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:
for name in values:
if not is_hashable(name):
raise TypeError(f"{type(self).__name__}.name must be a hashable type")
self._name = values[0]
names = property(fset=_set_names, fget=_get_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
Name(s) to set.
level : int, label or list of int or label, optional
If the index is a MultiIndex, level(s) to set (None for all
levels). Otherwise level must be None.
inplace : bool, default False
Modifies the object directly, instead of creating a new Index or
MultiIndex.
Returns
-------
Index
The same type as the caller or None if inplace is 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'])
"""
if level is not None and not isinstance(self, ABCMultiIndex):
raise ValueError("Level must be None for non-MultiIndex")
if 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.")
if not is_list_like(names) and level is None and self.nlevels > 1:
raise TypeError("Must pass list-like as `names`.")
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._shallow_copy()
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
The same type as the caller or None if inplace is 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):
"""
Compat with MultiIndex.
"""
return self
def _validate_index_level(self, level):
"""
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 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
"""
return self.sort_values(return_indexer=True, ascending=ascending)
def _get_level_values(self, level):
"""
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
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.
.. versionadded:: 0.23.1 (support for non-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
"""
if not isinstance(level, (tuple, list)):
level = [level]
levnums = sorted(self._get_level_number(lev) for lev in level)[::-1]
if len(level) == 0:
return self
if len(level) >= self.nlevels:
raise ValueError(
f"Cannot remove {len(level)} levels from an index with {self.nlevels} "
"levels: at least one level must be left."
)
# The two checks above guarantee that here self is a MultiIndex
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:
# 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
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
@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
@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
@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
@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
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"]
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"]
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"]
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"]
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)
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"]
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"]
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=2,
)
return self.inferred_type in ["mixed"]
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))
# --------------------------------------------------------------------
# Pickle Methods
def __reduce__(self):
d = dict(data=self._data)
d.update(self._get_attributes_dict())
return _new_Index, (type(self), d), None
# --------------------------------------------------------------------
# Null Handling Methods
_na_value = np.nan
"""The expected NA value to use with this index."""
@cache_readonly
def _isnan(self):
"""
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 _nan_idxs(self):
if self._can_hold_na:
return self._isnan.nonzero()[0]
else:
return np.array([], dtype=np.int64)
@cache_readonly
def hasnans(self) -> bool:
"""
Return if I have any nans; enables various perf speedups.
"""
if self._can_hold_na:
return bool(self._isnan.any())
else:
return False
def isna(self):
"""
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
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
def notna(self):
"""
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
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.
"""
self._assert_can_do_op(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
return Index(result, name=self.name)
return self._shallow_copy()
def dropna(self, how="any"):
"""
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:
return self._shallow_copy(self._values[~self._isnan])
return self._shallow_copy()
# --------------------------------------------------------------------
# Uniqueness Methods
def unique(self, level=None):
"""
Return unique values in the index.
Unique values are returned in order of appearance, this does NOT sort.
Parameters
----------
level : int or str, optional, default None
Only return values from specified level (for MultiIndex).
.. versionadded:: 0.23.0
Returns
-------
Index without duplicates
See Also
--------
unique
Series.unique
"""
if level is not None:
self._validate_index_level(level)
result = super().unique()
return self._shallow_copy(result)
def drop_duplicates(self, keep="first"):
"""
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')
"""
return super().drop_duplicates(keep=keep)
def duplicated(self, keep="first"):
"""
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
-------
numpy.ndarray
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])
"""
return super().duplicated(keep=keep)
def _get_unique_index(self, dropna: bool = False):
"""
Returns an index containing unique values.
Parameters
----------
dropna : bool, default False
If True, NaN values are dropped.
Returns
-------
uniques : index
"""
if self.is_unique and not dropna:
return self
if not self.is_unique:
values = self.unique()
if not isinstance(self, ABCMultiIndex):
# extract an array to pass to _shallow_copy
values = values._data
else:
values = self._values
if dropna:
try:
if self.hasnans:
values = values[~isna(values)]
except NotImplementedError:
pass
return self._shallow_copy(values)
# --------------------------------------------------------------------
# Arithmetic & Logical Methods
def __add__(self, other):
if isinstance(other, (ABCSeries, ABCDataFrame)):
return NotImplemented
from pandas import Series
return Index(Series(self) + other)
def __radd__(self, other):
from pandas import Series
return Index(other + Series(self))
def __iadd__(self, other):
# alias for __add__
return self + other
def __sub__(self, other):
return Index(np.array(self) - other)
def __rsub__(self, other):
# wrap Series to ensure we pin name correctly
from pandas import Series
return Index(other - Series(self))
def __and__(self, other):
return self.intersection(other)
def __or__(self, other):
return self.union(other)
def __xor__(self, other):
return self.symmetric_difference(other)
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 != name:
return self._shallow_copy(name=name)
return self
def _union_incompatible_dtypes(self, other, sort):
"""
Casts this and other index to object dtype to allow the formation
of a union between incompatible types.
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
"""
this = self.astype(object, copy=False)
# cast to Index for when `other` is list-like
other = Index(other).astype(object, copy=False)
return Index.union(this, other, sort=sort).astype(object, copy=False)
def _is_compatible_with_other(self, other) -> bool:
"""
Check whether this and the other dtype are compatible with each other.
Meaning a union can be formed between them without needing to be cast
to dtype object.
Parameters
----------
other : Index or array-like
Returns
-------
bool
"""
return type(self) is type(other) and is_dtype_equal(self.dtype, other.dtype)
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."
)
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.
.. versionadded:: 0.24.0
.. versionchanged:: 0.24.1
Changed the default value from ``True`` to ``None``
(without change in behaviour).
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')
"""
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
if not self._is_compatible_with_other(other):
return self._union_incompatible_dtypes(other, sort=sort)
return self._union(other, sort=sort)
def _union(self, other, 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
"""
if not len(other) or self.equals(other):
return self._get_reconciled_name_object(other)
if not len(self):
return other._get_reconciled_name_object(self)
# TODO(EA): setops-refactor, clean all this up
lvals = self._values
rvals = other._values
if sort is None and self.is_monotonic and other.is_monotonic:
try:
result = self._outer_indexer(lvals, rvals)[0]
except TypeError:
# incomparable objects
result = list(lvals)
# worth making this faster? a very unusual case
value_set = set(lvals)
result.extend([x for x in rvals if x not in value_set])
result = Index(result)._values # do type inference here
else:
# find indexes of things in "other" that are not in "self"
if self.is_unique:
indexer = self.get_indexer(other)
indexer = (indexer == -1).nonzero()[0]
else:
indexer = algos.unique1d(self.get_indexer_non_unique(other)[1])
if len(indexer) > 0:
other_diff = algos.take_nd(rvals, indexer, allow_fill=False)
result = concat_compat((lvals, other_diff))
else:
result = lvals
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=3,
)
# for subclasses
return self._wrap_setop_result(other, result)
def _wrap_setop_result(self, other, result):
name = get_op_result_name(self, other)
return self._shallow_copy(result, name=name)
# TODO: standardize return type of non-union setops type(self vs other)
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.
.. versionadded:: 0.24.0
.. versionchanged:: 0.24.1
Changed the default from ``True`` to ``False``, to match
the behaviour of 0.23.4 and earlier.
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 = ensure_index(other)
if self.equals(other) and not self.has_duplicates:
return self._get_reconciled_name_object(other)
if not is_dtype_equal(self.dtype, other.dtype):
this = self.astype("O")
other = other.astype("O")
return this.intersection(other, sort=sort)
# TODO(EA): setops-refactor, clean all this up
lvals = self._values
rvals = other._values
if self.is_monotonic and other.is_monotonic:
try:
result = self._inner_indexer(lvals, rvals)[0]
except TypeError:
pass
else:
return self._wrap_setop_result(other, algos.unique1d(result))
try:
indexer = Index(rvals).get_indexer(lvals)
indexer = indexer.take((indexer != -1).nonzero()[0])
except (InvalidIndexError, IncompatibleFrequency):
# InvalidIndexError raised by get_indexer if non-unique
# IncompatibleFrequency raised by PeriodIndex.get_indexer
indexer = algos.unique1d(Index(rvals).get_indexer_non_unique(lvals)[0])
indexer = indexer[indexer != -1]
taken = other.take(indexer).unique()
res_name = get_op_result_name(self, other)
if sort is None:
taken = algos.safe_sort(taken.values)
return self._shallow_copy(taken, name=res_name)
# Intersection has to be unique
assert algos.unique(taken._values).shape == taken._values.shape
taken.name = res_name
return taken
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.
.. versionadded:: 0.24.0
.. versionchanged:: 0.24.1
Changed the default value from ``True`` to ``None``
(without change in behaviour).
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)
if self.equals(other):
# pass an empty np.ndarray with the appropriate dtype
return self._shallow_copy(self._data[:0])
other, result_name = self._convert_can_do_setop(other)
this = self._get_unique_index()
indexer = this.get_indexer(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)
if sort is None:
try:
the_diff = algos.safe_sort(the_diff)
except TypeError:
pass
return this._shallow_copy(the_diff, name=result_name)
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.
.. versionadded:: 0.24.0
.. versionchanged:: 0.24.1
Changed the default value from ``True`` to ``None``
(without change in behaviour).
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')
You can also use the ``^`` operator:
>>> idx1 ^ 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
this = self._get_unique_index()
other = other._get_unique_index()
indexer = this.get_indexer(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)
the_diff = concat_compat([left_diff, right_diff])
if sort is None:
try:
the_diff = algos.safe_sort(the_diff)
except TypeError:
pass
return Index(the_diff, dtype=self.dtype, name=result_name)
def _assert_can_do_setop(self, other):
if not is_list_like(other):
raise TypeError("Input must be Index or array-like")
return True
def _convert_can_do_setop(self, other):
if not isinstance(other, Index):
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 most 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
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 most
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 : ndarray of int
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
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)
def get_indexer(
self, target, method=None, limit=None, tolerance=None
) -> np.ndarray:
method = missing.clean_reindex_fill_method(method)
target = ensure_index(target)
if tolerance is not None:
tolerance = self._convert_tolerance(tolerance, target)
# 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.
# (GH #16877)
if target.is_boolean() and self.is_numeric():
return ensure_platform_int(np.repeat(-1, target.size))
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 not is_dtype_equal(self.dtype, target.dtype):
this = self.astype(object)
target = target.astype(object)
return this.get_indexer(
target, method=method, limit=limit, tolerance=tolerance
)
if not self.is_unique:
raise InvalidIndexError(
"Reindexing only valid with uniquely valued Index objects"
)
if method == "pad" or method == "backfill":
indexer = self._get_fill_indexer(target, method, limit, tolerance)
elif method == "nearest":
indexer = self._get_nearest_indexer(target, limit, tolerance)
else:
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"
)
indexer = self._engine.get_indexer(target._get_engine_target())
return ensure_platform_int(indexer)
def _convert_tolerance(self, tolerance, target):
# 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
def _get_fill_indexer(
self, target: "Index", method: str_t, limit=None, tolerance=None
) -> np.ndarray:
target_values = target._get_engine_target()
if self.is_monotonic_increasing and target.is_monotonic_increasing:
engine_method = (
self._engine.get_pad_indexer
if method == "pad"
else self._engine.get_backfill_indexer
)
indexer = engine_method(target_values, limit)
else:
indexer = self._get_fill_indexer_searchsorted(target, method, limit)
if tolerance is not None:
indexer = self._filter_indexer_tolerance(target_values, indexer, tolerance)
return indexer
def _get_fill_indexer_searchsorted(
self, target: "Index", method: str_t, limit=None
) -> np.ndarray:
"""
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 = "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
def _get_nearest_indexer(self, target: "Index", limit, tolerance) -> np.ndarray:
"""
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).
"""
left_indexer = self.get_indexer(target, "pad", limit=limit)
right_indexer = self.get_indexer(target, "backfill", limit=limit)
target_values = target._values
left_distances = np.abs(self._values[left_indexer] - target_values)
right_distances = np.abs(self._values[right_indexer] - target_values)
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_values, indexer, tolerance)
return indexer
def _filter_indexer_tolerance(
self,
target: Union["Index", np.ndarray, ExtensionArray],
indexer: np.ndarray,
tolerance,
) -> np.ndarray:
distance = abs(self._values[indexer] - target)
indexer = np.where(distance <= tolerance, indexer, -1)
return indexer
# --------------------------------------------------------------------
# Indexer Conversion Methods
def _get_partial_string_timestamp_match_key(self, key):
"""
Translate any partial string timestamp matches in key, returning the
new key.
Only relevant for MultiIndex.
"""
# GH#10331
return key
def _validate_positional_slice(self, key: slice):
"""
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=6,
)
indexer = key
else:
indexer = self.slice_indexer(start, stop, step, kind=kind)
return indexer
def _convert_listlike_indexer(self, keyarr):
"""
Parameters
----------
keyarr : list-like
Indexer to convert.
Returns
-------
indexer : numpy.ndarray or None
Return an ndarray or None if cannot convert.
keyarr : numpy.ndarray
Return tuple-safe keys.
"""
if isinstance(keyarr, Index):
keyarr = self._convert_index_indexer(keyarr)
else:
keyarr = self._convert_arr_indexer(keyarr)
indexer = self._convert_list_indexer(keyarr)
return indexer, keyarr
def _convert_arr_indexer(self, keyarr):
"""
Convert an array-like indexer to the appropriate dtype.
Parameters
----------
keyarr : array-like
Indexer to convert.
Returns
-------
converted_keyarr : array-like
"""
keyarr = com.asarray_tuplesafe(keyarr)
return keyarr
def _convert_index_indexer(self, keyarr):
"""
Convert an Index indexer to the appropriate dtype.
Parameters
----------
keyarr : Index (or sub-class)
Indexer to convert.
Returns
-------
converted_keyarr : Index (or sub-class)
"""
return keyarr
def _convert_list_indexer(self, keyarr):
"""
Convert a list-like indexer to the appropriate dtype.
Parameters
----------
keyarr : Index (or sub-class)
Indexer to convert.
kind : iloc, loc, optional
Returns
-------
positional indexer or None
"""
return None
def _invalid_indexer(self, form: str_t, key):
"""
Consistent invalid indexer message.
"""
raise TypeError(
f"cannot do {form} indexing on {type(self).__name__} with these "
f"indexers [{key}] of type {type(key).__name__}"
)
# --------------------------------------------------------------------
# Reindex Methods
def _can_reindex(self, indexer):
"""
Check if we are allowing reindexing with this particular indexer.
Parameters
----------
indexer : an integer indexer
Raises
------
ValueError if its a duplicate axis
"""
# trying to reindex on an axis with duplicates
if not self.is_unique and len(indexer):
raise ValueError("cannot reindex from a duplicate axis")
def reindex(self, target, method=None, level=None, limit=None, tolerance=None):
"""
Create index with target's values.
Parameters
----------
target : an iterable
Returns
-------
new_index : pd.Index
Resulting index.
indexer : np.ndarray or None
Indices of output values in original index.
"""
# 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 isinstance(self, ABCRangeIndex):
values = range(0)
else:
values = self._data[:0] # appropriately-dtyped empty array
target = self._simple_new(values, name=self.name)
else:
target = ensure_index(target)
if level is not None:
if method is not None:
raise TypeError("Fill method not supported if level passed")
_, indexer, _ = self._join_level(
target, level, how="right", return_indexers=True
)
else:
if self.equals(target):
indexer = None
else:
# check is_overlapping for IntervalIndex compat
if self.is_unique and not getattr(self, "is_overlapping", False):
indexer = self.get_indexer(
target, method=method, limit=limit, tolerance=tolerance
)
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, missing = self.get_indexer_non_unique(target)
if preserve_names and target.nlevels == 1 and target.name != self.name:
target = target.copy()
target.name = self.name
return target, indexer
def _reindex_non_unique(self, target):
"""
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 or None
Indices of output values in original index.
"""
target = ensure_index(target)
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))
missing = ensure_platform_int(missing)
missing_labels = target.take(missing)
missing_indexer = ensure_int64(length[~check])
cur_labels = self.take(indexer[check]).values
cur_indexer = ensure_int64(length[check])
new_labels = np.empty(tuple([len(indexer)]), dtype=object)
new_labels[cur_indexer] = cur_labels
new_labels[missing_indexer] = missing_labels
# a unique indexer
if target.is_unique:
# see GH5553, make sure we use the right indexer
new_indexer = np.arange(len(indexer))
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)))
new_indexer[~check] = -1
if isinstance(self, ABCMultiIndex):
new_index = type(self).from_tuples(new_labels, names=self.names)
else:
new_index = Index(new_labels, name=self.name)
return new_index, indexer, new_indexer
# --------------------------------------------------------------------
# Join Methods
def join(self, other, how="left", level=None, return_indexers=False, sort=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)
self_is_mi = isinstance(self, ABCMultiIndex)
other_is_mi = isinstance(other, ABCMultiIndex)
# try to figure out the join level
# GH3662
if level is None and (self_is_mi or other_is_mi):
# have the same levels/names so a simple join
if self.names == other.names:
pass
else:
return self._join_multi(other, how=how, return_indexers=return_indexers)
# join on the level
if level is not None and (self_is_mi or other_is_mi):
return self._join_level(
other, level, how=how, return_indexers=return_indexers
)
if len(other) == 0 and how in ("left", "outer"):
join_index = self._shallow_copy()
if return_indexers:
rindexer = np.repeat(-1, len(join_index))
return join_index, None, rindexer
else:
return join_index
if len(self) == 0 and how in ("right", "outer"):
join_index = other._shallow_copy()
if return_indexers:
lindexer = np.repeat(-1, len(join_index))
return join_index, lindexer, None
else:
return join_index
if self._join_precedence < other._join_precedence:
how = {"right": "left", "left": "right"}.get(how, how)
result = other.join(
self, how=how, level=level, return_indexers=return_indexers
)
if return_indexers:
x, y, z = result
result = x, z, y
return result
if not is_dtype_equal(self.dtype, other.dtype):
this = self.astype("O")
other = other.astype("O")
return this.join(other, how=how, return_indexers=return_indexers)
_validate_join_method(how)
if not self.is_unique and not other.is_unique:
return self._join_non_unique(
other, how=how, return_indexers=return_indexers
)
elif not self.is_unique or not other.is_unique:
if self.is_monotonic and other.is_monotonic:
return self._join_monotonic(
other, how=how, return_indexers=return_indexers
)
else:
return self._join_non_unique(
other, how=how, return_indexers=return_indexers
)
elif self.is_monotonic and other.is_monotonic:
try:
return self._join_monotonic(
other, how=how, return_indexers=return_indexers
)
except TypeError:
pass
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 return_indexers:
if join_index is self:
lindexer = None
else:
lindexer = self.get_indexer(join_index)
if join_index is other:
rindexer = None
else:
rindexer = other.get_indexer(join_index)
return join_index, lindexer, rindexer
else:
return join_index
def _join_multi(self, other, how, return_indexers=True):
from pandas.core.indexes.multi import MultiIndex
from pandas.core.reshape.merge import _restore_dropped_levels_multijoin
# figure out join names
self_names = set(com.not_none(*self.names))
other_names = set(com.not_none(*other.names))
overlap = self_names & other_names
# need at least 1 in common
if not overlap:
raise ValueError("cannot join with no overlapping index names")
self_is_mi = isinstance(self, ABCMultiIndex)
other_is_mi = isinstance(other, ABCMultiIndex)
if self_is_mi and other_is_mi:
# Drop the non-matching levels from left and right respectively
ldrop_names = list(self_names - overlap)
rdrop_names = list(other_names - overlap)
# 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()
if return_indexers:
return multi_join_idx, lidx, ridx
else:
return multi_join_idx
jl = list(overlap)[0]
# Case where only one index is multi
# make the indices into mi's that match
flip_order = False
if self_is_mi:
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, return_indexers=return_indexers
)
if flip_order:
if isinstance(result, tuple):
return result[0], result[2], result[1]
return result
def _join_non_unique(self, other, how="left", return_indexers=False):
from pandas.core.reshape.merge import _get_join_indexers
# We only get here if dtypes match
assert self.dtype == other.dtype
lvalues = self._get_engine_target()
rvalues = other._get_engine_target()
left_idx, right_idx = _get_join_indexers(
[lvalues], [rvalues], how=how, sort=True
)
left_idx = ensure_platform_int(left_idx)
right_idx = ensure_platform_int(right_idx)
join_index = np.asarray(lvalues.take(left_idx))
mask = left_idx == -1
np.putmask(join_index, mask, rvalues.take(right_idx))
join_index = self._wrap_joined_index(join_index, other)
if return_indexers:
return join_index, left_idx, right_idx
else:
return join_index
def _join_level(
self, other, level, how="left", return_indexers=False, keep_order=True
):
"""
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):
"""
Returns sorter for the inner most level while preserving the
order of higher levels.
"""
if labels[0].size == 0:
return np.empty(0, dtype="int64")
if len(labels) == 1:
lab = ensure_int64(labels[0])
sorter, _ = libalgos.groupsort_indexer(lab, 1 + lab.max())
return sorter
# 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_int64(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)
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_int64(left_lev_indexer)
rev_indexer = lib.get_reverse_indexer(left_lev_indexer, len(old_level))
new_lev_codes = algos.take_nd(
rev_indexer, left.codes[level], allow_fill=False
)
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)
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
ngroups = 1 + new_lev_codes.max()
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 = algos.take_nd(
right_lev_indexer, join_index.codes[level], allow_fill=False
)
else:
right_indexer = join_index.codes[level]
if flip_order:
left_indexer, right_indexer = right_indexer, left_indexer
if return_indexers:
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
else:
return join_index
def _join_monotonic(self, other, how="left", return_indexers=False):
# We only get here with matching dtypes
assert other.dtype == self.dtype
if self.equals(other):
ret_index = other if how == "right" else self
if return_indexers:
return ret_index, None, None
else:
return ret_index
sv = self._get_engine_target()
ov = other._get_engine_target()
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(sv, ov)
elif how == "right":
join_index = other
lidx = self._left_indexer_unique(ov, sv)
ridx = None
elif how == "inner":
join_index, lidx, ridx = self._inner_indexer(sv, ov)
join_index = self._wrap_joined_index(join_index, other)
elif how == "outer":
join_index, lidx, ridx = self._outer_indexer(sv, ov)
join_index = self._wrap_joined_index(join_index, other)
else:
if how == "left":
join_index, lidx, ridx = self._left_indexer(sv, ov)
elif how == "right":
join_index, ridx, lidx = self._left_indexer(ov, sv)
elif how == "inner":
join_index, lidx, ridx = self._inner_indexer(sv, ov)
elif how == "outer":
join_index, lidx, ridx = self._outer_indexer(sv, ov)
join_index = self._wrap_joined_index(join_index, other)
if return_indexers:
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
else:
return join_index
def _wrap_joined_index(self, joined, other):
name = get_op_result_name(self, other)
return Index(joined, name=name)
# --------------------------------------------------------------------
# Uncategorized Methods
@property
def values(self) -> np.ndarray:
"""
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.view(np.ndarray)
@cache_readonly
@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) -> Union[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
"""
return self._data
def _get_engine_target(self) -> np.ndarray:
"""
Get the ndarray that we can pass to the IndexEngine constructor.
"""
return self._values
@doc(IndexOpsMixin.memory_usage)
def memory_usage(self, deep: bool = False) -> int:
result = super().memory_usage(deep=deep)
# include our engine hashtable
result += self._engine.sizeof(deep=deep)
return result
def where(self, cond, other=None):
"""
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 other is None:
other = self._na_value
dtype = self.dtype
values = self.values
if is_bool(other) or is_bool_dtype(other):
# bools force casting
values = values.astype(object)
dtype = None
values = np.where(cond, values, other)
if self._is_numeric_dtype and np.any(isna(values)):
# We can't coerce to the numeric dtype of "self" (unless
# it's float) if there are NaN values in our output.
dtype = None
return Index(values, dtype=dtype, name=self.name)
# construction helpers
@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"
)
@classmethod
def _string_data_error(cls, data):
raise TypeError(
"String dtype not supported, you may need "
"to explicitly cast to a numeric type"
)
def _coerce_scalar_to_index(self, item):
"""
We need to coerce a scalar to a compat for our index type.
Parameters
----------
item : scalar item to coerce
"""
dtype = self.dtype
if self._is_numeric_dtype and isna(item):
# We can't coerce to the numeric dtype of "self" (unless
# it's float) if there are NaN values in our output.
dtype = None
return Index([item], dtype=dtype, **self._get_attributes_dict())
def _to_safe_for_reshape(self):
"""
Convert to object if we are a categorical.
"""
return self
def _convert_for_op(self, value):
"""
Convert value to be insertable to ndarray.
"""
return value
def _assert_can_do_op(self, value):
"""
Check value is valid for scalar op.
"""
if not is_scalar(value):
raise TypeError(f"'value' must be a scalar, passed: {type(value).__name__}")
@property
def _has_complex_internals(self) -> bool:
"""
Indicates if an index is not directly backed by a numpy array
"""
# used to avoid libreduction code paths, which raise or require conversion
return False
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) -> bool:
"""
Whether the index type is compatible with the provided type.
"""
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
def __hash__(self):
raise TypeError(f"unhashable type: {repr(type(self).__name__)}")
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.
"""
# There's no custom logic to be implemented in __getslice__, so it's
# not overloaded intentionally.
getitem = self._data.__getitem__
promote = self._shallow_copy
if is_scalar(key):
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 of basic indexing.
return promote(getitem(key))
if com.is_bool_indexer(key):
key = np.asarray(key, dtype=bool)
result = getitem(key)
if not is_scalar(result):
if np.ndim(result) > 1:
deprecate_ndim_indexing(result)
return result
return promote(result)
else:
return result
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):
"""
Append a collection of Index options together.
Parameters
----------
other : Index or list/tuple of indices
Returns
-------
appended : Index
"""
to_concat = [self]
if isinstance(other, (list, tuple)):
to_concat = to_concat + list(other)
else:
to_concat.append(other)
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, name):
"""
Concatenate multiple Index objects.
"""
to_concat = [x._values if isinstance(x, Index) else x for x in to_concat]
result = _concat.concat_compat(to_concat)
return Index(result, name=name)
def putmask(self, mask, value):
"""
Return a new Index of the values set with the mask.
Returns
-------
Index
See Also
--------
numpy.ndarray.putmask
"""
values = self.values.copy()
try:
np.putmask(values, mask, self._convert_for_op(value))
if is_period_dtype(self.dtype):
# .values cast to object, so we need to cast back
values = type(self)(values)._data
return self._shallow_copy(values)
except (ValueError, TypeError) as err:
if is_object_dtype(self):
raise err
# coerces to object
return self.astype(object).putmask(mask, value)
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 is_extension_array_dtype(other.dtype):
# All EA-backed Index subclasses override equals
return other.equals(self)
return array_equivalent(self._values, other._values)
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)
)
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
"""
try:
loc = self.get_loc(label, method="pad")
except KeyError:
return self._na_value
else:
if isinstance(loc, slice):
loc = loc.indices(len(self))[-1]
return self[loc]
def asof_locs(self, where, mask):
"""
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 : array-like
Array of booleans denoting where values in the original
data are not NA.
Returns
-------
numpy.ndarray
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`.
"""
locs = self.values[mask].searchsorted(where.values, side="right")
locs = np.where(locs > 0, locs - 1, 0)
result = np.arange(len(self))[mask].take(locs)
first = mask.argmax()
result[(locs == 0) & (where.values < self.values[first])] = -1
return result
def sort_values(
self, return_indexer=False, ascending=True, key: Optional[Callable] = 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.
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)
_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
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"Not supported for type {type(self).__name__}")
def argsort(self, *args, **kwargs) -> np.ndarray:
"""
Return the integer indices that would sort the index.
Parameters
----------
*args
Passed to `numpy.ndarray.argsort`.
**kwargs
Passed to `numpy.ndarray.argsort`.
Returns
-------
numpy.ndarray
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')
"""
result = self.asi8
if result is None:
result = np.array(self)
return result.argsort(*args, **kwargs)
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=2,
)
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)
def _should_fallback_to_positional(self) -> bool:
"""
Should an integer key be treated as positional?
"""
if self.holds_integer() or self.is_boolean():
return False
return True
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]
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=2,
)
loc = self._engine.get_loc(key)
validate_numeric_casting(arr.dtype, value)
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 : ndarray of int
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 : ndarray of int
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):
target = ensure_index(target)
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 self._is_comparable_dtype(target.dtype):
no_matches = -1 * np.ones(self.shape, dtype=np.intp)
return no_matches, no_matches
if is_categorical_dtype(target.dtype):
tgt_values = np.asarray(target)
else:
tgt_values = target._get_engine_target()
indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
return ensure_platform_int(indexer), missing
def get_indexer_for(self, target, **kwargs):
"""
Guaranteed return of an indexer even when non-unique.
This dispatches to get_indexer or get_indexer_non_unique
as appropriate.
Returns
-------
numpy.ndarray
List of indices.
"""
if self.is_unique:
return self.get_indexer(target, **kwargs)
indexer, _ = self.get_indexer_non_unique(target, **kwargs)
return indexer
def _maybe_promote(self, other: "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 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.inferred_type == "boolean":
if not is_object_dtype(self.dtype):
return self.astype("object"), other.astype("object")
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
def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
"""
Can we compare values of the given dtype to our own?
"""
return True
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 input correspondence (a dict, Series, 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 = super()._map_values(mapper, na_action=na_action)
attributes = self._get_attributes_dict()
# we can return a MultiIndex
if new_values.size and isinstance(new_values[0], tuple):
if isinstance(self, MultiIndex):
names = self.names
elif attributes.get("name"):
names = [attributes.get("name")] * len(new_values[0])
else:
names = None
return MultiIndex.from_tuples(new_values, names=names)
attributes["copy"] = False
if not new_values.size:
# empty
attributes["dtype"] = self.dtype
return Index(new_values, **attributes)
# TODO: De-duplicate with map, xref GH#32349
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:
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):
"""
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
-------
is_contained : ndarray
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)
def _get_string_slice(self, key: str_t, use_lhs: bool = True, use_rhs: bool = True):
# this is for partial string indexing,
# overridden in DatetimeIndex, TimedeltaIndex and PeriodIndex
raise NotImplementedError
def slice_indexer(self, start=None, end=None, step=None, kind=None):
"""
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
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)
"""
start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind)
# 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 _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 None:
pass
elif is_integer(key):
pass
else:
self._invalid_indexer(form, key)
def _maybe_cast_slice_bound(self, label, side: str_t, kind):
"""
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
Returns
-------
label : object
Notes
-----
Value of `side` parameter should be validated in caller.
"""
assert kind in ["loc", "getitem", None]
# 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 is_float(label):
self._invalid_indexer("slice", label)
# we are trying to find integer bounds on a non-integer based index
# this is rejected (generally .loc gets you here)
elif is_integer(label):
self._invalid_indexer("slice", label)
return label
def _searchsorted_monotonic(self, label, side="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: str_t, kind) -> 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
Returns
-------
int
Index of label.
"""
assert kind in ["loc", "getitem", None]
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, kind)
# 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 or an array of indices, which
# is OK as long as they are representable by a slice.
if is_bool_dtype(slc):
slc = lib.maybe_booleans_to_slice(slc.view("u1"))
else:
slc = lib.maybe_indices_to_slice(
slc.astype(np.intp, copy=False), len(self)
)
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=None):
"""
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
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)
"""
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", kind)
if start_slice is None:
start_slice = 0
end_slice = None
if end is not None:
end_slice = self.get_slice_bound(end, "right", kind)
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, loc):
"""
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
New Index with passed location(-s) deleted.
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')
"""
return self._shallow_copy(np.delete(self._data, loc))
def insert(self, loc: int, item):
"""
Make new Index inserting new item at location.
Follows Python list.append semantics for negative values.
Parameters
----------
loc : int
item : object
Returns
-------
new_index : Index
"""
# Note: this method is overridden by all ExtensionIndex subclasses,
# so self is never backed by an EA.
arr = np.asarray(self)
item = self._coerce_scalar_to_index(item)._values
idx = np.concatenate((arr[:loc], item, arr[loc:]))
return Index(idx, name=self.name)
def drop(self, labels, errors: str_t = "raise"):
"""
Make new Index with passed list of labels deleted.
Parameters
----------
labels : array-like
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
Returns
-------
dropped : Index
Raises
------
KeyError
If not all of the labels are found in the selected axis
"""
arr_dtype = "object" if self.dtype == "object" else None
labels = com.index_labels_to_array(labels, dtype=arr_dtype)
indexer = self.get_indexer(labels)
mask = indexer == -1
if mask.any():
if errors != "ignore":
raise KeyError(f"{labels[mask]} not found in axis")
indexer = indexer[~mask]
return self.delete(indexer)
# --------------------------------------------------------------------
# Generated Arithmetic, Comparison, and Unary Methods
@classmethod
def _add_comparison_methods(cls):
"""
Add in comparison methods.
"""
cls.__eq__ = _make_comparison_op(operator.eq, cls)
cls.__ne__ = _make_comparison_op(operator.ne, cls)
cls.__lt__ = _make_comparison_op(operator.lt, cls)
cls.__gt__ = _make_comparison_op(operator.gt, cls)
cls.__le__ = _make_comparison_op(operator.le, cls)
cls.__ge__ = _make_comparison_op(operator.ge, cls)
@classmethod
def _add_numeric_methods_add_sub_disabled(cls):
"""
Add in the numeric add/sub methods to disable.
"""
cls.__add__ = make_invalid_op("__add__")
cls.__radd__ = make_invalid_op("__radd__")
cls.__iadd__ = make_invalid_op("__iadd__")
cls.__sub__ = make_invalid_op("__sub__")
cls.__rsub__ = make_invalid_op("__rsub__")
cls.__isub__ = make_invalid_op("__isub__")
@classmethod
def _add_numeric_methods_disabled(cls):
"""
Add in numeric methods to disable other than add/sub.
"""
cls.__pow__ = make_invalid_op("__pow__")
cls.__rpow__ = make_invalid_op("__rpow__")
cls.__mul__ = make_invalid_op("__mul__")
cls.__rmul__ = make_invalid_op("__rmul__")
cls.__floordiv__ = make_invalid_op("__floordiv__")
cls.__rfloordiv__ = make_invalid_op("__rfloordiv__")
cls.__truediv__ = make_invalid_op("__truediv__")
cls.__rtruediv__ = make_invalid_op("__rtruediv__")
cls.__mod__ = make_invalid_op("__mod__")
cls.__divmod__ = make_invalid_op("__divmod__")
cls.__neg__ = make_invalid_op("__neg__")
cls.__pos__ = make_invalid_op("__pos__")
cls.__abs__ = make_invalid_op("__abs__")
cls.__inv__ = make_invalid_op("__inv__")
@classmethod
def _add_numeric_methods_binary(cls):
"""
Add in numeric methods.
"""
cls.__add__ = _make_arithmetic_op(operator.add, cls)
cls.__radd__ = _make_arithmetic_op(ops.radd, cls)
cls.__sub__ = _make_arithmetic_op(operator.sub, cls)
cls.__rsub__ = _make_arithmetic_op(ops.rsub, cls)
cls.__rpow__ = _make_arithmetic_op(ops.rpow, cls)
cls.__pow__ = _make_arithmetic_op(operator.pow, cls)
cls.__truediv__ = _make_arithmetic_op(operator.truediv, cls)
cls.__rtruediv__ = _make_arithmetic_op(ops.rtruediv, cls)
# TODO: rmod? rdivmod?
cls.__mod__ = _make_arithmetic_op(operator.mod, cls)
cls.__floordiv__ = _make_arithmetic_op(operator.floordiv, cls)
cls.__rfloordiv__ = _make_arithmetic_op(ops.rfloordiv, cls)
cls.__divmod__ = _make_arithmetic_op(divmod, cls)
cls.__mul__ = _make_arithmetic_op(operator.mul, cls)
cls.__rmul__ = _make_arithmetic_op(ops.rmul, cls)
@classmethod
def _add_numeric_methods_unary(cls):
"""
Add in numeric unary methods.
"""
def _make_evaluate_unary(op, opstr: str_t):
def _evaluate_numeric_unary(self):
attrs = self._get_attributes_dict()
return Index(op(self.values), **attrs)
_evaluate_numeric_unary.__name__ = opstr
return _evaluate_numeric_unary
cls.__neg__ = _make_evaluate_unary(operator.neg, "__neg__")
cls.__pos__ = _make_evaluate_unary(operator.pos, "__pos__")
cls.__abs__ = _make_evaluate_unary(np.abs, "__abs__")
cls.__inv__ = _make_evaluate_unary(lambda x: -x, "__inv__")
@classmethod
def _add_numeric_methods(cls):
cls._add_numeric_methods_unary()
cls._add_numeric_methods_binary()
@classmethod
def _add_logical_methods(cls):
"""
Add in logical methods.
"""
_doc = """
%(desc)s
Parameters
----------
*args
These parameters will be passed to numpy.%(outname)s.
**kwargs
These parameters will be passed to numpy.%(outname)s.
Returns
-------
%(outname)s : bool or array_like (if axis is specified)
A single element array_like may be converted to bool."""
_index_shared_docs["index_all"] = dedent(
"""
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
--------
**all**
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
**any**
True, because ``1`` is considered True.
>>> pd.Index([0, 0, 1]).any()
True
False, because ``0`` is considered False.
>>> pd.Index([0, 0, 0]).any()
False
"""
)
_index_shared_docs["index_any"] = dedent(
"""
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
"""
)
def _make_logical_function(name: str_t, desc: str_t, f):
@Substitution(outname=name, desc=desc)
@Appender(_index_shared_docs["index_" + name])
@Appender(_doc)
def logical_func(self, *args, **kwargs):
result = f(self.values)
if (
isinstance(result, (np.ndarray, ABCSeries, Index))
and result.ndim == 0
):
# return NumPy type
return result.dtype.type(result.item())
else: # pragma: no cover
return result
logical_func.__name__ = name
return logical_func
cls.all = _make_logical_function(
"all", "Return whether all elements are True.", np.all
)
cls.any = _make_logical_function(
"any", "Return whether any element is True.", np.any
)
@classmethod
def _add_logical_methods_disabled(cls):
"""
Add in logical methods to disable.
"""
cls.all = make_invalid_op("all")
cls.any = make_invalid_op("any")
@property
def shape(self):
"""
Return a tuple of the shape of the underlying data.
"""
# not using "(len(self), )" to return "correct" shape if the values
# consists of a >1 D array (see GH-27775)
# overridden in MultiIndex.shape to avoid materializing the values
return self._values.shape
Index._add_numeric_methods_disabled()
Index._add_logical_methods()
Index._add_comparison_methods()
def ensure_index_from_sequences(sequences, names=None):
"""
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, copy: bool = False):
"""
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 hasattr(index_like, "name"):
return Index(index_like, name=index_like.name, copy=copy)
if is_iterator(index_like):
index_like = list(index_like)
# must check for exactly list here because of strict type
# check in clean_index_list
if isinstance(index_like, list):
if type(index_like) != list:
index_like = list(index_like)
converted, all_arrays = lib.clean_index_list(index_like)
if len(converted) > 0 and all_arrays:
from pandas.core.indexes.multi import MultiIndex
return MultiIndex.from_arrays(converted)
else:
index_like = converted
else:
# clean_index_list does the equivalent of copying
# so only need to do this if not list instance
if copy:
index_like = copy_func(index_like)
return Index(index_like)
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.
"""
trimmed = strings
while len(strings) > 0 and all(x[0] == " " for x in trimmed):
trimmed = [x[1:] for x in trimmed]
return trimmed
def _validate_join_method(method: str):
if method not in ["left", "right", "inner", "outer"]:
raise ValueError(f"do not recognize join method {method}")
def default_index(n):
from pandas.core.indexes.range import RangeIndex
return RangeIndex(0, n, name=None)
def maybe_extract_name(name, obj, cls) -> Label:
"""
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
def _maybe_cast_with_dtype(data: np.ndarray, dtype: np.dtype, copy: bool) -> np.ndarray:
"""
If a dtype is passed, cast to the closest matching dtype that is supported
by Index.
Parameters
----------
data : np.ndarray
dtype : np.dtype
copy : bool
Returns
-------
np.ndarray
"""
# 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
if is_integer_dtype(dtype):
inferred = lib.infer_dtype(data, skipna=False)
if inferred == "integer":
data = maybe_cast_to_integer_array(data, dtype, copy=copy)
elif inferred in ["floating", "mixed-integer-float"]:
if isna(data).any():
raise ValueError("cannot convert float NaN to integer")
if inferred == "mixed-integer-float":
data = maybe_cast_to_integer_array(data, dtype)
# If we are actually all equal to integers,
# then coerce to integer.
try:
data = _try_convert_to_int_array(data, copy, dtype)
except ValueError:
data = np.array(data, dtype=np.float64, copy=copy)
elif inferred == "string":
pass
else:
data = data.astype(dtype)
elif is_float_dtype(dtype):
inferred = lib.infer_dtype(data, skipna=False)
if inferred == "string":
pass
else:
data = data.astype(dtype)
else:
data = np.array(data, dtype=dtype, copy=copy)
return data
def _maybe_cast_data_without_dtype(subarr):
"""
If we have an arraylike input but no passed dtype, try to infer
a supported dtype.
Parameters
----------
subarr : np.ndarray, Index, or Series
Returns
-------
converted : np.ndarray or ExtensionArray
dtype : np.dtype or ExtensionDtype
"""
# Runtime import needed bc IntervalArray imports Index
from pandas.core.arrays import (
DatetimeArray,
IntervalArray,
PeriodArray,
TimedeltaArray,
)
inferred = lib.infer_dtype(subarr, skipna=False)
if inferred == "integer":
try:
data = _try_convert_to_int_array(subarr, False, None)
return data, data.dtype
except ValueError:
pass
return subarr, object
elif inferred in ["floating", "mixed-integer-float", "integer-na"]:
# TODO: Returns IntegerArray for integer-na case in the future
return subarr, np.float64
elif inferred == "interval":
try:
data = IntervalArray._from_sequence(subarr, copy=False)
return data, data.dtype
except ValueError:
# GH27172: mixed closed Intervals --> object dtype
pass
elif inferred == "boolean":
# don't support boolean explicitly ATM
pass
elif inferred != "string":
if inferred.startswith("datetime"):
try:
data = DatetimeArray._from_sequence(subarr, copy=False)
return data, data.dtype
except (ValueError, OutOfBoundsDatetime):
# GH 27011
# If we have mixed timezones, just send it
# down the base constructor
pass
elif inferred.startswith("timedelta"):
data = TimedeltaArray._from_sequence(subarr, copy=False)
return data, data.dtype
elif inferred == "period":
try:
data = PeriodArray._from_sequence(subarr)
return data, data.dtype
except IncompatibleFrequency:
pass
return subarr, subarr.dtype
def _try_convert_to_int_array(
data: np.ndarray, copy: bool, dtype: np.dtype
) -> np.ndarray:
"""
Attempt to convert an array of data into an integer array.
Parameters
----------
data : The data to convert.
copy : bool
Whether to copy the data or not.
dtype : np.dtype
Returns
-------
int_array : data converted to either an ndarray[int64] or ndarray[uint64]
Raises
------
ValueError if the conversion was not successful.
"""
if not is_unsigned_integer_dtype(dtype):
# skip int64 conversion attempt if uint-like dtype is passed, as
# this could return Int64Index when UInt64Index is what's desired
try:
res = data.astype("i8", copy=False)
if (res == data).all():
return res # TODO: might still need to copy
except (OverflowError, TypeError, ValueError):
pass
# Conversion to int64 failed (possibly due to overflow) or was skipped,
# so let's try now with uint64.
try:
res = data.astype("u8", copy=False)
if (res == data).all():
return res # TODO: might still need to copy
except (OverflowError, TypeError, ValueError):
pass
raise ValueError
def _maybe_asobject(dtype, klass, data, copy: bool, name: Label, **kwargs):
"""
If an object dtype was specified, create the non-object Index
and then convert it to object.
Parameters
----------
dtype : np.dtype, ExtensionDtype, str
klass : Index subclass
data : list-like
copy : bool
name : hashable
**kwargs
Returns
-------
Index
Notes
-----
We assume that calling .astype(object) on this klass will make a copy.
"""
# GH#23524 passing `dtype=object` to DatetimeIndex is invalid,
# will raise in the where `data` is already tz-aware. So
# we leave it out of this step and cast to object-dtype after
# the DatetimeIndex construction.
if is_dtype_equal(_o_dtype, dtype):
# Note we can pass copy=False because the .astype below
# will always make a copy
index = klass(data, copy=False, name=name, **kwargs)
return index.astype(object)
return klass(data, dtype=dtype, copy=copy, name=name, **kwargs)