3970 lines
130 KiB
Python
3970 lines
130 KiB
Python
![]() |
from functools import wraps
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from sys import getsizeof
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Hashable,
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Iterable,
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List,
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Optional,
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Sequence,
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Tuple,
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Union,
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)
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import warnings
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import numpy as np
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from pandas._config import get_option
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from pandas._libs import algos as libalgos, index as libindex, lib
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from pandas._libs.hashtable import duplicated_int64
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from pandas._typing import AnyArrayLike, DtypeObj, Label, Scalar, Shape
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from pandas.compat.numpy import function as nv
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from pandas.errors import InvalidIndexError, PerformanceWarning, UnsortedIndexError
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from pandas.util._decorators import Appender, cache_readonly, doc
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from pandas.core.dtypes.cast import coerce_indexer_dtype
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from pandas.core.dtypes.common import (
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ensure_int64,
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ensure_platform_int,
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is_categorical_dtype,
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is_hashable,
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is_integer,
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is_iterator,
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is_list_like,
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is_object_dtype,
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is_scalar,
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pandas_dtype,
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)
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from pandas.core.dtypes.dtypes import ExtensionDtype
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from pandas.core.dtypes.generic import ABCDataFrame, ABCDatetimeIndex, ABCTimedeltaIndex
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from pandas.core.dtypes.missing import array_equivalent, isna
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import pandas.core.algorithms as algos
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from pandas.core.arrays import Categorical
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from pandas.core.arrays.categorical import factorize_from_iterables
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import pandas.core.common as com
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import pandas.core.indexes.base as ibase
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from pandas.core.indexes.base import (
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Index,
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_index_shared_docs,
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ensure_index,
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get_unanimous_names,
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)
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from pandas.core.indexes.frozen import FrozenList
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from pandas.core.indexes.numeric import Int64Index
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import pandas.core.missing as missing
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from pandas.core.ops.invalid import make_invalid_op
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from pandas.core.sorting import (
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get_group_index,
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indexer_from_factorized,
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lexsort_indexer,
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)
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from pandas.io.formats.printing import (
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format_object_attrs,
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format_object_summary,
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pprint_thing,
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)
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if TYPE_CHECKING:
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from pandas import Series
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_index_doc_kwargs = dict(ibase._index_doc_kwargs)
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_index_doc_kwargs.update(
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{"klass": "MultiIndex", "target_klass": "MultiIndex or list of tuples"}
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)
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class MultiIndexUIntEngine(libindex.BaseMultiIndexCodesEngine, libindex.UInt64Engine):
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"""
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This class manages a MultiIndex by mapping label combinations to positive
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integers.
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"""
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_base = libindex.UInt64Engine
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def _codes_to_ints(self, codes):
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"""
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Transform combination(s) of uint64 in one uint64 (each), in a strictly
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monotonic way (i.e. respecting the lexicographic order of integer
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combinations): see BaseMultiIndexCodesEngine documentation.
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Parameters
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----------
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codes : 1- or 2-dimensional array of dtype uint64
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Combinations of integers (one per row)
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Returns
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-------
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scalar or 1-dimensional array, of dtype uint64
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Integer(s) representing one combination (each).
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"""
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# Shift the representation of each level by the pre-calculated number
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# of bits:
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codes <<= self.offsets
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# Now sum and OR are in fact interchangeable. This is a simple
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# composition of the (disjunct) significant bits of each level (i.e.
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# each column in "codes") in a single positive integer:
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if codes.ndim == 1:
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# Single key
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return np.bitwise_or.reduce(codes)
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# Multiple keys
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return np.bitwise_or.reduce(codes, axis=1)
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class MultiIndexPyIntEngine(libindex.BaseMultiIndexCodesEngine, libindex.ObjectEngine):
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"""
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This class manages those (extreme) cases in which the number of possible
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label combinations overflows the 64 bits integers, and uses an ObjectEngine
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containing Python integers.
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"""
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_base = libindex.ObjectEngine
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def _codes_to_ints(self, codes):
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"""
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Transform combination(s) of uint64 in one Python integer (each), in a
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strictly monotonic way (i.e. respecting the lexicographic order of
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integer combinations): see BaseMultiIndexCodesEngine documentation.
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Parameters
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----------
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codes : 1- or 2-dimensional array of dtype uint64
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Combinations of integers (one per row)
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Returns
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-------
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int, or 1-dimensional array of dtype object
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Integer(s) representing one combination (each).
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"""
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# Shift the representation of each level by the pre-calculated number
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# of bits. Since this can overflow uint64, first make sure we are
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# working with Python integers:
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codes = codes.astype("object") << self.offsets
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# Now sum and OR are in fact interchangeable. This is a simple
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# composition of the (disjunct) significant bits of each level (i.e.
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# each column in "codes") in a single positive integer (per row):
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if codes.ndim == 1:
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# Single key
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return np.bitwise_or.reduce(codes)
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# Multiple keys
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return np.bitwise_or.reduce(codes, axis=1)
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def names_compat(meth):
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"""
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A decorator to allow either `name` or `names` keyword but not both.
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This makes it easier to share code with base class.
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"""
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@wraps(meth)
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def new_meth(self_or_cls, *args, **kwargs):
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if "name" in kwargs and "names" in kwargs:
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raise TypeError("Can only provide one of `names` and `name`")
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elif "name" in kwargs:
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kwargs["names"] = kwargs.pop("name")
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return meth(self_or_cls, *args, **kwargs)
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return new_meth
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class MultiIndex(Index):
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"""
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A multi-level, or hierarchical, index object for pandas objects.
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Parameters
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----------
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levels : sequence of arrays
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The unique labels for each level.
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codes : sequence of arrays
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Integers for each level designating which label at each location.
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.. versionadded:: 0.24.0
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sortorder : optional int
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Level of sortedness (must be lexicographically sorted by that
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level).
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names : optional sequence of objects
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Names for each of the index levels. (name is accepted for compat).
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copy : bool, default False
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Copy the meta-data.
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verify_integrity : bool, default True
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Check that the levels/codes are consistent and valid.
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Attributes
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----------
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names
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levels
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codes
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nlevels
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levshape
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Methods
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-------
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from_arrays
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from_tuples
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from_product
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from_frame
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set_levels
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set_codes
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to_frame
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to_flat_index
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is_lexsorted
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sortlevel
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droplevel
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swaplevel
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reorder_levels
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remove_unused_levels
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get_locs
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See Also
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--------
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MultiIndex.from_arrays : Convert list of arrays to MultiIndex.
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MultiIndex.from_product : Create a MultiIndex from the cartesian product
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of iterables.
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MultiIndex.from_tuples : Convert list of tuples to a MultiIndex.
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MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
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Index : The base pandas Index type.
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Notes
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-----
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See the `user guide
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<https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html>`_
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for more.
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Examples
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--------
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A new ``MultiIndex`` is typically constructed using one of the helper
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methods :meth:`MultiIndex.from_arrays`, :meth:`MultiIndex.from_product`
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and :meth:`MultiIndex.from_tuples`. For example (using ``.from_arrays``):
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>>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
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>>> pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))
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MultiIndex([(1, 'red'),
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(1, 'blue'),
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(2, 'red'),
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(2, 'blue')],
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names=['number', 'color'])
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See further examples for how to construct a MultiIndex in the doc strings
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of the mentioned helper methods.
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"""
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_hidden_attrs = Index._hidden_attrs | frozenset()
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# initialize to zero-length tuples to make everything work
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_typ = "multiindex"
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_names = FrozenList()
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_levels = FrozenList()
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_codes = FrozenList()
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_comparables = ["names"]
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rename = Index.set_names
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sortorder: Optional[int]
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# --------------------------------------------------------------------
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# Constructors
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def __new__(
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cls,
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levels=None,
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codes=None,
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sortorder=None,
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names=None,
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dtype=None,
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copy=False,
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name=None,
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verify_integrity: bool = True,
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):
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# compat with Index
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if name is not None:
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names = name
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if levels is None or codes is None:
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raise TypeError("Must pass both levels and codes")
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if len(levels) != len(codes):
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raise ValueError("Length of levels and codes must be the same.")
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if len(levels) == 0:
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raise ValueError("Must pass non-zero number of levels/codes")
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result = object.__new__(MultiIndex)
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result._cache = {}
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# we've already validated levels and codes, so shortcut here
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result._set_levels(levels, copy=copy, validate=False)
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result._set_codes(codes, copy=copy, validate=False)
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result._names = [None] * len(levels)
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if names is not None:
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# handles name validation
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result._set_names(names)
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if sortorder is not None:
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result.sortorder = int(sortorder)
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else:
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result.sortorder = sortorder
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if verify_integrity:
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new_codes = result._verify_integrity()
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result._codes = new_codes
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result._reset_identity()
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return result
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def _validate_codes(self, level: List, code: List):
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"""
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Reassign code values as -1 if their corresponding levels are NaN.
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Parameters
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----------
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code : list
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Code to reassign.
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level : list
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Level to check for missing values (NaN, NaT, None).
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Returns
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-------
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new code where code value = -1 if it corresponds
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to a level with missing values (NaN, NaT, None).
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"""
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null_mask = isna(level)
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if np.any(null_mask):
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code = np.where(null_mask[code], -1, code)
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return code
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def _verify_integrity(
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self, codes: Optional[List] = None, levels: Optional[List] = None
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):
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"""
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Parameters
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----------
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codes : optional list
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Codes to check for validity. Defaults to current codes.
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levels : optional list
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Levels to check for validity. Defaults to current levels.
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Raises
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------
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ValueError
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If length of levels and codes don't match, if the codes for any
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level would exceed level bounds, or there are any duplicate levels.
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Returns
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-------
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new codes where code value = -1 if it corresponds to a
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NaN level.
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"""
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# NOTE: Currently does not check, among other things, that cached
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# nlevels matches nor that sortorder matches actually sortorder.
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codes = codes or self.codes
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levels = levels or self.levels
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if len(levels) != len(codes):
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raise ValueError(
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"Length of levels and codes must match. NOTE: "
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"this index is in an inconsistent state."
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)
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codes_length = len(codes[0])
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for i, (level, level_codes) in enumerate(zip(levels, codes)):
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if len(level_codes) != codes_length:
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raise ValueError(
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f"Unequal code lengths: {[len(code_) for code_ in codes]}"
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)
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if len(level_codes) and level_codes.max() >= len(level):
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raise ValueError(
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f"On level {i}, code max ({level_codes.max()}) >= length of "
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f"level ({len(level)}). NOTE: this index is in an "
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"inconsistent state"
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)
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if len(level_codes) and level_codes.min() < -1:
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raise ValueError(f"On level {i}, code value ({level_codes.min()}) < -1")
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if not level.is_unique:
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raise ValueError(
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f"Level values must be unique: {list(level)} on level {i}"
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)
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if self.sortorder is not None:
|
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|
if self.sortorder > self._lexsort_depth():
|
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raise ValueError(
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"Value for sortorder must be inferior or equal to actual "
|
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|
f"lexsort_depth: sortorder {self.sortorder} "
|
||
|
f"with lexsort_depth {self._lexsort_depth()}"
|
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|
)
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codes = [
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self._validate_codes(level, code) for level, code in zip(levels, codes)
|
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]
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new_codes = FrozenList(codes)
|
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return new_codes
|
||
|
|
||
|
@classmethod
|
||
|
def from_arrays(cls, arrays, sortorder=None, names=lib.no_default) -> "MultiIndex":
|
||
|
"""
|
||
|
Convert arrays to MultiIndex.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
arrays : list / sequence of array-likes
|
||
|
Each array-like gives one level's value for each data point.
|
||
|
len(arrays) is the number of levels.
|
||
|
sortorder : int or None
|
||
|
Level of sortedness (must be lexicographically sorted by that
|
||
|
level).
|
||
|
names : list / sequence of str, optional
|
||
|
Names for the levels in the index.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
MultiIndex
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
|
||
|
MultiIndex.from_product : Make a MultiIndex from cartesian product
|
||
|
of iterables.
|
||
|
MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
|
||
|
>>> pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))
|
||
|
MultiIndex([(1, 'red'),
|
||
|
(1, 'blue'),
|
||
|
(2, 'red'),
|
||
|
(2, 'blue')],
|
||
|
names=['number', 'color'])
|
||
|
"""
|
||
|
error_msg = "Input must be a list / sequence of array-likes."
|
||
|
if not is_list_like(arrays):
|
||
|
raise TypeError(error_msg)
|
||
|
elif is_iterator(arrays):
|
||
|
arrays = list(arrays)
|
||
|
|
||
|
# Check if elements of array are list-like
|
||
|
for array in arrays:
|
||
|
if not is_list_like(array):
|
||
|
raise TypeError(error_msg)
|
||
|
|
||
|
# Check if lengths of all arrays are equal or not,
|
||
|
# raise ValueError, if not
|
||
|
for i in range(1, len(arrays)):
|
||
|
if len(arrays[i]) != len(arrays[i - 1]):
|
||
|
raise ValueError("all arrays must be same length")
|
||
|
|
||
|
codes, levels = factorize_from_iterables(arrays)
|
||
|
if names is lib.no_default:
|
||
|
names = [getattr(arr, "name", None) for arr in arrays]
|
||
|
|
||
|
return cls(
|
||
|
levels=levels,
|
||
|
codes=codes,
|
||
|
sortorder=sortorder,
|
||
|
names=names,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
|
||
|
@classmethod
|
||
|
@names_compat
|
||
|
def from_tuples(
|
||
|
cls,
|
||
|
tuples,
|
||
|
sortorder: Optional[int] = None,
|
||
|
names: Optional[Sequence[Label]] = None,
|
||
|
):
|
||
|
"""
|
||
|
Convert list of tuples to MultiIndex.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
tuples : list / sequence of tuple-likes
|
||
|
Each tuple is the index of one row/column.
|
||
|
sortorder : int or None
|
||
|
Level of sortedness (must be lexicographically sorted by that
|
||
|
level).
|
||
|
names : list / sequence of str, optional
|
||
|
Names for the levels in the index.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
MultiIndex
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
MultiIndex.from_arrays : Convert list of arrays to MultiIndex.
|
||
|
MultiIndex.from_product : Make a MultiIndex from cartesian product
|
||
|
of iterables.
|
||
|
MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> tuples = [(1, 'red'), (1, 'blue'),
|
||
|
... (2, 'red'), (2, 'blue')]
|
||
|
>>> pd.MultiIndex.from_tuples(tuples, names=('number', 'color'))
|
||
|
MultiIndex([(1, 'red'),
|
||
|
(1, 'blue'),
|
||
|
(2, 'red'),
|
||
|
(2, 'blue')],
|
||
|
names=['number', 'color'])
|
||
|
"""
|
||
|
if not is_list_like(tuples):
|
||
|
raise TypeError("Input must be a list / sequence of tuple-likes.")
|
||
|
elif is_iterator(tuples):
|
||
|
tuples = list(tuples)
|
||
|
|
||
|
arrays: List[Sequence[Label]]
|
||
|
if len(tuples) == 0:
|
||
|
if names is None:
|
||
|
raise TypeError("Cannot infer number of levels from empty list")
|
||
|
arrays = [[]] * len(names)
|
||
|
elif isinstance(tuples, (np.ndarray, Index)):
|
||
|
if isinstance(tuples, Index):
|
||
|
tuples = tuples._values
|
||
|
|
||
|
arrays = list(lib.tuples_to_object_array(tuples).T)
|
||
|
elif isinstance(tuples, list):
|
||
|
arrays = list(lib.to_object_array_tuples(tuples).T)
|
||
|
else:
|
||
|
arrays = zip(*tuples)
|
||
|
|
||
|
return cls.from_arrays(arrays, sortorder=sortorder, names=names)
|
||
|
|
||
|
@classmethod
|
||
|
def from_product(cls, iterables, sortorder=None, names=lib.no_default):
|
||
|
"""
|
||
|
Make a MultiIndex from the cartesian product of multiple iterables.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
iterables : list / sequence of iterables
|
||
|
Each iterable has unique labels for each level of the index.
|
||
|
sortorder : int or None
|
||
|
Level of sortedness (must be lexicographically sorted by that
|
||
|
level).
|
||
|
names : list / sequence of str, optional
|
||
|
Names for the levels in the index.
|
||
|
|
||
|
.. versionchanged:: 1.0.0
|
||
|
|
||
|
If not explicitly provided, names will be inferred from the
|
||
|
elements of iterables if an element has a name attribute
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
MultiIndex
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
MultiIndex.from_arrays : Convert list of arrays to MultiIndex.
|
||
|
MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
|
||
|
MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> numbers = [0, 1, 2]
|
||
|
>>> colors = ['green', 'purple']
|
||
|
>>> pd.MultiIndex.from_product([numbers, colors],
|
||
|
... names=['number', 'color'])
|
||
|
MultiIndex([(0, 'green'),
|
||
|
(0, 'purple'),
|
||
|
(1, 'green'),
|
||
|
(1, 'purple'),
|
||
|
(2, 'green'),
|
||
|
(2, 'purple')],
|
||
|
names=['number', 'color'])
|
||
|
"""
|
||
|
from pandas.core.reshape.util import cartesian_product
|
||
|
|
||
|
if not is_list_like(iterables):
|
||
|
raise TypeError("Input must be a list / sequence of iterables.")
|
||
|
elif is_iterator(iterables):
|
||
|
iterables = list(iterables)
|
||
|
|
||
|
codes, levels = factorize_from_iterables(iterables)
|
||
|
if names is lib.no_default:
|
||
|
names = [getattr(it, "name", None) for it in iterables]
|
||
|
|
||
|
# codes are all ndarrays, so cartesian_product is lossless
|
||
|
codes = cartesian_product(codes)
|
||
|
return cls(levels, codes, sortorder=sortorder, names=names)
|
||
|
|
||
|
@classmethod
|
||
|
def from_frame(cls, df, sortorder=None, names=None):
|
||
|
"""
|
||
|
Make a MultiIndex from a DataFrame.
|
||
|
|
||
|
.. versionadded:: 0.24.0
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
df : DataFrame
|
||
|
DataFrame to be converted to MultiIndex.
|
||
|
sortorder : int, optional
|
||
|
Level of sortedness (must be lexicographically sorted by that
|
||
|
level).
|
||
|
names : list-like, optional
|
||
|
If no names are provided, use the column names, or tuple of column
|
||
|
names if the columns is a MultiIndex. If a sequence, overwrite
|
||
|
names with the given sequence.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
MultiIndex
|
||
|
The MultiIndex representation of the given DataFrame.
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
MultiIndex.from_arrays : Convert list of arrays to MultiIndex.
|
||
|
MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
|
||
|
MultiIndex.from_product : Make a MultiIndex from cartesian product
|
||
|
of iterables.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> df = pd.DataFrame([['HI', 'Temp'], ['HI', 'Precip'],
|
||
|
... ['NJ', 'Temp'], ['NJ', 'Precip']],
|
||
|
... columns=['a', 'b'])
|
||
|
>>> df
|
||
|
a b
|
||
|
0 HI Temp
|
||
|
1 HI Precip
|
||
|
2 NJ Temp
|
||
|
3 NJ Precip
|
||
|
|
||
|
>>> pd.MultiIndex.from_frame(df)
|
||
|
MultiIndex([('HI', 'Temp'),
|
||
|
('HI', 'Precip'),
|
||
|
('NJ', 'Temp'),
|
||
|
('NJ', 'Precip')],
|
||
|
names=['a', 'b'])
|
||
|
|
||
|
Using explicit names, instead of the column names
|
||
|
|
||
|
>>> pd.MultiIndex.from_frame(df, names=['state', 'observation'])
|
||
|
MultiIndex([('HI', 'Temp'),
|
||
|
('HI', 'Precip'),
|
||
|
('NJ', 'Temp'),
|
||
|
('NJ', 'Precip')],
|
||
|
names=['state', 'observation'])
|
||
|
"""
|
||
|
if not isinstance(df, ABCDataFrame):
|
||
|
raise TypeError("Input must be a DataFrame")
|
||
|
|
||
|
column_names, columns = zip(*df.items())
|
||
|
names = column_names if names is None else names
|
||
|
return cls.from_arrays(columns, sortorder=sortorder, names=names)
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
|
||
|
@cache_readonly
|
||
|
def _values(self):
|
||
|
# We override here, since our parent uses _data, which we don't use.
|
||
|
values = []
|
||
|
|
||
|
for i in range(self.nlevels):
|
||
|
vals = self._get_level_values(i)
|
||
|
if is_categorical_dtype(vals.dtype):
|
||
|
vals = vals._internal_get_values()
|
||
|
if isinstance(vals.dtype, ExtensionDtype) or isinstance(
|
||
|
vals, (ABCDatetimeIndex, ABCTimedeltaIndex)
|
||
|
):
|
||
|
vals = vals.astype(object)
|
||
|
vals = np.array(vals, copy=False)
|
||
|
values.append(vals)
|
||
|
|
||
|
arr = lib.fast_zip(values)
|
||
|
return arr
|
||
|
|
||
|
@property
|
||
|
def values(self):
|
||
|
return self._values
|
||
|
|
||
|
@property
|
||
|
def array(self):
|
||
|
"""
|
||
|
Raises a ValueError for `MultiIndex` because there's no single
|
||
|
array backing a MultiIndex.
|
||
|
|
||
|
Raises
|
||
|
------
|
||
|
ValueError
|
||
|
"""
|
||
|
raise ValueError(
|
||
|
"MultiIndex has no single backing array. Use "
|
||
|
"'MultiIndex.to_numpy()' to get a NumPy array of tuples."
|
||
|
)
|
||
|
|
||
|
@property
|
||
|
def shape(self) -> Shape:
|
||
|
"""
|
||
|
Return a tuple of the shape of the underlying data.
|
||
|
"""
|
||
|
# overriding the base Index.shape definition to avoid materializing
|
||
|
# the values (GH-27384, GH-27775)
|
||
|
return (len(self),)
|
||
|
|
||
|
def __len__(self) -> int:
|
||
|
return len(self.codes[0])
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
# Levels Methods
|
||
|
|
||
|
@cache_readonly
|
||
|
def levels(self):
|
||
|
# Use cache_readonly to ensure that self.get_locs doesn't repeatedly
|
||
|
# create new IndexEngine
|
||
|
# https://github.com/pandas-dev/pandas/issues/31648
|
||
|
result = [
|
||
|
x._shallow_copy(name=name) for x, name in zip(self._levels, self._names)
|
||
|
]
|
||
|
for level in result:
|
||
|
# disallow midx.levels[0].name = "foo"
|
||
|
level._no_setting_name = True
|
||
|
return FrozenList(result)
|
||
|
|
||
|
def _set_levels(
|
||
|
self,
|
||
|
levels,
|
||
|
level=None,
|
||
|
copy: bool = False,
|
||
|
validate: bool = True,
|
||
|
verify_integrity: bool = False,
|
||
|
) -> None:
|
||
|
# This is NOT part of the levels property because it should be
|
||
|
# externally not allowed to set levels. User beware if you change
|
||
|
# _levels directly
|
||
|
if validate:
|
||
|
if len(levels) == 0:
|
||
|
raise ValueError("Must set non-zero number of levels.")
|
||
|
if level is None and len(levels) != self.nlevels:
|
||
|
raise ValueError("Length of levels must match number of levels.")
|
||
|
if level is not None and len(levels) != len(level):
|
||
|
raise ValueError("Length of levels must match length of level.")
|
||
|
|
||
|
if level is None:
|
||
|
new_levels = FrozenList(
|
||
|
ensure_index(lev, copy=copy)._shallow_copy() for lev in levels
|
||
|
)
|
||
|
else:
|
||
|
level_numbers = [self._get_level_number(lev) for lev in level]
|
||
|
new_levels_list = list(self._levels)
|
||
|
for lev_num, lev in zip(level_numbers, levels):
|
||
|
new_levels_list[lev_num] = ensure_index(lev, copy=copy)._shallow_copy()
|
||
|
new_levels = FrozenList(new_levels_list)
|
||
|
|
||
|
if verify_integrity:
|
||
|
new_codes = self._verify_integrity(levels=new_levels)
|
||
|
self._codes = new_codes
|
||
|
|
||
|
names = self.names
|
||
|
self._levels = new_levels
|
||
|
if any(names):
|
||
|
self._set_names(names)
|
||
|
|
||
|
self._reset_cache()
|
||
|
|
||
|
def set_levels(self, levels, level=None, inplace=None, verify_integrity=True):
|
||
|
"""
|
||
|
Set new levels on MultiIndex. Defaults to returning new index.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
levels : sequence or list of sequence
|
||
|
New level(s) to apply.
|
||
|
level : int, level name, or sequence of int/level names (default None)
|
||
|
Level(s) to set (None for all levels).
|
||
|
inplace : bool
|
||
|
If True, mutates in place.
|
||
|
|
||
|
.. deprecated:: 1.2.0
|
||
|
verify_integrity : bool, default True
|
||
|
If True, checks that levels and codes are compatible.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
new index (of same type and class...etc) or None
|
||
|
The same type as the caller or None if ``inplace=True``.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> idx = pd.MultiIndex.from_tuples(
|
||
|
... [
|
||
|
... (1, "one"),
|
||
|
... (1, "two"),
|
||
|
... (2, "one"),
|
||
|
... (2, "two"),
|
||
|
... (3, "one"),
|
||
|
... (3, "two")
|
||
|
... ],
|
||
|
... names=["foo", "bar"]
|
||
|
... )
|
||
|
>>> idx
|
||
|
MultiIndex([(1, 'one'),
|
||
|
(1, 'two'),
|
||
|
(2, 'one'),
|
||
|
(2, 'two'),
|
||
|
(3, 'one'),
|
||
|
(3, 'two')],
|
||
|
names=['foo', 'bar'])
|
||
|
|
||
|
>>> idx.set_levels([['a', 'b', 'c'], [1, 2]])
|
||
|
MultiIndex([('a', 1),
|
||
|
('a', 2),
|
||
|
('b', 1),
|
||
|
('b', 2),
|
||
|
('c', 1),
|
||
|
('c', 2)],
|
||
|
names=['foo', 'bar'])
|
||
|
>>> idx.set_levels(['a', 'b', 'c'], level=0)
|
||
|
MultiIndex([('a', 'one'),
|
||
|
('a', 'two'),
|
||
|
('b', 'one'),
|
||
|
('b', 'two'),
|
||
|
('c', 'one'),
|
||
|
('c', 'two')],
|
||
|
names=['foo', 'bar'])
|
||
|
>>> idx.set_levels(['a', 'b'], level='bar')
|
||
|
MultiIndex([(1, 'a'),
|
||
|
(1, 'b'),
|
||
|
(2, 'a'),
|
||
|
(2, 'b'),
|
||
|
(3, 'a'),
|
||
|
(3, 'b')],
|
||
|
names=['foo', 'bar'])
|
||
|
|
||
|
If any of the levels passed to ``set_levels()`` exceeds the
|
||
|
existing length, all of the values from that argument will
|
||
|
be stored in the MultiIndex levels, though the values will
|
||
|
be truncated in the MultiIndex output.
|
||
|
|
||
|
>>> idx.set_levels([['a', 'b', 'c'], [1, 2, 3, 4]], level=[0, 1])
|
||
|
MultiIndex([('a', 1),
|
||
|
('a', 2),
|
||
|
('b', 1),
|
||
|
('b', 2),
|
||
|
('c', 1),
|
||
|
('c', 2)],
|
||
|
names=['foo', 'bar'])
|
||
|
>>> idx.set_levels([['a', 'b', 'c'], [1, 2, 3, 4]], level=[0, 1]).levels
|
||
|
FrozenList([['a', 'b', 'c'], [1, 2, 3, 4]])
|
||
|
"""
|
||
|
if inplace is not None:
|
||
|
warnings.warn(
|
||
|
"inplace is deprecated and will be removed in a future version.",
|
||
|
FutureWarning,
|
||
|
stacklevel=2,
|
||
|
)
|
||
|
else:
|
||
|
inplace = False
|
||
|
|
||
|
if is_list_like(levels) and not isinstance(levels, Index):
|
||
|
levels = list(levels)
|
||
|
|
||
|
if level is not None and not is_list_like(level):
|
||
|
if not is_list_like(levels):
|
||
|
raise TypeError("Levels must be list-like")
|
||
|
if is_list_like(levels[0]):
|
||
|
raise TypeError("Levels must be list-like")
|
||
|
level = [level]
|
||
|
levels = [levels]
|
||
|
elif level is None or is_list_like(level):
|
||
|
if not is_list_like(levels) or not is_list_like(levels[0]):
|
||
|
raise TypeError("Levels must be list of lists-like")
|
||
|
|
||
|
if inplace:
|
||
|
idx = self
|
||
|
else:
|
||
|
idx = self._shallow_copy()
|
||
|
idx._reset_identity()
|
||
|
idx._set_levels(
|
||
|
levels, level=level, validate=True, verify_integrity=verify_integrity
|
||
|
)
|
||
|
if not inplace:
|
||
|
return idx
|
||
|
|
||
|
@property
|
||
|
def nlevels(self) -> int:
|
||
|
"""
|
||
|
Integer number of levels in this MultiIndex.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_arrays([['a'], ['b'], ['c']])
|
||
|
>>> mi
|
||
|
MultiIndex([('a', 'b', 'c')],
|
||
|
)
|
||
|
>>> mi.nlevels
|
||
|
3
|
||
|
"""
|
||
|
return len(self._levels)
|
||
|
|
||
|
@property
|
||
|
def levshape(self):
|
||
|
"""
|
||
|
A tuple with the length of each level.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_arrays([['a'], ['b'], ['c']])
|
||
|
>>> mi
|
||
|
MultiIndex([('a', 'b', 'c')],
|
||
|
)
|
||
|
>>> mi.levshape
|
||
|
(1, 1, 1)
|
||
|
"""
|
||
|
return tuple(len(x) for x in self.levels)
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
# Codes Methods
|
||
|
|
||
|
@property
|
||
|
def codes(self):
|
||
|
return self._codes
|
||
|
|
||
|
def _set_codes(
|
||
|
self,
|
||
|
codes,
|
||
|
level=None,
|
||
|
copy: bool = False,
|
||
|
validate: bool = True,
|
||
|
verify_integrity: bool = False,
|
||
|
) -> None:
|
||
|
if validate:
|
||
|
if level is None and len(codes) != self.nlevels:
|
||
|
raise ValueError("Length of codes must match number of levels")
|
||
|
if level is not None and len(codes) != len(level):
|
||
|
raise ValueError("Length of codes must match length of levels.")
|
||
|
|
||
|
if level is None:
|
||
|
new_codes = FrozenList(
|
||
|
_coerce_indexer_frozen(level_codes, lev, copy=copy).view()
|
||
|
for lev, level_codes in zip(self._levels, codes)
|
||
|
)
|
||
|
else:
|
||
|
level_numbers = [self._get_level_number(lev) for lev in level]
|
||
|
new_codes_list = list(self._codes)
|
||
|
for lev_num, level_codes in zip(level_numbers, codes):
|
||
|
lev = self.levels[lev_num]
|
||
|
new_codes_list[lev_num] = _coerce_indexer_frozen(
|
||
|
level_codes, lev, copy=copy
|
||
|
)
|
||
|
new_codes = FrozenList(new_codes_list)
|
||
|
|
||
|
if verify_integrity:
|
||
|
new_codes = self._verify_integrity(codes=new_codes)
|
||
|
|
||
|
self._codes = new_codes
|
||
|
|
||
|
self._reset_cache()
|
||
|
|
||
|
def set_codes(self, codes, level=None, inplace=None, verify_integrity=True):
|
||
|
"""
|
||
|
Set new codes on MultiIndex. Defaults to returning new index.
|
||
|
|
||
|
.. versionadded:: 0.24.0
|
||
|
|
||
|
New name for deprecated method `set_labels`.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
codes : sequence or list of sequence
|
||
|
New codes to apply.
|
||
|
level : int, level name, or sequence of int/level names (default None)
|
||
|
Level(s) to set (None for all levels).
|
||
|
inplace : bool
|
||
|
If True, mutates in place.
|
||
|
|
||
|
.. deprecated:: 1.2.0
|
||
|
verify_integrity : bool (default True)
|
||
|
If True, checks that levels and codes are compatible.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
new index (of same type and class...etc) or None
|
||
|
The same type as the caller or None if ``inplace=True``.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> idx = pd.MultiIndex.from_tuples(
|
||
|
... [(1, "one"), (1, "two"), (2, "one"), (2, "two")], names=["foo", "bar"]
|
||
|
... )
|
||
|
>>> idx
|
||
|
MultiIndex([(1, 'one'),
|
||
|
(1, 'two'),
|
||
|
(2, 'one'),
|
||
|
(2, 'two')],
|
||
|
names=['foo', 'bar'])
|
||
|
|
||
|
>>> idx.set_codes([[1, 0, 1, 0], [0, 0, 1, 1]])
|
||
|
MultiIndex([(2, 'one'),
|
||
|
(1, 'one'),
|
||
|
(2, 'two'),
|
||
|
(1, 'two')],
|
||
|
names=['foo', 'bar'])
|
||
|
>>> idx.set_codes([1, 0, 1, 0], level=0)
|
||
|
MultiIndex([(2, 'one'),
|
||
|
(1, 'two'),
|
||
|
(2, 'one'),
|
||
|
(1, 'two')],
|
||
|
names=['foo', 'bar'])
|
||
|
>>> idx.set_codes([0, 0, 1, 1], level='bar')
|
||
|
MultiIndex([(1, 'one'),
|
||
|
(1, 'one'),
|
||
|
(2, 'two'),
|
||
|
(2, 'two')],
|
||
|
names=['foo', 'bar'])
|
||
|
>>> idx.set_codes([[1, 0, 1, 0], [0, 0, 1, 1]], level=[0, 1])
|
||
|
MultiIndex([(2, 'one'),
|
||
|
(1, 'one'),
|
||
|
(2, 'two'),
|
||
|
(1, 'two')],
|
||
|
names=['foo', 'bar'])
|
||
|
"""
|
||
|
if inplace is not None:
|
||
|
warnings.warn(
|
||
|
"inplace is deprecated and will be removed in a future version.",
|
||
|
FutureWarning,
|
||
|
stacklevel=2,
|
||
|
)
|
||
|
else:
|
||
|
inplace = False
|
||
|
|
||
|
if level is not None and not is_list_like(level):
|
||
|
if not is_list_like(codes):
|
||
|
raise TypeError("Codes must be list-like")
|
||
|
if is_list_like(codes[0]):
|
||
|
raise TypeError("Codes must be list-like")
|
||
|
level = [level]
|
||
|
codes = [codes]
|
||
|
elif level is None or is_list_like(level):
|
||
|
if not is_list_like(codes) or not is_list_like(codes[0]):
|
||
|
raise TypeError("Codes must be list of lists-like")
|
||
|
|
||
|
if inplace:
|
||
|
idx = self
|
||
|
else:
|
||
|
idx = self._shallow_copy()
|
||
|
idx._reset_identity()
|
||
|
idx._set_codes(codes, level=level, verify_integrity=verify_integrity)
|
||
|
if not inplace:
|
||
|
return idx
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
# Index Internals
|
||
|
|
||
|
@cache_readonly
|
||
|
def _engine(self):
|
||
|
# Calculate the number of bits needed to represent labels in each
|
||
|
# level, as log2 of their sizes (including -1 for NaN):
|
||
|
sizes = np.ceil(np.log2([len(level) + 1 for level in self.levels]))
|
||
|
|
||
|
# Sum bit counts, starting from the _right_....
|
||
|
lev_bits = np.cumsum(sizes[::-1])[::-1]
|
||
|
|
||
|
# ... in order to obtain offsets such that sorting the combination of
|
||
|
# shifted codes (one for each level, resulting in a unique integer) is
|
||
|
# equivalent to sorting lexicographically the codes themselves. Notice
|
||
|
# that each level needs to be shifted by the number of bits needed to
|
||
|
# represent the _previous_ ones:
|
||
|
offsets = np.concatenate([lev_bits[1:], [0]]).astype("uint64")
|
||
|
|
||
|
# Check the total number of bits needed for our representation:
|
||
|
if lev_bits[0] > 64:
|
||
|
# The levels would overflow a 64 bit uint - use Python integers:
|
||
|
return MultiIndexPyIntEngine(self.levels, self.codes, offsets)
|
||
|
return MultiIndexUIntEngine(self.levels, self.codes, offsets)
|
||
|
|
||
|
@property
|
||
|
def _constructor(self):
|
||
|
return type(self).from_tuples
|
||
|
|
||
|
@doc(Index._shallow_copy)
|
||
|
def _shallow_copy(self, values=None, name=lib.no_default):
|
||
|
names = name if name is not lib.no_default else self.names
|
||
|
|
||
|
if values is not None:
|
||
|
return type(self).from_tuples(values, sortorder=None, names=names)
|
||
|
|
||
|
result = type(self)(
|
||
|
levels=self.levels,
|
||
|
codes=self.codes,
|
||
|
sortorder=None,
|
||
|
names=names,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
result._cache = self._cache.copy()
|
||
|
result._cache.pop("levels", None) # GH32669
|
||
|
return result
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
|
||
|
def copy(
|
||
|
self,
|
||
|
names=None,
|
||
|
dtype=None,
|
||
|
levels=None,
|
||
|
codes=None,
|
||
|
deep=False,
|
||
|
name=None,
|
||
|
):
|
||
|
"""
|
||
|
Make a copy of this object. Names, dtype, levels and codes can be
|
||
|
passed and will be set on new copy.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
names : sequence, optional
|
||
|
dtype : numpy dtype or pandas type, optional
|
||
|
|
||
|
.. deprecated:: 1.2.0
|
||
|
levels : sequence, optional
|
||
|
|
||
|
.. deprecated:: 1.2.0
|
||
|
codes : sequence, optional
|
||
|
|
||
|
.. deprecated:: 1.2.0
|
||
|
deep : bool, default False
|
||
|
name : Label
|
||
|
Kept for compatibility with 1-dimensional Index. Should not be used.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
MultiIndex
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
In most cases, there should be no functional difference from using
|
||
|
``deep``, but if ``deep`` is passed it will attempt to deepcopy.
|
||
|
This could be potentially expensive on large MultiIndex objects.
|
||
|
"""
|
||
|
names = self._validate_names(name=name, names=names, deep=deep)
|
||
|
if levels is not None:
|
||
|
warnings.warn(
|
||
|
"parameter levels is deprecated and will be removed in a future "
|
||
|
"version. Use the set_levels method instead.",
|
||
|
FutureWarning,
|
||
|
stacklevel=2,
|
||
|
)
|
||
|
if codes is not None:
|
||
|
warnings.warn(
|
||
|
"parameter codes is deprecated and will be removed in a future "
|
||
|
"version. Use the set_codes method instead.",
|
||
|
FutureWarning,
|
||
|
stacklevel=2,
|
||
|
)
|
||
|
|
||
|
if deep:
|
||
|
from copy import deepcopy
|
||
|
|
||
|
if levels is None:
|
||
|
levels = deepcopy(self.levels)
|
||
|
if codes is None:
|
||
|
codes = deepcopy(self.codes)
|
||
|
|
||
|
levels = levels if levels is not None else self.levels
|
||
|
codes = codes if codes is not None else self.codes
|
||
|
|
||
|
new_index = type(self)(
|
||
|
levels=levels,
|
||
|
codes=codes,
|
||
|
sortorder=self.sortorder,
|
||
|
names=names,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
new_index._cache = self._cache.copy()
|
||
|
new_index._cache.pop("levels", None) # GH32669
|
||
|
|
||
|
if dtype:
|
||
|
warnings.warn(
|
||
|
"parameter dtype is deprecated and will be removed in a future "
|
||
|
"version. Use the astype method instead.",
|
||
|
FutureWarning,
|
||
|
stacklevel=2,
|
||
|
)
|
||
|
new_index = new_index.astype(dtype)
|
||
|
return new_index
|
||
|
|
||
|
def __array__(self, dtype=None) -> np.ndarray:
|
||
|
""" the array interface, return my values """
|
||
|
return self.values
|
||
|
|
||
|
def view(self, cls=None):
|
||
|
""" this is defined as a copy with the same identity """
|
||
|
result = self.copy()
|
||
|
result._id = self._id
|
||
|
return result
|
||
|
|
||
|
@doc(Index.__contains__)
|
||
|
def __contains__(self, key: Any) -> bool:
|
||
|
hash(key)
|
||
|
try:
|
||
|
self.get_loc(key)
|
||
|
return True
|
||
|
except (LookupError, TypeError, ValueError):
|
||
|
return False
|
||
|
|
||
|
@cache_readonly
|
||
|
def dtype(self) -> np.dtype:
|
||
|
return np.dtype("O")
|
||
|
|
||
|
def _is_memory_usage_qualified(self) -> bool:
|
||
|
""" return a boolean if we need a qualified .info display """
|
||
|
|
||
|
def f(level):
|
||
|
return "mixed" in level or "string" in level or "unicode" in level
|
||
|
|
||
|
return any(f(level) for level in self._inferred_type_levels)
|
||
|
|
||
|
@doc(Index.memory_usage)
|
||
|
def memory_usage(self, deep: bool = False) -> int:
|
||
|
# we are overwriting our base class to avoid
|
||
|
# computing .values here which could materialize
|
||
|
# a tuple representation unnecessarily
|
||
|
return self._nbytes(deep)
|
||
|
|
||
|
@cache_readonly
|
||
|
def nbytes(self) -> int:
|
||
|
""" return the number of bytes in the underlying data """
|
||
|
return self._nbytes(False)
|
||
|
|
||
|
def _nbytes(self, deep: bool = False) -> int:
|
||
|
"""
|
||
|
return the number of bytes in the underlying data
|
||
|
deeply introspect the level data if deep=True
|
||
|
|
||
|
include the engine hashtable
|
||
|
|
||
|
*this is in internal routine*
|
||
|
|
||
|
"""
|
||
|
# for implementations with no useful getsizeof (PyPy)
|
||
|
objsize = 24
|
||
|
|
||
|
level_nbytes = sum(i.memory_usage(deep=deep) for i in self.levels)
|
||
|
label_nbytes = sum(i.nbytes for i in self.codes)
|
||
|
names_nbytes = sum(getsizeof(i, objsize) for i in self.names)
|
||
|
result = level_nbytes + label_nbytes + names_nbytes
|
||
|
|
||
|
# include our engine hashtable
|
||
|
result += self._engine.sizeof(deep=deep)
|
||
|
return result
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
# Rendering Methods
|
||
|
|
||
|
def _formatter_func(self, tup):
|
||
|
"""
|
||
|
Formats each item in tup according to its level's formatter function.
|
||
|
"""
|
||
|
formatter_funcs = [level._formatter_func for level in self.levels]
|
||
|
return tuple(func(val) for func, val in zip(formatter_funcs, tup))
|
||
|
|
||
|
def _format_data(self, name=None):
|
||
|
"""
|
||
|
Return the formatted data as a unicode string
|
||
|
"""
|
||
|
return format_object_summary(
|
||
|
self, self._formatter_func, name=name, line_break_each_value=True
|
||
|
)
|
||
|
|
||
|
def _format_attrs(self):
|
||
|
"""
|
||
|
Return a list of tuples of the (attr,formatted_value).
|
||
|
"""
|
||
|
return format_object_attrs(self, include_dtype=False)
|
||
|
|
||
|
def _format_native_types(self, na_rep="nan", **kwargs):
|
||
|
new_levels = []
|
||
|
new_codes = []
|
||
|
|
||
|
# go through the levels and format them
|
||
|
for level, level_codes in zip(self.levels, self.codes):
|
||
|
level_strs = level._format_native_types(na_rep=na_rep, **kwargs)
|
||
|
# add nan values, if there are any
|
||
|
mask = level_codes == -1
|
||
|
if mask.any():
|
||
|
nan_index = len(level_strs)
|
||
|
# numpy 1.21 deprecated implicit string casting
|
||
|
level_strs = level_strs.astype(str)
|
||
|
level_strs = np.append(level_strs, na_rep)
|
||
|
assert not level_codes.flags.writeable # i.e. copy is needed
|
||
|
level_codes = level_codes.copy() # make writeable
|
||
|
level_codes[mask] = nan_index
|
||
|
new_levels.append(level_strs)
|
||
|
new_codes.append(level_codes)
|
||
|
|
||
|
if len(new_levels) == 1:
|
||
|
# a single-level multi-index
|
||
|
return Index(new_levels[0].take(new_codes[0]))._format_native_types()
|
||
|
else:
|
||
|
# reconstruct the multi-index
|
||
|
mi = MultiIndex(
|
||
|
levels=new_levels,
|
||
|
codes=new_codes,
|
||
|
names=self.names,
|
||
|
sortorder=self.sortorder,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
return mi._values
|
||
|
|
||
|
def format(
|
||
|
self,
|
||
|
name: Optional[bool] = None,
|
||
|
formatter: Optional[Callable] = None,
|
||
|
na_rep: Optional[str] = None,
|
||
|
names: bool = False,
|
||
|
space: int = 2,
|
||
|
sparsify=None,
|
||
|
adjoin: bool = True,
|
||
|
) -> List:
|
||
|
if name is not None:
|
||
|
names = name
|
||
|
|
||
|
if len(self) == 0:
|
||
|
return []
|
||
|
|
||
|
stringified_levels = []
|
||
|
for lev, level_codes in zip(self.levels, self.codes):
|
||
|
na = na_rep if na_rep is not None else _get_na_rep(lev.dtype.type)
|
||
|
|
||
|
if len(lev) > 0:
|
||
|
|
||
|
formatted = lev.take(level_codes).format(formatter=formatter)
|
||
|
|
||
|
# we have some NA
|
||
|
mask = level_codes == -1
|
||
|
if mask.any():
|
||
|
formatted = np.array(formatted, dtype=object)
|
||
|
formatted[mask] = na
|
||
|
formatted = formatted.tolist()
|
||
|
|
||
|
else:
|
||
|
# weird all NA case
|
||
|
formatted = [
|
||
|
pprint_thing(na if isna(x) else x, escape_chars=("\t", "\r", "\n"))
|
||
|
for x in algos.take_1d(lev._values, level_codes)
|
||
|
]
|
||
|
stringified_levels.append(formatted)
|
||
|
|
||
|
result_levels = []
|
||
|
for lev, lev_name in zip(stringified_levels, self.names):
|
||
|
level = []
|
||
|
|
||
|
if names:
|
||
|
level.append(
|
||
|
pprint_thing(lev_name, escape_chars=("\t", "\r", "\n"))
|
||
|
if lev_name is not None
|
||
|
else ""
|
||
|
)
|
||
|
|
||
|
level.extend(np.array(lev, dtype=object))
|
||
|
result_levels.append(level)
|
||
|
|
||
|
if sparsify is None:
|
||
|
sparsify = get_option("display.multi_sparse")
|
||
|
|
||
|
if sparsify:
|
||
|
sentinel = ""
|
||
|
# GH3547 use value of sparsify as sentinel if it's "Falsey"
|
||
|
assert isinstance(sparsify, bool) or sparsify is lib.no_default
|
||
|
if sparsify in [False, lib.no_default]:
|
||
|
sentinel = sparsify
|
||
|
# little bit of a kludge job for #1217
|
||
|
result_levels = sparsify_labels(
|
||
|
result_levels, start=int(names), sentinel=sentinel
|
||
|
)
|
||
|
|
||
|
if adjoin:
|
||
|
from pandas.io.formats.format import get_adjustment
|
||
|
|
||
|
adj = get_adjustment()
|
||
|
return adj.adjoin(space, *result_levels).split("\n")
|
||
|
else:
|
||
|
return result_levels
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
# Names Methods
|
||
|
|
||
|
def _get_names(self):
|
||
|
return FrozenList(self._names)
|
||
|
|
||
|
def _set_names(self, names, level=None, validate=True):
|
||
|
"""
|
||
|
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
|
||
|
validate : boolean, default True
|
||
|
validate that the names match level lengths
|
||
|
|
||
|
Raises
|
||
|
------
|
||
|
TypeError if each name is not hashable.
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
sets names on levels. WARNING: mutates!
|
||
|
|
||
|
Note that you generally want to set this *after* changing levels, so
|
||
|
that it only acts on copies
|
||
|
"""
|
||
|
# GH 15110
|
||
|
# Don't allow a single string for names in a MultiIndex
|
||
|
if names is not None and not is_list_like(names):
|
||
|
raise ValueError("Names should be list-like for a MultiIndex")
|
||
|
names = list(names)
|
||
|
|
||
|
if validate:
|
||
|
if level is not None and len(names) != len(level):
|
||
|
raise ValueError("Length of names must match length of level.")
|
||
|
if level is None and len(names) != self.nlevels:
|
||
|
raise ValueError(
|
||
|
"Length of names must match number of levels in MultiIndex."
|
||
|
)
|
||
|
|
||
|
if level is None:
|
||
|
level = range(self.nlevels)
|
||
|
else:
|
||
|
level = [self._get_level_number(lev) for lev in level]
|
||
|
|
||
|
# set the name
|
||
|
for lev, name in zip(level, names):
|
||
|
if name is not None:
|
||
|
# GH 20527
|
||
|
# All items in 'names' need to be hashable:
|
||
|
if not is_hashable(name):
|
||
|
raise TypeError(
|
||
|
f"{type(self).__name__}.name must be a hashable type"
|
||
|
)
|
||
|
# pandas\core\indexes\multi.py:1448: error: Cannot determine type
|
||
|
# of '__setitem__' [has-type]
|
||
|
self._names[lev] = name # type: ignore[has-type]
|
||
|
|
||
|
# If .levels has been accessed, the names in our cache will be stale.
|
||
|
self._reset_cache()
|
||
|
|
||
|
names = property(
|
||
|
fset=_set_names,
|
||
|
fget=_get_names,
|
||
|
doc="""
|
||
|
Names of levels in MultiIndex.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_arrays(
|
||
|
... [[1, 2], [3, 4], [5, 6]], names=['x', 'y', 'z'])
|
||
|
>>> mi
|
||
|
MultiIndex([(1, 3, 5),
|
||
|
(2, 4, 6)],
|
||
|
names=['x', 'y', 'z'])
|
||
|
>>> mi.names
|
||
|
FrozenList(['x', 'y', 'z'])
|
||
|
""",
|
||
|
)
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
|
||
|
@doc(Index._get_grouper_for_level)
|
||
|
def _get_grouper_for_level(self, mapper, level):
|
||
|
indexer = self.codes[level]
|
||
|
level_index = self.levels[level]
|
||
|
|
||
|
if mapper is not None:
|
||
|
# Handle group mapping function and return
|
||
|
level_values = self.levels[level].take(indexer)
|
||
|
grouper = level_values.map(mapper)
|
||
|
return grouper, None, None
|
||
|
|
||
|
codes, uniques = algos.factorize(indexer, sort=True)
|
||
|
|
||
|
if len(uniques) > 0 and uniques[0] == -1:
|
||
|
# Handle NAs
|
||
|
mask = indexer != -1
|
||
|
ok_codes, uniques = algos.factorize(indexer[mask], sort=True)
|
||
|
|
||
|
codes = np.empty(len(indexer), dtype=indexer.dtype)
|
||
|
codes[mask] = ok_codes
|
||
|
codes[~mask] = -1
|
||
|
|
||
|
if len(uniques) < len(level_index):
|
||
|
# Remove unobserved levels from level_index
|
||
|
level_index = level_index.take(uniques)
|
||
|
else:
|
||
|
# break references back to us so that setting the name
|
||
|
# on the output of a groupby doesn't reflect back here.
|
||
|
level_index = level_index.copy()
|
||
|
|
||
|
if level_index._can_hold_na:
|
||
|
grouper = level_index.take(codes, fill_value=True)
|
||
|
else:
|
||
|
grouper = level_index.take(codes)
|
||
|
|
||
|
return grouper, codes, level_index
|
||
|
|
||
|
@cache_readonly
|
||
|
def inferred_type(self) -> str:
|
||
|
return "mixed"
|
||
|
|
||
|
def _get_level_number(self, level) -> int:
|
||
|
count = self.names.count(level)
|
||
|
if (count > 1) and not is_integer(level):
|
||
|
raise ValueError(
|
||
|
f"The name {level} occurs multiple times, use a level number"
|
||
|
)
|
||
|
try:
|
||
|
level = self.names.index(level)
|
||
|
except ValueError as err:
|
||
|
if not is_integer(level):
|
||
|
raise KeyError(f"Level {level} not found") from err
|
||
|
elif level < 0:
|
||
|
level += self.nlevels
|
||
|
if level < 0:
|
||
|
orig_level = level - self.nlevels
|
||
|
raise IndexError(
|
||
|
f"Too many levels: Index has only {self.nlevels} levels, "
|
||
|
f"{orig_level} is not a valid level number"
|
||
|
) from err
|
||
|
# Note: levels are zero-based
|
||
|
elif level >= self.nlevels:
|
||
|
raise IndexError(
|
||
|
f"Too many levels: Index has only {self.nlevels} levels, "
|
||
|
f"not {level + 1}"
|
||
|
) from err
|
||
|
return level
|
||
|
|
||
|
@property
|
||
|
def _has_complex_internals(self) -> bool:
|
||
|
# used to avoid libreduction code paths, which raise or require conversion
|
||
|
return True
|
||
|
|
||
|
@cache_readonly
|
||
|
def is_monotonic_increasing(self) -> bool:
|
||
|
"""
|
||
|
return if the index is monotonic increasing (only equal or
|
||
|
increasing) values.
|
||
|
"""
|
||
|
if any(-1 in code for code in self.codes):
|
||
|
return False
|
||
|
|
||
|
if all(level.is_monotonic for level in self.levels):
|
||
|
# If each level is sorted, we can operate on the codes directly. GH27495
|
||
|
return libalgos.is_lexsorted(
|
||
|
[x.astype("int64", copy=False) for x in self.codes]
|
||
|
)
|
||
|
|
||
|
# reversed() because lexsort() wants the most significant key last.
|
||
|
values = [
|
||
|
self._get_level_values(i)._values for i in reversed(range(len(self.levels)))
|
||
|
]
|
||
|
try:
|
||
|
sort_order = np.lexsort(values)
|
||
|
return Index(sort_order).is_monotonic
|
||
|
except TypeError:
|
||
|
|
||
|
# we have mixed types and np.lexsort is not happy
|
||
|
return Index(self._values).is_monotonic
|
||
|
|
||
|
@cache_readonly
|
||
|
def is_monotonic_decreasing(self) -> bool:
|
||
|
"""
|
||
|
return if the index is monotonic decreasing (only equal or
|
||
|
decreasing) values.
|
||
|
"""
|
||
|
# monotonic decreasing if and only if reverse is monotonic increasing
|
||
|
return self[::-1].is_monotonic_increasing
|
||
|
|
||
|
@cache_readonly
|
||
|
def _inferred_type_levels(self):
|
||
|
""" return a list of the inferred types, one for each level """
|
||
|
return [i.inferred_type for i in self.levels]
|
||
|
|
||
|
@doc(Index.duplicated)
|
||
|
def duplicated(self, keep="first"):
|
||
|
shape = map(len, self.levels)
|
||
|
ids = get_group_index(self.codes, shape, sort=False, xnull=False)
|
||
|
|
||
|
return duplicated_int64(ids, keep)
|
||
|
|
||
|
def fillna(self, value=None, downcast=None):
|
||
|
"""
|
||
|
fillna is not implemented for MultiIndex
|
||
|
"""
|
||
|
raise NotImplementedError("isna is not defined for MultiIndex")
|
||
|
|
||
|
@doc(Index.dropna)
|
||
|
def dropna(self, how="any"):
|
||
|
nans = [level_codes == -1 for level_codes in self.codes]
|
||
|
if how == "any":
|
||
|
indexer = np.any(nans, axis=0)
|
||
|
elif how == "all":
|
||
|
indexer = np.all(nans, axis=0)
|
||
|
else:
|
||
|
raise ValueError(f"invalid how option: {how}")
|
||
|
|
||
|
new_codes = [level_codes[~indexer] for level_codes in self.codes]
|
||
|
return self.set_codes(codes=new_codes)
|
||
|
|
||
|
def _get_level_values(self, level, unique=False):
|
||
|
"""
|
||
|
Return vector of label values for requested level,
|
||
|
equal to the length of the index
|
||
|
|
||
|
**this is an internal method**
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
level : int level
|
||
|
unique : bool, default False
|
||
|
if True, drop duplicated values
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
values : ndarray
|
||
|
"""
|
||
|
lev = self.levels[level]
|
||
|
level_codes = self.codes[level]
|
||
|
name = self._names[level]
|
||
|
if unique:
|
||
|
level_codes = algos.unique(level_codes)
|
||
|
filled = algos.take_1d(lev._values, level_codes, fill_value=lev._na_value)
|
||
|
return lev._shallow_copy(filled, name=name)
|
||
|
|
||
|
def get_level_values(self, level):
|
||
|
"""
|
||
|
Return vector of label values for requested level.
|
||
|
|
||
|
Length of returned vector is equal to the length of the index.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
level : int or str
|
||
|
``level`` is either the integer position of the level in the
|
||
|
MultiIndex, or the name of the level.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
values : Index
|
||
|
Values is a level of this MultiIndex converted to
|
||
|
a single :class:`Index` (or subclass thereof).
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
Create a MultiIndex:
|
||
|
|
||
|
>>> mi = pd.MultiIndex.from_arrays((list('abc'), list('def')))
|
||
|
>>> mi.names = ['level_1', 'level_2']
|
||
|
|
||
|
Get level values by supplying level as either integer or name:
|
||
|
|
||
|
>>> mi.get_level_values(0)
|
||
|
Index(['a', 'b', 'c'], dtype='object', name='level_1')
|
||
|
>>> mi.get_level_values('level_2')
|
||
|
Index(['d', 'e', 'f'], dtype='object', name='level_2')
|
||
|
"""
|
||
|
level = self._get_level_number(level)
|
||
|
values = self._get_level_values(level)
|
||
|
return values
|
||
|
|
||
|
@doc(Index.unique)
|
||
|
def unique(self, level=None):
|
||
|
|
||
|
if level is None:
|
||
|
return super().unique()
|
||
|
else:
|
||
|
level = self._get_level_number(level)
|
||
|
return self._get_level_values(level=level, unique=True)
|
||
|
|
||
|
def to_frame(self, index=True, name=None):
|
||
|
"""
|
||
|
Create a DataFrame with the levels of the MultiIndex as columns.
|
||
|
|
||
|
Column ordering is determined by the DataFrame constructor with data as
|
||
|
a dict.
|
||
|
|
||
|
.. versionadded:: 0.24.0
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
index : bool, default True
|
||
|
Set the index of the returned DataFrame as the original MultiIndex.
|
||
|
|
||
|
name : list / sequence of str, optional
|
||
|
The passed names should substitute index level names.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
DataFrame : a DataFrame containing the original MultiIndex data.
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
DataFrame : Two-dimensional, size-mutable, potentially heterogeneous
|
||
|
tabular data.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_arrays([['a', 'b'], ['c', 'd']])
|
||
|
>>> mi
|
||
|
MultiIndex([('a', 'c'),
|
||
|
('b', 'd')],
|
||
|
)
|
||
|
|
||
|
>>> df = mi.to_frame()
|
||
|
>>> df
|
||
|
0 1
|
||
|
a c a c
|
||
|
b d b d
|
||
|
|
||
|
>>> df = mi.to_frame(index=False)
|
||
|
>>> df
|
||
|
0 1
|
||
|
0 a c
|
||
|
1 b d
|
||
|
|
||
|
>>> df = mi.to_frame(name=['x', 'y'])
|
||
|
>>> df
|
||
|
x y
|
||
|
a c a c
|
||
|
b d b d
|
||
|
"""
|
||
|
from pandas import DataFrame
|
||
|
|
||
|
if name is not None:
|
||
|
if not is_list_like(name):
|
||
|
raise TypeError("'name' must be a list / sequence of column names.")
|
||
|
|
||
|
if len(name) != len(self.levels):
|
||
|
raise ValueError(
|
||
|
"'name' should have same length as number of levels on index."
|
||
|
)
|
||
|
idx_names = name
|
||
|
else:
|
||
|
idx_names = self.names
|
||
|
|
||
|
# Guarantee resulting column order - PY36+ dict maintains insertion order
|
||
|
result = DataFrame(
|
||
|
{
|
||
|
(level if lvlname is None else lvlname): self._get_level_values(level)
|
||
|
for lvlname, level in zip(idx_names, range(len(self.levels)))
|
||
|
},
|
||
|
copy=False,
|
||
|
)
|
||
|
|
||
|
if index:
|
||
|
result.index = self
|
||
|
return result
|
||
|
|
||
|
def to_flat_index(self):
|
||
|
"""
|
||
|
Convert a MultiIndex to an Index of Tuples containing the level values.
|
||
|
|
||
|
.. versionadded:: 0.24.0
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
pd.Index
|
||
|
Index with the MultiIndex data represented in Tuples.
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
This method will simply return the caller if called by anything other
|
||
|
than a MultiIndex.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> index = pd.MultiIndex.from_product(
|
||
|
... [['foo', 'bar'], ['baz', 'qux']],
|
||
|
... names=['a', 'b'])
|
||
|
>>> index.to_flat_index()
|
||
|
Index([('foo', 'baz'), ('foo', 'qux'),
|
||
|
('bar', 'baz'), ('bar', 'qux')],
|
||
|
dtype='object')
|
||
|
"""
|
||
|
return Index(self._values, tupleize_cols=False)
|
||
|
|
||
|
@property
|
||
|
def _is_all_dates(self) -> bool:
|
||
|
return False
|
||
|
|
||
|
def is_lexsorted(self) -> bool:
|
||
|
"""
|
||
|
Return True if the codes are lexicographically sorted.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
bool
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
In the below examples, the first level of the MultiIndex is sorted because
|
||
|
a<b<c, so there is no need to look at the next level.
|
||
|
|
||
|
>>> pd.MultiIndex.from_arrays([['a', 'b', 'c'], ['d', 'e', 'f']]).is_lexsorted()
|
||
|
True
|
||
|
>>> pd.MultiIndex.from_arrays([['a', 'b', 'c'], ['d', 'f', 'e']]).is_lexsorted()
|
||
|
True
|
||
|
|
||
|
In case there is a tie, the lexicographical sorting looks
|
||
|
at the next level of the MultiIndex.
|
||
|
|
||
|
>>> pd.MultiIndex.from_arrays([[0, 1, 1], ['a', 'b', 'c']]).is_lexsorted()
|
||
|
True
|
||
|
>>> pd.MultiIndex.from_arrays([[0, 1, 1], ['a', 'c', 'b']]).is_lexsorted()
|
||
|
False
|
||
|
>>> pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'],
|
||
|
... ['aa', 'bb', 'aa', 'bb']]).is_lexsorted()
|
||
|
True
|
||
|
>>> pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'],
|
||
|
... ['bb', 'aa', 'aa', 'bb']]).is_lexsorted()
|
||
|
False
|
||
|
"""
|
||
|
return self.lexsort_depth == self.nlevels
|
||
|
|
||
|
@cache_readonly
|
||
|
def lexsort_depth(self):
|
||
|
if self.sortorder is not None:
|
||
|
return self.sortorder
|
||
|
|
||
|
return self._lexsort_depth()
|
||
|
|
||
|
def _lexsort_depth(self) -> int:
|
||
|
"""
|
||
|
Compute and return the lexsort_depth, the number of levels of the
|
||
|
MultiIndex that are sorted lexically
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
int
|
||
|
"""
|
||
|
int64_codes = [ensure_int64(level_codes) for level_codes in self.codes]
|
||
|
for k in range(self.nlevels, 0, -1):
|
||
|
if libalgos.is_lexsorted(int64_codes[:k]):
|
||
|
return k
|
||
|
return 0
|
||
|
|
||
|
def _sort_levels_monotonic(self):
|
||
|
"""
|
||
|
This is an *internal* function.
|
||
|
|
||
|
Create a new MultiIndex from the current to monotonically sorted
|
||
|
items IN the levels. This does not actually make the entire MultiIndex
|
||
|
monotonic, JUST the levels.
|
||
|
|
||
|
The resulting MultiIndex will have the same outward
|
||
|
appearance, meaning the same .values and ordering. It will also
|
||
|
be .equals() to the original.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
MultiIndex
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
|
||
|
... codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
|
||
|
>>> mi
|
||
|
MultiIndex([('a', 'bb'),
|
||
|
('a', 'aa'),
|
||
|
('b', 'bb'),
|
||
|
('b', 'aa')],
|
||
|
)
|
||
|
|
||
|
>>> mi.sort_values()
|
||
|
MultiIndex([('a', 'aa'),
|
||
|
('a', 'bb'),
|
||
|
('b', 'aa'),
|
||
|
('b', 'bb')],
|
||
|
)
|
||
|
"""
|
||
|
if self.is_lexsorted() and self.is_monotonic:
|
||
|
return self
|
||
|
|
||
|
new_levels = []
|
||
|
new_codes = []
|
||
|
|
||
|
for lev, level_codes in zip(self.levels, self.codes):
|
||
|
|
||
|
if not lev.is_monotonic:
|
||
|
try:
|
||
|
# indexer to reorder the levels
|
||
|
indexer = lev.argsort()
|
||
|
except TypeError:
|
||
|
pass
|
||
|
else:
|
||
|
lev = lev.take(indexer)
|
||
|
|
||
|
# indexer to reorder the level codes
|
||
|
indexer = ensure_int64(indexer)
|
||
|
ri = lib.get_reverse_indexer(indexer, len(indexer))
|
||
|
level_codes = algos.take_1d(ri, level_codes)
|
||
|
|
||
|
new_levels.append(lev)
|
||
|
new_codes.append(level_codes)
|
||
|
|
||
|
return MultiIndex(
|
||
|
new_levels,
|
||
|
new_codes,
|
||
|
names=self.names,
|
||
|
sortorder=self.sortorder,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
|
||
|
def remove_unused_levels(self):
|
||
|
"""
|
||
|
Create new MultiIndex from current that removes unused levels.
|
||
|
|
||
|
Unused level(s) means levels that are not expressed in the
|
||
|
labels. The resulting MultiIndex will have the same outward
|
||
|
appearance, meaning the same .values and ordering. It will
|
||
|
also be .equals() to the original.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
MultiIndex
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_product([range(2), list('ab')])
|
||
|
>>> mi
|
||
|
MultiIndex([(0, 'a'),
|
||
|
(0, 'b'),
|
||
|
(1, 'a'),
|
||
|
(1, 'b')],
|
||
|
)
|
||
|
|
||
|
>>> mi[2:]
|
||
|
MultiIndex([(1, 'a'),
|
||
|
(1, 'b')],
|
||
|
)
|
||
|
|
||
|
The 0 from the first level is not represented
|
||
|
and can be removed
|
||
|
|
||
|
>>> mi2 = mi[2:].remove_unused_levels()
|
||
|
>>> mi2.levels
|
||
|
FrozenList([[1], ['a', 'b']])
|
||
|
"""
|
||
|
new_levels = []
|
||
|
new_codes = []
|
||
|
|
||
|
changed = False
|
||
|
for lev, level_codes in zip(self.levels, self.codes):
|
||
|
|
||
|
# Since few levels are typically unused, bincount() is more
|
||
|
# efficient than unique() - however it only accepts positive values
|
||
|
# (and drops order):
|
||
|
uniques = np.where(np.bincount(level_codes + 1) > 0)[0] - 1
|
||
|
has_na = int(len(uniques) and (uniques[0] == -1))
|
||
|
|
||
|
if len(uniques) != len(lev) + has_na:
|
||
|
# We have unused levels
|
||
|
changed = True
|
||
|
|
||
|
# Recalculate uniques, now preserving order.
|
||
|
# Can easily be cythonized by exploiting the already existing
|
||
|
# "uniques" and stop parsing "level_codes" when all items
|
||
|
# are found:
|
||
|
uniques = algos.unique(level_codes)
|
||
|
if has_na:
|
||
|
na_idx = np.where(uniques == -1)[0]
|
||
|
# Just ensure that -1 is in first position:
|
||
|
uniques[[0, na_idx[0]]] = uniques[[na_idx[0], 0]]
|
||
|
|
||
|
# codes get mapped from uniques to 0:len(uniques)
|
||
|
# -1 (if present) is mapped to last position
|
||
|
code_mapping = np.zeros(len(lev) + has_na)
|
||
|
# ... and reassigned value -1:
|
||
|
code_mapping[uniques] = np.arange(len(uniques)) - has_na
|
||
|
|
||
|
level_codes = code_mapping[level_codes]
|
||
|
|
||
|
# new levels are simple
|
||
|
lev = lev.take(uniques[has_na:])
|
||
|
|
||
|
new_levels.append(lev)
|
||
|
new_codes.append(level_codes)
|
||
|
|
||
|
result = self.view()
|
||
|
|
||
|
if changed:
|
||
|
result._reset_identity()
|
||
|
result._set_levels(new_levels, validate=False)
|
||
|
result._set_codes(new_codes, validate=False)
|
||
|
|
||
|
return result
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
# Pickling Methods
|
||
|
|
||
|
def __reduce__(self):
|
||
|
"""Necessary for making this object picklable"""
|
||
|
d = {
|
||
|
"levels": list(self.levels),
|
||
|
"codes": list(self.codes),
|
||
|
"sortorder": self.sortorder,
|
||
|
"names": list(self.names),
|
||
|
}
|
||
|
return ibase._new_Index, (type(self), d), None
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
|
||
|
def __getitem__(self, key):
|
||
|
if is_scalar(key):
|
||
|
key = com.cast_scalar_indexer(key, warn_float=True)
|
||
|
|
||
|
retval = []
|
||
|
for lev, level_codes in zip(self.levels, self.codes):
|
||
|
if level_codes[key] == -1:
|
||
|
retval.append(np.nan)
|
||
|
else:
|
||
|
retval.append(lev[level_codes[key]])
|
||
|
|
||
|
return tuple(retval)
|
||
|
else:
|
||
|
if com.is_bool_indexer(key):
|
||
|
key = np.asarray(key, dtype=bool)
|
||
|
sortorder = self.sortorder
|
||
|
else:
|
||
|
# cannot be sure whether the result will be sorted
|
||
|
sortorder = None
|
||
|
|
||
|
if isinstance(key, Index):
|
||
|
key = np.asarray(key)
|
||
|
|
||
|
new_codes = [level_codes[key] for level_codes in self.codes]
|
||
|
|
||
|
return MultiIndex(
|
||
|
levels=self.levels,
|
||
|
codes=new_codes,
|
||
|
names=self.names,
|
||
|
sortorder=sortorder,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
|
||
|
@Appender(_index_shared_docs["take"] % _index_doc_kwargs)
|
||
|
def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs):
|
||
|
nv.validate_take((), kwargs)
|
||
|
indices = ensure_platform_int(indices)
|
||
|
|
||
|
# only fill if we are passing a non-None fill_value
|
||
|
allow_fill = self._maybe_disallow_fill(allow_fill, fill_value, indices)
|
||
|
|
||
|
na_value = -1
|
||
|
|
||
|
if allow_fill:
|
||
|
taken = [lab.take(indices) for lab in self.codes]
|
||
|
mask = indices == -1
|
||
|
if mask.any():
|
||
|
masked = []
|
||
|
for new_label in taken:
|
||
|
label_values = new_label
|
||
|
label_values[mask] = na_value
|
||
|
masked.append(np.asarray(label_values))
|
||
|
taken = masked
|
||
|
else:
|
||
|
taken = [lab.take(indices) for lab in self.codes]
|
||
|
|
||
|
return MultiIndex(
|
||
|
levels=self.levels, codes=taken, names=self.names, verify_integrity=False
|
||
|
)
|
||
|
|
||
|
def append(self, other):
|
||
|
"""
|
||
|
Append a collection of Index options together
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
other : Index or list/tuple of indices
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
appended : Index
|
||
|
"""
|
||
|
if not isinstance(other, (list, tuple)):
|
||
|
other = [other]
|
||
|
|
||
|
if all(
|
||
|
(isinstance(o, MultiIndex) and o.nlevels >= self.nlevels) for o in other
|
||
|
):
|
||
|
arrays = []
|
||
|
for i in range(self.nlevels):
|
||
|
label = self._get_level_values(i)
|
||
|
appended = [o._get_level_values(i) for o in other]
|
||
|
arrays.append(label.append(appended))
|
||
|
return MultiIndex.from_arrays(arrays, names=self.names)
|
||
|
|
||
|
to_concat = (self._values,) + tuple(k._values for k in other)
|
||
|
new_tuples = np.concatenate(to_concat)
|
||
|
|
||
|
# if all(isinstance(x, MultiIndex) for x in other):
|
||
|
try:
|
||
|
return MultiIndex.from_tuples(new_tuples, names=self.names)
|
||
|
except (TypeError, IndexError):
|
||
|
return Index(new_tuples)
|
||
|
|
||
|
def argsort(self, *args, **kwargs) -> np.ndarray:
|
||
|
return self._values.argsort(*args, **kwargs)
|
||
|
|
||
|
@Appender(_index_shared_docs["repeat"] % _index_doc_kwargs)
|
||
|
def repeat(self, repeats, axis=None):
|
||
|
nv.validate_repeat((), {"axis": axis})
|
||
|
repeats = ensure_platform_int(repeats)
|
||
|
return MultiIndex(
|
||
|
levels=self.levels,
|
||
|
codes=[
|
||
|
level_codes.view(np.ndarray).astype(np.intp).repeat(repeats)
|
||
|
for level_codes in self.codes
|
||
|
],
|
||
|
names=self.names,
|
||
|
sortorder=self.sortorder,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
|
||
|
def where(self, cond, other=None):
|
||
|
raise NotImplementedError(".where is not supported for MultiIndex operations")
|
||
|
|
||
|
def drop(self, codes, level=None, errors="raise"):
|
||
|
"""
|
||
|
Make new MultiIndex with passed list of codes deleted
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
codes : array-like
|
||
|
Must be a list of tuples when level is not specified
|
||
|
level : int or level name, default None
|
||
|
errors : str, default 'raise'
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
dropped : MultiIndex
|
||
|
"""
|
||
|
if level is not None:
|
||
|
return self._drop_from_level(codes, level, errors)
|
||
|
|
||
|
if not isinstance(codes, (np.ndarray, Index)):
|
||
|
try:
|
||
|
codes = com.index_labels_to_array(codes, dtype=object)
|
||
|
except ValueError:
|
||
|
pass
|
||
|
|
||
|
inds = []
|
||
|
for level_codes in codes:
|
||
|
try:
|
||
|
loc = self.get_loc(level_codes)
|
||
|
# get_loc returns either an integer, a slice, or a boolean
|
||
|
# mask
|
||
|
if isinstance(loc, int):
|
||
|
inds.append(loc)
|
||
|
elif isinstance(loc, slice):
|
||
|
step = loc.step if loc.step is not None else 1
|
||
|
inds.extend(range(loc.start, loc.stop, step))
|
||
|
elif com.is_bool_indexer(loc):
|
||
|
if self.lexsort_depth == 0:
|
||
|
warnings.warn(
|
||
|
"dropping on a non-lexsorted multi-index "
|
||
|
"without a level parameter may impact performance.",
|
||
|
PerformanceWarning,
|
||
|
stacklevel=3,
|
||
|
)
|
||
|
loc = loc.nonzero()[0]
|
||
|
inds.extend(loc)
|
||
|
else:
|
||
|
msg = f"unsupported indexer of type {type(loc)}"
|
||
|
raise AssertionError(msg)
|
||
|
except KeyError:
|
||
|
if errors != "ignore":
|
||
|
raise
|
||
|
|
||
|
return self.delete(inds)
|
||
|
|
||
|
def _drop_from_level(self, codes, level, errors="raise"):
|
||
|
codes = com.index_labels_to_array(codes)
|
||
|
i = self._get_level_number(level)
|
||
|
index = self.levels[i]
|
||
|
values = index.get_indexer(codes)
|
||
|
# If nan should be dropped it will equal -1 here. We have to check which values
|
||
|
# are not nan and equal -1, this means they are missing in the index
|
||
|
nan_codes = isna(codes)
|
||
|
values[(np.equal(nan_codes, False)) & (values == -1)] = -2
|
||
|
if index.shape[0] == self.shape[0]:
|
||
|
values[np.equal(nan_codes, True)] = -2
|
||
|
|
||
|
not_found = codes[values == -2]
|
||
|
if len(not_found) != 0 and errors != "ignore":
|
||
|
raise KeyError(f"labels {not_found} not found in level")
|
||
|
mask = ~algos.isin(self.codes[i], values)
|
||
|
|
||
|
return self[mask]
|
||
|
|
||
|
def swaplevel(self, i=-2, j=-1):
|
||
|
"""
|
||
|
Swap level i with level j.
|
||
|
|
||
|
Calling this method does not change the ordering of the values.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
i : int, str, default -2
|
||
|
First level of index to be swapped. Can pass level name as string.
|
||
|
Type of parameters can be mixed.
|
||
|
j : int, str, default -1
|
||
|
Second level of index to be swapped. Can pass level name as string.
|
||
|
Type of parameters can be mixed.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
MultiIndex
|
||
|
A new MultiIndex.
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
Series.swaplevel : Swap levels i and j in a MultiIndex.
|
||
|
Dataframe.swaplevel : Swap levels i and j in a MultiIndex on a
|
||
|
particular axis.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
|
||
|
... codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
|
||
|
>>> mi
|
||
|
MultiIndex([('a', 'bb'),
|
||
|
('a', 'aa'),
|
||
|
('b', 'bb'),
|
||
|
('b', 'aa')],
|
||
|
)
|
||
|
>>> mi.swaplevel(0, 1)
|
||
|
MultiIndex([('bb', 'a'),
|
||
|
('aa', 'a'),
|
||
|
('bb', 'b'),
|
||
|
('aa', 'b')],
|
||
|
)
|
||
|
"""
|
||
|
new_levels = list(self.levels)
|
||
|
new_codes = list(self.codes)
|
||
|
new_names = list(self.names)
|
||
|
|
||
|
i = self._get_level_number(i)
|
||
|
j = self._get_level_number(j)
|
||
|
|
||
|
new_levels[i], new_levels[j] = new_levels[j], new_levels[i]
|
||
|
new_codes[i], new_codes[j] = new_codes[j], new_codes[i]
|
||
|
new_names[i], new_names[j] = new_names[j], new_names[i]
|
||
|
|
||
|
return MultiIndex(
|
||
|
levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
|
||
|
)
|
||
|
|
||
|
def reorder_levels(self, order):
|
||
|
"""
|
||
|
Rearrange levels using input order. May not drop or duplicate levels.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
order : list of int or list of str
|
||
|
List representing new level order. Reference level by number
|
||
|
(position) or by key (label).
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
MultiIndex
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_arrays([[1, 2], [3, 4]], names=['x', 'y'])
|
||
|
>>> mi
|
||
|
MultiIndex([(1, 3),
|
||
|
(2, 4)],
|
||
|
names=['x', 'y'])
|
||
|
|
||
|
>>> mi.reorder_levels(order=[1, 0])
|
||
|
MultiIndex([(3, 1),
|
||
|
(4, 2)],
|
||
|
names=['y', 'x'])
|
||
|
|
||
|
>>> mi.reorder_levels(order=['y', 'x'])
|
||
|
MultiIndex([(3, 1),
|
||
|
(4, 2)],
|
||
|
names=['y', 'x'])
|
||
|
"""
|
||
|
order = [self._get_level_number(i) for i in order]
|
||
|
if len(order) != self.nlevels:
|
||
|
raise AssertionError(
|
||
|
f"Length of order must be same as number of levels ({self.nlevels}), "
|
||
|
f"got {len(order)}"
|
||
|
)
|
||
|
new_levels = [self.levels[i] for i in order]
|
||
|
new_codes = [self.codes[i] for i in order]
|
||
|
new_names = [self.names[i] for i in order]
|
||
|
|
||
|
return MultiIndex(
|
||
|
levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
|
||
|
)
|
||
|
|
||
|
def _get_codes_for_sorting(self):
|
||
|
"""
|
||
|
we are categorizing our codes by using the
|
||
|
available categories (all, not just observed)
|
||
|
excluding any missing ones (-1); this is in preparation
|
||
|
for sorting, where we need to disambiguate that -1 is not
|
||
|
a valid valid
|
||
|
"""
|
||
|
|
||
|
def cats(level_codes):
|
||
|
return np.arange(
|
||
|
np.array(level_codes).max() + 1 if len(level_codes) else 0,
|
||
|
dtype=level_codes.dtype,
|
||
|
)
|
||
|
|
||
|
return [
|
||
|
Categorical.from_codes(level_codes, cats(level_codes), ordered=True)
|
||
|
for level_codes in self.codes
|
||
|
]
|
||
|
|
||
|
def sortlevel(self, level=0, ascending=True, sort_remaining=True):
|
||
|
"""
|
||
|
Sort MultiIndex at the requested level.
|
||
|
|
||
|
The result will respect the original ordering of the associated
|
||
|
factor at that level.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
level : list-like, int or str, default 0
|
||
|
If a string is given, must be a name of the level.
|
||
|
If list-like must be names or ints of levels.
|
||
|
ascending : bool, default True
|
||
|
False to sort in descending order.
|
||
|
Can also be a list to specify a directed ordering.
|
||
|
sort_remaining : sort by the remaining levels after level
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
sorted_index : pd.MultiIndex
|
||
|
Resulting index.
|
||
|
indexer : np.ndarray
|
||
|
Indices of output values in original index.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_arrays([[0, 0], [2, 1]])
|
||
|
>>> mi
|
||
|
MultiIndex([(0, 2),
|
||
|
(0, 1)],
|
||
|
)
|
||
|
|
||
|
>>> mi.sortlevel()
|
||
|
(MultiIndex([(0, 1),
|
||
|
(0, 2)],
|
||
|
), array([1, 0]))
|
||
|
|
||
|
>>> mi.sortlevel(sort_remaining=False)
|
||
|
(MultiIndex([(0, 2),
|
||
|
(0, 1)],
|
||
|
), array([0, 1]))
|
||
|
|
||
|
>>> mi.sortlevel(1)
|
||
|
(MultiIndex([(0, 1),
|
||
|
(0, 2)],
|
||
|
), array([1, 0]))
|
||
|
|
||
|
>>> mi.sortlevel(1, ascending=False)
|
||
|
(MultiIndex([(0, 2),
|
||
|
(0, 1)],
|
||
|
), array([0, 1]))
|
||
|
"""
|
||
|
if isinstance(level, (str, int)):
|
||
|
level = [level]
|
||
|
level = [self._get_level_number(lev) for lev in level]
|
||
|
sortorder = None
|
||
|
|
||
|
# we have a directed ordering via ascending
|
||
|
if isinstance(ascending, list):
|
||
|
if not len(level) == len(ascending):
|
||
|
raise ValueError("level must have same length as ascending")
|
||
|
|
||
|
indexer = lexsort_indexer(
|
||
|
[self.codes[lev] for lev in level], orders=ascending
|
||
|
)
|
||
|
|
||
|
# level ordering
|
||
|
else:
|
||
|
|
||
|
codes = list(self.codes)
|
||
|
shape = list(self.levshape)
|
||
|
|
||
|
# partition codes and shape
|
||
|
primary = tuple(codes[lev] for lev in level)
|
||
|
primshp = tuple(shape[lev] for lev in level)
|
||
|
|
||
|
# Reverse sorted to retain the order of
|
||
|
# smaller indices that needs to be removed
|
||
|
for lev in sorted(level, reverse=True):
|
||
|
codes.pop(lev)
|
||
|
shape.pop(lev)
|
||
|
|
||
|
if sort_remaining:
|
||
|
primary += primary + tuple(codes)
|
||
|
primshp += primshp + tuple(shape)
|
||
|
else:
|
||
|
sortorder = level[0]
|
||
|
|
||
|
indexer = indexer_from_factorized(primary, primshp, compress=False)
|
||
|
|
||
|
if not ascending:
|
||
|
indexer = indexer[::-1]
|
||
|
|
||
|
indexer = ensure_platform_int(indexer)
|
||
|
new_codes = [level_codes.take(indexer) for level_codes in self.codes]
|
||
|
|
||
|
new_index = MultiIndex(
|
||
|
codes=new_codes,
|
||
|
levels=self.levels,
|
||
|
names=self.names,
|
||
|
sortorder=sortorder,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
|
||
|
return new_index, indexer
|
||
|
|
||
|
def reindex(self, target, method=None, level=None, limit=None, tolerance=None):
|
||
|
"""
|
||
|
Create index with target's values (move/add/delete values as necessary)
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
new_index : pd.MultiIndex
|
||
|
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, "names")
|
||
|
|
||
|
if level is not None:
|
||
|
if method is not None:
|
||
|
raise TypeError("Fill method not supported if level passed")
|
||
|
|
||
|
# GH7774: preserve dtype/tz if target is empty and not an Index.
|
||
|
# target may be an iterator
|
||
|
target = ibase.ensure_has_len(target)
|
||
|
if len(target) == 0 and not isinstance(target, Index):
|
||
|
idx = self.levels[level]
|
||
|
attrs = idx._get_attributes_dict()
|
||
|
attrs.pop("freq", None) # don't preserve freq
|
||
|
target = type(idx)._simple_new(np.empty(0, dtype=idx.dtype), **attrs)
|
||
|
else:
|
||
|
target = ensure_index(target)
|
||
|
target, indexer, _ = self._join_level(
|
||
|
target, level, how="right", return_indexers=True, keep_order=False
|
||
|
)
|
||
|
else:
|
||
|
target = ensure_index(target)
|
||
|
if self.equals(target):
|
||
|
indexer = None
|
||
|
else:
|
||
|
if self.is_unique:
|
||
|
indexer = self.get_indexer(
|
||
|
target, method=method, limit=limit, tolerance=tolerance
|
||
|
)
|
||
|
else:
|
||
|
raise ValueError("cannot handle a non-unique multi-index!")
|
||
|
|
||
|
if not isinstance(target, MultiIndex):
|
||
|
if indexer is None:
|
||
|
target = self
|
||
|
elif (indexer >= 0).all():
|
||
|
target = self.take(indexer)
|
||
|
else:
|
||
|
# hopefully?
|
||
|
target = MultiIndex.from_tuples(target)
|
||
|
|
||
|
if (
|
||
|
preserve_names
|
||
|
and target.nlevels == self.nlevels
|
||
|
and target.names != self.names
|
||
|
):
|
||
|
target = target.copy(deep=False)
|
||
|
target.names = self.names
|
||
|
|
||
|
return target, indexer
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
# Indexing Methods
|
||
|
|
||
|
def _check_indexing_error(self, key):
|
||
|
if not is_hashable(key) or is_iterator(key):
|
||
|
# We allow tuples if they are hashable, whereas other Index
|
||
|
# subclasses require scalar.
|
||
|
# We have to explicitly exclude generators, as these are hashable.
|
||
|
raise InvalidIndexError(key)
|
||
|
|
||
|
def _should_fallback_to_positional(self) -> bool:
|
||
|
"""
|
||
|
Should integer key(s) be treated as positional?
|
||
|
"""
|
||
|
# GH#33355
|
||
|
return self.levels[0]._should_fallback_to_positional()
|
||
|
|
||
|
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`
|
||
|
"""
|
||
|
new_values = series._values[loc]
|
||
|
if is_scalar(loc):
|
||
|
return new_values
|
||
|
|
||
|
if len(new_values) == 1 and not self.nlevels > 1:
|
||
|
# If more than one level left, we can not return a scalar
|
||
|
return new_values[0]
|
||
|
|
||
|
new_index = self[loc]
|
||
|
new_index = maybe_droplevels(new_index, key)
|
||
|
new_ser = series._constructor(new_values, index=new_index, name=series.name)
|
||
|
return new_ser.__finalize__(series)
|
||
|
|
||
|
def _convert_listlike_indexer(self, keyarr):
|
||
|
"""
|
||
|
Parameters
|
||
|
----------
|
||
|
keyarr : list-like
|
||
|
Indexer to convert.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
tuple (indexer, keyarr)
|
||
|
indexer is an ndarray or None if cannot convert
|
||
|
keyarr are tuple-safe keys
|
||
|
"""
|
||
|
indexer, keyarr = super()._convert_listlike_indexer(keyarr)
|
||
|
|
||
|
# are we indexing a specific level
|
||
|
if indexer is None and len(keyarr) and not isinstance(keyarr[0], tuple):
|
||
|
level = 0
|
||
|
_, indexer = self.reindex(keyarr, level=level)
|
||
|
|
||
|
# take all
|
||
|
if indexer is None:
|
||
|
indexer = np.arange(len(self))
|
||
|
|
||
|
check = self.levels[0].get_indexer(keyarr)
|
||
|
mask = check == -1
|
||
|
if mask.any():
|
||
|
raise KeyError(f"{keyarr[mask]} not in index")
|
||
|
|
||
|
return indexer, keyarr
|
||
|
|
||
|
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
|
||
|
if isinstance(key, str) and self.levels[0]._supports_partial_string_indexing:
|
||
|
# Convert key '2016-01-01' to
|
||
|
# ('2016-01-01'[, slice(None, None, None)]+)
|
||
|
key = (key,) + (slice(None),) * (len(self.levels) - 1)
|
||
|
|
||
|
if isinstance(key, tuple):
|
||
|
# Convert (..., '2016-01-01', ...) in tuple to
|
||
|
# (..., slice('2016-01-01', '2016-01-01', None), ...)
|
||
|
new_key = []
|
||
|
for i, component in enumerate(key):
|
||
|
if (
|
||
|
isinstance(component, str)
|
||
|
and self.levels[i]._supports_partial_string_indexing
|
||
|
):
|
||
|
new_key.append(slice(component, component, None))
|
||
|
else:
|
||
|
new_key.append(component)
|
||
|
key = tuple(new_key)
|
||
|
|
||
|
return key
|
||
|
|
||
|
@Appender(_index_shared_docs["get_indexer"] % _index_doc_kwargs)
|
||
|
def get_indexer(self, target, method=None, limit=None, tolerance=None):
|
||
|
method = missing.clean_reindex_fill_method(method)
|
||
|
target = ensure_index(target)
|
||
|
|
||
|
# empty indexer
|
||
|
if is_list_like(target) and not len(target):
|
||
|
return ensure_platform_int(np.array([]))
|
||
|
|
||
|
if not isinstance(target, MultiIndex):
|
||
|
try:
|
||
|
target = MultiIndex.from_tuples(target)
|
||
|
except (TypeError, ValueError):
|
||
|
|
||
|
# let's instead try with a straight Index
|
||
|
if method is None:
|
||
|
return Index(self._values).get_indexer(
|
||
|
target, method=method, limit=limit, tolerance=tolerance
|
||
|
)
|
||
|
|
||
|
if not self.is_unique:
|
||
|
raise ValueError("Reindexing only valid with uniquely valued Index objects")
|
||
|
|
||
|
if method == "pad" or method == "backfill":
|
||
|
if tolerance is not None:
|
||
|
raise NotImplementedError(
|
||
|
"tolerance not implemented yet for MultiIndex"
|
||
|
)
|
||
|
indexer = self._engine.get_indexer(
|
||
|
values=self._values, target=target, method=method, limit=limit
|
||
|
)
|
||
|
elif method == "nearest":
|
||
|
raise NotImplementedError(
|
||
|
"method='nearest' not implemented yet "
|
||
|
"for MultiIndex; see GitHub issue 9365"
|
||
|
)
|
||
|
else:
|
||
|
indexer = self._engine.get_indexer(target)
|
||
|
|
||
|
return ensure_platform_int(indexer)
|
||
|
|
||
|
def get_slice_bound(
|
||
|
self, label: Union[Hashable, Sequence[Hashable]], side: str, kind: str
|
||
|
) -> int:
|
||
|
"""
|
||
|
For an ordered MultiIndex, compute slice bound
|
||
|
that corresponds to given label.
|
||
|
|
||
|
Returns leftmost (one-past-the-rightmost if `side=='right') position
|
||
|
of given label.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
label : object or tuple of objects
|
||
|
side : {'left', 'right'}
|
||
|
kind : {'loc', 'getitem'}
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
int
|
||
|
Index of label.
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
This method only works if level 0 index of the MultiIndex is lexsorted.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_arrays([list('abbc'), list('gefd')])
|
||
|
|
||
|
Get the locations from the leftmost 'b' in the first level
|
||
|
until the end of the multiindex:
|
||
|
|
||
|
>>> mi.get_slice_bound('b', side="left", kind="loc")
|
||
|
1
|
||
|
|
||
|
Like above, but if you get the locations from the rightmost
|
||
|
'b' in the first level and 'f' in the second level:
|
||
|
|
||
|
>>> mi.get_slice_bound(('b','f'), side="right", kind="loc")
|
||
|
3
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
MultiIndex.get_loc : Get location for a label or a tuple of labels.
|
||
|
MultiIndex.get_locs : Get location for a label/slice/list/mask or a
|
||
|
sequence of such.
|
||
|
"""
|
||
|
if not isinstance(label, tuple):
|
||
|
label = (label,)
|
||
|
return self._partial_tup_index(label, side=side)
|
||
|
|
||
|
def slice_locs(self, start=None, end=None, step=None, kind=None):
|
||
|
"""
|
||
|
For an ordered MultiIndex, compute the slice locations for input
|
||
|
labels.
|
||
|
|
||
|
The input labels can be tuples representing partial levels, e.g. for a
|
||
|
MultiIndex with 3 levels, you can pass a single value (corresponding to
|
||
|
the first level), or a 1-, 2-, or 3-tuple.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
start : label or tuple, default None
|
||
|
If None, defaults to the beginning
|
||
|
end : label or tuple
|
||
|
If None, defaults to the end
|
||
|
step : int or None
|
||
|
Slice step
|
||
|
kind : string, optional, defaults None
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
(start, end) : (int, int)
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
This method only works if the MultiIndex is properly lexsorted. So,
|
||
|
if only the first 2 levels of a 3-level MultiIndex are lexsorted,
|
||
|
you can only pass two levels to ``.slice_locs``.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_arrays([list('abbd'), list('deff')],
|
||
|
... names=['A', 'B'])
|
||
|
|
||
|
Get the slice locations from the beginning of 'b' in the first level
|
||
|
until the end of the multiindex:
|
||
|
|
||
|
>>> mi.slice_locs(start='b')
|
||
|
(1, 4)
|
||
|
|
||
|
Like above, but stop at the end of 'b' in the first level and 'f' in
|
||
|
the second level:
|
||
|
|
||
|
>>> mi.slice_locs(start='b', end=('b', 'f'))
|
||
|
(1, 3)
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
MultiIndex.get_loc : Get location for a label or a tuple of labels.
|
||
|
MultiIndex.get_locs : Get location for a label/slice/list/mask or a
|
||
|
sequence of such.
|
||
|
"""
|
||
|
# This function adds nothing to its parent implementation (the magic
|
||
|
# happens in get_slice_bound method), but it adds meaningful doc.
|
||
|
return super().slice_locs(start, end, step, kind=kind)
|
||
|
|
||
|
def _partial_tup_index(self, tup, side="left"):
|
||
|
if len(tup) > self.lexsort_depth:
|
||
|
raise UnsortedIndexError(
|
||
|
f"Key length ({len(tup)}) was greater than MultiIndex lexsort depth "
|
||
|
f"({self.lexsort_depth})"
|
||
|
)
|
||
|
|
||
|
n = len(tup)
|
||
|
start, end = 0, len(self)
|
||
|
zipped = zip(tup, self.levels, self.codes)
|
||
|
for k, (lab, lev, labs) in enumerate(zipped):
|
||
|
section = labs[start:end]
|
||
|
|
||
|
if lab not in lev and not isna(lab):
|
||
|
if not lev.is_type_compatible(lib.infer_dtype([lab], skipna=False)):
|
||
|
raise TypeError(f"Level type mismatch: {lab}")
|
||
|
|
||
|
# short circuit
|
||
|
loc = lev.searchsorted(lab, side=side)
|
||
|
if side == "right" and loc >= 0:
|
||
|
loc -= 1
|
||
|
return start + section.searchsorted(loc, side=side)
|
||
|
|
||
|
idx = self._get_loc_single_level_index(lev, lab)
|
||
|
if isinstance(idx, slice) and k < n - 1:
|
||
|
# Get start and end value from slice, necessary when a non-integer
|
||
|
# interval is given as input GH#37707
|
||
|
start = idx.start
|
||
|
end = idx.stop
|
||
|
elif k < n - 1:
|
||
|
end = start + section.searchsorted(idx, side="right")
|
||
|
start = start + section.searchsorted(idx, side="left")
|
||
|
elif isinstance(idx, slice):
|
||
|
idx = idx.start
|
||
|
return start + section.searchsorted(idx, side=side)
|
||
|
else:
|
||
|
return start + section.searchsorted(idx, side=side)
|
||
|
|
||
|
def _get_loc_single_level_index(self, level_index: Index, key: Hashable) -> int:
|
||
|
"""
|
||
|
If key is NA value, location of index unify as -1.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
level_index: Index
|
||
|
key : label
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
loc : int
|
||
|
If key is NA value, loc is -1
|
||
|
Else, location of key in index.
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
Index.get_loc : The get_loc method for (single-level) index.
|
||
|
"""
|
||
|
if is_scalar(key) and isna(key):
|
||
|
return -1
|
||
|
else:
|
||
|
return level_index.get_loc(key)
|
||
|
|
||
|
def get_loc(self, key, method=None):
|
||
|
"""
|
||
|
Get location for a label or a tuple of labels.
|
||
|
|
||
|
The location is returned as an integer/slice or boolean
|
||
|
mask.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
key : label or tuple of labels (one for each level)
|
||
|
method : None
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
loc : int, slice object or boolean mask
|
||
|
If the key is past the lexsort depth, the return may be a
|
||
|
boolean mask array, otherwise it is always a slice or int.
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
Index.get_loc : The get_loc method for (single-level) index.
|
||
|
MultiIndex.slice_locs : Get slice location given start label(s) and
|
||
|
end label(s).
|
||
|
MultiIndex.get_locs : Get location for a label/slice/list/mask or a
|
||
|
sequence of such.
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
The key cannot be a slice, list of same-level labels, a boolean mask,
|
||
|
or a sequence of such. If you want to use those, use
|
||
|
:meth:`MultiIndex.get_locs` instead.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])
|
||
|
|
||
|
>>> mi.get_loc('b')
|
||
|
slice(1, 3, None)
|
||
|
|
||
|
>>> mi.get_loc(('b', 'e'))
|
||
|
1
|
||
|
"""
|
||
|
if method is not None:
|
||
|
raise NotImplementedError(
|
||
|
"only the default get_loc method is "
|
||
|
"currently supported for MultiIndex"
|
||
|
)
|
||
|
|
||
|
hash(key)
|
||
|
|
||
|
def _maybe_to_slice(loc):
|
||
|
"""convert integer indexer to boolean mask or slice if possible"""
|
||
|
if not isinstance(loc, np.ndarray) or loc.dtype != np.intp:
|
||
|
return loc
|
||
|
|
||
|
loc = lib.maybe_indices_to_slice(loc, len(self))
|
||
|
if isinstance(loc, slice):
|
||
|
return loc
|
||
|
|
||
|
mask = np.empty(len(self), dtype="bool")
|
||
|
mask.fill(False)
|
||
|
mask[loc] = True
|
||
|
return mask
|
||
|
|
||
|
if not isinstance(key, tuple):
|
||
|
loc = self._get_level_indexer(key, level=0)
|
||
|
return _maybe_to_slice(loc)
|
||
|
|
||
|
keylen = len(key)
|
||
|
if self.nlevels < keylen:
|
||
|
raise KeyError(
|
||
|
f"Key length ({keylen}) exceeds index depth ({self.nlevels})"
|
||
|
)
|
||
|
|
||
|
if keylen == self.nlevels and self.is_unique:
|
||
|
return self._engine.get_loc(key)
|
||
|
|
||
|
# -- partial selection or non-unique index
|
||
|
# break the key into 2 parts based on the lexsort_depth of the index;
|
||
|
# the first part returns a continuous slice of the index; the 2nd part
|
||
|
# needs linear search within the slice
|
||
|
i = self.lexsort_depth
|
||
|
lead_key, follow_key = key[:i], key[i:]
|
||
|
start, stop = (
|
||
|
self.slice_locs(lead_key, lead_key) if lead_key else (0, len(self))
|
||
|
)
|
||
|
|
||
|
if start == stop:
|
||
|
raise KeyError(key)
|
||
|
|
||
|
if not follow_key:
|
||
|
return slice(start, stop)
|
||
|
|
||
|
warnings.warn(
|
||
|
"indexing past lexsort depth may impact performance.",
|
||
|
PerformanceWarning,
|
||
|
stacklevel=10,
|
||
|
)
|
||
|
|
||
|
loc = np.arange(start, stop, dtype=np.intp)
|
||
|
|
||
|
for i, k in enumerate(follow_key, len(lead_key)):
|
||
|
mask = self.codes[i][loc] == self._get_loc_single_level_index(
|
||
|
self.levels[i], k
|
||
|
)
|
||
|
if not mask.all():
|
||
|
loc = loc[mask]
|
||
|
if not len(loc):
|
||
|
raise KeyError(key)
|
||
|
|
||
|
return _maybe_to_slice(loc) if len(loc) != stop - start else slice(start, stop)
|
||
|
|
||
|
def get_loc_level(self, key, level=0, drop_level: bool = True):
|
||
|
"""
|
||
|
Get location and sliced index for requested label(s)/level(s).
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
key : label or sequence of labels
|
||
|
level : int/level name or list thereof, optional
|
||
|
drop_level : bool, default True
|
||
|
If ``False``, the resulting index will not drop any level.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
loc : A 2-tuple where the elements are:
|
||
|
Element 0: int, slice object or boolean array
|
||
|
Element 1: The resulting sliced multiindex/index. If the key
|
||
|
contains all levels, this will be ``None``.
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
MultiIndex.get_loc : Get location for a label or a tuple of labels.
|
||
|
MultiIndex.get_locs : Get location for a label/slice/list/mask or a
|
||
|
sequence of such.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')],
|
||
|
... names=['A', 'B'])
|
||
|
|
||
|
>>> mi.get_loc_level('b')
|
||
|
(slice(1, 3, None), Index(['e', 'f'], dtype='object', name='B'))
|
||
|
|
||
|
>>> mi.get_loc_level('e', level='B')
|
||
|
(array([False, True, False]), Index(['b'], dtype='object', name='A'))
|
||
|
|
||
|
>>> mi.get_loc_level(['b', 'e'])
|
||
|
(1, None)
|
||
|
"""
|
||
|
if not isinstance(level, (list, tuple)):
|
||
|
level = self._get_level_number(level)
|
||
|
else:
|
||
|
level = [self._get_level_number(lev) for lev in level]
|
||
|
return self._get_loc_level(key, level=level, drop_level=drop_level)
|
||
|
|
||
|
def _get_loc_level(
|
||
|
self, key, level: Union[int, List[int]] = 0, drop_level: bool = True
|
||
|
):
|
||
|
"""
|
||
|
get_loc_level but with `level` known to be positional, not name-based.
|
||
|
"""
|
||
|
|
||
|
# different name to distinguish from maybe_droplevels
|
||
|
def maybe_mi_droplevels(indexer, levels, drop_level: bool):
|
||
|
if not drop_level:
|
||
|
return self[indexer]
|
||
|
# kludge around
|
||
|
orig_index = new_index = self[indexer]
|
||
|
|
||
|
for i in sorted(levels, reverse=True):
|
||
|
try:
|
||
|
new_index = new_index._drop_level_numbers([i])
|
||
|
except ValueError:
|
||
|
|
||
|
# no dropping here
|
||
|
return orig_index
|
||
|
return new_index
|
||
|
|
||
|
if isinstance(level, (tuple, list)):
|
||
|
if len(key) != len(level):
|
||
|
raise AssertionError(
|
||
|
"Key for location must have same length as number of levels"
|
||
|
)
|
||
|
result = None
|
||
|
for lev, k in zip(level, key):
|
||
|
loc, new_index = self._get_loc_level(k, level=lev)
|
||
|
if isinstance(loc, slice):
|
||
|
mask = np.zeros(len(self), dtype=bool)
|
||
|
mask[loc] = True
|
||
|
loc = mask
|
||
|
|
||
|
result = loc if result is None else result & loc
|
||
|
|
||
|
return result, maybe_mi_droplevels(result, level, drop_level)
|
||
|
|
||
|
# kludge for #1796
|
||
|
if isinstance(key, list):
|
||
|
key = tuple(key)
|
||
|
|
||
|
if isinstance(key, tuple) and level == 0:
|
||
|
|
||
|
try:
|
||
|
if key in self.levels[0]:
|
||
|
indexer = self._get_level_indexer(key, level=level)
|
||
|
new_index = maybe_mi_droplevels(indexer, [0], drop_level)
|
||
|
return indexer, new_index
|
||
|
except (TypeError, InvalidIndexError):
|
||
|
pass
|
||
|
|
||
|
if not any(isinstance(k, slice) for k in key):
|
||
|
|
||
|
# partial selection
|
||
|
# optionally get indexer to avoid re-calculation
|
||
|
def partial_selection(key, indexer=None):
|
||
|
if indexer is None:
|
||
|
indexer = self.get_loc(key)
|
||
|
ilevels = [
|
||
|
i for i in range(len(key)) if key[i] != slice(None, None)
|
||
|
]
|
||
|
return indexer, maybe_mi_droplevels(indexer, ilevels, drop_level)
|
||
|
|
||
|
if len(key) == self.nlevels and self.is_unique:
|
||
|
# Complete key in unique index -> standard get_loc
|
||
|
try:
|
||
|
return (self._engine.get_loc(key), None)
|
||
|
except KeyError as e:
|
||
|
raise KeyError(key) from e
|
||
|
else:
|
||
|
return partial_selection(key)
|
||
|
else:
|
||
|
indexer = None
|
||
|
for i, k in enumerate(key):
|
||
|
if not isinstance(k, slice):
|
||
|
k = self._get_level_indexer(k, level=i)
|
||
|
if isinstance(k, slice):
|
||
|
# everything
|
||
|
if k.start == 0 and k.stop == len(self):
|
||
|
k = slice(None, None)
|
||
|
else:
|
||
|
k_index = k
|
||
|
|
||
|
if isinstance(k, slice):
|
||
|
if k == slice(None, None):
|
||
|
continue
|
||
|
else:
|
||
|
raise TypeError(key)
|
||
|
|
||
|
if indexer is None:
|
||
|
indexer = k_index
|
||
|
else: # pragma: no cover
|
||
|
indexer &= k_index
|
||
|
if indexer is None:
|
||
|
indexer = slice(None, None)
|
||
|
ilevels = [i for i in range(len(key)) if key[i] != slice(None, None)]
|
||
|
return indexer, maybe_mi_droplevels(indexer, ilevels, drop_level)
|
||
|
else:
|
||
|
indexer = self._get_level_indexer(key, level=level)
|
||
|
return indexer, maybe_mi_droplevels(indexer, [level], drop_level)
|
||
|
|
||
|
def _get_level_indexer(self, key, level: int = 0, indexer=None):
|
||
|
# `level` kwarg is _always_ positional, never name
|
||
|
# return an indexer, boolean array or a slice showing where the key is
|
||
|
# in the totality of values
|
||
|
# if the indexer is provided, then use this
|
||
|
|
||
|
level_index = self.levels[level]
|
||
|
level_codes = self.codes[level]
|
||
|
|
||
|
def convert_indexer(start, stop, step, indexer=indexer, codes=level_codes):
|
||
|
# given the inputs and the codes/indexer, compute an indexer set
|
||
|
# if we have a provided indexer, then this need not consider
|
||
|
# the entire labels set
|
||
|
if step is not None and step < 0:
|
||
|
# Switch elements for negative step size
|
||
|
start, stop = stop - 1, start - 1
|
||
|
r = np.arange(start, stop, step)
|
||
|
|
||
|
if indexer is not None and len(indexer) != len(codes):
|
||
|
|
||
|
# we have an indexer which maps the locations in the labels
|
||
|
# that we have already selected (and is not an indexer for the
|
||
|
# entire set) otherwise this is wasteful so we only need to
|
||
|
# examine locations that are in this set the only magic here is
|
||
|
# that the result are the mappings to the set that we have
|
||
|
# selected
|
||
|
from pandas import Series
|
||
|
|
||
|
mapper = Series(indexer)
|
||
|
indexer = codes.take(ensure_platform_int(indexer))
|
||
|
result = Series(Index(indexer).isin(r).nonzero()[0])
|
||
|
m = result.map(mapper)
|
||
|
m = np.asarray(m)
|
||
|
|
||
|
else:
|
||
|
m = np.zeros(len(codes), dtype=bool)
|
||
|
m[np.in1d(codes, r, assume_unique=Index(codes).is_unique)] = True
|
||
|
|
||
|
return m
|
||
|
|
||
|
if isinstance(key, slice):
|
||
|
# handle a slice, returning a slice if we can
|
||
|
# otherwise a boolean indexer
|
||
|
|
||
|
try:
|
||
|
if key.start is not None:
|
||
|
start = level_index.get_loc(key.start)
|
||
|
else:
|
||
|
start = 0
|
||
|
if key.stop is not None:
|
||
|
stop = level_index.get_loc(key.stop)
|
||
|
elif isinstance(start, slice):
|
||
|
stop = len(level_index)
|
||
|
else:
|
||
|
stop = len(level_index) - 1
|
||
|
step = key.step
|
||
|
except KeyError:
|
||
|
|
||
|
# we have a partial slice (like looking up a partial date
|
||
|
# string)
|
||
|
start = stop = level_index.slice_indexer(
|
||
|
key.start, key.stop, key.step, kind="loc"
|
||
|
)
|
||
|
step = start.step
|
||
|
|
||
|
if isinstance(start, slice) or isinstance(stop, slice):
|
||
|
# we have a slice for start and/or stop
|
||
|
# a partial date slicer on a DatetimeIndex generates a slice
|
||
|
# note that the stop ALREADY includes the stopped point (if
|
||
|
# it was a string sliced)
|
||
|
start = getattr(start, "start", start)
|
||
|
stop = getattr(stop, "stop", stop)
|
||
|
return convert_indexer(start, stop, step)
|
||
|
|
||
|
elif level > 0 or self.lexsort_depth == 0 or step is not None:
|
||
|
# need to have like semantics here to right
|
||
|
# searching as when we are using a slice
|
||
|
# so include the stop+1 (so we include stop)
|
||
|
return convert_indexer(start, stop + 1, step)
|
||
|
else:
|
||
|
# sorted, so can return slice object -> view
|
||
|
i = level_codes.searchsorted(start, side="left")
|
||
|
j = level_codes.searchsorted(stop, side="right")
|
||
|
return slice(i, j, step)
|
||
|
|
||
|
else:
|
||
|
|
||
|
idx = self._get_loc_single_level_index(level_index, key)
|
||
|
|
||
|
if level > 0 or self.lexsort_depth == 0:
|
||
|
# Desired level is not sorted
|
||
|
locs = np.array(level_codes == idx, dtype=bool, copy=False)
|
||
|
if not locs.any():
|
||
|
# The label is present in self.levels[level] but unused:
|
||
|
raise KeyError(key)
|
||
|
return locs
|
||
|
|
||
|
if isinstance(idx, slice):
|
||
|
start = idx.start
|
||
|
end = idx.stop
|
||
|
else:
|
||
|
start = level_codes.searchsorted(idx, side="left")
|
||
|
end = level_codes.searchsorted(idx, side="right")
|
||
|
|
||
|
if start == end:
|
||
|
# The label is present in self.levels[level] but unused:
|
||
|
raise KeyError(key)
|
||
|
return slice(start, end)
|
||
|
|
||
|
def get_locs(self, seq):
|
||
|
"""
|
||
|
Get location for a sequence of labels.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
seq : label, slice, list, mask or a sequence of such
|
||
|
You should use one of the above for each level.
|
||
|
If a level should not be used, set it to ``slice(None)``.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
numpy.ndarray
|
||
|
NumPy array of integers suitable for passing to iloc.
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
MultiIndex.get_loc : Get location for a label or a tuple of labels.
|
||
|
MultiIndex.slice_locs : Get slice location given start label(s) and
|
||
|
end label(s).
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])
|
||
|
|
||
|
>>> mi.get_locs('b') # doctest: +SKIP
|
||
|
array([1, 2], dtype=int64)
|
||
|
|
||
|
>>> mi.get_locs([slice(None), ['e', 'f']]) # doctest: +SKIP
|
||
|
array([1, 2], dtype=int64)
|
||
|
|
||
|
>>> mi.get_locs([[True, False, True], slice('e', 'f')]) # doctest: +SKIP
|
||
|
array([2], dtype=int64)
|
||
|
"""
|
||
|
|
||
|
# must be lexsorted to at least as many levels
|
||
|
true_slices = [i for (i, s) in enumerate(com.is_true_slices(seq)) if s]
|
||
|
if true_slices and true_slices[-1] >= self.lexsort_depth:
|
||
|
raise UnsortedIndexError(
|
||
|
"MultiIndex slicing requires the index to be lexsorted: slicing "
|
||
|
f"on levels {true_slices}, lexsort depth {self.lexsort_depth}"
|
||
|
)
|
||
|
# indexer
|
||
|
# this is the list of all values that we want to select
|
||
|
n = len(self)
|
||
|
indexer = None
|
||
|
|
||
|
def _convert_to_indexer(r) -> Int64Index:
|
||
|
# return an indexer
|
||
|
if isinstance(r, slice):
|
||
|
m = np.zeros(n, dtype=bool)
|
||
|
m[r] = True
|
||
|
r = m.nonzero()[0]
|
||
|
elif com.is_bool_indexer(r):
|
||
|
if len(r) != n:
|
||
|
raise ValueError(
|
||
|
"cannot index with a boolean indexer "
|
||
|
"that is not the same length as the "
|
||
|
"index"
|
||
|
)
|
||
|
r = r.nonzero()[0]
|
||
|
return Int64Index(r)
|
||
|
|
||
|
def _update_indexer(
|
||
|
idxr: Optional[Index], indexer: Optional[Index], key
|
||
|
) -> Index:
|
||
|
if indexer is None:
|
||
|
indexer = Index(np.arange(n))
|
||
|
if idxr is None:
|
||
|
return indexer
|
||
|
indexer_intersection = indexer.intersection(idxr)
|
||
|
if indexer_intersection.empty and not idxr.empty and not indexer.empty:
|
||
|
raise KeyError(key)
|
||
|
return indexer_intersection
|
||
|
|
||
|
for i, k in enumerate(seq):
|
||
|
|
||
|
if com.is_bool_indexer(k):
|
||
|
# a boolean indexer, must be the same length!
|
||
|
k = np.asarray(k)
|
||
|
indexer = _update_indexer(
|
||
|
_convert_to_indexer(k), indexer=indexer, key=seq
|
||
|
)
|
||
|
|
||
|
elif is_list_like(k):
|
||
|
# a collection of labels to include from this level (these
|
||
|
# are or'd)
|
||
|
indexers: Optional[Int64Index] = None
|
||
|
for x in k:
|
||
|
try:
|
||
|
idxrs = _convert_to_indexer(
|
||
|
self._get_level_indexer(x, level=i, indexer=indexer)
|
||
|
)
|
||
|
indexers = (idxrs if indexers is None else indexers).union(
|
||
|
idxrs, sort=False
|
||
|
)
|
||
|
except KeyError:
|
||
|
|
||
|
# ignore not founds
|
||
|
continue
|
||
|
|
||
|
if indexers is not None:
|
||
|
indexer = _update_indexer(indexers, indexer=indexer, key=seq)
|
||
|
else:
|
||
|
# no matches we are done
|
||
|
return np.array([], dtype=np.int64)
|
||
|
|
||
|
elif com.is_null_slice(k):
|
||
|
# empty slice
|
||
|
indexer = _update_indexer(None, indexer=indexer, key=seq)
|
||
|
|
||
|
elif isinstance(k, slice):
|
||
|
|
||
|
# a slice, include BOTH of the labels
|
||
|
indexer = _update_indexer(
|
||
|
_convert_to_indexer(
|
||
|
self._get_level_indexer(k, level=i, indexer=indexer)
|
||
|
),
|
||
|
indexer=indexer,
|
||
|
key=seq,
|
||
|
)
|
||
|
else:
|
||
|
# a single label
|
||
|
indexer = _update_indexer(
|
||
|
_convert_to_indexer(
|
||
|
self.get_loc_level(k, level=i, drop_level=False)[0]
|
||
|
),
|
||
|
indexer=indexer,
|
||
|
key=seq,
|
||
|
)
|
||
|
|
||
|
# empty indexer
|
||
|
if indexer is None:
|
||
|
return np.array([], dtype=np.int64)
|
||
|
|
||
|
assert isinstance(indexer, Int64Index), type(indexer)
|
||
|
indexer = self._reorder_indexer(seq, indexer)
|
||
|
|
||
|
return indexer._values
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
|
||
|
def _reorder_indexer(
|
||
|
self,
|
||
|
seq: Tuple[Union[Scalar, Iterable, AnyArrayLike], ...],
|
||
|
indexer: Int64Index,
|
||
|
) -> Int64Index:
|
||
|
"""
|
||
|
Reorder an indexer of a MultiIndex (self) so that the label are in the
|
||
|
same order as given in seq
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
seq : label/slice/list/mask or a sequence of such
|
||
|
indexer: an Int64Index indexer of self
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
indexer : a sorted Int64Index indexer of self ordered as seq
|
||
|
"""
|
||
|
# If the index is lexsorted and the list_like label in seq are sorted
|
||
|
# then we do not need to sort
|
||
|
if self.is_lexsorted():
|
||
|
need_sort = False
|
||
|
for i, k in enumerate(seq):
|
||
|
if is_list_like(k):
|
||
|
if not need_sort:
|
||
|
k_codes = self.levels[i].get_indexer(k)
|
||
|
k_codes = k_codes[k_codes >= 0] # Filter absent keys
|
||
|
# True if the given codes are not ordered
|
||
|
need_sort = (k_codes[:-1] > k_codes[1:]).any()
|
||
|
elif isinstance(k, slice) and k.step is not None and k.step < 0:
|
||
|
need_sort = True
|
||
|
# Bail out if both index and seq are sorted
|
||
|
if not need_sort:
|
||
|
return indexer
|
||
|
|
||
|
n = len(self)
|
||
|
keys: Tuple[np.ndarray, ...] = ()
|
||
|
# For each level of the sequence in seq, map the level codes with the
|
||
|
# order they appears in a list-like sequence
|
||
|
# This mapping is then use to reorder the indexer
|
||
|
for i, k in enumerate(seq):
|
||
|
if is_scalar(k):
|
||
|
# GH#34603 we want to treat a scalar the same as an all equal list
|
||
|
k = [k]
|
||
|
if com.is_bool_indexer(k):
|
||
|
new_order = np.arange(n)[indexer]
|
||
|
elif is_list_like(k):
|
||
|
# Generate a map with all level codes as sorted initially
|
||
|
key_order_map = np.ones(len(self.levels[i]), dtype=np.uint64) * len(
|
||
|
self.levels[i]
|
||
|
)
|
||
|
# Set order as given in the indexer list
|
||
|
level_indexer = self.levels[i].get_indexer(k)
|
||
|
level_indexer = level_indexer[level_indexer >= 0] # Filter absent keys
|
||
|
key_order_map[level_indexer] = np.arange(len(level_indexer))
|
||
|
|
||
|
new_order = key_order_map[self.codes[i][indexer]]
|
||
|
elif isinstance(k, slice) and k.step is not None and k.step < 0:
|
||
|
new_order = np.arange(n)[k][indexer]
|
||
|
elif isinstance(k, slice) and k.start is None and k.stop is None:
|
||
|
# slice(None) should not determine order GH#31330
|
||
|
new_order = np.ones((n,))[indexer]
|
||
|
else:
|
||
|
# For all other case, use the same order as the level
|
||
|
new_order = np.arange(n)[indexer]
|
||
|
keys = (new_order,) + keys
|
||
|
|
||
|
# Find the reordering using lexsort on the keys mapping
|
||
|
ind = np.lexsort(keys)
|
||
|
return indexer[ind]
|
||
|
|
||
|
def truncate(self, before=None, after=None):
|
||
|
"""
|
||
|
Slice index between two labels / tuples, return new MultiIndex
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
before : label or tuple, can be partial. Default None
|
||
|
None defaults to start
|
||
|
after : label or tuple, can be partial. Default None
|
||
|
None defaults to end
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
truncated : MultiIndex
|
||
|
"""
|
||
|
if after and before and after < before:
|
||
|
raise ValueError("after < before")
|
||
|
|
||
|
i, j = self.levels[0].slice_locs(before, after)
|
||
|
left, right = self.slice_locs(before, after)
|
||
|
|
||
|
new_levels = list(self.levels)
|
||
|
new_levels[0] = new_levels[0][i:j]
|
||
|
|
||
|
new_codes = [level_codes[left:right] for level_codes in self.codes]
|
||
|
new_codes[0] = new_codes[0] - i
|
||
|
|
||
|
return MultiIndex(
|
||
|
levels=new_levels,
|
||
|
codes=new_codes,
|
||
|
names=self._names,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
|
||
|
def equals(self, other: object) -> bool:
|
||
|
"""
|
||
|
Determines if two MultiIndex objects have the same labeling information
|
||
|
(the levels themselves do not necessarily have to be the same)
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
equal_levels
|
||
|
"""
|
||
|
if self.is_(other):
|
||
|
return True
|
||
|
|
||
|
if not isinstance(other, Index):
|
||
|
return False
|
||
|
|
||
|
if len(self) != len(other):
|
||
|
return False
|
||
|
|
||
|
if not isinstance(other, MultiIndex):
|
||
|
# d-level MultiIndex can equal d-tuple Index
|
||
|
if not is_object_dtype(other.dtype):
|
||
|
# other cannot contain tuples, so cannot match self
|
||
|
return False
|
||
|
return array_equivalent(self._values, other._values)
|
||
|
|
||
|
if self.nlevels != other.nlevels:
|
||
|
return False
|
||
|
|
||
|
for i in range(self.nlevels):
|
||
|
self_codes = self.codes[i]
|
||
|
self_codes = self_codes[self_codes != -1]
|
||
|
self_values = algos.take_nd(
|
||
|
np.asarray(self.levels[i]._values), self_codes, allow_fill=False
|
||
|
)
|
||
|
|
||
|
other_codes = other.codes[i]
|
||
|
other_codes = other_codes[other_codes != -1]
|
||
|
other_values = algos.take_nd(
|
||
|
np.asarray(other.levels[i]._values), other_codes, allow_fill=False
|
||
|
)
|
||
|
|
||
|
# since we use NaT both datetime64 and timedelta64 we can have a
|
||
|
# situation where a level is typed say timedelta64 in self (IOW it
|
||
|
# has other values than NaT) but types datetime64 in other (where
|
||
|
# its all NaT) but these are equivalent
|
||
|
if len(self_values) == 0 and len(other_values) == 0:
|
||
|
continue
|
||
|
|
||
|
if not array_equivalent(self_values, other_values):
|
||
|
return False
|
||
|
|
||
|
return True
|
||
|
|
||
|
def equal_levels(self, other) -> bool:
|
||
|
"""
|
||
|
Return True if the levels of both MultiIndex objects are the same
|
||
|
|
||
|
"""
|
||
|
if self.nlevels != other.nlevels:
|
||
|
return False
|
||
|
|
||
|
for i in range(self.nlevels):
|
||
|
if not self.levels[i].equals(other.levels[i]):
|
||
|
return False
|
||
|
return True
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
# Set Methods
|
||
|
|
||
|
def union(self, other, sort=None):
|
||
|
"""
|
||
|
Form the union of two MultiIndex objects
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
other : MultiIndex or array / Index of tuples
|
||
|
sort : False or None, default None
|
||
|
Whether to sort the resulting Index.
|
||
|
|
||
|
* None : Sort the result, except when
|
||
|
|
||
|
1. `self` and `other` are equal.
|
||
|
2. `self` 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
|
||
|
-------
|
||
|
Index
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> idx1 = pd.MultiIndex.from_arrays(
|
||
|
... [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]
|
||
|
... )
|
||
|
>>> idx1
|
||
|
MultiIndex([(1, 'Red'),
|
||
|
(1, 'Blue'),
|
||
|
(2, 'Red'),
|
||
|
(2, 'Blue')],
|
||
|
)
|
||
|
>>> idx2 = pd.MultiIndex.from_arrays(
|
||
|
... [[3, 3, 2, 2], ["Red", "Green", "Red", "Green"]]
|
||
|
... )
|
||
|
>>> idx2
|
||
|
MultiIndex([(3, 'Red'),
|
||
|
(3, 'Green'),
|
||
|
(2, 'Red'),
|
||
|
(2, 'Green')],
|
||
|
)
|
||
|
|
||
|
>>> idx1.union(idx2)
|
||
|
MultiIndex([(1, 'Blue'),
|
||
|
(1, 'Red'),
|
||
|
(2, 'Blue'),
|
||
|
(2, 'Green'),
|
||
|
(2, 'Red'),
|
||
|
(3, 'Green'),
|
||
|
(3, 'Red')],
|
||
|
)
|
||
|
|
||
|
>>> idx1.union(idx2, sort=False)
|
||
|
MultiIndex([(1, 'Red'),
|
||
|
(1, 'Blue'),
|
||
|
(2, 'Red'),
|
||
|
(2, 'Blue'),
|
||
|
(3, 'Red'),
|
||
|
(3, 'Green'),
|
||
|
(2, 'Green')],
|
||
|
)
|
||
|
"""
|
||
|
self._validate_sort_keyword(sort)
|
||
|
self._assert_can_do_setop(other)
|
||
|
other, result_names = self._convert_can_do_setop(other)
|
||
|
|
||
|
if len(other) == 0 or self.equals(other):
|
||
|
return self.rename(result_names)
|
||
|
|
||
|
return self._union(other, sort=sort)
|
||
|
|
||
|
def _union(self, other, sort):
|
||
|
other, result_names = self._convert_can_do_setop(other)
|
||
|
|
||
|
# TODO: Index.union returns other when `len(self)` is 0.
|
||
|
|
||
|
if not is_object_dtype(other.dtype):
|
||
|
raise NotImplementedError(
|
||
|
"Can only union MultiIndex with MultiIndex or Index of tuples, "
|
||
|
"try mi.to_flat_index().union(other) instead."
|
||
|
)
|
||
|
|
||
|
uniq_tuples = lib.fast_unique_multiple([self._values, other._values], sort=sort)
|
||
|
|
||
|
return MultiIndex.from_arrays(
|
||
|
zip(*uniq_tuples), sortorder=0, names=result_names
|
||
|
)
|
||
|
|
||
|
def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
|
||
|
return is_object_dtype(dtype)
|
||
|
|
||
|
def intersection(self, other, sort=False):
|
||
|
"""
|
||
|
Form the intersection of two MultiIndex objects.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
other : MultiIndex or array / Index of tuples
|
||
|
sort : False or None, default False
|
||
|
Sort the resulting MultiIndex if possible
|
||
|
|
||
|
.. versionadded:: 0.24.0
|
||
|
|
||
|
.. versionchanged:: 0.24.1
|
||
|
|
||
|
Changed the default from ``True`` to ``False``, to match
|
||
|
behaviour from before 0.24.0
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
Index
|
||
|
"""
|
||
|
self._validate_sort_keyword(sort)
|
||
|
self._assert_can_do_setop(other)
|
||
|
other, result_names = self._convert_can_do_setop(other)
|
||
|
|
||
|
if self.equals(other):
|
||
|
if self.has_duplicates:
|
||
|
return self.unique().rename(result_names)
|
||
|
return self.rename(result_names)
|
||
|
|
||
|
return self._intersection(other, sort=sort)
|
||
|
|
||
|
def _intersection(self, other, sort=False):
|
||
|
other, result_names = self._convert_can_do_setop(other)
|
||
|
|
||
|
if not self._is_comparable_dtype(other.dtype):
|
||
|
# The intersection is empty
|
||
|
return self[:0].rename(result_names)
|
||
|
|
||
|
lvals = self._values
|
||
|
rvals = other._values
|
||
|
|
||
|
uniq_tuples = None # flag whether _inner_indexer was successful
|
||
|
if self.is_monotonic and other.is_monotonic:
|
||
|
try:
|
||
|
inner_tuples = self._inner_indexer(lvals, rvals)[0]
|
||
|
sort = False # inner_tuples is already sorted
|
||
|
except TypeError:
|
||
|
pass
|
||
|
else:
|
||
|
uniq_tuples = algos.unique(inner_tuples)
|
||
|
|
||
|
if uniq_tuples is None:
|
||
|
other_uniq = set(rvals)
|
||
|
seen = set()
|
||
|
# pandas\core\indexes\multi.py:3503: error: "add" of "set" does not
|
||
|
# return a value [func-returns-value]
|
||
|
uniq_tuples = [
|
||
|
x
|
||
|
for x in lvals
|
||
|
if x in other_uniq
|
||
|
and not (x in seen or seen.add(x)) # type: ignore[func-returns-value]
|
||
|
]
|
||
|
|
||
|
if sort is None:
|
||
|
uniq_tuples = sorted(uniq_tuples)
|
||
|
|
||
|
if len(uniq_tuples) == 0:
|
||
|
return MultiIndex(
|
||
|
levels=self.levels,
|
||
|
codes=[[]] * self.nlevels,
|
||
|
names=result_names,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
else:
|
||
|
return MultiIndex.from_arrays(
|
||
|
zip(*uniq_tuples), sortorder=0, names=result_names
|
||
|
)
|
||
|
|
||
|
def difference(self, other, sort=None):
|
||
|
"""
|
||
|
Compute set difference of two MultiIndex objects
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
other : MultiIndex
|
||
|
sort : False or None, default None
|
||
|
Sort the resulting MultiIndex if possible
|
||
|
|
||
|
.. versionadded:: 0.24.0
|
||
|
|
||
|
.. versionchanged:: 0.24.1
|
||
|
|
||
|
Changed the default value from ``True`` to ``None``
|
||
|
(without change in behaviour).
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
diff : MultiIndex
|
||
|
"""
|
||
|
self._validate_sort_keyword(sort)
|
||
|
self._assert_can_do_setop(other)
|
||
|
other, result_names = self._convert_can_do_setop(other)
|
||
|
|
||
|
if len(other) == 0:
|
||
|
return self.rename(result_names)
|
||
|
|
||
|
if self.equals(other):
|
||
|
return MultiIndex(
|
||
|
levels=self.levels,
|
||
|
codes=[[]] * self.nlevels,
|
||
|
names=result_names,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
|
||
|
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)
|
||
|
difference = this._values.take(label_diff)
|
||
|
if sort is None:
|
||
|
difference = sorted(difference)
|
||
|
|
||
|
if len(difference) == 0:
|
||
|
return MultiIndex(
|
||
|
levels=[[]] * self.nlevels,
|
||
|
codes=[[]] * self.nlevels,
|
||
|
names=result_names,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
else:
|
||
|
return MultiIndex.from_tuples(difference, sortorder=0, names=result_names)
|
||
|
|
||
|
def _convert_can_do_setop(self, other):
|
||
|
result_names = self.names
|
||
|
|
||
|
if not isinstance(other, Index):
|
||
|
|
||
|
if len(other) == 0:
|
||
|
return self[:0], self.names
|
||
|
else:
|
||
|
msg = "other must be a MultiIndex or a list of tuples"
|
||
|
try:
|
||
|
other = MultiIndex.from_tuples(other, names=self.names)
|
||
|
except (ValueError, TypeError) as err:
|
||
|
# ValueError raised by tuples_to_object_array if we
|
||
|
# have non-object dtype
|
||
|
raise TypeError(msg) from err
|
||
|
else:
|
||
|
result_names = get_unanimous_names(self, other)
|
||
|
|
||
|
return other, result_names
|
||
|
|
||
|
def symmetric_difference(self, other, result_name=None, sort=None):
|
||
|
# On equal symmetric_difference MultiIndexes the difference is empty.
|
||
|
# Therefore, an empty MultiIndex is returned GH13490
|
||
|
tups = Index.symmetric_difference(self, other, result_name, sort)
|
||
|
if len(tups) == 0:
|
||
|
return type(self)(
|
||
|
levels=[[] for _ in range(self.nlevels)],
|
||
|
codes=[[] for _ in range(self.nlevels)],
|
||
|
names=tups.name,
|
||
|
)
|
||
|
return type(self).from_tuples(tups, names=tups.name)
|
||
|
|
||
|
# --------------------------------------------------------------------
|
||
|
|
||
|
@doc(Index.astype)
|
||
|
def astype(self, dtype, copy=True):
|
||
|
dtype = pandas_dtype(dtype)
|
||
|
if is_categorical_dtype(dtype):
|
||
|
msg = "> 1 ndim Categorical are not supported at this time"
|
||
|
raise NotImplementedError(msg)
|
||
|
elif not is_object_dtype(dtype):
|
||
|
raise TypeError(
|
||
|
"Setting a MultiIndex dtype to anything other than object "
|
||
|
"is not supported"
|
||
|
)
|
||
|
elif copy is True:
|
||
|
return self._shallow_copy()
|
||
|
return self
|
||
|
|
||
|
def _validate_fill_value(self, item):
|
||
|
if not isinstance(item, tuple):
|
||
|
# Pad the key with empty strings if lower levels of the key
|
||
|
# aren't specified:
|
||
|
item = (item,) + ("",) * (self.nlevels - 1)
|
||
|
elif len(item) != self.nlevels:
|
||
|
raise ValueError("Item must have length equal to number of levels.")
|
||
|
return item
|
||
|
|
||
|
def insert(self, loc: int, item):
|
||
|
"""
|
||
|
Make new MultiIndex inserting new item at location
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
loc : int
|
||
|
item : tuple
|
||
|
Must be same length as number of levels in the MultiIndex
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
new_index : Index
|
||
|
"""
|
||
|
item = self._validate_fill_value(item)
|
||
|
|
||
|
new_levels = []
|
||
|
new_codes = []
|
||
|
for k, level, level_codes in zip(item, self.levels, self.codes):
|
||
|
if k not in level:
|
||
|
# have to insert into level
|
||
|
# must insert at end otherwise you have to recompute all the
|
||
|
# other codes
|
||
|
lev_loc = len(level)
|
||
|
try:
|
||
|
level = level.insert(lev_loc, k)
|
||
|
except TypeError:
|
||
|
# TODO: Should this be done inside insert?
|
||
|
# TODO: smarter casting rules?
|
||
|
level = level.astype(object).insert(lev_loc, k)
|
||
|
else:
|
||
|
lev_loc = level.get_loc(k)
|
||
|
|
||
|
new_levels.append(level)
|
||
|
new_codes.append(np.insert(ensure_int64(level_codes), loc, lev_loc))
|
||
|
|
||
|
return MultiIndex(
|
||
|
levels=new_levels, codes=new_codes, names=self.names, verify_integrity=False
|
||
|
)
|
||
|
|
||
|
def delete(self, loc):
|
||
|
"""
|
||
|
Make new index with passed location deleted
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
new_index : MultiIndex
|
||
|
"""
|
||
|
new_codes = [np.delete(level_codes, loc) for level_codes in self.codes]
|
||
|
return MultiIndex(
|
||
|
levels=self.levels,
|
||
|
codes=new_codes,
|
||
|
names=self.names,
|
||
|
verify_integrity=False,
|
||
|
)
|
||
|
|
||
|
@doc(Index.isin)
|
||
|
def isin(self, values, level=None):
|
||
|
if level is None:
|
||
|
values = MultiIndex.from_tuples(values, names=self.names)._values
|
||
|
return algos.isin(self._values, values)
|
||
|
else:
|
||
|
num = self._get_level_number(level)
|
||
|
levs = self.get_level_values(num)
|
||
|
|
||
|
if levs.size == 0:
|
||
|
return np.zeros(len(levs), dtype=np.bool_)
|
||
|
return levs.isin(values)
|
||
|
|
||
|
# ---------------------------------------------------------------
|
||
|
# Arithmetic/Numeric Methods - Disabled
|
||
|
|
||
|
__add__ = make_invalid_op("__add__")
|
||
|
__radd__ = make_invalid_op("__radd__")
|
||
|
__iadd__ = make_invalid_op("__iadd__")
|
||
|
__sub__ = make_invalid_op("__sub__")
|
||
|
__rsub__ = make_invalid_op("__rsub__")
|
||
|
__isub__ = make_invalid_op("__isub__")
|
||
|
__pow__ = make_invalid_op("__pow__")
|
||
|
__rpow__ = make_invalid_op("__rpow__")
|
||
|
__mul__ = make_invalid_op("__mul__")
|
||
|
__rmul__ = make_invalid_op("__rmul__")
|
||
|
__floordiv__ = make_invalid_op("__floordiv__")
|
||
|
__rfloordiv__ = make_invalid_op("__rfloordiv__")
|
||
|
__truediv__ = make_invalid_op("__truediv__")
|
||
|
__rtruediv__ = make_invalid_op("__rtruediv__")
|
||
|
__mod__ = make_invalid_op("__mod__")
|
||
|
__rmod__ = make_invalid_op("__rmod__")
|
||
|
__divmod__ = make_invalid_op("__divmod__")
|
||
|
__rdivmod__ = make_invalid_op("__rdivmod__")
|
||
|
# Unary methods disabled
|
||
|
__neg__ = make_invalid_op("__neg__")
|
||
|
__pos__ = make_invalid_op("__pos__")
|
||
|
__abs__ = make_invalid_op("__abs__")
|
||
|
__inv__ = make_invalid_op("__inv__")
|
||
|
|
||
|
|
||
|
def sparsify_labels(label_list, start: int = 0, sentinel=""):
|
||
|
pivoted = list(zip(*label_list))
|
||
|
k = len(label_list)
|
||
|
|
||
|
result = pivoted[: start + 1]
|
||
|
prev = pivoted[start]
|
||
|
|
||
|
for cur in pivoted[start + 1 :]:
|
||
|
sparse_cur = []
|
||
|
|
||
|
for i, (p, t) in enumerate(zip(prev, cur)):
|
||
|
if i == k - 1:
|
||
|
sparse_cur.append(t)
|
||
|
result.append(sparse_cur)
|
||
|
break
|
||
|
|
||
|
if p == t:
|
||
|
sparse_cur.append(sentinel)
|
||
|
else:
|
||
|
sparse_cur.extend(cur[i:])
|
||
|
result.append(sparse_cur)
|
||
|
break
|
||
|
|
||
|
prev = cur
|
||
|
|
||
|
return list(zip(*result))
|
||
|
|
||
|
|
||
|
def _get_na_rep(dtype) -> str:
|
||
|
return {np.datetime64: "NaT", np.timedelta64: "NaT"}.get(dtype, "NaN")
|
||
|
|
||
|
|
||
|
def maybe_droplevels(index, key):
|
||
|
"""
|
||
|
Attempt to drop level or levels from the given index.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
index: Index
|
||
|
key : scalar or tuple
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
Index
|
||
|
"""
|
||
|
# drop levels
|
||
|
original_index = index
|
||
|
if isinstance(key, tuple):
|
||
|
for _ in key:
|
||
|
try:
|
||
|
index = index._drop_level_numbers([0])
|
||
|
except ValueError:
|
||
|
# we have dropped too much, so back out
|
||
|
return original_index
|
||
|
else:
|
||
|
try:
|
||
|
index = index._drop_level_numbers([0])
|
||
|
except ValueError:
|
||
|
pass
|
||
|
|
||
|
return index
|
||
|
|
||
|
|
||
|
def _coerce_indexer_frozen(array_like, categories, copy: bool = False) -> np.ndarray:
|
||
|
"""
|
||
|
Coerce the array_like indexer to the smallest integer dtype that can encode all
|
||
|
of the given categories.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
array_like : array-like
|
||
|
categories : array-like
|
||
|
copy : bool
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
np.ndarray
|
||
|
Non-writeable.
|
||
|
"""
|
||
|
array_like = coerce_indexer_dtype(array_like, categories)
|
||
|
if copy:
|
||
|
array_like = array_like.copy()
|
||
|
array_like.flags.writeable = False
|
||
|
return array_like
|