cfcfbca965
### What changes were proposed in this pull request? Adds more type annotations in the file `python/pyspark/pandas/series.py` and fixes the mypy check failures. ### Why are the changes needed? We should enable more disallow_untyped_defs mypy checks. ### Does this PR introduce _any_ user-facing change? Yes. This PR adds more type annotations in pandas APIs on Spark module, which can impact interaction with development tools for users. ### How was this patch tested? The mypy check with a new configuration and existing tests should pass. Closes #33045 from ueshin/issues/SPARK-35476/disallow_untyped_defs_series. Authored-by: Takuya UESHIN <ueshin@databricks.com> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
1207 lines
41 KiB
Python
1207 lines
41 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from distutils.version import LooseVersion
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from functools import partial
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from typing import Any, Callable, Iterator, List, Optional, Tuple, Union, cast, no_type_check
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import pandas as pd
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from pandas.api.types import is_list_like
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from pandas.api.types import is_hashable
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from pyspark import sql as spark
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from pyspark.sql import functions as F, Window
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from pyspark.sql.types import DataType
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# For running doctests and reference resolution in PyCharm.
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from pyspark import pandas as ps # noqa: F401
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from pyspark.pandas.exceptions import PandasNotImplementedError
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from pyspark.pandas.frame import DataFrame
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from pyspark.pandas.indexes.base import Index
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from pyspark.pandas.missing.indexes import MissingPandasLikeMultiIndex
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from pyspark.pandas.series import Series, first_series
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from pyspark.pandas.utils import (
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compare_disallow_null,
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is_name_like_tuple,
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name_like_string,
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scol_for,
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verify_temp_column_name,
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)
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from pyspark.pandas.internal import (
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InternalField,
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InternalFrame,
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NATURAL_ORDER_COLUMN_NAME,
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SPARK_INDEX_NAME_FORMAT,
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)
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from pyspark.pandas.typedef import Scalar
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class MultiIndex(Index):
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"""
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pandas-on-Spark MultiIndex that corresponds to pandas MultiIndex logically. This might hold
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Spark Column internally.
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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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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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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 : A single-level Index.
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Examples
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--------
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>>> ps.DataFrame({'a': ['a', 'b', 'c']}, index=[[1, 2, 3], [4, 5, 6]]).index # doctest: +SKIP
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MultiIndex([(1, 4),
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(2, 5),
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(3, 6)],
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)
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>>> ps.DataFrame({'a': [1, 2, 3]}, index=[list('abc'), list('def')]).index # doctest: +SKIP
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MultiIndex([('a', 'd'),
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('b', 'e'),
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('c', 'f')],
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)
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"""
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@no_type_check
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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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) -> "MultiIndex":
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if LooseVersion(pd.__version__) < LooseVersion("0.24"):
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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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pidx = pd.MultiIndex(
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levels=levels,
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labels=codes,
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sortorder=sortorder,
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names=names,
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dtype=dtype,
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copy=copy,
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name=name,
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verify_integrity=verify_integrity,
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)
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else:
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pidx = pd.MultiIndex(
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levels=levels,
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codes=codes,
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sortorder=sortorder,
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names=names,
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dtype=dtype,
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copy=copy,
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name=name,
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verify_integrity=verify_integrity,
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)
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return ps.from_pandas(pidx)
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@property
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def _internal(self) -> InternalFrame:
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internal = self._psdf._internal
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scol = F.struct(*internal.index_spark_columns)
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return internal.copy(
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column_labels=[None],
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data_spark_columns=[scol],
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data_fields=[None],
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column_label_names=None,
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)
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@property
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def _column_label(self) -> Optional[Tuple]:
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return None
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def __abs__(self) -> "MultiIndex":
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raise TypeError("TypeError: cannot perform __abs__ with this index type: MultiIndex")
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def _with_new_scol(
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self, scol: spark.Column, *, field: Optional[InternalField] = None
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) -> "MultiIndex":
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raise NotImplementedError("Not supported for type MultiIndex")
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@no_type_check
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def any(self, *args, **kwargs) -> None:
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raise TypeError("cannot perform any with this index type: MultiIndex")
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@no_type_check
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def all(self, *args, **kwargs) -> None:
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raise TypeError("cannot perform all with this index type: MultiIndex")
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@staticmethod
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def from_tuples(
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tuples: List[Tuple],
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sortorder: Optional[int] = None,
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names: Optional[List[Union[Any, Tuple]]] = None,
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) -> "MultiIndex":
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"""
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Convert list of tuples to MultiIndex.
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Parameters
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----------
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tuples : list / sequence of tuple-likes
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Each tuple is the index of one row/column.
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sortorder : int or None
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Level of sortedness (must be lexicographically sorted by that level).
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names : list / sequence of str, optional
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Names for the levels in the index.
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Returns
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-------
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index : MultiIndex
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Examples
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--------
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>>> tuples = [(1, 'red'), (1, 'blue'),
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... (2, 'red'), (2, 'blue')]
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>>> ps.MultiIndex.from_tuples(tuples, names=('number', 'color')) # doctest: +SKIP
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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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"""
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return cast(
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MultiIndex,
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ps.from_pandas(
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pd.MultiIndex.from_tuples(tuples=tuples, sortorder=sortorder, names=names)
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),
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)
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@staticmethod
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def from_arrays(
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arrays: List[List],
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sortorder: Optional[int] = None,
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names: Optional[List[Union[Any, Tuple]]] = None,
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) -> "MultiIndex":
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"""
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Convert arrays to MultiIndex.
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Parameters
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----------
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arrays: list / sequence of array-likes
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Each array-like gives one level’s value for each data point. len(arrays)
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is the number of levels.
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sortorder: int or None
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Level of sortedness (must be lexicographically sorted by that level).
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names: list / sequence of str, optional
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Names for the levels in the index.
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Returns
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-------
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index: MultiIndex
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Examples
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--------
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>>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
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>>> ps.MultiIndex.from_arrays(arrays, names=('number', 'color')) # doctest: +SKIP
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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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"""
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return cast(
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MultiIndex,
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ps.from_pandas(
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pd.MultiIndex.from_arrays(arrays=arrays, sortorder=sortorder, names=names)
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),
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)
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@staticmethod
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def from_product(
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iterables: List[List],
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sortorder: Optional[int] = None,
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names: Optional[List[Union[Any, Tuple]]] = None,
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) -> "MultiIndex":
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"""
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Make a MultiIndex from the cartesian product of multiple iterables.
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Parameters
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----------
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iterables : list / sequence of iterables
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Each iterable has unique labels for each level of the index.
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sortorder : int or None
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Level of sortedness (must be lexicographically sorted by that
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level).
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names : list / sequence of str, optional
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Names for the levels in the index.
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Returns
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-------
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index : MultiIndex
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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_tuples : Convert list of tuples to MultiIndex.
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Examples
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--------
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>>> numbers = [0, 1, 2]
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>>> colors = ['green', 'purple']
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>>> ps.MultiIndex.from_product([numbers, colors],
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... names=['number', 'color']) # doctest: +SKIP
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MultiIndex([(0, 'green'),
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(0, 'purple'),
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(1, 'green'),
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(1, 'purple'),
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(2, 'green'),
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(2, 'purple')],
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names=['number', 'color'])
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"""
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return cast(
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MultiIndex,
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ps.from_pandas(
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pd.MultiIndex.from_product(iterables=iterables, sortorder=sortorder, names=names)
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),
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)
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@staticmethod
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def from_frame(df: DataFrame, names: Optional[List[Union[Any, Tuple]]] = None) -> "MultiIndex":
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"""
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Make a MultiIndex from a DataFrame.
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Parameters
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----------
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df : DataFrame
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DataFrame to be converted to MultiIndex.
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names : list-like, optional
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If no names are provided, use the column names, or tuple of column
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names if the columns is a MultiIndex. If a sequence, overwrite
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names with the given sequence.
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Returns
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-------
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MultiIndex
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The MultiIndex representation of the given DataFrame.
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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_tuples : Convert list of tuples to MultiIndex.
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MultiIndex.from_product : Make a MultiIndex from cartesian product
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of iterables.
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Examples
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--------
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>>> df = ps.DataFrame([['HI', 'Temp'], ['HI', 'Precip'],
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... ['NJ', 'Temp'], ['NJ', 'Precip']],
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... columns=['a', 'b'])
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>>> df # doctest: +SKIP
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a b
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0 HI Temp
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1 HI Precip
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2 NJ Temp
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3 NJ Precip
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>>> ps.MultiIndex.from_frame(df) # doctest: +SKIP
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MultiIndex([('HI', 'Temp'),
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('HI', 'Precip'),
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('NJ', 'Temp'),
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('NJ', 'Precip')],
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names=['a', 'b'])
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Using explicit names, instead of the column names
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>>> ps.MultiIndex.from_frame(df, names=['state', 'observation']) # doctest: +SKIP
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MultiIndex([('HI', 'Temp'),
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('HI', 'Precip'),
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('NJ', 'Temp'),
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('NJ', 'Precip')],
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names=['state', 'observation'])
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"""
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if not isinstance(df, DataFrame):
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raise TypeError("Input must be a DataFrame")
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sdf = df.to_spark()
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if names is None:
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names = df._internal.column_labels
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elif not is_list_like(names):
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raise TypeError("Names should be list-like for a MultiIndex")
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else:
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names = [name if is_name_like_tuple(name) else (name,) for name in names]
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internal = InternalFrame(
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spark_frame=sdf,
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index_spark_columns=[scol_for(sdf, col) for col in sdf.columns],
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index_names=names,
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)
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return cast(MultiIndex, DataFrame(internal).index)
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@property
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def name(self) -> Union[Any, Tuple]:
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raise PandasNotImplementedError(class_name="pd.MultiIndex", property_name="name")
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@name.setter
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def name(self, name: Union[Any, Tuple]) -> None:
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raise PandasNotImplementedError(class_name="pd.MultiIndex", property_name="name")
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def _verify_for_rename( # type: ignore[override]
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self, name: List[Union[Any, Tuple]]
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) -> List[Tuple]:
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if is_list_like(name):
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if self._internal.index_level != len(name):
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raise ValueError(
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"Length of new names must be {}, got {}".format(
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self._internal.index_level, len(name)
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)
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)
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if any(not is_hashable(n) for n in name):
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raise TypeError("MultiIndex.name must be a hashable type")
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return [n if is_name_like_tuple(n) else (n,) for n in name]
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else:
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raise TypeError("Must pass list-like as `names`.")
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def swaplevel(self, i: int = -2, j: int = -1) -> "MultiIndex":
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"""
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Swap level i with level j.
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Calling this method does not change the ordering of the values.
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Parameters
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----------
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i : int, str, default -2
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First level of index to be swapped. Can pass level name as string.
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Type of parameters can be mixed.
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j : int, str, default -1
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Second level of index to be swapped. Can pass level name as string.
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Type of parameters can be mixed.
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Returns
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-------
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MultiIndex
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A new MultiIndex.
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Examples
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--------
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>>> midx = ps.MultiIndex.from_arrays([['a', 'b'], [1, 2]], names = ['word', 'number'])
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>>> midx # doctest: +SKIP
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MultiIndex([('a', 1),
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('b', 2)],
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names=['word', 'number'])
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>>> midx.swaplevel(0, 1) # doctest: +SKIP
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MultiIndex([(1, 'a'),
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(2, 'b')],
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names=['number', 'word'])
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>>> midx.swaplevel('number', 'word') # doctest: +SKIP
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MultiIndex([(1, 'a'),
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(2, 'b')],
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names=['number', 'word'])
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"""
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for index in (i, j):
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if not isinstance(index, int) and index not in self.names:
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raise KeyError("Level %s not found" % index)
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i = i if isinstance(i, int) else self.names.index(i)
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j = j if isinstance(j, int) else self.names.index(j)
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|
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for index in (i, j):
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if index >= len(self.names) or index < -len(self.names):
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raise IndexError(
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"Too many levels: Index has only %s levels, "
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"%s is not a valid level number" % (len(self.names), index)
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)
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||
|
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index_map = list(
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zip(
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self._internal.index_spark_columns,
|
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self._internal.index_names,
|
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self._internal.index_fields,
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)
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)
|
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index_map[i], index_map[j] = index_map[j], index_map[i]
|
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index_spark_columns, index_names, index_fields = zip(*index_map)
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internal = self._internal.copy(
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index_spark_columns=list(index_spark_columns),
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index_names=list(index_names),
|
||
index_fields=list(index_fields),
|
||
column_labels=[],
|
||
data_spark_columns=[],
|
||
data_fields=[],
|
||
)
|
||
return cast(MultiIndex, DataFrame(internal).index)
|
||
|
||
@property
|
||
def levshape(self) -> Tuple[int, ...]:
|
||
"""
|
||
A tuple with the length of each level.
|
||
|
||
Examples
|
||
--------
|
||
>>> midx = ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])
|
||
>>> midx # doctest: +SKIP
|
||
MultiIndex([('a', 'x'),
|
||
('b', 'y'),
|
||
('c', 'z')],
|
||
)
|
||
|
||
>>> midx.levshape
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(3, 3)
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"""
|
||
result = self._internal.spark_frame.agg(
|
||
*(F.countDistinct(c) for c in self._internal.index_spark_columns)
|
||
).collect()[0]
|
||
return tuple(result)
|
||
|
||
@staticmethod
|
||
def _comparator_for_monotonic_increasing(
|
||
data_type: DataType,
|
||
) -> Callable[
|
||
[spark.Column, spark.Column, Callable[[spark.Column, spark.Column], spark.Column]],
|
||
spark.Column,
|
||
]:
|
||
return compare_disallow_null
|
||
|
||
def _is_monotonic(self, order: str) -> bool:
|
||
if order == "increasing":
|
||
return self._is_monotonic_increasing().all()
|
||
else:
|
||
return self._is_monotonic_decreasing().all()
|
||
|
||
def _is_monotonic_increasing(self) -> Series:
|
||
window = Window.orderBy(NATURAL_ORDER_COLUMN_NAME).rowsBetween(-1, -1)
|
||
|
||
cond = F.lit(True)
|
||
has_not_null = F.lit(True)
|
||
for scol in self._internal.index_spark_columns[::-1]:
|
||
data_type = self._internal.spark_type_for(scol)
|
||
prev = F.lag(scol, 1).over(window)
|
||
compare = MultiIndex._comparator_for_monotonic_increasing(data_type)
|
||
# Since pandas 1.1.4, null value is not allowed at any levels of MultiIndex.
|
||
# Therefore, we should check `has_not_null` over the all levels.
|
||
has_not_null = has_not_null & scol.isNotNull()
|
||
cond = F.when(scol.eqNullSafe(prev), cond).otherwise(
|
||
compare(scol, prev, spark.Column.__gt__)
|
||
)
|
||
|
||
cond = has_not_null & (prev.isNull() | cond)
|
||
|
||
cond_name = verify_temp_column_name(
|
||
self._internal.spark_frame.select(self._internal.index_spark_columns),
|
||
"__is_monotonic_increasing_cond__",
|
||
)
|
||
|
||
sdf = self._internal.spark_frame.select(
|
||
self._internal.index_spark_columns + [cond.alias(cond_name)]
|
||
)
|
||
|
||
internal = InternalFrame(
|
||
spark_frame=sdf,
|
||
index_spark_columns=[
|
||
scol_for(sdf, col) for col in self._internal.index_spark_column_names
|
||
],
|
||
index_names=self._internal.index_names,
|
||
index_fields=self._internal.index_fields,
|
||
)
|
||
|
||
return first_series(DataFrame(internal))
|
||
|
||
@staticmethod
|
||
def _comparator_for_monotonic_decreasing(
|
||
data_type: DataType,
|
||
) -> Callable[
|
||
[spark.Column, spark.Column, Callable[[spark.Column, spark.Column], spark.Column]],
|
||
spark.Column,
|
||
]:
|
||
return compare_disallow_null
|
||
|
||
def _is_monotonic_decreasing(self) -> Series:
|
||
window = Window.orderBy(NATURAL_ORDER_COLUMN_NAME).rowsBetween(-1, -1)
|
||
|
||
cond = F.lit(True)
|
||
has_not_null = F.lit(True)
|
||
for scol in self._internal.index_spark_columns[::-1]:
|
||
data_type = self._internal.spark_type_for(scol)
|
||
prev = F.lag(scol, 1).over(window)
|
||
compare = MultiIndex._comparator_for_monotonic_increasing(data_type)
|
||
# Since pandas 1.1.4, null value is not allowed at any levels of MultiIndex.
|
||
# Therefore, we should check `has_not_null` over the all levels.
|
||
has_not_null = has_not_null & scol.isNotNull()
|
||
cond = F.when(scol.eqNullSafe(prev), cond).otherwise(
|
||
compare(scol, prev, spark.Column.__lt__)
|
||
)
|
||
|
||
cond = has_not_null & (prev.isNull() | cond)
|
||
|
||
cond_name = verify_temp_column_name(
|
||
self._internal.spark_frame.select(self._internal.index_spark_columns),
|
||
"__is_monotonic_decreasing_cond__",
|
||
)
|
||
|
||
sdf = self._internal.spark_frame.select(
|
||
self._internal.index_spark_columns + [cond.alias(cond_name)]
|
||
)
|
||
|
||
internal = InternalFrame(
|
||
spark_frame=sdf,
|
||
index_spark_columns=[
|
||
scol_for(sdf, col) for col in self._internal.index_spark_column_names
|
||
],
|
||
index_names=self._internal.index_names,
|
||
index_fields=self._internal.index_fields,
|
||
)
|
||
|
||
return first_series(DataFrame(internal))
|
||
|
||
def to_frame( # type: ignore[override]
|
||
self, index: bool = True, name: Optional[List[Union[Any, Tuple]]] = None
|
||
) -> DataFrame:
|
||
"""
|
||
Create a DataFrame with the levels of the MultiIndex as columns.
|
||
Column ordering is determined by the DataFrame constructor with data as
|
||
a dict.
|
||
|
||
Parameters
|
||
----------
|
||
index : boolean, default True
|
||
Set the index of the returned DataFrame as the original MultiIndex.
|
||
name : list / sequence of strings, optional
|
||
The passed names should substitute index level names.
|
||
|
||
Returns
|
||
-------
|
||
DataFrame : a DataFrame containing the original MultiIndex data.
|
||
|
||
See Also
|
||
--------
|
||
DataFrame
|
||
|
||
Examples
|
||
--------
|
||
>>> tuples = [(1, 'red'), (1, 'blue'),
|
||
... (2, 'red'), (2, 'blue')]
|
||
>>> idx = ps.MultiIndex.from_tuples(tuples, names=('number', 'color'))
|
||
>>> idx # doctest: +SKIP
|
||
MultiIndex([(1, 'red'),
|
||
(1, 'blue'),
|
||
(2, 'red'),
|
||
(2, 'blue')],
|
||
names=['number', 'color'])
|
||
>>> idx.to_frame() # doctest: +NORMALIZE_WHITESPACE
|
||
number color
|
||
number color
|
||
1 red 1 red
|
||
blue 1 blue
|
||
2 red 2 red
|
||
blue 2 blue
|
||
|
||
By default, the original Index is reused. To enforce a new Index:
|
||
|
||
>>> idx.to_frame(index=False)
|
||
number color
|
||
0 1 red
|
||
1 1 blue
|
||
2 2 red
|
||
3 2 blue
|
||
|
||
To override the name of the resulting column, specify `name`:
|
||
|
||
>>> idx.to_frame(name=['n', 'c']) # doctest: +NORMALIZE_WHITESPACE
|
||
n c
|
||
number color
|
||
1 red 1 red
|
||
blue 1 blue
|
||
2 red 2 red
|
||
blue 2 blue
|
||
"""
|
||
if name is None:
|
||
name = [
|
||
name if name is not None else (i,)
|
||
for i, name in enumerate(self._internal.index_names)
|
||
]
|
||
elif is_list_like(name):
|
||
if len(name) != self._internal.index_level:
|
||
raise ValueError("'name' should have same length as number of levels on index.")
|
||
name = [n if is_name_like_tuple(n) else (n,) for n in name]
|
||
else:
|
||
raise TypeError("'name' must be a list / sequence of column names.")
|
||
|
||
return self._to_frame(index=index, names=name)
|
||
|
||
def to_pandas(self) -> pd.MultiIndex:
|
||
"""
|
||
Return a pandas MultiIndex.
|
||
|
||
.. note:: This method should only be used if the resulting pandas object is expected
|
||
to be small, as all the data is loaded into the driver's memory.
|
||
|
||
Examples
|
||
--------
|
||
>>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
|
||
... columns=['dogs', 'cats'],
|
||
... index=[list('abcd'), list('efgh')])
|
||
>>> df['dogs'].index.to_pandas() # doctest: +SKIP
|
||
MultiIndex([('a', 'e'),
|
||
('b', 'f'),
|
||
('c', 'g'),
|
||
('d', 'h')],
|
||
)
|
||
"""
|
||
# TODO: We might need to handle internal state change.
|
||
# So far, we don't have any functions to change the internal state of MultiIndex except for
|
||
# series-like operations. In that case, it creates new Index object instead of MultiIndex.
|
||
return super().to_pandas()
|
||
|
||
def nunique(self, dropna: bool = True, approx: bool = False, rsd: float = 0.05) -> int:
|
||
raise NotImplementedError("nunique is not defined for MultiIndex")
|
||
|
||
# TODO: add 'name' parameter after pd.MultiIndex.name is implemented
|
||
def copy(self, deep: Optional[bool] = None) -> "MultiIndex": # type: ignore[override]
|
||
"""
|
||
Make a copy of this object.
|
||
|
||
Parameters
|
||
----------
|
||
deep : None
|
||
this parameter is not supported but just dummy parameter to match pandas.
|
||
|
||
Examples
|
||
--------
|
||
>>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
|
||
... columns=['dogs', 'cats'],
|
||
... index=[list('abcd'), list('efgh')])
|
||
>>> df['dogs'].index # doctest: +SKIP
|
||
MultiIndex([('a', 'e'),
|
||
('b', 'f'),
|
||
('c', 'g'),
|
||
('d', 'h')],
|
||
)
|
||
|
||
Copy index
|
||
|
||
>>> df.index.copy() # doctest: +SKIP
|
||
MultiIndex([('a', 'e'),
|
||
('b', 'f'),
|
||
('c', 'g'),
|
||
('d', 'h')],
|
||
)
|
||
"""
|
||
return super().copy(deep=deep) # type: ignore
|
||
|
||
def symmetric_difference( # type: ignore[override]
|
||
self,
|
||
other: Index,
|
||
result_name: Optional[List[Union[Any, Tuple]]] = None,
|
||
sort: Optional[bool] = None,
|
||
) -> "MultiIndex":
|
||
"""
|
||
Compute the symmetric difference of two MultiIndex objects.
|
||
|
||
Parameters
|
||
----------
|
||
other : Index or array-like
|
||
result_name : list
|
||
sort : True or None, default None
|
||
Whether to sort the resulting index.
|
||
* True : Attempt to sort the result.
|
||
* None : Do not sort the result.
|
||
|
||
Returns
|
||
-------
|
||
symmetric_difference : MiltiIndex
|
||
|
||
Notes
|
||
-----
|
||
``symmetric_difference`` contains elements that appear in either
|
||
``idx1`` or ``idx2`` but not both. Equivalent to the Index created by
|
||
``idx1.difference(idx2) | idx2.difference(idx1)`` with duplicates
|
||
dropped.
|
||
|
||
Examples
|
||
--------
|
||
>>> midx1 = pd.MultiIndex([['lama', 'cow', 'falcon'],
|
||
... ['speed', 'weight', 'length']],
|
||
... [[0, 0, 0, 1, 1, 1, 2, 2, 2],
|
||
... [0, 0, 0, 0, 1, 2, 0, 1, 2]])
|
||
>>> midx2 = pd.MultiIndex([['pandas-on-Spark', 'cow', 'falcon'],
|
||
... ['speed', 'weight', 'length']],
|
||
... [[0, 0, 0, 1, 1, 1, 2, 2, 2],
|
||
... [0, 0, 0, 0, 1, 2, 0, 1, 2]])
|
||
>>> s1 = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
|
||
... index=midx1)
|
||
>>> s2 = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
|
||
... index=midx2)
|
||
|
||
>>> s1.index.symmetric_difference(s2.index) # doctest: +SKIP
|
||
MultiIndex([('pandas-on-Spark', 'speed'),
|
||
( 'lama', 'speed')],
|
||
)
|
||
|
||
You can set names of result Index.
|
||
|
||
>>> s1.index.symmetric_difference(s2.index, result_name=['a', 'b']) # doctest: +SKIP
|
||
MultiIndex([('pandas-on-Spark', 'speed'),
|
||
( 'lama', 'speed')],
|
||
names=['a', 'b'])
|
||
|
||
You can set sort to `True`, if you want to sort the resulting index.
|
||
|
||
>>> s1.index.symmetric_difference(s2.index, sort=True) # doctest: +SKIP
|
||
MultiIndex([('pandas-on-Spark', 'speed'),
|
||
( 'lama', 'speed')],
|
||
)
|
||
|
||
You can also use the ``^`` operator:
|
||
|
||
>>> s1.index ^ s2.index # doctest: +SKIP
|
||
MultiIndex([('pandas-on-Spark', 'speed'),
|
||
( 'lama', 'speed')],
|
||
)
|
||
"""
|
||
if type(self) != type(other):
|
||
raise NotImplementedError(
|
||
"Doesn't support symmetric_difference between Index & MultiIndex for now"
|
||
)
|
||
|
||
sdf_self = self._psdf._internal.spark_frame.select(self._internal.index_spark_columns)
|
||
sdf_other = other._psdf._internal.spark_frame.select(other._internal.index_spark_columns)
|
||
|
||
sdf_symdiff = sdf_self.union(sdf_other).subtract(sdf_self.intersect(sdf_other))
|
||
|
||
if sort:
|
||
sdf_symdiff = sdf_symdiff.sort(*self._internal.index_spark_columns)
|
||
|
||
internal = InternalFrame( # TODO: dtypes?
|
||
spark_frame=sdf_symdiff,
|
||
index_spark_columns=[
|
||
scol_for(sdf_symdiff, col) for col in self._internal.index_spark_column_names
|
||
],
|
||
index_names=self._internal.index_names,
|
||
)
|
||
result = cast(MultiIndex, DataFrame(internal).index)
|
||
|
||
if result_name:
|
||
result.names = result_name
|
||
|
||
return result
|
||
|
||
# TODO: ADD error parameter
|
||
def drop(
|
||
self, codes: List[Any], level: Optional[Union[int, Any, Tuple]] = None
|
||
) -> "MultiIndex":
|
||
"""
|
||
Make new MultiIndex with passed list of labels deleted
|
||
|
||
Parameters
|
||
----------
|
||
codes : array-like
|
||
Must be a list of tuples
|
||
level : int or level name, default None
|
||
|
||
Returns
|
||
-------
|
||
dropped : MultiIndex
|
||
|
||
Examples
|
||
--------
|
||
>>> index = ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])
|
||
>>> index # doctest: +SKIP
|
||
MultiIndex([('a', 'x'),
|
||
('b', 'y'),
|
||
('c', 'z')],
|
||
)
|
||
|
||
>>> index.drop(['a']) # doctest: +SKIP
|
||
MultiIndex([('b', 'y'),
|
||
('c', 'z')],
|
||
)
|
||
|
||
>>> index.drop(['x', 'y'], level=1) # doctest: +SKIP
|
||
MultiIndex([('c', 'z')],
|
||
)
|
||
"""
|
||
internal = self._internal.resolved_copy
|
||
sdf = internal.spark_frame
|
||
index_scols = internal.index_spark_columns
|
||
if level is None:
|
||
scol = index_scols[0]
|
||
elif isinstance(level, int):
|
||
scol = index_scols[level]
|
||
else:
|
||
scol = None
|
||
for index_spark_column, index_name in zip(
|
||
internal.index_spark_columns, internal.index_names
|
||
):
|
||
if not isinstance(level, tuple):
|
||
level = (level,)
|
||
if level == index_name:
|
||
if scol is not None:
|
||
raise ValueError(
|
||
"The name {} occurs multiple times, use a level number".format(
|
||
name_like_string(level)
|
||
)
|
||
)
|
||
scol = index_spark_column
|
||
if scol is None:
|
||
raise KeyError("Level {} not found".format(name_like_string(level)))
|
||
sdf = sdf[~scol.isin(codes)]
|
||
|
||
internal = InternalFrame(
|
||
spark_frame=sdf,
|
||
index_spark_columns=[scol_for(sdf, col) for col in internal.index_spark_column_names],
|
||
index_names=internal.index_names,
|
||
index_fields=internal.index_fields,
|
||
column_labels=[],
|
||
data_spark_columns=[],
|
||
data_fields=[],
|
||
)
|
||
return cast(MultiIndex, DataFrame(internal).index)
|
||
|
||
def argmax(self) -> None:
|
||
raise TypeError("reduction operation 'argmax' not allowed for this dtype")
|
||
|
||
def argmin(self) -> None:
|
||
raise TypeError("reduction operation 'argmin' not allowed for this dtype")
|
||
|
||
def asof(self, label: Any) -> None:
|
||
raise NotImplementedError(
|
||
"only the default get_loc method is currently supported for MultiIndex"
|
||
)
|
||
|
||
@property
|
||
def is_all_dates(self) -> bool:
|
||
"""
|
||
is_all_dates always returns False for MultiIndex
|
||
|
||
Examples
|
||
--------
|
||
>>> from datetime import datetime
|
||
|
||
>>> idx = ps.MultiIndex.from_tuples(
|
||
... [(datetime(2019, 1, 1, 0, 0, 0), datetime(2019, 1, 1, 0, 0, 0)),
|
||
... (datetime(2019, 1, 1, 0, 0, 0), datetime(2019, 1, 1, 0, 0, 0))])
|
||
>>> idx # doctest: +SKIP
|
||
MultiIndex([('2019-01-01', '2019-01-01'),
|
||
('2019-01-01', '2019-01-01')],
|
||
)
|
||
|
||
>>> idx.is_all_dates
|
||
False
|
||
"""
|
||
return False
|
||
|
||
def __getattr__(self, item: str) -> Any:
|
||
if hasattr(MissingPandasLikeMultiIndex, item):
|
||
property_or_func = getattr(MissingPandasLikeMultiIndex, item)
|
||
if isinstance(property_or_func, property):
|
||
return property_or_func.fget(self) # type: ignore
|
||
else:
|
||
return partial(property_or_func, self)
|
||
raise AttributeError("'MultiIndex' object has no attribute '{}'".format(item))
|
||
|
||
def _get_level_number(self, level: Union[int, Any, Tuple]) -> int:
|
||
"""
|
||
Return the level number if a valid level is given.
|
||
"""
|
||
count = self.names.count(level)
|
||
if (count > 1) and not isinstance(level, int):
|
||
raise ValueError("The name %s occurs multiple times, use a level number" % level)
|
||
if level in self.names:
|
||
level = self.names.index(level)
|
||
elif isinstance(level, int):
|
||
nlevels = self.nlevels
|
||
if level >= nlevels:
|
||
raise IndexError(
|
||
"Too many levels: Index has only %d "
|
||
"levels, %d is not a valid level number" % (nlevels, level)
|
||
)
|
||
if level < 0:
|
||
if (level + nlevels) < 0:
|
||
raise IndexError(
|
||
"Too many levels: Index has only %d levels, "
|
||
"not %d" % (nlevels, level + 1)
|
||
)
|
||
level = level + nlevels
|
||
else:
|
||
raise KeyError("Level %s not found" % str(level))
|
||
|
||
return level
|
||
|
||
def get_level_values(self, level: Union[int, Any, Tuple]) -> Index:
|
||
"""
|
||
Return vector of label values for requested level,
|
||
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 = ps.MultiIndex.from_tuples([('x', 'a'), ('x', 'b'), ('y', 'a')])
|
||
>>> mi.names = ['level_1', 'level_2']
|
||
|
||
Get level values by supplying level as either integer or name:
|
||
|
||
>>> mi.get_level_values(0)
|
||
Index(['x', 'x', 'y'], dtype='object', name='level_1')
|
||
|
||
>>> mi.get_level_values('level_2')
|
||
Index(['a', 'b', 'a'], dtype='object', name='level_2')
|
||
"""
|
||
level = self._get_level_number(level)
|
||
index_scol = self._internal.index_spark_columns[level]
|
||
index_name = self._internal.index_names[level]
|
||
index_field = self._internal.index_fields[level]
|
||
internal = self._internal.copy(
|
||
index_spark_columns=[index_scol],
|
||
index_names=[index_name],
|
||
index_fields=[index_field],
|
||
column_labels=[],
|
||
data_spark_columns=[],
|
||
data_fields=[],
|
||
)
|
||
return DataFrame(internal).index
|
||
|
||
def insert(self, loc: int, item: Any) -> Index:
|
||
"""
|
||
Make new MultiIndex inserting new item at location.
|
||
|
||
Follows Python list.append semantics for negative values.
|
||
|
||
Parameters
|
||
----------
|
||
loc : int
|
||
item : object
|
||
|
||
Returns
|
||
-------
|
||
new_index : MultiIndex
|
||
|
||
Examples
|
||
--------
|
||
>>> psmidx = ps.MultiIndex.from_tuples([("a", "x"), ("b", "y"), ("c", "z")])
|
||
>>> psmidx.insert(3, ("h", "j")) # doctest: +SKIP
|
||
MultiIndex([('a', 'x'),
|
||
('b', 'y'),
|
||
('c', 'z'),
|
||
('h', 'j')],
|
||
)
|
||
|
||
For negative values
|
||
|
||
>>> psmidx.insert(-2, ("h", "j")) # doctest: +SKIP
|
||
MultiIndex([('a', 'x'),
|
||
('h', 'j'),
|
||
('b', 'y'),
|
||
('c', 'z')],
|
||
)
|
||
"""
|
||
length = len(self)
|
||
if loc < 0:
|
||
loc = loc + length
|
||
if loc < 0:
|
||
raise IndexError(
|
||
"index {} is out of bounds for axis 0 with size {}".format(
|
||
(loc - length), length
|
||
)
|
||
)
|
||
else:
|
||
if loc > length:
|
||
raise IndexError(
|
||
"index {} is out of bounds for axis 0 with size {}".format(loc, length)
|
||
)
|
||
|
||
index_name = [
|
||
(name,) for name in self._internal.index_spark_column_names
|
||
] # type: List[Tuple]
|
||
sdf_before = self.to_frame(name=index_name)[:loc].to_spark()
|
||
sdf_middle = Index([item]).to_frame(name=index_name).to_spark()
|
||
sdf_after = self.to_frame(name=index_name)[loc:].to_spark()
|
||
sdf = sdf_before.union(sdf_middle).union(sdf_after)
|
||
|
||
internal = InternalFrame( # TODO: dtypes?
|
||
spark_frame=sdf,
|
||
index_spark_columns=[
|
||
scol_for(sdf, col) for col in self._internal.index_spark_column_names
|
||
],
|
||
index_names=self._internal.index_names,
|
||
)
|
||
return DataFrame(internal).index
|
||
|
||
def item(self) -> Tuple[Scalar, ...]:
|
||
"""
|
||
Return the first element of the underlying data as a python tuple.
|
||
|
||
Returns
|
||
-------
|
||
tuple
|
||
The first element of MultiIndex.
|
||
|
||
Raises
|
||
------
|
||
ValueError
|
||
If the data is not length-1.
|
||
|
||
Examples
|
||
--------
|
||
>>> psmidx = ps.MultiIndex.from_tuples([('a', 'x')])
|
||
>>> psmidx.item()
|
||
('a', 'x')
|
||
"""
|
||
return self._psdf.head(2)._to_internal_pandas().index.item()
|
||
|
||
def intersection(self, other: Union[DataFrame, Series, Index, List]) -> "MultiIndex":
|
||
"""
|
||
Form the intersection of two Index objects.
|
||
|
||
This returns a new Index with elements common to the index and `other`.
|
||
|
||
Parameters
|
||
----------
|
||
other : Index or array-like
|
||
|
||
Returns
|
||
-------
|
||
intersection : MultiIndex
|
||
|
||
Examples
|
||
--------
|
||
>>> midx1 = ps.MultiIndex.from_tuples([("a", "x"), ("b", "y"), ("c", "z")])
|
||
>>> midx2 = ps.MultiIndex.from_tuples([("c", "z"), ("d", "w")])
|
||
>>> midx1.intersection(midx2).sort_values() # doctest: +SKIP
|
||
MultiIndex([('c', 'z')],
|
||
)
|
||
"""
|
||
if isinstance(other, Series) or not is_list_like(other):
|
||
raise TypeError("other must be a MultiIndex or a list of tuples")
|
||
elif isinstance(other, DataFrame):
|
||
raise ValueError("Index data must be 1-dimensional")
|
||
elif isinstance(other, MultiIndex):
|
||
spark_frame_other = other.to_frame().to_spark()
|
||
keep_name = self.names == other.names
|
||
elif isinstance(other, Index):
|
||
# Always returns an empty MultiIndex if `other` is Index.
|
||
return self.to_frame().head(0).index # type: ignore
|
||
elif not all(isinstance(item, tuple) for item in other):
|
||
raise TypeError("other must be a MultiIndex or a list of tuples")
|
||
else:
|
||
spark_frame_other = MultiIndex.from_tuples(list(other)).to_frame().to_spark()
|
||
keep_name = True
|
||
|
||
default_name = [SPARK_INDEX_NAME_FORMAT(i) for i in range(self.nlevels)] # type: List
|
||
spark_frame_self = self.to_frame(name=default_name).to_spark()
|
||
spark_frame_intersected = spark_frame_self.intersect(spark_frame_other)
|
||
if keep_name:
|
||
index_names = self._internal.index_names
|
||
else:
|
||
index_names = None
|
||
internal = InternalFrame( # TODO: dtypes?
|
||
spark_frame=spark_frame_intersected,
|
||
index_spark_columns=[scol_for(spark_frame_intersected, col) for col in default_name],
|
||
index_names=index_names,
|
||
)
|
||
return cast(MultiIndex, DataFrame(internal).index)
|
||
|
||
@property
|
||
def hasnans(self) -> bool:
|
||
raise NotImplementedError("hasnans is not defined for MultiIndex")
|
||
|
||
@property
|
||
def inferred_type(self) -> str:
|
||
"""
|
||
Return a string of the type inferred from the values.
|
||
"""
|
||
# Always returns "mixed" for MultiIndex
|
||
return "mixed"
|
||
|
||
@property
|
||
def asi8(self) -> None:
|
||
"""
|
||
Integer representation of the values.
|
||
"""
|
||
# Always returns None for MultiIndex
|
||
return None
|
||
|
||
def factorize(
|
||
self, sort: bool = True, na_sentinel: Optional[int] = -1
|
||
) -> Tuple["MultiIndex", pd.Index]:
|
||
return MissingPandasLikeMultiIndex.factorize(self, sort=sort, na_sentinel=na_sentinel)
|
||
|
||
def __iter__(self) -> Iterator:
|
||
return MissingPandasLikeMultiIndex.__iter__(self)
|
||
|
||
|
||
def _test() -> None:
|
||
import os
|
||
import doctest
|
||
import sys
|
||
import numpy
|
||
from pyspark.sql import SparkSession
|
||
import pyspark.pandas.indexes.multi
|
||
|
||
os.chdir(os.environ["SPARK_HOME"])
|
||
|
||
globs = pyspark.pandas.indexes.multi.__dict__.copy()
|
||
globs["np"] = numpy
|
||
globs["ps"] = pyspark.pandas
|
||
spark = (
|
||
SparkSession.builder.master("local[4]")
|
||
.appName("pyspark.pandas.indexes.multi tests")
|
||
.getOrCreate()
|
||
)
|
||
(failure_count, test_count) = doctest.testmod(
|
||
pyspark.pandas.indexes.multi,
|
||
globs=globs,
|
||
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
|
||
)
|
||
spark.stop()
|
||
if failure_count:
|
||
sys.exit(-1)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
_test()
|