7ff9d2e3ee
### What changes were proposed in this pull request? This PR proposes to rename Koalas to pandas-on-Spark in main codes ### Why are the changes needed? To have the correct name in PySpark. NOTE that the official name in the main documentation will be pandas APIs on Spark to be extra clear. pandas-on-Spark is not the official term. ### Does this PR introduce _any_ user-facing change? No, it's master-only change. It changes the docstring and class names. ### How was this patch tested? Manually tested via: ```bash ./python/run-tests --python-executable=python3 --modules pyspark-pandas ``` Closes #32166 from HyukjinKwon/rename-koalas. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
219 lines
7.5 KiB
Python
219 lines
7.5 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 functools import partial
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from typing import Any
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import pandas as pd
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from pandas.api.types import is_hashable
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from pyspark import pandas as ps
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from pyspark.pandas.indexes.base import Index
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from pyspark.pandas.missing.indexes import MissingPandasLikeCategoricalIndex
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from pyspark.pandas.series import Series
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class CategoricalIndex(Index):
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"""
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Index based on an underlying `Categorical`.
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CategoricalIndex can only take on a limited,
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and usually fixed, number of possible values (`categories`). Also,
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it might have an order, but numerical operations
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(additions, divisions, ...) are not possible.
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Parameters
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----------
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data : array-like (1-dimensional)
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The values of the categorical. If `categories` are given, values not in
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`categories` will be replaced with NaN.
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categories : index-like, optional
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The categories for the categorical. Items need to be unique.
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If the categories are not given here (and also not in `dtype`), they
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will be inferred from the `data`.
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ordered : bool, optional
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Whether or not this categorical is treated as an ordered
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categorical. If not given here or in `dtype`, the resulting
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categorical will be unordered.
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dtype : CategoricalDtype or "category", optional
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If :class:`CategoricalDtype`, cannot be used together with
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`categories` or `ordered`.
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copy : bool, default False
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Make a copy of input ndarray.
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name : object, optional
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Name to be stored in the index.
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See Also
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--------
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Index : The base pandas-on-Spark Index type.
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Examples
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--------
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>>> ps.CategoricalIndex(["a", "b", "c", "a", "b", "c"]) # doctest: +NORMALIZE_WHITESPACE
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CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
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categories=['a', 'b', 'c'], ordered=False, dtype='category')
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``CategoricalIndex`` can also be instantiated from a ``Categorical``:
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>>> c = pd.Categorical(["a", "b", "c", "a", "b", "c"])
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>>> ps.CategoricalIndex(c) # doctest: +NORMALIZE_WHITESPACE
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CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
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categories=['a', 'b', 'c'], ordered=False, dtype='category')
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Ordered ``CategoricalIndex`` can have a min and max value.
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>>> ci = ps.CategoricalIndex(
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... ["a", "b", "c", "a", "b", "c"], ordered=True, categories=["c", "b", "a"]
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... )
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>>> ci # doctest: +NORMALIZE_WHITESPACE
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CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
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categories=['c', 'b', 'a'], ordered=True, dtype='category')
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From a Series:
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>>> s = ps.Series(["a", "b", "c", "a", "b", "c"], index=[10, 20, 30, 40, 50, 60])
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>>> ps.CategoricalIndex(s) # doctest: +NORMALIZE_WHITESPACE
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CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
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categories=['a', 'b', 'c'], ordered=False, dtype='category')
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From an Index:
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>>> idx = ps.Index(["a", "b", "c", "a", "b", "c"])
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>>> ps.CategoricalIndex(idx) # doctest: +NORMALIZE_WHITESPACE
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CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
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categories=['a', 'b', 'c'], ordered=False, dtype='category')
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"""
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def __new__(cls, data=None, categories=None, ordered=None, dtype=None, copy=False, name=None):
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if not is_hashable(name):
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raise TypeError("Index.name must be a hashable type")
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if isinstance(data, (Series, Index)):
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if dtype is None:
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dtype = "category"
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return Index(data, dtype=dtype, copy=copy, name=name)
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return ps.from_pandas(
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pd.CategoricalIndex(
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data=data, categories=categories, ordered=ordered, dtype=dtype, name=name
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)
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)
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@property
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def codes(self) -> Index:
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"""
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The category codes of this categorical.
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Codes are an Index of integers which are the positions of the actual
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values in the categories Index.
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There is no setter, use the other categorical methods and the normal item
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setter to change values in the categorical.
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Returns
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-------
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Index
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A non-writable view of the `codes` Index.
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Examples
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--------
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>>> idx = ps.CategoricalIndex(list("abbccc"))
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>>> idx # doctest: +NORMALIZE_WHITESPACE
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CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
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categories=['a', 'b', 'c'], ordered=False, dtype='category')
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>>> idx.codes
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Int64Index([0, 1, 1, 2, 2, 2], dtype='int64')
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"""
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return self._with_new_scol(self.spark.column).rename(None)
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@property
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def categories(self) -> pd.Index:
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"""
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The categories of this categorical.
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Examples
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--------
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>>> idx = ps.CategoricalIndex(list("abbccc"))
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>>> idx # doctest: +NORMALIZE_WHITESPACE
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CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
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categories=['a', 'b', 'c'], ordered=False, dtype='category')
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>>> idx.categories
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Index(['a', 'b', 'c'], dtype='object')
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"""
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return self.dtype.categories
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@categories.setter
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def categories(self, categories):
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raise NotImplementedError()
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@property
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def ordered(self) -> bool:
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"""
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Whether the categories have an ordered relationship.
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Examples
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--------
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>>> idx = ps.CategoricalIndex(list("abbccc"))
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>>> idx # doctest: +NORMALIZE_WHITESPACE
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CategoricalIndex(['a', 'b', 'b', 'c', 'c', 'c'],
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categories=['a', 'b', 'c'], ordered=False, dtype='category')
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>>> idx.ordered
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False
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"""
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return self.dtype.ordered
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def __getattr__(self, item: str) -> Any:
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if hasattr(MissingPandasLikeCategoricalIndex, item):
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property_or_func = getattr(MissingPandasLikeCategoricalIndex, item)
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if isinstance(property_or_func, property):
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return property_or_func.fget(self) # type: ignore
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else:
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return partial(property_or_func, self)
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raise AttributeError("'CategoricalIndex' object has no attribute '{}'".format(item))
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def _test():
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import os
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import doctest
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import sys
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from pyspark.sql import SparkSession
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import pyspark.pandas.indexes.category
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os.chdir(os.environ["SPARK_HOME"])
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globs = pyspark.pandas.indexes.category.__dict__.copy()
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globs["ps"] = pyspark.pandas
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spark = (
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SparkSession.builder.master("local[4]")
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.appName("pyspark.pandas.indexes.category tests")
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.getOrCreate()
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)
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(failure_count, test_count) = doctest.testmod(
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pyspark.pandas.indexes.category,
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globs=globs,
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optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
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)
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spark.stop()
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if failure_count:
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sys.exit(-1)
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if __name__ == "__main__":
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_test()
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