2dc15d9d84
This PR proposes to enable the tests, disabled since different behavior with pandas 1.3.
- `inplace` argument for `CategoricalDtype` functions is deprecated from pandas 1.3, and seems they have bug. So we manually created the expected result and test them.
- Fixed the `GroupBy.transform` since it doesn't work properly for `CategoricalDtype`.
We should enable the tests as much as possible even if pandas has a bug.
And we should follow the behavior of latest pandas.
Yes, `GroupBy.transform` now follow the behavior of latest pandas.
Unittests.
Closes #33817 from itholic/SPARK-36537.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit fe486185c4)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
806 lines
29 KiB
Python
806 lines
29 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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import numpy as np
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import pandas as pd
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from pandas.api.types import CategoricalDtype
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import pyspark.pandas as ps
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from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils
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class CategoricalTest(PandasOnSparkTestCase, TestUtils):
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@property
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def pdf(self):
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return pd.DataFrame(
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{
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"a": pd.Categorical([1, 2, 3, 1, 2, 3]),
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"b": pd.Categorical(
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["b", "a", "c", "c", "b", "a"], categories=["c", "b", "d", "a"]
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),
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},
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)
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@property
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def psdf(self):
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return ps.from_pandas(self.pdf)
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@property
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def df_pair(self):
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return self.pdf, self.psdf
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def test_categorical_frame(self):
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pdf, psdf = self.df_pair
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self.assert_eq(psdf, pdf)
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self.assert_eq(psdf.a, pdf.a)
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self.assert_eq(psdf.b, pdf.b)
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self.assert_eq(psdf.index, pdf.index)
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self.assert_eq(psdf.sort_index(), pdf.sort_index())
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self.assert_eq(psdf.sort_values("b"), pdf.sort_values("b"))
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def test_categorical_series(self):
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pser = pd.Series([1, 2, 3], dtype="category")
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psser = ps.Series([1, 2, 3], dtype="category")
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self.assert_eq(psser, pser)
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self.assert_eq(psser.cat.categories, pser.cat.categories)
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self.assert_eq(psser.cat.codes, pser.cat.codes)
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self.assert_eq(psser.cat.ordered, pser.cat.ordered)
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def test_categories_setter(self):
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pdf, psdf = self.df_pair
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pser = pdf.a
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psser = psdf.a
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pser.cat.categories = ["z", "y", "x"]
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psser.cat.categories = ["z", "y", "x"]
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
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pser = pser.astype(CategoricalDtype(categories=["x", "y", "z"]))
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self.assert_eq(pser, psser)
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self.assert_eq(pdf, psdf)
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with self.assertRaises(ValueError):
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psser.cat.categories = [1, 2, 3, 4]
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def test_add_categories(self):
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pdf, psdf = self.df_pair
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pser = pdf.a
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psser = psdf.a
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self.assert_eq(pser.cat.add_categories(4), psser.cat.add_categories(4))
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self.assert_eq(pser.cat.add_categories([4, 5]), psser.cat.add_categories([4, 5]))
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self.assert_eq(pser.cat.add_categories([]), psser.cat.add_categories([]))
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pser.cat.add_categories(4, inplace=True)
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psser.cat.add_categories(4, inplace=True)
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
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pser = pser.astype(CategoricalDtype(categories=[1, 2, 3, 4]))
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self.assert_eq(pser, psser)
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self.assert_eq(pdf, psdf)
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self.assertRaises(ValueError, lambda: psser.cat.add_categories(4))
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self.assertRaises(ValueError, lambda: psser.cat.add_categories([5, 5]))
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def test_remove_categories(self):
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pdf, psdf = self.df_pair
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pser = pdf.a
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psser = psdf.a
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self.assert_eq(pser.cat.remove_categories(2), psser.cat.remove_categories(2))
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self.assert_eq(pser.cat.remove_categories([1, 3]), psser.cat.remove_categories([1, 3]))
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self.assert_eq(pser.cat.remove_categories([]), psser.cat.remove_categories([]))
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self.assert_eq(pser.cat.remove_categories([2, 2]), psser.cat.remove_categories([2, 2]))
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self.assert_eq(
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pser.cat.remove_categories([1, 2, 3]), psser.cat.remove_categories([1, 2, 3])
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)
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self.assert_eq(pser.cat.remove_categories(None), psser.cat.remove_categories(None))
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self.assert_eq(pser.cat.remove_categories([None]), psser.cat.remove_categories([None]))
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pser.cat.remove_categories(2, inplace=True)
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psser.cat.remove_categories(2, inplace=True)
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
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pser = pser.astype(CategoricalDtype(categories=[1, 3]))
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self.assert_eq(pser, psser)
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self.assert_eq(pdf, psdf)
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self.assertRaises(ValueError, lambda: psser.cat.remove_categories(4))
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self.assertRaises(ValueError, lambda: psser.cat.remove_categories([4, None]))
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def test_remove_unused_categories(self):
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pdf, psdf = self.df_pair
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pser = pdf.a
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psser = psdf.a
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self.assert_eq(pser.cat.remove_unused_categories(), psser.cat.remove_unused_categories())
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pser.cat.add_categories(4, inplace=True)
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pser.cat.remove_categories(2, inplace=True)
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psser.cat.add_categories(4, inplace=True)
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psser.cat.remove_categories(2, inplace=True)
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self.assert_eq(pser.cat.remove_unused_categories(), psser.cat.remove_unused_categories())
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pser.cat.remove_unused_categories(inplace=True)
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psser.cat.remove_unused_categories(inplace=True)
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
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pser = pser.astype(CategoricalDtype(categories=[1, 3]))
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self.assert_eq(pser, psser)
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self.assert_eq(pdf, psdf)
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def test_reorder_categories(self):
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pdf, psdf = self.df_pair
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pser = pdf.a
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psser = psdf.a
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self.assert_eq(
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pser.cat.reorder_categories([1, 2, 3]), psser.cat.reorder_categories([1, 2, 3])
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)
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self.assert_eq(
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pser.cat.reorder_categories([1, 2, 3], ordered=True),
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psser.cat.reorder_categories([1, 2, 3], ordered=True),
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)
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self.assert_eq(
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pser.cat.reorder_categories([3, 2, 1]), psser.cat.reorder_categories([3, 2, 1])
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)
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self.assert_eq(
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pser.cat.reorder_categories([3, 2, 1], ordered=True),
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psser.cat.reorder_categories([3, 2, 1], ordered=True),
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)
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pser.cat.reorder_categories([1, 2, 3], inplace=True)
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psser.cat.reorder_categories([1, 2, 3], inplace=True)
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self.assert_eq(pser, psser)
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self.assert_eq(pdf, psdf)
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pser.cat.reorder_categories([3, 2, 1], ordered=True, inplace=True)
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psser.cat.reorder_categories([3, 2, 1], ordered=True, inplace=True)
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
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pser = pser.astype(CategoricalDtype(categories=[3, 2, 1], ordered=True))
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self.assert_eq(pser, psser)
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self.assert_eq(pdf, psdf)
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self.assertRaises(ValueError, lambda: psser.cat.reorder_categories([1, 2]))
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self.assertRaises(ValueError, lambda: psser.cat.reorder_categories([1, 2, 4]))
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self.assertRaises(ValueError, lambda: psser.cat.reorder_categories([1, 2, 2]))
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self.assertRaises(TypeError, lambda: psser.cat.reorder_categories(1))
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self.assertRaises(TypeError, lambda: psdf.b.cat.reorder_categories("abcd"))
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def test_as_ordered_unordered(self):
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pdf, psdf = self.df_pair
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pser = pdf.a
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psser = psdf.a
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# as_ordered
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self.assert_eq(pser.cat.as_ordered(), psser.cat.as_ordered())
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pser.cat.as_ordered(inplace=True)
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psser.cat.as_ordered(inplace=True)
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
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pser = pser.astype(CategoricalDtype(categories=[1, 2, 3], ordered=True))
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self.assert_eq(pser, psser)
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self.assert_eq(pdf, psdf)
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# as_unordered
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self.assert_eq(pser.cat.as_unordered(), psser.cat.as_unordered())
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pser.cat.as_unordered(inplace=True)
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psser.cat.as_unordered(inplace=True)
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
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pser = pser.astype(CategoricalDtype(categories=[1, 2, 3], ordered=False))
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pdf.a = pser
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self.assert_eq(pser, psser)
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self.assert_eq(pdf, psdf)
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def test_astype(self):
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pser = pd.Series(["a", "b", "c"])
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psser = ps.from_pandas(pser)
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self.assert_eq(psser.astype("category"), pser.astype("category"))
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self.assert_eq(
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psser.astype(CategoricalDtype(["c", "a", "b"])),
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pser.astype(CategoricalDtype(["c", "a", "b"])),
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)
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pcser = pser.astype(CategoricalDtype(["c", "a", "b"]))
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pscser = psser.astype(CategoricalDtype(["c", "a", "b"]))
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self.assert_eq(pscser.astype("category"), pcser.astype("category"))
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# CategoricalDtype is not updated if the dtype is same from pandas 1.3.
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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self.assert_eq(
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pscser.astype(CategoricalDtype(["b", "c", "a"])),
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pcser.astype(CategoricalDtype(["b", "c", "a"])),
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)
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else:
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self.assert_eq(
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pscser.astype(CategoricalDtype(["b", "c", "a"])),
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pcser,
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)
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self.assert_eq(pscser.astype(str), pcser.astype(str))
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def test_factorize(self):
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pser = pd.Series(["a", "b", "c", None], dtype=CategoricalDtype(["c", "a", "d", "b"]))
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psser = ps.from_pandas(pser)
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pcodes, puniques = pser.factorize()
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kcodes, kuniques = psser.factorize()
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self.assert_eq(kcodes.tolist(), pcodes.tolist())
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self.assert_eq(kuniques, puniques)
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pcodes, puniques = pser.factorize(na_sentinel=-2)
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kcodes, kuniques = psser.factorize(na_sentinel=-2)
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self.assert_eq(kcodes.tolist(), pcodes.tolist())
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self.assert_eq(kuniques, puniques)
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def test_frame_apply(self):
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pdf, psdf = self.df_pair
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self.assert_eq(psdf.apply(lambda x: x).sort_index(), pdf.apply(lambda x: x).sort_index())
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self.assert_eq(
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psdf.apply(lambda x: x, axis=1).sort_index(),
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pdf.apply(lambda x: x, axis=1).sort_index(),
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)
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def test_frame_apply_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_frame_apply()
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pdf = pd.DataFrame(
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{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
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)
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psdf = ps.from_pandas(pdf)
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dtype = CategoricalDtype(categories=["a", "b", "c"])
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def categorize(ser) -> ps.Series[dtype]:
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return ser.astype(dtype)
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self.assert_eq(
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psdf.apply(categorize).sort_values(["a", "b"]).reset_index(drop=True),
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pdf.apply(categorize).sort_values(["a", "b"]).reset_index(drop=True),
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)
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def test_frame_transform(self):
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pdf, psdf = self.df_pair
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self.assert_eq(psdf.transform(lambda x: x), pdf.transform(lambda x: x))
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self.assert_eq(psdf.transform(lambda x: x.cat.codes), pdf.transform(lambda x: x.cat.codes))
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pdf = pd.DataFrame(
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{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
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)
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psdf = ps.from_pandas(pdf)
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dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
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self.assert_eq(
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psdf.transform(lambda x: x.astype(dtype)).sort_index(),
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pdf.transform(lambda x: x.astype(dtype)).sort_index(),
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)
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def test_frame_transform_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_frame_transform()
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pdf, psdf = self.df_pair
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def codes(pser) -> ps.Series[np.int8]:
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return pser.cat.codes
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self.assert_eq(psdf.transform(codes), pdf.transform(codes))
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pdf = pd.DataFrame(
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{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
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)
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psdf = ps.from_pandas(pdf)
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dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
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def to_category(pser) -> ps.Series[dtype]:
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return pser.astype(dtype)
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self.assert_eq(
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psdf.transform(to_category).sort_index(), pdf.transform(to_category).sort_index()
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)
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def test_series_apply(self):
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pdf, psdf = self.df_pair
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self.assert_eq(
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psdf.a.apply(lambda x: x).sort_index(), pdf.a.apply(lambda x: x).sort_index()
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)
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def test_series_apply_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_series_apply()
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pdf, psdf = self.df_pair
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ret = psdf.a.dtype
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def identity(pser) -> ret:
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return pser
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self.assert_eq(psdf.a.apply(identity).sort_index(), pdf.a.apply(identity).sort_index())
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# TODO: The return type is still category.
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# def to_str(x) -> str:
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# return str(x)
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#
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# self.assert_eq(
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# psdf.a.apply(to_str).sort_index(), pdf.a.apply(to_str).sort_index()
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# )
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def test_groupby_apply(self):
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pdf, psdf = self.df_pair
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self.assert_eq(
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psdf.groupby("a").apply(lambda df: df).sort_index(),
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pdf.groupby("a").apply(lambda df: df).sort_index(),
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)
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self.assert_eq(
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psdf.groupby("b").apply(lambda df: df[["a"]]).sort_index(),
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pdf.groupby("b").apply(lambda df: df[["a"]]).sort_index(),
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)
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self.assert_eq(
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psdf.groupby(["a", "b"]).apply(lambda df: df).sort_index(),
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pdf.groupby(["a", "b"]).apply(lambda df: df).sort_index(),
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)
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self.assert_eq(
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psdf.groupby("a").apply(lambda df: df.b.cat.codes).sort_index(),
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pdf.groupby("a").apply(lambda df: df.b.cat.codes).sort_index(),
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)
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self.assert_eq(
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psdf.groupby("a")["b"].apply(lambda b: b.cat.codes).sort_index(),
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pdf.groupby("a")["b"].apply(lambda b: b.cat.codes).sort_index(),
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)
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# TODO: grouping by a categorical type sometimes preserves unused categories.
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# self.assert_eq(
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# psdf.groupby("a").apply(len).sort_index(), pdf.groupby("a").apply(len).sort_index(),
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# )
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def test_groupby_apply_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_groupby_apply()
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pdf, psdf = self.df_pair
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def identity(df) -> ps.DataFrame[zip(psdf.columns, psdf.dtypes)]:
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return df
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self.assert_eq(
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psdf.groupby("a").apply(identity).sort_values(["a", "b"]).reset_index(drop=True),
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pdf.groupby("a").apply(identity).sort_values(["a", "b"]).reset_index(drop=True),
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)
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def test_groupby_transform(self):
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pdf, psdf = self.df_pair
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self.assert_eq(
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psdf.groupby("a").transform(lambda x: x).sort_index(),
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pdf.groupby("a").transform(lambda x: x).sort_index(),
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)
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|
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
|
|
|
|
self.assert_eq(
|
|
psdf.groupby("a").transform(lambda x: x.astype(dtype)).sort_index(),
|
|
pdf.groupby("a").transform(lambda x: x.astype(dtype)).sort_index(),
|
|
)
|
|
|
|
def test_groupby_transform_without_shortcut(self):
|
|
with ps.option_context("compute.shortcut_limit", 0):
|
|
self.test_groupby_transform()
|
|
|
|
pdf, psdf = self.df_pair
|
|
|
|
def identity(x) -> ps.Series[psdf.b.dtype]: # type: ignore
|
|
return x
|
|
|
|
self.assert_eq(
|
|
psdf.groupby("a").transform(identity).sort_values("b").reset_index(drop=True),
|
|
pdf.groupby("a").transform(identity).sort_values("b").reset_index(drop=True),
|
|
)
|
|
|
|
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
|
|
|
|
# The behavior for CategoricalDtype is changed from pandas 1.3
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
|
|
ret_dtype = pdf.b.dtype
|
|
else:
|
|
ret_dtype = dtype
|
|
|
|
def astype(x) -> ps.Series[ret_dtype]:
|
|
return x.astype(dtype)
|
|
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.2"):
|
|
self.assert_eq(
|
|
psdf.groupby("a").transform(astype).sort_values("b").reset_index(drop=True),
|
|
pdf.groupby("a").transform(astype).sort_values("b").reset_index(drop=True),
|
|
)
|
|
else:
|
|
expected = pdf.groupby("a").transform(astype)
|
|
expected["b"] = dtype.categories.take(expected["b"].cat.codes).astype(dtype)
|
|
self.assert_eq(
|
|
psdf.groupby("a").transform(astype).sort_values("b").reset_index(drop=True),
|
|
expected.sort_values("b").reset_index(drop=True),
|
|
)
|
|
|
|
def test_frame_apply_batch(self):
|
|
pdf, psdf = self.df_pair
|
|
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.apply_batch(lambda pdf: pdf.astype(str)).sort_index(),
|
|
pdf.astype(str).sort_index(),
|
|
)
|
|
|
|
pdf = pd.DataFrame(
|
|
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
|
|
)
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
|
|
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.apply_batch(lambda pdf: pdf.astype(dtype)).sort_index(),
|
|
pdf.astype(dtype).sort_index(),
|
|
)
|
|
|
|
def test_frame_apply_batch_without_shortcut(self):
|
|
with ps.option_context("compute.shortcut_limit", 0):
|
|
self.test_frame_apply_batch()
|
|
|
|
pdf, psdf = self.df_pair
|
|
|
|
def to_str(pdf) -> 'ps.DataFrame["a":str, "b":str]': # noqa: F405
|
|
return pdf.astype(str)
|
|
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.apply_batch(to_str).sort_values(["a", "b"]).reset_index(drop=True),
|
|
to_str(pdf).sort_values(["a", "b"]).reset_index(drop=True),
|
|
)
|
|
|
|
pdf = pd.DataFrame(
|
|
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
|
|
)
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
|
|
ret = ps.DataFrame["a":dtype, "b":dtype]
|
|
|
|
def to_category(pdf) -> ret:
|
|
return pdf.astype(dtype)
|
|
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.apply_batch(to_category)
|
|
.sort_values(["a", "b"])
|
|
.reset_index(drop=True),
|
|
to_category(pdf).sort_values(["a", "b"]).reset_index(drop=True),
|
|
)
|
|
|
|
def test_frame_transform_batch(self):
|
|
pdf, psdf = self.df_pair
|
|
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.transform_batch(lambda pdf: pdf.astype(str)).sort_index(),
|
|
pdf.astype(str).sort_index(),
|
|
)
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.transform_batch(lambda pdf: pdf.b.cat.codes).sort_index(),
|
|
pdf.b.cat.codes.sort_index(),
|
|
)
|
|
|
|
pdf = pd.DataFrame(
|
|
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
|
|
)
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
|
|
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.transform_batch(lambda pdf: pdf.astype(dtype)).sort_index(),
|
|
pdf.astype(dtype).sort_index(),
|
|
)
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.transform_batch(lambda pdf: pdf.b.astype(dtype)).sort_index(),
|
|
pdf.b.astype(dtype).sort_index(),
|
|
)
|
|
|
|
def test_frame_transform_batch_without_shortcut(self):
|
|
with ps.option_context("compute.shortcut_limit", 0):
|
|
self.test_frame_transform_batch()
|
|
|
|
pdf, psdf = self.df_pair
|
|
|
|
def to_str(pdf) -> 'ps.DataFrame["a":str, "b":str]': # noqa: F405
|
|
return pdf.astype(str)
|
|
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.transform_batch(to_str).sort_index(),
|
|
to_str(pdf).sort_index(),
|
|
)
|
|
|
|
def to_codes(pdf) -> ps.Series[np.int8]:
|
|
return pdf.b.cat.codes
|
|
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.transform_batch(to_codes).sort_index(),
|
|
to_codes(pdf).sort_index(),
|
|
)
|
|
|
|
pdf = pd.DataFrame(
|
|
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
|
|
)
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
|
|
ret = ps.DataFrame["a":dtype, "b":dtype]
|
|
|
|
def to_category(pdf) -> ret:
|
|
return pdf.astype(dtype)
|
|
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.transform_batch(to_category).sort_index(),
|
|
to_category(pdf).sort_index(),
|
|
)
|
|
|
|
def to_category(pdf) -> ps.Series[dtype]:
|
|
return pdf.b.astype(dtype)
|
|
|
|
self.assert_eq(
|
|
psdf.pandas_on_spark.transform_batch(to_category).sort_index(),
|
|
to_category(pdf).rename().sort_index(),
|
|
)
|
|
|
|
def test_series_transform_batch(self):
|
|
pdf, psdf = self.df_pair
|
|
|
|
self.assert_eq(
|
|
psdf.a.pandas_on_spark.transform_batch(lambda pser: pser.astype(str)).sort_index(),
|
|
pdf.a.astype(str).sort_index(),
|
|
)
|
|
|
|
pdf = pd.DataFrame(
|
|
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
|
|
)
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
|
|
|
|
self.assert_eq(
|
|
psdf.a.pandas_on_spark.transform_batch(lambda pser: pser.astype(dtype)).sort_index(),
|
|
pdf.a.astype(dtype).sort_index(),
|
|
)
|
|
|
|
def test_series_transform_batch_without_shortcut(self):
|
|
with ps.option_context("compute.shortcut_limit", 0):
|
|
self.test_series_transform_batch()
|
|
|
|
pdf, psdf = self.df_pair
|
|
|
|
def to_str(pser) -> ps.Series[str]:
|
|
return pser.astype(str)
|
|
|
|
self.assert_eq(
|
|
psdf.a.pandas_on_spark.transform_batch(to_str).sort_index(), to_str(pdf.a).sort_index()
|
|
)
|
|
|
|
pdf = pd.DataFrame(
|
|
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
|
|
)
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
|
|
|
|
def to_category(pser) -> ps.Series[dtype]:
|
|
return pser.astype(dtype)
|
|
|
|
self.assert_eq(
|
|
psdf.a.pandas_on_spark.transform_batch(to_category).sort_index(),
|
|
to_category(pdf.a).sort_index(),
|
|
)
|
|
|
|
def test_unstack(self):
|
|
pdf = self.pdf
|
|
index = pd.MultiIndex.from_tuples(
|
|
[("x", "a"), ("x", "b"), ("x", "c"), ("y", "a"), ("y", "b"), ("y", "d")]
|
|
)
|
|
pdf.index = index
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
self.assert_eq(psdf.a.unstack().sort_index(), pdf.a.unstack().sort_index())
|
|
self.assert_eq(psdf.b.unstack().sort_index(), pdf.b.unstack().sort_index())
|
|
|
|
def test_rename_categories(self):
|
|
pdf, psdf = self.df_pair
|
|
|
|
pser = pdf.b
|
|
psser = psdf.b
|
|
|
|
self.assert_eq(
|
|
pser.cat.rename_categories([0, 1, 3, 2]), psser.cat.rename_categories([0, 1, 3, 2])
|
|
)
|
|
self.assert_eq(
|
|
pser.cat.rename_categories({"a": "A", "c": "C"}),
|
|
psser.cat.rename_categories({"a": "A", "c": "C"}),
|
|
)
|
|
self.assert_eq(
|
|
pser.cat.rename_categories(lambda x: x.upper()),
|
|
psser.cat.rename_categories(lambda x: x.upper()),
|
|
)
|
|
|
|
pser.cat.rename_categories({"a": "A", "c": "C"}, inplace=True)
|
|
psser.cat.rename_categories({"a": "A", "c": "C"}, inplace=True)
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
|
|
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
|
|
pser = pser.astype(CategoricalDtype(categories=["C", "b", "d", "A"]))
|
|
|
|
self.assert_eq(pser, psser)
|
|
self.assert_eq(pdf, psdf)
|
|
|
|
pser.cat.rename_categories(lambda x: x.upper(), inplace=True)
|
|
psser.cat.rename_categories(lambda x: x.upper(), inplace=True)
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
|
|
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
|
|
pser = pser.astype(CategoricalDtype(categories=["C", "B", "D", "A"]))
|
|
pdf.b = pser
|
|
|
|
self.assert_eq(pser, psser)
|
|
self.assert_eq(pdf, psdf)
|
|
|
|
pser.cat.rename_categories([0, 1, 3, 2], inplace=True)
|
|
psser.cat.rename_categories([0, 1, 3, 2], inplace=True)
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
|
|
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
|
|
pser = pser.astype(CategoricalDtype(categories=[0, 1, 3, 2]))
|
|
pdf.b = pser
|
|
|
|
self.assert_eq(pser, psser)
|
|
self.assert_eq(pdf, psdf)
|
|
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
"new categories need to have the same number of items as the old categories",
|
|
lambda: psser.cat.rename_categories([0, 1, 2]),
|
|
)
|
|
self.assertRaises(
|
|
TypeError,
|
|
lambda: psser.cat.rename_categories(None),
|
|
)
|
|
self.assertRaises(
|
|
TypeError,
|
|
lambda: psser.cat.rename_categories(1),
|
|
)
|
|
self.assertRaises(
|
|
TypeError,
|
|
lambda: psser.cat.rename_categories("x"),
|
|
)
|
|
|
|
def test_set_categories(self):
|
|
pdf, psdf = self.df_pair
|
|
|
|
pser = pdf.b
|
|
psser = psdf.b
|
|
|
|
self.assert_eq(
|
|
pser.cat.set_categories(["a", "c", "b", "o"]),
|
|
psser.cat.set_categories(["a", "c", "b", "o"]),
|
|
)
|
|
self.assert_eq(
|
|
pser.cat.set_categories(["a", "c", "b"]),
|
|
psser.cat.set_categories(["a", "c", "b"]),
|
|
)
|
|
self.assert_eq(
|
|
pser.cat.set_categories(["a", "c", "b", "d", "e"]),
|
|
psser.cat.set_categories(["a", "c", "b", "d", "e"]),
|
|
)
|
|
|
|
self.assert_eq(
|
|
pser.cat.set_categories([0, 1, 3, 2], rename=True),
|
|
psser.cat.set_categories([0, 1, 3, 2], rename=True),
|
|
)
|
|
self.assert_eq(
|
|
pser.cat.set_categories([0, 1, 3], rename=True),
|
|
psser.cat.set_categories([0, 1, 3], rename=True),
|
|
)
|
|
self.assert_eq(
|
|
pser.cat.set_categories([0, 1, 3, 2, 4], rename=True),
|
|
psser.cat.set_categories([0, 1, 3, 2, 4], rename=True),
|
|
)
|
|
|
|
self.assert_eq(
|
|
pser.cat.set_categories(["a", "c", "b", "o"], ordered=True),
|
|
psser.cat.set_categories(["a", "c", "b", "o"], ordered=True),
|
|
)
|
|
self.assert_eq(
|
|
pser.cat.set_categories(["a", "c", "b"], ordered=True),
|
|
psser.cat.set_categories(["a", "c", "b"], ordered=True),
|
|
)
|
|
self.assert_eq(
|
|
pser.cat.set_categories(["a", "c", "b", "d", "e"], ordered=True),
|
|
psser.cat.set_categories(["a", "c", "b", "d", "e"], ordered=True),
|
|
)
|
|
|
|
self.assert_eq(
|
|
pser.cat.set_categories(["a", "c", "b", "o"], inplace=True, rename=True),
|
|
psser.cat.set_categories(["a", "c", "b", "o"], inplace=True, rename=True),
|
|
)
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
|
|
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
|
|
pser = pser.astype(CategoricalDtype(categories=["a", "c", "b", "o"]))
|
|
|
|
self.assert_eq(pser, psser)
|
|
self.assert_eq(pdf, psdf)
|
|
|
|
pser.cat.set_categories([2, 3, 1, 0], inplace=True, rename=False),
|
|
psser.cat.set_categories([2, 3, 1, 0], inplace=True, rename=False),
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
|
|
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
|
|
pser = pser.astype(CategoricalDtype(categories=[2, 3, 1, 0]))
|
|
pdf.b = pser
|
|
|
|
self.assert_eq(pser, psser)
|
|
self.assert_eq(pdf, psdf)
|
|
|
|
self.assertRaisesRegex(
|
|
TypeError,
|
|
"Parameter 'new_categories' must be list-like, was",
|
|
lambda: psser.cat.set_categories(None),
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
from pyspark.pandas.tests.test_categorical import * # noqa: F401
|
|
|
|
try:
|
|
import xmlrunner # type: ignore[import]
|
|
|
|
testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
|
|
except ImportError:
|
|
testRunner = None
|
|
unittest.main(testRunner=testRunner, verbosity=2)
|