58feb85145
### What changes were proposed in this pull request? Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas miscellaneous unit tests to PySpark. ### Why are the changes needed? Currently, the pandas-on-Spark modules are not tested fully. We should enable miscellaneous unit tests. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Enable miscellaneous unit tests. Closes #32152 from xinrong-databricks/port.misc_tests. Lead-authored-by: xinrong-databricks <47337188+xinrong-databricks@users.noreply.github.com> Co-authored-by: Xinrong Meng <xinrong.meng@databricks.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
314 lines
12 KiB
Python
314 lines
12 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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import datetime
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from decimal import Decimal
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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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import pyspark
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from pyspark import pandas as ps
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from pyspark.pandas.testing.utils import ReusedSQLTestCase, SPARK_CONF_ARROW_ENABLED
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from pyspark.pandas.utils import name_like_string
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class ReshapeTest(ReusedSQLTestCase):
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def test_get_dummies(self):
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for pdf_or_ps in [
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pd.Series([1, 1, 1, 2, 2, 1, 3, 4]),
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# pd.Series([1, 1, 1, 2, 2, 1, 3, 4], dtype='category'),
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# pd.Series(pd.Categorical([1, 1, 1, 2, 2, 1, 3, 4],
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# categories=[4, 3, 2, 1])),
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pd.DataFrame(
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{
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"a": [1, 2, 3, 4, 4, 3, 2, 1],
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# 'b': pd.Categorical(list('abcdabcd')),
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"b": list("abcdabcd"),
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}
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),
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pd.DataFrame({10: [1, 2, 3, 4, 4, 3, 2, 1], 20: list("abcdabcd")}),
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]:
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kdf_or_kser = ps.from_pandas(pdf_or_ps)
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self.assert_eq(ps.get_dummies(kdf_or_kser), pd.get_dummies(pdf_or_ps, dtype=np.int8))
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kser = ps.Series([1, 1, 1, 2, 2, 1, 3, 4])
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with self.assertRaisesRegex(
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NotImplementedError, "get_dummies currently does not support sparse"
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):
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ps.get_dummies(kser, sparse=True)
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def test_get_dummies_object(self):
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pdf = pd.DataFrame(
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{
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"a": [1, 2, 3, 4, 4, 3, 2, 1],
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# 'a': pd.Categorical([1, 2, 3, 4, 4, 3, 2, 1]),
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"b": list("abcdabcd"),
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# 'c': pd.Categorical(list('abcdabcd')),
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"c": list("abcdabcd"),
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}
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)
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kdf = ps.from_pandas(pdf)
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# Explicitly exclude object columns
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self.assert_eq(
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ps.get_dummies(kdf, columns=["a", "c"]),
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pd.get_dummies(pdf, columns=["a", "c"], dtype=np.int8),
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)
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self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8))
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self.assert_eq(ps.get_dummies(kdf.b), pd.get_dummies(pdf.b, dtype=np.int8))
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self.assert_eq(
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ps.get_dummies(kdf, columns=["b"]), pd.get_dummies(pdf, columns=["b"], dtype=np.int8)
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)
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self.assertRaises(KeyError, lambda: ps.get_dummies(kdf, columns=("a", "c")))
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self.assertRaises(TypeError, lambda: ps.get_dummies(kdf, columns="b"))
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# non-string names
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pdf = pd.DataFrame(
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{10: [1, 2, 3, 4, 4, 3, 2, 1], 20: list("abcdabcd"), 30: list("abcdabcd")}
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)
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kdf = ps.from_pandas(pdf)
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self.assert_eq(
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ps.get_dummies(kdf, columns=[10, 30]),
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pd.get_dummies(pdf, columns=[10, 30], dtype=np.int8),
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)
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self.assertRaises(TypeError, lambda: ps.get_dummies(kdf, columns=10))
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def test_get_dummies_date_datetime(self):
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pdf = pd.DataFrame(
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{
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"d": [
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datetime.date(2019, 1, 1),
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datetime.date(2019, 1, 2),
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datetime.date(2019, 1, 1),
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],
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"dt": [
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datetime.datetime(2019, 1, 1, 0, 0, 0),
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datetime.datetime(2019, 1, 1, 0, 0, 1),
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datetime.datetime(2019, 1, 1, 0, 0, 0),
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],
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}
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)
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kdf = ps.from_pandas(pdf)
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if LooseVersion(pyspark.__version__) >= LooseVersion("2.4"):
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self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8))
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self.assert_eq(ps.get_dummies(kdf.d), pd.get_dummies(pdf.d, dtype=np.int8))
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self.assert_eq(ps.get_dummies(kdf.dt), pd.get_dummies(pdf.dt, dtype=np.int8))
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else:
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with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
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self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8))
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self.assert_eq(ps.get_dummies(kdf.d), pd.get_dummies(pdf.d, dtype=np.int8))
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self.assert_eq(ps.get_dummies(kdf.dt), pd.get_dummies(pdf.dt, dtype=np.int8))
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def test_get_dummies_boolean(self):
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pdf = pd.DataFrame({"b": [True, False, True]})
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kdf = ps.from_pandas(pdf)
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if LooseVersion(pyspark.__version__) >= LooseVersion("2.4"):
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self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8))
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self.assert_eq(ps.get_dummies(kdf.b), pd.get_dummies(pdf.b, dtype=np.int8))
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else:
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with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
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self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8))
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self.assert_eq(ps.get_dummies(kdf.b), pd.get_dummies(pdf.b, dtype=np.int8))
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def test_get_dummies_decimal(self):
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pdf = pd.DataFrame({"d": [Decimal(1.0), Decimal(2.0), Decimal(1)]})
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kdf = ps.from_pandas(pdf)
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if LooseVersion(pyspark.__version__) >= LooseVersion("2.4"):
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self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8))
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self.assert_eq(ps.get_dummies(kdf.d), pd.get_dummies(pdf.d, dtype=np.int8), almost=True)
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else:
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with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
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self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8))
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self.assert_eq(
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ps.get_dummies(kdf.d), pd.get_dummies(pdf.d, dtype=np.int8), almost=True
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)
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def test_get_dummies_kwargs(self):
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# pser = pd.Series([1, 1, 1, 2, 2, 1, 3, 4], dtype='category')
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pser = pd.Series([1, 1, 1, 2, 2, 1, 3, 4])
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kser = ps.from_pandas(pser)
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self.assert_eq(
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ps.get_dummies(kser, prefix="X", prefix_sep="-"),
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pd.get_dummies(pser, prefix="X", prefix_sep="-", dtype=np.int8),
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)
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self.assert_eq(
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ps.get_dummies(kser, drop_first=True),
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pd.get_dummies(pser, drop_first=True, dtype=np.int8),
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)
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# nan
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# pser = pd.Series([1, 1, 1, 2, np.nan, 3, np.nan, 5], dtype='category')
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pser = pd.Series([1, 1, 1, 2, np.nan, 3, np.nan, 5])
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kser = ps.from_pandas(pser)
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self.assert_eq(ps.get_dummies(kser), pd.get_dummies(pser, dtype=np.int8), almost=True)
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# dummy_na
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self.assert_eq(
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ps.get_dummies(kser, dummy_na=True), pd.get_dummies(pser, dummy_na=True, dtype=np.int8)
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)
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def test_get_dummies_prefix(self):
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pdf = pd.DataFrame({"A": ["a", "b", "a"], "B": ["b", "a", "c"], "D": [0, 0, 1]})
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kdf = ps.from_pandas(pdf)
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self.assert_eq(
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ps.get_dummies(kdf, prefix=["foo", "bar"]),
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pd.get_dummies(pdf, prefix=["foo", "bar"], dtype=np.int8),
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)
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self.assert_eq(
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ps.get_dummies(kdf, prefix=["foo"], columns=["B"]),
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pd.get_dummies(pdf, prefix=["foo"], columns=["B"], dtype=np.int8),
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)
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self.assert_eq(
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ps.get_dummies(kdf, prefix={"A": "foo", "B": "bar"}),
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pd.get_dummies(pdf, prefix={"A": "foo", "B": "bar"}, dtype=np.int8),
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)
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self.assert_eq(
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ps.get_dummies(kdf, prefix={"B": "foo", "A": "bar"}),
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pd.get_dummies(pdf, prefix={"B": "foo", "A": "bar"}, dtype=np.int8),
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)
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self.assert_eq(
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ps.get_dummies(kdf, prefix={"A": "foo", "B": "bar"}, columns=["A", "B"]),
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pd.get_dummies(pdf, prefix={"A": "foo", "B": "bar"}, columns=["A", "B"], dtype=np.int8),
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)
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with self.assertRaisesRegex(NotImplementedError, "string types"):
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ps.get_dummies(kdf, prefix="foo")
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with self.assertRaisesRegex(ValueError, "Length of 'prefix' \\(1\\) .* \\(2\\)"):
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ps.get_dummies(kdf, prefix=["foo"])
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with self.assertRaisesRegex(ValueError, "Length of 'prefix' \\(2\\) .* \\(1\\)"):
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ps.get_dummies(kdf, prefix=["foo", "bar"], columns=["B"])
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pser = pd.Series([1, 1, 1, 2, 2, 1, 3, 4], name="A")
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kser = ps.from_pandas(pser)
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self.assert_eq(
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ps.get_dummies(kser, prefix="foo"), pd.get_dummies(pser, prefix="foo", dtype=np.int8)
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)
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# columns are ignored.
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self.assert_eq(
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ps.get_dummies(kser, prefix=["foo"], columns=["B"]),
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pd.get_dummies(pser, prefix=["foo"], columns=["B"], dtype=np.int8),
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)
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def test_get_dummies_dtype(self):
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pdf = pd.DataFrame(
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{
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# "A": pd.Categorical(['a', 'b', 'a'], categories=['a', 'b', 'c']),
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"A": ["a", "b", "a"],
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"B": [0, 0, 1],
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}
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)
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kdf = ps.from_pandas(pdf)
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if LooseVersion("0.23.0") <= LooseVersion(pd.__version__):
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exp = pd.get_dummies(pdf, dtype="float64")
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else:
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exp = pd.get_dummies(pdf)
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exp = exp.astype({"A_a": "float64", "A_b": "float64"})
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res = ps.get_dummies(kdf, dtype="float64")
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self.assert_eq(res, exp)
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def test_get_dummies_multiindex_columns(self):
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pdf = pd.DataFrame(
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{
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("x", "a", "1"): [1, 2, 3, 4, 4, 3, 2, 1],
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("x", "b", "2"): list("abcdabcd"),
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("y", "c", "3"): list("abcdabcd"),
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}
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)
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kdf = ps.from_pandas(pdf)
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self.assert_eq(
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ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8).rename(columns=name_like_string)
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)
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self.assert_eq(
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ps.get_dummies(kdf, columns=[("y", "c", "3"), ("x", "a", "1")]),
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pd.get_dummies(pdf, columns=[("y", "c", "3"), ("x", "a", "1")], dtype=np.int8).rename(
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columns=name_like_string
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),
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)
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self.assert_eq(
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ps.get_dummies(kdf, columns=["x"]),
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pd.get_dummies(pdf, columns=["x"], dtype=np.int8).rename(columns=name_like_string),
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)
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self.assert_eq(
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ps.get_dummies(kdf, columns=("x", "a")),
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pd.get_dummies(pdf, columns=("x", "a"), dtype=np.int8).rename(columns=name_like_string),
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)
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self.assertRaises(KeyError, lambda: ps.get_dummies(kdf, columns=["z"]))
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self.assertRaises(KeyError, lambda: ps.get_dummies(kdf, columns=("x", "c")))
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self.assertRaises(ValueError, lambda: ps.get_dummies(kdf, columns=[("x",), "c"]))
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self.assertRaises(TypeError, lambda: ps.get_dummies(kdf, columns="x"))
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# non-string names
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pdf = pd.DataFrame(
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{
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("x", 1, "a"): [1, 2, 3, 4, 4, 3, 2, 1],
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("x", 2, "b"): list("abcdabcd"),
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("y", 3, "c"): list("abcdabcd"),
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}
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)
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kdf = ps.from_pandas(pdf)
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self.assert_eq(
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ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8).rename(columns=name_like_string)
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)
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self.assert_eq(
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ps.get_dummies(kdf, columns=[("y", 3, "c"), ("x", 1, "a")]),
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pd.get_dummies(pdf, columns=[("y", 3, "c"), ("x", 1, "a")], dtype=np.int8).rename(
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columns=name_like_string
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),
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)
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self.assert_eq(
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ps.get_dummies(kdf, columns=["x"]),
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pd.get_dummies(pdf, columns=["x"], dtype=np.int8).rename(columns=name_like_string),
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)
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self.assert_eq(
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ps.get_dummies(kdf, columns=("x", 1)),
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pd.get_dummies(pdf, columns=("x", 1), dtype=np.int8).rename(columns=name_like_string),
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)
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if __name__ == "__main__":
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import unittest
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from pyspark.pandas.tests.test_reshape import * # noqa: F401
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try:
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import xmlrunner # type: ignore[import]
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testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
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except ImportError:
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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