4fcbf59079
### What changes were proposed in this pull request? Removes PySpark version dependent codes from pyspark.pandas test codes. ### Why are the changes needed? There are several places to check the PySpark version and switch the logic, but now those are not necessary. We should remove them. We will do the same thing after we finish porting tests. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Existing tests. Closes #32300 from xinrong-databricks/port.rmv_spark_version_chk_in_tests. Authored-by: Xinrong Meng <xinrong.meng@databricks.com> Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
295 lines
11 KiB
Python
295 lines
11 KiB
Python
#
|
|
# Licensed to the Apache Software Foundation (ASF) under one or more
|
|
# contributor license agreements. See the NOTICE file distributed with
|
|
# this work for additional information regarding copyright ownership.
|
|
# The ASF licenses this file to You under the Apache License, Version 2.0
|
|
# (the "License"); you may not use this file except in compliance with
|
|
# the License. You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
import datetime
|
|
from decimal import Decimal
|
|
from distutils.version import LooseVersion
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
from pyspark import pandas as ps
|
|
from pyspark.pandas.utils import name_like_string
|
|
from pyspark.testing.pandasutils import PandasOnSparkTestCase
|
|
|
|
|
|
class ReshapeTest(PandasOnSparkTestCase):
|
|
def test_get_dummies(self):
|
|
for pdf_or_ps in [
|
|
pd.Series([1, 1, 1, 2, 2, 1, 3, 4]),
|
|
# pd.Series([1, 1, 1, 2, 2, 1, 3, 4], dtype='category'),
|
|
# pd.Series(pd.Categorical([1, 1, 1, 2, 2, 1, 3, 4],
|
|
# categories=[4, 3, 2, 1])),
|
|
pd.DataFrame(
|
|
{
|
|
"a": [1, 2, 3, 4, 4, 3, 2, 1],
|
|
# 'b': pd.Categorical(list('abcdabcd')),
|
|
"b": list("abcdabcd"),
|
|
}
|
|
),
|
|
pd.DataFrame({10: [1, 2, 3, 4, 4, 3, 2, 1], 20: list("abcdabcd")}),
|
|
]:
|
|
kdf_or_kser = ps.from_pandas(pdf_or_ps)
|
|
|
|
self.assert_eq(ps.get_dummies(kdf_or_kser), pd.get_dummies(pdf_or_ps, dtype=np.int8))
|
|
|
|
kser = ps.Series([1, 1, 1, 2, 2, 1, 3, 4])
|
|
with self.assertRaisesRegex(
|
|
NotImplementedError, "get_dummies currently does not support sparse"
|
|
):
|
|
ps.get_dummies(kser, sparse=True)
|
|
|
|
def test_get_dummies_object(self):
|
|
pdf = pd.DataFrame(
|
|
{
|
|
"a": [1, 2, 3, 4, 4, 3, 2, 1],
|
|
# 'a': pd.Categorical([1, 2, 3, 4, 4, 3, 2, 1]),
|
|
"b": list("abcdabcd"),
|
|
# 'c': pd.Categorical(list('abcdabcd')),
|
|
"c": list("abcdabcd"),
|
|
}
|
|
)
|
|
kdf = ps.from_pandas(pdf)
|
|
|
|
# Explicitly exclude object columns
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, columns=["a", "c"]),
|
|
pd.get_dummies(pdf, columns=["a", "c"], dtype=np.int8),
|
|
)
|
|
|
|
self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8))
|
|
self.assert_eq(ps.get_dummies(kdf.b), pd.get_dummies(pdf.b, dtype=np.int8))
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, columns=["b"]), pd.get_dummies(pdf, columns=["b"], dtype=np.int8)
|
|
)
|
|
|
|
self.assertRaises(KeyError, lambda: ps.get_dummies(kdf, columns=("a", "c")))
|
|
self.assertRaises(TypeError, lambda: ps.get_dummies(kdf, columns="b"))
|
|
|
|
# non-string names
|
|
pdf = pd.DataFrame(
|
|
{10: [1, 2, 3, 4, 4, 3, 2, 1], 20: list("abcdabcd"), 30: list("abcdabcd")}
|
|
)
|
|
kdf = ps.from_pandas(pdf)
|
|
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, columns=[10, 30]),
|
|
pd.get_dummies(pdf, columns=[10, 30], dtype=np.int8),
|
|
)
|
|
|
|
self.assertRaises(TypeError, lambda: ps.get_dummies(kdf, columns=10))
|
|
|
|
def test_get_dummies_date_datetime(self):
|
|
pdf = pd.DataFrame(
|
|
{
|
|
"d": [
|
|
datetime.date(2019, 1, 1),
|
|
datetime.date(2019, 1, 2),
|
|
datetime.date(2019, 1, 1),
|
|
],
|
|
"dt": [
|
|
datetime.datetime(2019, 1, 1, 0, 0, 0),
|
|
datetime.datetime(2019, 1, 1, 0, 0, 1),
|
|
datetime.datetime(2019, 1, 1, 0, 0, 0),
|
|
],
|
|
}
|
|
)
|
|
kdf = ps.from_pandas(pdf)
|
|
|
|
self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8))
|
|
self.assert_eq(ps.get_dummies(kdf.d), pd.get_dummies(pdf.d, dtype=np.int8))
|
|
self.assert_eq(ps.get_dummies(kdf.dt), pd.get_dummies(pdf.dt, dtype=np.int8))
|
|
|
|
def test_get_dummies_boolean(self):
|
|
pdf = pd.DataFrame({"b": [True, False, True]})
|
|
kdf = ps.from_pandas(pdf)
|
|
|
|
self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8))
|
|
self.assert_eq(ps.get_dummies(kdf.b), pd.get_dummies(pdf.b, dtype=np.int8))
|
|
|
|
def test_get_dummies_decimal(self):
|
|
pdf = pd.DataFrame({"d": [Decimal(1.0), Decimal(2.0), Decimal(1)]})
|
|
kdf = ps.from_pandas(pdf)
|
|
|
|
self.assert_eq(ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8))
|
|
self.assert_eq(ps.get_dummies(kdf.d), pd.get_dummies(pdf.d, dtype=np.int8), almost=True)
|
|
|
|
def test_get_dummies_kwargs(self):
|
|
# pser = pd.Series([1, 1, 1, 2, 2, 1, 3, 4], dtype='category')
|
|
pser = pd.Series([1, 1, 1, 2, 2, 1, 3, 4])
|
|
kser = ps.from_pandas(pser)
|
|
self.assert_eq(
|
|
ps.get_dummies(kser, prefix="X", prefix_sep="-"),
|
|
pd.get_dummies(pser, prefix="X", prefix_sep="-", dtype=np.int8),
|
|
)
|
|
|
|
self.assert_eq(
|
|
ps.get_dummies(kser, drop_first=True),
|
|
pd.get_dummies(pser, drop_first=True, dtype=np.int8),
|
|
)
|
|
|
|
# nan
|
|
# pser = pd.Series([1, 1, 1, 2, np.nan, 3, np.nan, 5], dtype='category')
|
|
pser = pd.Series([1, 1, 1, 2, np.nan, 3, np.nan, 5])
|
|
kser = ps.from_pandas(pser)
|
|
self.assert_eq(ps.get_dummies(kser), pd.get_dummies(pser, dtype=np.int8), almost=True)
|
|
|
|
# dummy_na
|
|
self.assert_eq(
|
|
ps.get_dummies(kser, dummy_na=True), pd.get_dummies(pser, dummy_na=True, dtype=np.int8)
|
|
)
|
|
|
|
def test_get_dummies_prefix(self):
|
|
pdf = pd.DataFrame({"A": ["a", "b", "a"], "B": ["b", "a", "c"], "D": [0, 0, 1]})
|
|
kdf = ps.from_pandas(pdf)
|
|
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, prefix=["foo", "bar"]),
|
|
pd.get_dummies(pdf, prefix=["foo", "bar"], dtype=np.int8),
|
|
)
|
|
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, prefix=["foo"], columns=["B"]),
|
|
pd.get_dummies(pdf, prefix=["foo"], columns=["B"], dtype=np.int8),
|
|
)
|
|
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, prefix={"A": "foo", "B": "bar"}),
|
|
pd.get_dummies(pdf, prefix={"A": "foo", "B": "bar"}, dtype=np.int8),
|
|
)
|
|
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, prefix={"B": "foo", "A": "bar"}),
|
|
pd.get_dummies(pdf, prefix={"B": "foo", "A": "bar"}, dtype=np.int8),
|
|
)
|
|
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, prefix={"A": "foo", "B": "bar"}, columns=["A", "B"]),
|
|
pd.get_dummies(pdf, prefix={"A": "foo", "B": "bar"}, columns=["A", "B"], dtype=np.int8),
|
|
)
|
|
|
|
with self.assertRaisesRegex(NotImplementedError, "string types"):
|
|
ps.get_dummies(kdf, prefix="foo")
|
|
with self.assertRaisesRegex(ValueError, "Length of 'prefix' \\(1\\) .* \\(2\\)"):
|
|
ps.get_dummies(kdf, prefix=["foo"])
|
|
with self.assertRaisesRegex(ValueError, "Length of 'prefix' \\(2\\) .* \\(1\\)"):
|
|
ps.get_dummies(kdf, prefix=["foo", "bar"], columns=["B"])
|
|
|
|
pser = pd.Series([1, 1, 1, 2, 2, 1, 3, 4], name="A")
|
|
kser = ps.from_pandas(pser)
|
|
|
|
self.assert_eq(
|
|
ps.get_dummies(kser, prefix="foo"), pd.get_dummies(pser, prefix="foo", dtype=np.int8)
|
|
)
|
|
|
|
# columns are ignored.
|
|
self.assert_eq(
|
|
ps.get_dummies(kser, prefix=["foo"], columns=["B"]),
|
|
pd.get_dummies(pser, prefix=["foo"], columns=["B"], dtype=np.int8),
|
|
)
|
|
|
|
def test_get_dummies_dtype(self):
|
|
pdf = pd.DataFrame(
|
|
{
|
|
# "A": pd.Categorical(['a', 'b', 'a'], categories=['a', 'b', 'c']),
|
|
"A": ["a", "b", "a"],
|
|
"B": [0, 0, 1],
|
|
}
|
|
)
|
|
kdf = ps.from_pandas(pdf)
|
|
|
|
if LooseVersion("0.23.0") <= LooseVersion(pd.__version__):
|
|
exp = pd.get_dummies(pdf, dtype="float64")
|
|
else:
|
|
exp = pd.get_dummies(pdf)
|
|
exp = exp.astype({"A_a": "float64", "A_b": "float64"})
|
|
res = ps.get_dummies(kdf, dtype="float64")
|
|
self.assert_eq(res, exp)
|
|
|
|
def test_get_dummies_multiindex_columns(self):
|
|
pdf = pd.DataFrame(
|
|
{
|
|
("x", "a", "1"): [1, 2, 3, 4, 4, 3, 2, 1],
|
|
("x", "b", "2"): list("abcdabcd"),
|
|
("y", "c", "3"): list("abcdabcd"),
|
|
}
|
|
)
|
|
kdf = ps.from_pandas(pdf)
|
|
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8).rename(columns=name_like_string)
|
|
)
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, columns=[("y", "c", "3"), ("x", "a", "1")]),
|
|
pd.get_dummies(pdf, columns=[("y", "c", "3"), ("x", "a", "1")], dtype=np.int8).rename(
|
|
columns=name_like_string
|
|
),
|
|
)
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, columns=["x"]),
|
|
pd.get_dummies(pdf, columns=["x"], dtype=np.int8).rename(columns=name_like_string),
|
|
)
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, columns=("x", "a")),
|
|
pd.get_dummies(pdf, columns=("x", "a"), dtype=np.int8).rename(columns=name_like_string),
|
|
)
|
|
|
|
self.assertRaises(KeyError, lambda: ps.get_dummies(kdf, columns=["z"]))
|
|
self.assertRaises(KeyError, lambda: ps.get_dummies(kdf, columns=("x", "c")))
|
|
self.assertRaises(ValueError, lambda: ps.get_dummies(kdf, columns=[("x",), "c"]))
|
|
self.assertRaises(TypeError, lambda: ps.get_dummies(kdf, columns="x"))
|
|
|
|
# non-string names
|
|
pdf = pd.DataFrame(
|
|
{
|
|
("x", 1, "a"): [1, 2, 3, 4, 4, 3, 2, 1],
|
|
("x", 2, "b"): list("abcdabcd"),
|
|
("y", 3, "c"): list("abcdabcd"),
|
|
}
|
|
)
|
|
kdf = ps.from_pandas(pdf)
|
|
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf), pd.get_dummies(pdf, dtype=np.int8).rename(columns=name_like_string)
|
|
)
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, columns=[("y", 3, "c"), ("x", 1, "a")]),
|
|
pd.get_dummies(pdf, columns=[("y", 3, "c"), ("x", 1, "a")], dtype=np.int8).rename(
|
|
columns=name_like_string
|
|
),
|
|
)
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, columns=["x"]),
|
|
pd.get_dummies(pdf, columns=["x"], dtype=np.int8).rename(columns=name_like_string),
|
|
)
|
|
self.assert_eq(
|
|
ps.get_dummies(kdf, columns=("x", 1)),
|
|
pd.get_dummies(pdf, columns=("x", 1), dtype=np.int8).rename(columns=name_like_string),
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
from pyspark.pandas.tests.test_reshape 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)
|