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#
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# 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
#
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# 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 unittest
import pandas as pd
from pyspark import pandas as ps
from pyspark.testing.pandasutils import PandasOnSparkTestCase
from pyspark.testing.sqlutils import SQLTestUtils
class GroupbyIndexMixin:
def test_groupby_multiindex_columns(self):
pdf = pd.DataFrame(
{
(10, "a"): [1, 2, 6, 4, 4, 6, 4, 3, 7],
(10, "b"): [4, 2, 7, 3, 3, 1, 1, 1, 2],
(20, "c"): [4, 2, 7, 3, None, 1, 1, 1, 2],
(30, "d"): list("abcdefght"),
},
index=[0, 1, 3, 5, 6, 8, 9, 9, 9],
)
psdf = ps.from_pandas(pdf)
self.assert_eq(
psdf.groupby((10, "a")).sum().sort_index(), pdf.groupby((10, "a")).sum().sort_index()
)
self.assert_eq(
psdf.groupby((10, "a"), as_index=False)
.sum()
.sort_values((10, "a"))
.reset_index(drop=True),
pdf.groupby((10, "a"), as_index=False)
.sum()
.sort_values((10, "a"))
.reset_index(drop=True),
)
self.assert_eq(
psdf.groupby((10, "a"))[[(20, "c")]].sum().sort_index(),
pdf.groupby((10, "a"))[[(20, "c")]].sum().sort_index(),
)
# TODO: a pandas bug?
# expected = pdf.groupby((10, "a"))[(20, "c")].sum().sort_index()
expected = pd.Series(
[4.0, 2.0, 1.0, 4.0, 8.0, 2.0],
name=(20, "c"),
index=pd.Index([1, 2, 3, 4, 6, 7], name=(10, "a")),
)
self.assert_eq(psdf.groupby((10, "a"))[(20, "c")].sum().sort_index(), expected)
self.assert_eq(
psdf[(20, "c")].groupby(psdf[(10, "a")]).sum().sort_index(),
pdf[(20, "c")].groupby(pdf[(10, "a")]).sum().sort_index(),
)
def test_idxmax(self):
pdf = pd.DataFrame(
{"a": [1, 1, 2, 2, 3] * 3, "b": [1, 2, 3, 4, 5] * 3, "c": [5, 4, 3, 2, 1] * 3}
)
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.groupby(["a"]).idxmax().sort_index(), psdf.groupby(["a"]).idxmax().sort_index()
)
self.assert_eq(
pdf.groupby(["a"]).idxmax(skipna=False).sort_index(),
psdf.groupby(["a"]).idxmax(skipna=False).sort_index(),
)
self.assert_eq(
pdf.groupby(["a"])["b"].idxmax().sort_index(),
psdf.groupby(["a"])["b"].idxmax().sort_index(),
)
self.assert_eq(
pdf.b.rename().groupby(pdf.a).idxmax().sort_index(),
psdf.b.rename().groupby(psdf.a).idxmax().sort_index(),
)
self.assert_eq(
pdf.b.groupby(pdf.a.rename()).idxmax().sort_index(),
psdf.b.groupby(psdf.a.rename()).idxmax().sort_index(),
)
self.assert_eq(
pdf.b.rename().groupby(pdf.a.rename()).idxmax().sort_index(),
psdf.b.rename().groupby(psdf.a.rename()).idxmax().sort_index(),
)
with self.assertRaisesRegex(ValueError, "idxmax only support one-level index now"):
psdf.set_index(["a", "b"]).groupby(["c"]).idxmax()
# multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
pdf.groupby(("x", "a")).idxmax().sort_index(),
psdf.groupby(("x", "a")).idxmax().sort_index(),
)
self.assert_eq(
pdf.groupby(("x", "a")).idxmax(skipna=False).sort_index(),
psdf.groupby(("x", "a")).idxmax(skipna=False).sort_index(),
)
def test_idxmin(self):
pdf = pd.DataFrame(
{"a": [1, 1, 2, 2, 3] * 3, "b": [1, 2, 3, 4, 5] * 3, "c": [5, 4, 3, 2, 1] * 3}
)
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.groupby(["a"]).idxmin().sort_index(), psdf.groupby(["a"]).idxmin().sort_index()
)
self.assert_eq(
pdf.groupby(["a"]).idxmin(skipna=False).sort_index(),
psdf.groupby(["a"]).idxmin(skipna=False).sort_index(),
)
self.assert_eq(
pdf.groupby(["a"])["b"].idxmin().sort_index(),
psdf.groupby(["a"])["b"].idxmin().sort_index(),
)
self.assert_eq(
pdf.b.rename().groupby(pdf.a).idxmin().sort_index(),
psdf.b.rename().groupby(psdf.a).idxmin().sort_index(),
)
self.assert_eq(
pdf.b.groupby(pdf.a.rename()).idxmin().sort_index(),
psdf.b.groupby(psdf.a.rename()).idxmin().sort_index(),
)
self.assert_eq(
pdf.b.rename().groupby(pdf.a.rename()).idxmin().sort_index(),
psdf.b.rename().groupby(psdf.a.rename()).idxmin().sort_index(),
)
with self.assertRaisesRegex(ValueError, "idxmin only support one-level index now"):
psdf.set_index(["a", "b"]).groupby(["c"]).idxmin()
# multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
pdf.groupby(("x", "a")).idxmin().sort_index(),
psdf.groupby(("x", "a")).idxmin().sort_index(),
)
self.assert_eq(
pdf.groupby(("x", "a")).idxmin(skipna=False).sort_index(),
psdf.groupby(("x", "a")).idxmin(skipna=False).sort_index(),
)
class GroupbyIndexTests(
GroupbyIndexMixin,
PandasOnSparkTestCase,
SQLTestUtils,
):
pass
if __name__ == "__main__":
from pyspark.pandas.tests.groupby.test_index import * # noqa: F401
try:
import xmlrunner
testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)