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spark-instrumented-optimizer/python/pyspark/pandas/tests/test_expanding.py
itholic 6b912e4179 [SPARK-35364][PYTHON] Renaming the existing Koalas related codes 
### What changes were proposed in this pull request?

There are still naming related to Koalas in test and function name. This PR addressed them to fit pandas-on-spark.
- kdf -> psdf
- kser -> psser
- kidx -> psidx
- kmidx -> psmidx
- to_koalas() -> to_pandas_on_spark()

### Why are the changes needed?

This is because the name Koalas is no longer used in PySpark.

### Does this PR introduce _any_ user-facing change?

`to_koalas()` function is renamed to `to_pandas_on_spark()`

### How was this patch tested?

Tested in local manually.
After changing the related naming, I checked them one by one.

Closes #32516 from itholic/SPARK-35364.

Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
2021-05-20 15:08:30 -07:00

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#
# 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.
#
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pyspark.pandas as ps
from pyspark.pandas.window import Expanding
from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils
class ExpandingTest(PandasOnSparkTestCase, TestUtils):
def _test_expanding_func(self, f):
pser = pd.Series([1, 2, 3], index=np.random.rand(3))
psser = ps.from_pandas(pser)
self.assert_eq(getattr(psser.expanding(2), f)(), getattr(pser.expanding(2), f)())
self.assert_eq(
getattr(psser.expanding(2), f)().sum(), getattr(pser.expanding(2), f)().sum()
)
# Multiindex
pser = pd.Series(
[1, 2, 3], index=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z")])
)
psser = ps.from_pandas(pser)
self.assert_eq(getattr(psser.expanding(2), f)(), getattr(pser.expanding(2), f)())
pdf = pd.DataFrame(
{"a": [1.0, 2.0, 3.0, 2.0], "b": [4.0, 2.0, 3.0, 1.0]}, index=np.random.rand(4)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(getattr(psdf.expanding(2), f)(), getattr(pdf.expanding(2), f)())
self.assert_eq(getattr(psdf.expanding(2), f)().sum(), getattr(pdf.expanding(2), f)().sum())
# Multiindex column
columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(getattr(psdf.expanding(2), f)(), getattr(pdf.expanding(2), f)())
def test_expanding_error(self):
with self.assertRaisesRegex(ValueError, "min_periods must be >= 0"):
ps.range(10).expanding(-1)
with self.assertRaisesRegex(
TypeError, "psdf_or_psser must be a series or dataframe; however, got:.*int"
):
Expanding(1, 2)
def test_expanding_repr(self):
self.assertEqual(repr(ps.range(10).expanding(5)), "Expanding [min_periods=5]")
def test_expanding_count(self):
# The behaviour of Expanding.count are different between pandas>=1.0.0 and lower,
# and we're following the behaviour of latest version of pandas.
if LooseVersion(pd.__version__) >= LooseVersion("1.0.0"):
self._test_expanding_func("count")
else:
# Series
idx = np.random.rand(3)
psser = ps.Series([1, 2, 3], index=idx, name="a")
expected_result = pd.Series([None, 2.0, 3.0], index=idx, name="a")
self.assert_eq(psser.expanding(2).count().sort_index(), expected_result.sort_index())
self.assert_eq(psser.expanding(2).count().sum(), expected_result.sum())
# MultiIndex
midx = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z")])
psser = ps.Series([1, 2, 3], index=midx, name="a")
expected_result = pd.Series([None, 2.0, 3.0], index=midx, name="a")
self.assert_eq(psser.expanding(2).count().sort_index(), expected_result.sort_index())
# DataFrame
psdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]})
expected_result = pd.DataFrame({"a": [None, 2.0, 3.0, 4.0], "b": [None, 2.0, 3.0, 4.0]})
self.assert_eq(psdf.expanding(2).count().sort_index(), expected_result.sort_index())
self.assert_eq(psdf.expanding(2).count().sum(), expected_result.sum())
# MultiIndex columns
idx = np.random.rand(4)
psdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]}, index=idx)
psdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
expected_result = pd.DataFrame(
{("a", "x"): [None, 2.0, 3.0, 4.0], ("a", "y"): [None, 2.0, 3.0, 4.0]}, index=idx,
)
self.assert_eq(psdf.expanding(2).count().sort_index(), expected_result.sort_index())
def test_expanding_min(self):
self._test_expanding_func("min")
def test_expanding_max(self):
self._test_expanding_func("max")
def test_expanding_mean(self):
self._test_expanding_func("mean")
def test_expanding_sum(self):
self._test_expanding_func("sum")
def test_expanding_std(self):
self._test_expanding_func("std")
def test_expanding_var(self):
self._test_expanding_func("var")
def _test_groupby_expanding_func(self, f):
pser = pd.Series([1, 2, 3, 2], index=np.random.rand(4), name="a")
psser = ps.from_pandas(pser)
self.assert_eq(
getattr(psser.groupby(psser).expanding(2), f)().sort_index(),
getattr(pser.groupby(pser).expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(psser.groupby(psser).expanding(2), f)().sum(),
getattr(pser.groupby(pser).expanding(2), f)().sum(),
)
# Multiindex
pser = pd.Series(
[1, 2, 3, 2],
index=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z"), ("c", "z")]),
name="a",
)
psser = ps.from_pandas(pser)
self.assert_eq(
getattr(psser.groupby(psser).expanding(2), f)().sort_index(),
getattr(pser.groupby(pser).expanding(2), f)().sort_index(),
)
pdf = pd.DataFrame({"a": [1.0, 2.0, 3.0, 2.0], "b": [4.0, 2.0, 3.0, 1.0]})
psdf = ps.from_pandas(pdf)
self.assert_eq(
getattr(psdf.groupby(psdf.a).expanding(2), f)().sort_index(),
getattr(pdf.groupby(pdf.a).expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(psdf.groupby(psdf.a).expanding(2), f)().sum(),
getattr(pdf.groupby(pdf.a).expanding(2), f)().sum(),
)
self.assert_eq(
getattr(psdf.groupby(psdf.a + 1).expanding(2), f)().sort_index(),
getattr(pdf.groupby(pdf.a + 1).expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(psdf.b.groupby(psdf.a).expanding(2), f)().sort_index(),
getattr(pdf.b.groupby(pdf.a).expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(psdf.groupby(psdf.a)["b"].expanding(2), f)().sort_index(),
getattr(pdf.groupby(pdf.a)["b"].expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(psdf.groupby(psdf.a)[["b"]].expanding(2), f)().sort_index(),
getattr(pdf.groupby(pdf.a)[["b"]].expanding(2), f)().sort_index(),
)
# Multiindex column
columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
getattr(psdf.groupby(("a", "x")).expanding(2), f)().sort_index(),
getattr(pdf.groupby(("a", "x")).expanding(2), f)().sort_index(),
)
self.assert_eq(
getattr(psdf.groupby([("a", "x"), ("a", "y")]).expanding(2), f)().sort_index(),
getattr(pdf.groupby([("a", "x"), ("a", "y")]).expanding(2), f)().sort_index(),
)
def test_groupby_expanding_count(self):
# The behaviour of ExpandingGroupby.count are different between pandas>=1.0.0 and lower,
# and we're following the behaviour of latest version of pandas.
if LooseVersion(pd.__version__) >= LooseVersion("1.0.0"):
self._test_groupby_expanding_func("count")
else:
# Series
psser = ps.Series([1, 2, 3, 2], index=np.random.rand(4))
midx = pd.MultiIndex.from_tuples(
list(zip(psser.to_pandas().values, psser.index.to_pandas().values))
)
expected_result = pd.Series([np.nan, np.nan, np.nan, 2], index=midx)
self.assert_eq(
psser.groupby(psser).expanding(2).count().sort_index(), expected_result.sort_index()
)
self.assert_eq(psser.groupby(psser).expanding(2).count().sum(), expected_result.sum())
# MultiIndex
psser = ps.Series(
[1, 2, 3, 2],
index=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z"), ("a", "y")]),
)
midx = pd.MultiIndex.from_tuples(
[(1, "a", "x"), (2, "a", "y"), (3, "b", "z"), (2, "a", "y")]
)
expected_result = pd.Series([np.nan, np.nan, np.nan, 2], index=midx)
self.assert_eq(
psser.groupby(psser).expanding(2).count().sort_index(), expected_result.sort_index()
)
# DataFrame
psdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]})
midx = pd.MultiIndex.from_tuples([(1, 0), (2, 1), (2, 3), (3, 2)], names=["a", None])
expected_result = pd.DataFrame(
{"a": [None, None, 2.0, None], "b": [None, None, 2.0, None]}, index=midx
)
self.assert_eq(
psdf.groupby(psdf.a).expanding(2).count().sort_index(), expected_result.sort_index()
)
self.assert_eq(psdf.groupby(psdf.a).expanding(2).count().sum(), expected_result.sum())
expected_result = pd.DataFrame(
{"a": [None, None, 2.0, None], "b": [None, None, 2.0, None]},
index=pd.MultiIndex.from_tuples(
[(2, 0), (3, 1), (3, 3), (4, 2)], names=["a", None]
),
)
self.assert_eq(
psdf.groupby(psdf.a + 1).expanding(2).count().sort_index(),
expected_result.sort_index(),
)
expected_result = pd.Series([None, None, 2.0, None], index=midx, name="b")
self.assert_eq(
psdf.b.groupby(psdf.a).expanding(2).count().sort_index(),
expected_result.sort_index(),
)
self.assert_eq(
psdf.groupby(psdf.a)["b"].expanding(2).count().sort_index(),
expected_result.sort_index(),
)
expected_result = pd.DataFrame({"b": [None, None, 2.0, None]}, index=midx)
self.assert_eq(
psdf.groupby(psdf.a)[["b"]].expanding(2).count().sort_index(),
expected_result.sort_index(),
)
# MultiIndex column
psdf = ps.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]})
psdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
midx = pd.MultiIndex.from_tuples(
[(1, 0), (2, 1), (2, 3), (3, 2)], names=[("a", "x"), None]
)
expected_result = pd.DataFrame(
{"a": [None, None, 2.0, None], "b": [None, None, 2.0, None]}, index=midx
)
expected_result.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
self.assert_eq(
psdf.groupby(("a", "x")).expanding(2).count().sort_index(),
expected_result.sort_index(),
)
midx = pd.MultiIndex.from_tuples(
[(1, 4.0, 0), (2, 1.0, 3), (2, 2.0, 1), (3, 3.0, 2)],
names=[("a", "x"), ("a", "y"), None],
)
expected_result = pd.DataFrame(
{
("a", "x"): [np.nan, np.nan, np.nan, np.nan],
("a", "y"): [np.nan, np.nan, np.nan, np.nan],
},
index=midx,
)
self.assert_eq(
psdf.groupby([("a", "x"), ("a", "y")]).expanding(2).count().sort_index(),
expected_result.sort_index(),
)
def test_groupby_expanding_min(self):
self._test_groupby_expanding_func("min")
def test_groupby_expanding_max(self):
self._test_groupby_expanding_func("max")
def test_groupby_expanding_mean(self):
self._test_groupby_expanding_func("mean")
def test_groupby_expanding_sum(self):
self._test_groupby_expanding_func("sum")
def test_groupby_expanding_std(self):
self._test_groupby_expanding_func("std")
def test_groupby_expanding_var(self):
self._test_groupby_expanding_func("var")
if __name__ == "__main__":
import unittest
from pyspark.pandas.tests.test_expanding 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)
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