ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity
spark-instrumented-optimizer/python/pyspark/pandas/tests/test_namespace.py
Yikun Jiang 4824c53398 [SPARK-35812][PYTHON] Throw ValueError if version and timestamp are used together in to_delta 
### What changes were proposed in this pull request?

Throw ValueError if version and timestamp are used together in to_delta

### Why are the changes needed?
read_delta has arguments named `version` and `timestamp`, but they cannot be used together.

We should raise the proper error message when they are used together.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
UT

Closes #33023 from Yikun/SPARK-35812.

Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-06-23 19:04:45 +09:00

351 lines
14 KiB
Python
Raw Blame History

#
# 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 itertools
import pandas as pd
from pyspark import pandas as ps
from pyspark.pandas.namespace import _get_index_map, read_delta
from pyspark.pandas.utils import spark_column_equals
from pyspark.testing.pandasutils import PandasOnSparkTestCase
from pyspark.testing.sqlutils import SQLTestUtils
class NamespaceTest(PandasOnSparkTestCase, SQLTestUtils):
def test_from_pandas(self):
pdf = pd.DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]})
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
pser = pdf.year
psser = ps.from_pandas(pser)
self.assert_eq(psser, pser)
pidx = pdf.index
psidx = ps.from_pandas(pidx)
self.assert_eq(psidx, pidx)
pmidx = pdf.set_index("year", append=True).index
psmidx = ps.from_pandas(pmidx)
self.assert_eq(psmidx, pmidx)
expected_error_message = "Unknown data type: {}".format(type(psidx).__name__)
with self.assertRaisesRegex(TypeError, expected_error_message):
ps.from_pandas(psidx)
def test_to_datetime(self):
pdf = pd.DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]})
psdf = ps.from_pandas(pdf)
dict_from_pdf = pdf.to_dict()
self.assert_eq(pd.to_datetime(pdf), ps.to_datetime(psdf))
self.assert_eq(pd.to_datetime(dict_from_pdf), ps.to_datetime(dict_from_pdf))
self.assert_eq(pd.to_datetime(1490195805, unit="s"), ps.to_datetime(1490195805, unit="s"))
self.assert_eq(
pd.to_datetime(1490195805433502912, unit="ns"),
ps.to_datetime(1490195805433502912, unit="ns"),
)
self.assert_eq(
pd.to_datetime([1, 2, 3], unit="D", origin=pd.Timestamp("1960-01-01")),
ps.to_datetime([1, 2, 3], unit="D", origin=pd.Timestamp("1960-01-01")),
)
def test_date_range(self):
self.assert_eq(
ps.date_range(start="1/1/2018", end="1/08/2018"),
pd.date_range(start="1/1/2018", end="1/08/2018"),
)
self.assert_eq(
ps.date_range(start="1/1/2018", periods=8), pd.date_range(start="1/1/2018", periods=8)
)
self.assert_eq(
ps.date_range(end="1/1/2018", periods=8), pd.date_range(end="1/1/2018", periods=8)
)
self.assert_eq(
ps.date_range(start="2018-04-24", end="2018-04-27", periods=3),
pd.date_range(start="2018-04-24", end="2018-04-27", periods=3),
)
self.assert_eq(
ps.date_range(start="1/1/2018", periods=5, freq="M"),
pd.date_range(start="1/1/2018", periods=5, freq="M"),
)
self.assert_eq(
ps.date_range(start="1/1/2018", periods=5, freq="3M"),
pd.date_range(start="1/1/2018", periods=5, freq="3M"),
)
self.assert_eq(
ps.date_range(start="1/1/2018", periods=5, freq=pd.offsets.MonthEnd(3)),
pd.date_range(start="1/1/2018", periods=5, freq=pd.offsets.MonthEnd(3)),
)
self.assert_eq(
ps.date_range(start="2017-01-01", end="2017-01-04", closed="left"),
pd.date_range(start="2017-01-01", end="2017-01-04", closed="left"),
)
self.assert_eq(
ps.date_range(start="2017-01-01", end="2017-01-04", closed="right"),
pd.date_range(start="2017-01-01", end="2017-01-04", closed="right"),
)
self.assertRaises(
AssertionError, lambda: ps.date_range(start="1/1/2018", periods=5, tz="Asia/Tokyo")
)
self.assertRaises(
AssertionError, lambda: ps.date_range(start="1/1/2018", periods=5, freq="ns")
)
self.assertRaises(
AssertionError, lambda: ps.date_range(start="1/1/2018", periods=5, freq="N")
)
def test_concat_index_axis(self):
pdf = pd.DataFrame({"A": [0, 2, 4], "B": [1, 3, 5], "C": [6, 7, 8]})
# TODO: pdf.columns.names = ["ABC"]
psdf = ps.from_pandas(pdf)
ignore_indexes = [True, False]
joins = ["inner", "outer"]
sorts = [True, False]
objs = [
([psdf, psdf], [pdf, pdf]),
([psdf, psdf.reset_index()], [pdf, pdf.reset_index()]),
([psdf.reset_index(), psdf], [pdf.reset_index(), pdf]),
([psdf, psdf[["C", "A"]]], [pdf, pdf[["C", "A"]]]),
([psdf[["C", "A"]], psdf], [pdf[["C", "A"]], pdf]),
([psdf, psdf["C"]], [pdf, pdf["C"]]),
([psdf["C"], psdf], [pdf["C"], pdf]),
([psdf["C"], psdf, psdf["A"]], [pdf["C"], pdf, pdf["A"]]),
([psdf, psdf["C"], psdf["A"]], [pdf, pdf["C"], pdf["A"]]),
]
for ignore_index, join, sort in itertools.product(ignore_indexes, joins, sorts):
for i, (psdfs, pdfs) in enumerate(objs):
with self.subTest(
ignore_index=ignore_index, join=join, sort=sort, pdfs=pdfs, pair=i
):
self.assert_eq(
ps.concat(psdfs, ignore_index=ignore_index, join=join, sort=sort),
pd.concat(pdfs, ignore_index=ignore_index, join=join, sort=sort),
almost=(join == "outer"),
)
self.assertRaisesRegex(TypeError, "first argument must be", lambda: ps.concat(psdf))
self.assertRaisesRegex(TypeError, "cannot concatenate object", lambda: ps.concat([psdf, 1]))
psdf2 = psdf.set_index("B", append=True)
self.assertRaisesRegex(
ValueError, "Index type and names should be same", lambda: ps.concat([psdf, psdf2])
)
self.assertRaisesRegex(ValueError, "No objects to concatenate", lambda: ps.concat([]))
self.assertRaisesRegex(ValueError, "All objects passed", lambda: ps.concat([None, None]))
pdf3 = pdf.copy()
psdf3 = psdf.copy()
columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C")])
# TODO: colums.names = ["XYZ", "ABC"]
pdf3.columns = columns
psdf3.columns = columns
objs = [
([psdf3, psdf3], [pdf3, pdf3]),
([psdf3, psdf3.reset_index()], [pdf3, pdf3.reset_index()]),
([psdf3.reset_index(), psdf3], [pdf3.reset_index(), pdf3]),
([psdf3, psdf3[[("Y", "C"), ("X", "A")]]], [pdf3, pdf3[[("Y", "C"), ("X", "A")]]]),
([psdf3[[("Y", "C"), ("X", "A")]], psdf3], [pdf3[[("Y", "C"), ("X", "A")]], pdf3]),
]
for ignore_index, sort in itertools.product(ignore_indexes, sorts):
for i, (psdfs, pdfs) in enumerate(objs):
with self.subTest(
ignore_index=ignore_index, join="outer", sort=sort, pdfs=pdfs, pair=i
):
self.assert_eq(
ps.concat(psdfs, ignore_index=ignore_index, join="outer", sort=sort),
pd.concat(pdfs, ignore_index=ignore_index, join="outer", sort=sort),
)
# Skip tests for `join="inner" and sort=False` since pandas is flaky.
for ignore_index in ignore_indexes:
for i, (psdfs, pdfs) in enumerate(objs):
with self.subTest(
ignore_index=ignore_index, join="inner", sort=True, pdfs=pdfs, pair=i
):
self.assert_eq(
ps.concat(psdfs, ignore_index=ignore_index, join="inner", sort=True),
pd.concat(pdfs, ignore_index=ignore_index, join="inner", sort=True),
)
self.assertRaisesRegex(
ValueError,
"MultiIndex columns should have the same levels",
lambda: ps.concat([psdf, psdf3]),
)
self.assertRaisesRegex(
ValueError,
"MultiIndex columns should have the same levels",
lambda: ps.concat([psdf3[("Y", "C")], psdf3]),
)
pdf4 = pd.DataFrame({"A": [0, 2, 4], "B": [1, 3, 5], "C": [10, 20, 30]})
psdf4 = ps.from_pandas(pdf4)
self.assertRaisesRegex(
ValueError,
r"Only can inner \(intersect\) or outer \(union\) join the other axis.",
lambda: ps.concat([psdf, psdf4], join=""),
)
self.assertRaisesRegex(
ValueError,
r"Only can inner \(intersect\) or outer \(union\) join the other axis.",
lambda: ps.concat([psdf, psdf4], join="", axis=1),
)
self.assertRaisesRegex(
ValueError,
r"Only can inner \(intersect\) or outer \(union\) join the other axis.",
lambda: ps.concat([psdf.A, psdf4.B], join="", axis=1),
)
self.assertRaisesRegex(
ValueError,
r"Labels have to be unique; however, got duplicated labels \['A'\].",
lambda: ps.concat([psdf.A, psdf4.A], join="inner", axis=1),
)
def test_concat_column_axis(self):
pdf1 = pd.DataFrame({"A": [0, 2, 4], "B": [1, 3, 5]}, index=[1, 2, 3])
pdf1.columns.names = ["AB"]
pdf2 = pd.DataFrame({"C": [1, 2, 3], "D": [4, 5, 6]}, index=[1, 3, 5])
pdf2.columns.names = ["CD"]
psdf1 = ps.from_pandas(pdf1)
psdf2 = ps.from_pandas(pdf2)
psdf3 = psdf1.copy()
psdf4 = psdf2.copy()
pdf3 = pdf1.copy()
pdf4 = pdf2.copy()
columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B")], names=["X", "AB"])
pdf3.columns = columns
psdf3.columns = columns
columns = pd.MultiIndex.from_tuples([("X", "C"), ("X", "D")], names=["Y", "CD"])
pdf4.columns = columns
psdf4.columns = columns
ignore_indexes = [True, False]
joins = ["inner", "outer"]
objs = [
([psdf1.A, psdf1.A.rename("B")], [pdf1.A, pdf1.A.rename("B")]),
(
[psdf3[("X", "A")], psdf3[("X", "B")]],
[pdf3[("X", "A")], pdf3[("X", "B")]],
),
(
[psdf3[("X", "A")], psdf3[("X", "B")].rename("ABC")],
[pdf3[("X", "A")], pdf3[("X", "B")].rename("ABC")],
),
(
[psdf3[("X", "A")].rename("ABC"), psdf3[("X", "B")]],
[pdf3[("X", "A")].rename("ABC"), pdf3[("X", "B")]],
),
]
for ignore_index, join in itertools.product(ignore_indexes, joins):
for i, (psdfs, pdfs) in enumerate(objs):
with self.subTest(ignore_index=ignore_index, join=join, pdfs=pdfs, pair=i):
actual = ps.concat(psdfs, axis=1, ignore_index=ignore_index, join=join)
expected = pd.concat(pdfs, axis=1, ignore_index=ignore_index, join=join)
self.assert_eq(
repr(actual.sort_values(list(actual.columns)).reset_index(drop=True)),
repr(expected.sort_values(list(expected.columns)).reset_index(drop=True)),
)
# test dataframes equality with broadcast hint.
def test_broadcast(self):
psdf = ps.DataFrame(
{"key": ["K0", "K1", "K2", "K3"], "A": ["A0", "A1", "A2", "A3"]}, columns=["key", "A"]
)
self.assert_eq(psdf, ps.broadcast(psdf))
psdf.columns = ["x", "y"]
self.assert_eq(psdf, ps.broadcast(psdf))
psdf.columns = [("a", "c"), ("b", "d")]
self.assert_eq(psdf, ps.broadcast(psdf))
psser = ps.Series([1, 2, 3])
expected_error_message = "Invalid type : expected DataFrame got {}".format(
type(psser).__name__
)
with self.assertRaisesRegex(TypeError, expected_error_message):
ps.broadcast(psser)
def test_get_index_map(self):
psdf = ps.DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]})
sdf = psdf.to_spark()
self.assertEqual(_get_index_map(sdf), (None, None))
def check(actual, expected):
actual_scols, actual_labels = actual
expected_column_names, expected_labels = expected
self.assertEqual(len(actual_scols), len(expected_column_names))
for actual_scol, expected_column_name in zip(actual_scols, expected_column_names):
expected_scol = sdf[expected_column_name]
self.assertTrue(spark_column_equals(actual_scol, expected_scol))
self.assertEqual(actual_labels, expected_labels)
check(_get_index_map(sdf, "year"), (["year"], [("year",)]))
check(_get_index_map(sdf, ["year", "month"]), (["year", "month"], [("year",), ("month",)]))
self.assertRaises(KeyError, lambda: _get_index_map(sdf, ["year", "hour"]))
def test_read_delta_with_wrong_input(self):
self.assertRaisesRegex(
ValueError,
"version and timestamp cannot be used together",
lambda: read_delta("fake_path", version="0", timestamp="2021-06-22"),
)
if __name__ == "__main__":
import unittest
from pyspark.pandas.tests.test_namespace 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)
Reference in a new issue View git blame Copy permalink