b8740a1d1e
### What changes were proposed in this pull request? This PR proposes applying `black` to pandas API on Spark codes, for improving static analysis. By executing the `./dev/reformat-python` in the spark home directory, all the code of the pandas API on Spark is fixed according to the static analysis rules. ### Why are the changes needed? This can be reduces the cost of static analysis during development. It has been used continuously for about a year in the Koalas project and its convenience has been proven. ### Does this PR introduce _any_ user-facing change? No, it's dev-only. ### How was this patch tested? Manually reformat the pandas API on Spark codes by running the `./dev/reformat-python`, and checked the `./dev/lint-python` is passed. Closes #32779 from itholic/SPARK-35499. Authored-by: itholic <haejoon.lee@databricks.com> Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
1969 lines
73 KiB
Python
1969 lines
73 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.
|
|
#
|
|
|
|
from distutils.version import LooseVersion
|
|
from itertools import product
|
|
import unittest
|
|
|
|
import pandas as pd
|
|
import numpy as np
|
|
|
|
from pyspark import pandas as ps
|
|
from pyspark.pandas.config import set_option, reset_option
|
|
from pyspark.pandas.frame import DataFrame
|
|
from pyspark.testing.pandasutils import PandasOnSparkTestCase
|
|
from pyspark.testing.sqlutils import SQLTestUtils
|
|
from pyspark.pandas.typedef.typehints import (
|
|
extension_dtypes,
|
|
extension_dtypes_available,
|
|
extension_float_dtypes_available,
|
|
extension_object_dtypes_available,
|
|
)
|
|
|
|
|
|
class OpsOnDiffFramesEnabledTest(PandasOnSparkTestCase, SQLTestUtils):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
super().setUpClass()
|
|
set_option("compute.ops_on_diff_frames", True)
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
reset_option("compute.ops_on_diff_frames")
|
|
super().tearDownClass()
|
|
|
|
@property
|
|
def pdf1(self):
|
|
return pd.DataFrame(
|
|
{"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]},
|
|
index=[0, 1, 3, 5, 6, 8, 9, 10, 11],
|
|
)
|
|
|
|
@property
|
|
def pdf2(self):
|
|
return pd.DataFrame(
|
|
{"a": [9, 8, 7, 6, 5, 4, 3, 2, 1], "b": [0, 0, 0, 4, 5, 6, 1, 2, 3]},
|
|
index=list(range(9)),
|
|
)
|
|
|
|
@property
|
|
def pdf3(self):
|
|
return pd.DataFrame(
|
|
{"b": [1, 1, 1, 1, 1, 1, 1, 1, 1], "c": [1, 1, 1, 1, 1, 1, 1, 1, 1]},
|
|
index=list(range(9)),
|
|
)
|
|
|
|
@property
|
|
def pdf4(self):
|
|
return pd.DataFrame(
|
|
{"e": [2, 2, 2, 2, 2, 2, 2, 2, 2], "f": [2, 2, 2, 2, 2, 2, 2, 2, 2]},
|
|
index=list(range(9)),
|
|
)
|
|
|
|
@property
|
|
def pdf5(self):
|
|
return pd.DataFrame(
|
|
{
|
|
"a": [1, 2, 3, 4, 5, 6, 7, 8, 9],
|
|
"b": [4, 5, 6, 3, 2, 1, 0, 0, 0],
|
|
"c": [4, 5, 6, 3, 2, 1, 0, 0, 0],
|
|
},
|
|
index=[0, 1, 3, 5, 6, 8, 9, 10, 11],
|
|
).set_index(["a", "b"])
|
|
|
|
@property
|
|
def pdf6(self):
|
|
return pd.DataFrame(
|
|
{
|
|
"a": [9, 8, 7, 6, 5, 4, 3, 2, 1],
|
|
"b": [0, 0, 0, 4, 5, 6, 1, 2, 3],
|
|
"c": [9, 8, 7, 6, 5, 4, 3, 2, 1],
|
|
"e": [4, 5, 6, 3, 2, 1, 0, 0, 0],
|
|
},
|
|
index=list(range(9)),
|
|
).set_index(["a", "b"])
|
|
|
|
@property
|
|
def pser1(self):
|
|
midx = pd.MultiIndex(
|
|
[["lama", "cow", "falcon", "koala"], ["speed", "weight", "length", "power"]],
|
|
[[0, 3, 1, 1, 1, 2, 2, 2], [0, 2, 0, 3, 2, 0, 1, 3]],
|
|
)
|
|
return pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1], index=midx)
|
|
|
|
@property
|
|
def pser2(self):
|
|
midx = pd.MultiIndex(
|
|
[["lama", "cow", "falcon"], ["speed", "weight", "length"]],
|
|
[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
|
|
)
|
|
return pd.Series([-45, 200, -1.2, 30, -250, 1.5, 320, 1, -0.3], index=midx)
|
|
|
|
@property
|
|
def pser3(self):
|
|
midx = pd.MultiIndex(
|
|
[["koalas", "cow", "falcon"], ["speed", "weight", "length"]],
|
|
[[0, 0, 0, 1, 1, 1, 2, 2, 2], [1, 1, 2, 0, 0, 2, 2, 2, 1]],
|
|
)
|
|
return pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], index=midx)
|
|
|
|
@property
|
|
def psdf1(self):
|
|
return ps.from_pandas(self.pdf1)
|
|
|
|
@property
|
|
def psdf2(self):
|
|
return ps.from_pandas(self.pdf2)
|
|
|
|
@property
|
|
def psdf3(self):
|
|
return ps.from_pandas(self.pdf3)
|
|
|
|
@property
|
|
def psdf4(self):
|
|
return ps.from_pandas(self.pdf4)
|
|
|
|
@property
|
|
def psdf5(self):
|
|
return ps.from_pandas(self.pdf5)
|
|
|
|
@property
|
|
def psdf6(self):
|
|
return ps.from_pandas(self.pdf6)
|
|
|
|
@property
|
|
def psser1(self):
|
|
return ps.from_pandas(self.pser1)
|
|
|
|
@property
|
|
def psser2(self):
|
|
return ps.from_pandas(self.pser2)
|
|
|
|
@property
|
|
def psser3(self):
|
|
return ps.from_pandas(self.pser3)
|
|
|
|
def test_ranges(self):
|
|
self.assert_eq(
|
|
(ps.range(10) + ps.range(10)).sort_index(),
|
|
(
|
|
ps.DataFrame({"id": list(range(10))}) + ps.DataFrame({"id": list(range(10))})
|
|
).sort_index(),
|
|
)
|
|
|
|
def test_no_matched_index(self):
|
|
with self.assertRaisesRegex(ValueError, "Index names must be exactly matched"):
|
|
ps.DataFrame({"a": [1, 2, 3]}).set_index("a") + ps.DataFrame(
|
|
{"b": [1, 2, 3]}
|
|
).set_index("b")
|
|
|
|
def test_arithmetic(self):
|
|
self._test_arithmetic_frame(self.pdf1, self.pdf2, check_extension=False)
|
|
self._test_arithmetic_series(self.pser1, self.pser2, check_extension=False)
|
|
|
|
@unittest.skipIf(not extension_dtypes_available, "pandas extension dtypes are not available")
|
|
def test_arithmetic_extension_dtypes(self):
|
|
self._test_arithmetic_frame(
|
|
self.pdf1.astype("Int64"), self.pdf2.astype("Int64"), check_extension=True
|
|
)
|
|
self._test_arithmetic_series(
|
|
self.pser1.astype(int).astype("Int64"),
|
|
self.pser2.astype(int).astype("Int64"),
|
|
check_extension=True,
|
|
)
|
|
|
|
@unittest.skipIf(
|
|
not extension_float_dtypes_available, "pandas extension float dtypes are not available"
|
|
)
|
|
def test_arithmetic_extension_float_dtypes(self):
|
|
self._test_arithmetic_frame(
|
|
self.pdf1.astype("Float64"), self.pdf2.astype("Float64"), check_extension=True
|
|
)
|
|
self._test_arithmetic_series(
|
|
self.pser1.astype("Float64"), self.pser2.astype("Float64"), check_extension=True
|
|
)
|
|
|
|
def _test_arithmetic_frame(self, pdf1, pdf2, *, check_extension):
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
def assert_eq(actual, expected):
|
|
if LooseVersion("1.1") <= LooseVersion(pd.__version__) < LooseVersion("1.2.2"):
|
|
self.assert_eq(actual, expected, check_exact=not check_extension)
|
|
if check_extension:
|
|
if isinstance(actual, DataFrame):
|
|
for dtype in actual.dtypes:
|
|
self.assertTrue(isinstance(dtype, extension_dtypes))
|
|
else:
|
|
self.assertTrue(isinstance(actual.dtype, extension_dtypes))
|
|
else:
|
|
self.assert_eq(actual, expected)
|
|
|
|
# Series
|
|
assert_eq((psdf1.a - psdf2.b).sort_index(), (pdf1.a - pdf2.b).sort_index())
|
|
|
|
assert_eq((psdf1.a * psdf2.a).sort_index(), (pdf1.a * pdf2.a).sort_index())
|
|
|
|
if check_extension and not extension_float_dtypes_available:
|
|
self.assert_eq(
|
|
(psdf1["a"] / psdf2["a"]).sort_index(), (pdf1["a"] / pdf2["a"]).sort_index()
|
|
)
|
|
else:
|
|
assert_eq((psdf1["a"] / psdf2["a"]).sort_index(), (pdf1["a"] / pdf2["a"]).sort_index())
|
|
|
|
# DataFrame
|
|
assert_eq((psdf1 + psdf2).sort_index(), (pdf1 + pdf2).sort_index())
|
|
|
|
# Multi-index columns
|
|
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b")])
|
|
psdf1.columns = columns
|
|
psdf2.columns = columns
|
|
pdf1.columns = columns
|
|
pdf2.columns = columns
|
|
|
|
# Series
|
|
assert_eq(
|
|
(psdf1[("x", "a")] - psdf2[("x", "b")]).sort_index(),
|
|
(pdf1[("x", "a")] - pdf2[("x", "b")]).sort_index(),
|
|
)
|
|
|
|
assert_eq(
|
|
(psdf1[("x", "a")] - psdf2["x"]["b"]).sort_index(),
|
|
(pdf1[("x", "a")] - pdf2["x"]["b"]).sort_index(),
|
|
)
|
|
|
|
assert_eq(
|
|
(psdf1["x"]["a"] - psdf2[("x", "b")]).sort_index(),
|
|
(pdf1["x"]["a"] - pdf2[("x", "b")]).sort_index(),
|
|
)
|
|
|
|
# DataFrame
|
|
assert_eq((psdf1 + psdf2).sort_index(), (pdf1 + pdf2).sort_index())
|
|
|
|
def _test_arithmetic_series(self, pser1, pser2, *, check_extension):
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
|
|
def assert_eq(actual, expected):
|
|
if LooseVersion("1.1") <= LooseVersion(pd.__version__) < LooseVersion("1.2.2"):
|
|
self.assert_eq(actual, expected, check_exact=not check_extension)
|
|
if check_extension:
|
|
self.assertTrue(isinstance(actual.dtype, extension_dtypes))
|
|
else:
|
|
self.assert_eq(actual, expected)
|
|
|
|
# MultiIndex Series
|
|
assert_eq((psser1 + psser2).sort_index(), (pser1 + pser2).sort_index())
|
|
|
|
assert_eq((psser1 - psser2).sort_index(), (pser1 - pser2).sort_index())
|
|
|
|
assert_eq((psser1 * psser2).sort_index(), (pser1 * pser2).sort_index())
|
|
|
|
if check_extension and not extension_float_dtypes_available:
|
|
self.assert_eq((psser1 / psser2).sort_index(), (pser1 / pser2).sort_index())
|
|
else:
|
|
assert_eq((psser1 / psser2).sort_index(), (pser1 / pser2).sort_index())
|
|
|
|
def test_arithmetic_chain(self):
|
|
self._test_arithmetic_chain_frame(self.pdf1, self.pdf2, self.pdf3, check_extension=False)
|
|
self._test_arithmetic_chain_series(
|
|
self.pser1, self.pser2, self.pser3, check_extension=False
|
|
)
|
|
|
|
@unittest.skipIf(not extension_dtypes_available, "pandas extension dtypes are not available")
|
|
def test_arithmetic_chain_extension_dtypes(self):
|
|
self._test_arithmetic_chain_frame(
|
|
self.pdf1.astype("Int64"),
|
|
self.pdf2.astype("Int64"),
|
|
self.pdf3.astype("Int64"),
|
|
check_extension=True,
|
|
)
|
|
self._test_arithmetic_chain_series(
|
|
self.pser1.astype(int).astype("Int64"),
|
|
self.pser2.astype(int).astype("Int64"),
|
|
self.pser3.astype(int).astype("Int64"),
|
|
check_extension=True,
|
|
)
|
|
|
|
@unittest.skipIf(
|
|
not extension_float_dtypes_available, "pandas extension float dtypes are not available"
|
|
)
|
|
def test_arithmetic_chain_extension_float_dtypes(self):
|
|
self._test_arithmetic_chain_frame(
|
|
self.pdf1.astype("Float64"),
|
|
self.pdf2.astype("Float64"),
|
|
self.pdf3.astype("Float64"),
|
|
check_extension=True,
|
|
)
|
|
self._test_arithmetic_chain_series(
|
|
self.pser1.astype("Float64"),
|
|
self.pser2.astype("Float64"),
|
|
self.pser3.astype("Float64"),
|
|
check_extension=True,
|
|
)
|
|
|
|
def _test_arithmetic_chain_frame(self, pdf1, pdf2, pdf3, *, check_extension):
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
psdf3 = ps.from_pandas(pdf3)
|
|
|
|
common_columns = set(psdf1.columns).intersection(psdf2.columns).intersection(psdf3.columns)
|
|
|
|
def assert_eq(actual, expected):
|
|
if LooseVersion("1.1") <= LooseVersion(pd.__version__) < LooseVersion("1.2.2"):
|
|
self.assert_eq(actual, expected, check_exact=not check_extension)
|
|
if check_extension:
|
|
if isinstance(actual, DataFrame):
|
|
for column, dtype in zip(actual.columns, actual.dtypes):
|
|
if column in common_columns:
|
|
self.assertTrue(isinstance(dtype, extension_dtypes))
|
|
else:
|
|
self.assertFalse(isinstance(dtype, extension_dtypes))
|
|
else:
|
|
self.assertTrue(isinstance(actual.dtype, extension_dtypes))
|
|
else:
|
|
self.assert_eq(actual, expected)
|
|
|
|
# Series
|
|
assert_eq(
|
|
(psdf1.a - psdf2.b - psdf3.c).sort_index(), (pdf1.a - pdf2.b - pdf3.c).sort_index()
|
|
)
|
|
|
|
assert_eq(
|
|
(psdf1.a * (psdf2.a * psdf3.c)).sort_index(), (pdf1.a * (pdf2.a * pdf3.c)).sort_index()
|
|
)
|
|
|
|
if check_extension and not extension_float_dtypes_available:
|
|
self.assert_eq(
|
|
(psdf1["a"] / psdf2["a"] / psdf3["c"]).sort_index(),
|
|
(pdf1["a"] / pdf2["a"] / pdf3["c"]).sort_index(),
|
|
)
|
|
else:
|
|
assert_eq(
|
|
(psdf1["a"] / psdf2["a"] / psdf3["c"]).sort_index(),
|
|
(pdf1["a"] / pdf2["a"] / pdf3["c"]).sort_index(),
|
|
)
|
|
|
|
# DataFrame
|
|
if check_extension and (
|
|
LooseVersion("1.0") <= LooseVersion(pd.__version__) < LooseVersion("1.1")
|
|
):
|
|
self.assert_eq(
|
|
(psdf1 + psdf2 - psdf3).sort_index(), (pdf1 + pdf2 - pdf3).sort_index(), almost=True
|
|
)
|
|
else:
|
|
assert_eq((psdf1 + psdf2 - psdf3).sort_index(), (pdf1 + pdf2 - pdf3).sort_index())
|
|
|
|
# Multi-index columns
|
|
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b")])
|
|
psdf1.columns = columns
|
|
psdf2.columns = columns
|
|
pdf1.columns = columns
|
|
pdf2.columns = columns
|
|
columns = pd.MultiIndex.from_tuples([("x", "b"), ("y", "c")])
|
|
psdf3.columns = columns
|
|
pdf3.columns = columns
|
|
|
|
common_columns = set(psdf1.columns).intersection(psdf2.columns).intersection(psdf3.columns)
|
|
|
|
# Series
|
|
assert_eq(
|
|
(psdf1[("x", "a")] - psdf2[("x", "b")] - psdf3[("y", "c")]).sort_index(),
|
|
(pdf1[("x", "a")] - pdf2[("x", "b")] - pdf3[("y", "c")]).sort_index(),
|
|
)
|
|
|
|
assert_eq(
|
|
(psdf1[("x", "a")] * (psdf2[("x", "b")] * psdf3[("y", "c")])).sort_index(),
|
|
(pdf1[("x", "a")] * (pdf2[("x", "b")] * pdf3[("y", "c")])).sort_index(),
|
|
)
|
|
|
|
# DataFrame
|
|
if check_extension and (
|
|
LooseVersion("1.0") <= LooseVersion(pd.__version__) < LooseVersion("1.1")
|
|
):
|
|
self.assert_eq(
|
|
(psdf1 + psdf2 - psdf3).sort_index(), (pdf1 + pdf2 - pdf3).sort_index(), almost=True
|
|
)
|
|
else:
|
|
assert_eq((psdf1 + psdf2 - psdf3).sort_index(), (pdf1 + pdf2 - pdf3).sort_index())
|
|
|
|
def _test_arithmetic_chain_series(self, pser1, pser2, pser3, *, check_extension):
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
psser3 = ps.from_pandas(pser3)
|
|
|
|
def assert_eq(actual, expected):
|
|
if LooseVersion("1.1") <= LooseVersion(pd.__version__) < LooseVersion("1.2.2"):
|
|
self.assert_eq(actual, expected, check_exact=not check_extension)
|
|
if check_extension:
|
|
self.assertTrue(isinstance(actual.dtype, extension_dtypes))
|
|
else:
|
|
self.assert_eq(actual, expected)
|
|
|
|
# MultiIndex Series
|
|
assert_eq((psser1 + psser2 - psser3).sort_index(), (pser1 + pser2 - pser3).sort_index())
|
|
|
|
assert_eq((psser1 * psser2 * psser3).sort_index(), (pser1 * pser2 * pser3).sort_index())
|
|
|
|
if check_extension and not extension_float_dtypes_available:
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.0"):
|
|
self.assert_eq(
|
|
(psser1 - psser2 / psser3).sort_index(), (pser1 - pser2 / pser3).sort_index()
|
|
)
|
|
else:
|
|
expected = pd.Series(
|
|
[249.0, np.nan, 0.0, 0.88, np.nan, np.nan, np.nan, np.nan, np.nan, -np.inf]
|
|
+ [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
|
|
index=pd.MultiIndex(
|
|
[
|
|
["cow", "falcon", "koala", "koalas", "lama"],
|
|
["length", "power", "speed", "weight"],
|
|
],
|
|
[
|
|
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4],
|
|
[0, 1, 2, 2, 3, 0, 0, 1, 2, 3, 0, 0, 3, 3, 0, 2, 3],
|
|
],
|
|
),
|
|
)
|
|
self.assert_eq((psser1 - psser2 / psser3).sort_index(), expected)
|
|
else:
|
|
assert_eq((psser1 - psser2 / psser3).sort_index(), (pser1 - pser2 / pser3).sort_index())
|
|
|
|
assert_eq((psser1 + psser2 * psser3).sort_index(), (pser1 + pser2 * pser3).sort_index())
|
|
|
|
def test_mod(self):
|
|
pser = pd.Series([100, None, -300, None, 500, -700])
|
|
pser_other = pd.Series([-150] * 6)
|
|
psser = ps.from_pandas(pser)
|
|
psser_other = ps.from_pandas(pser_other)
|
|
|
|
self.assert_eq(psser.mod(psser_other).sort_index(), pser.mod(pser_other))
|
|
self.assert_eq(psser.mod(psser_other).sort_index(), pser.mod(pser_other))
|
|
self.assert_eq(psser.mod(psser_other).sort_index(), pser.mod(pser_other))
|
|
|
|
def test_rmod(self):
|
|
pser = pd.Series([100, None, -300, None, 500, -700])
|
|
pser_other = pd.Series([-150] * 6)
|
|
psser = ps.from_pandas(pser)
|
|
psser_other = ps.from_pandas(pser_other)
|
|
|
|
self.assert_eq(psser.rmod(psser_other).sort_index(), pser.rmod(pser_other))
|
|
self.assert_eq(psser.rmod(psser_other).sort_index(), pser.rmod(pser_other))
|
|
self.assert_eq(psser.rmod(psser_other).sort_index(), pser.rmod(pser_other))
|
|
|
|
def test_getitem_boolean_series(self):
|
|
pdf1 = pd.DataFrame(
|
|
{"A": [0, 1, 2, 3, 4], "B": [100, 200, 300, 400, 500]}, index=[20, 10, 30, 0, 50]
|
|
)
|
|
pdf2 = pd.DataFrame(
|
|
{"A": [0, -1, -2, -3, -4], "B": [-100, -200, -300, -400, -500]},
|
|
index=[0, 30, 10, 20, 50],
|
|
)
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
self.assert_eq(pdf1[pdf2.A > -3].sort_index(), psdf1[psdf2.A > -3].sort_index())
|
|
|
|
self.assert_eq(pdf1.A[pdf2.A > -3].sort_index(), psdf1.A[psdf2.A > -3].sort_index())
|
|
|
|
self.assert_eq(
|
|
(pdf1.A + 1)[pdf2.A > -3].sort_index(), (psdf1.A + 1)[psdf2.A > -3].sort_index()
|
|
)
|
|
|
|
def test_loc_getitem_boolean_series(self):
|
|
pdf1 = pd.DataFrame(
|
|
{"A": [0, 1, 2, 3, 4], "B": [100, 200, 300, 400, 500]}, index=[20, 10, 30, 0, 50]
|
|
)
|
|
pdf2 = pd.DataFrame(
|
|
{"A": [0, -1, -2, -3, -4], "B": [-100, -200, -300, -400, -500]},
|
|
index=[20, 10, 30, 0, 50],
|
|
)
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
self.assert_eq(pdf1.loc[pdf2.A > -3].sort_index(), psdf1.loc[psdf2.A > -3].sort_index())
|
|
|
|
self.assert_eq(pdf1.A.loc[pdf2.A > -3].sort_index(), psdf1.A.loc[psdf2.A > -3].sort_index())
|
|
|
|
self.assert_eq(
|
|
(pdf1.A + 1).loc[pdf2.A > -3].sort_index(), (psdf1.A + 1).loc[psdf2.A > -3].sort_index()
|
|
)
|
|
|
|
def test_bitwise(self):
|
|
pser1 = pd.Series([True, False, True, False, np.nan, np.nan, True, False, np.nan])
|
|
pser2 = pd.Series([True, False, False, True, True, False, np.nan, np.nan, np.nan])
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
|
|
self.assert_eq(pser1 | pser2, (psser1 | psser2).sort_index())
|
|
self.assert_eq(pser1 & pser2, (psser1 & psser2).sort_index())
|
|
|
|
pser1 = pd.Series([True, False, np.nan], index=list("ABC"))
|
|
pser2 = pd.Series([False, True, np.nan], index=list("DEF"))
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
|
|
self.assert_eq(pser1 | pser2, (psser1 | psser2).sort_index())
|
|
self.assert_eq(pser1 & pser2, (psser1 & psser2).sort_index())
|
|
|
|
@unittest.skipIf(
|
|
not extension_object_dtypes_available, "pandas extension object dtypes are not available"
|
|
)
|
|
def test_bitwise_extension_dtype(self):
|
|
def assert_eq(actual, expected):
|
|
if LooseVersion("1.1") <= LooseVersion(pd.__version__) < LooseVersion("1.2.2"):
|
|
self.assert_eq(actual, expected, check_exact=False)
|
|
self.assertTrue(isinstance(actual.dtype, extension_dtypes))
|
|
else:
|
|
self.assert_eq(actual, expected)
|
|
|
|
pser1 = pd.Series(
|
|
[True, False, True, False, np.nan, np.nan, True, False, np.nan], dtype="boolean"
|
|
)
|
|
pser2 = pd.Series(
|
|
[True, False, False, True, True, False, np.nan, np.nan, np.nan], dtype="boolean"
|
|
)
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
|
|
assert_eq((psser1 | psser2).sort_index(), pser1 | pser2)
|
|
assert_eq((psser1 & psser2).sort_index(), pser1 & pser2)
|
|
|
|
pser1 = pd.Series([True, False, np.nan], index=list("ABC"), dtype="boolean")
|
|
pser2 = pd.Series([False, True, np.nan], index=list("DEF"), dtype="boolean")
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
|
|
# a pandas bug?
|
|
# assert_eq((psser1 | psser2).sort_index(), pser1 | pser2)
|
|
# assert_eq((psser1 & psser2).sort_index(), pser1 & pser2)
|
|
assert_eq(
|
|
(psser1 | psser2).sort_index(),
|
|
pd.Series([True, None, None, None, True, None], index=list("ABCDEF"), dtype="boolean"),
|
|
)
|
|
assert_eq(
|
|
(psser1 & psser2).sort_index(),
|
|
pd.Series(
|
|
[None, False, None, False, None, None], index=list("ABCDEF"), dtype="boolean"
|
|
),
|
|
)
|
|
|
|
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
|
|
|
|
pdf5 = pd.DataFrame({"A": [0, 2, 4], "B": [1, 3, 5]}, index=[1, 2, 3])
|
|
pdf6 = pd.DataFrame({"C": [1, 2, 3]}, index=[1, 3, 5])
|
|
psdf5 = ps.from_pandas(pdf5)
|
|
psdf6 = ps.from_pandas(pdf6)
|
|
|
|
ignore_indexes = [True, False]
|
|
joins = ["inner", "outer"]
|
|
|
|
objs = [
|
|
([psdf1.A, psdf2.C], [pdf1.A, pdf2.C]),
|
|
# TODO: ([psdf1, psdf2.C], [pdf1, pdf2.C]),
|
|
([psdf1.A, psdf2], [pdf1.A, pdf2]),
|
|
([psdf1.A, psdf2.C], [pdf1.A, pdf2.C]),
|
|
([psdf3[("X", "A")], psdf4[("X", "C")]], [pdf3[("X", "A")], pdf4[("X", "C")]]),
|
|
([psdf3, psdf4[("X", "C")]], [pdf3, pdf4[("X", "C")]]),
|
|
([psdf3[("X", "A")], psdf4], [pdf3[("X", "A")], pdf4]),
|
|
([psdf3, psdf4], [pdf3, pdf4]),
|
|
([psdf5, psdf6], [pdf5, pdf6]),
|
|
([psdf6, psdf5], [pdf6, pdf5]),
|
|
]
|
|
|
|
for ignore_index, join in 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)),
|
|
)
|
|
|
|
def test_combine_first(self):
|
|
pser1 = pd.Series({"falcon": 330.0, "eagle": 160.0})
|
|
pser2 = pd.Series({"falcon": 345.0, "eagle": 200.0, "duck": 30.0})
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
|
|
self.assert_eq(
|
|
psser1.combine_first(psser2).sort_index(), pser1.combine_first(pser2).sort_index()
|
|
)
|
|
with self.assertRaisesRegex(
|
|
TypeError, "`combine_first` only allows `Series` for parameter `other`"
|
|
):
|
|
psser1.combine_first(50)
|
|
|
|
psser1.name = ("X", "A")
|
|
psser2.name = ("Y", "B")
|
|
pser1.name = ("X", "A")
|
|
pser2.name = ("Y", "B")
|
|
self.assert_eq(
|
|
psser1.combine_first(psser2).sort_index(), pser1.combine_first(pser2).sort_index()
|
|
)
|
|
|
|
# MultiIndex
|
|
midx1 = pd.MultiIndex(
|
|
[["lama", "cow", "falcon", "koala"], ["speed", "weight", "length", "power"]],
|
|
[[0, 3, 1, 1, 1, 2, 2, 2], [0, 2, 0, 3, 2, 0, 1, 3]],
|
|
)
|
|
midx2 = pd.MultiIndex(
|
|
[["lama", "cow", "falcon"], ["speed", "weight", "length"]],
|
|
[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
|
|
)
|
|
pser1 = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1], index=midx1)
|
|
pser2 = pd.Series([-45, 200, -1.2, 30, -250, 1.5, 320, 1, -0.3], index=midx2)
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
|
|
self.assert_eq(
|
|
psser1.combine_first(psser2).sort_index(), pser1.combine_first(pser2).sort_index()
|
|
)
|
|
|
|
# Series come from same DataFrame
|
|
pdf = pd.DataFrame(
|
|
{
|
|
"A": {"falcon": 330.0, "eagle": 160.0},
|
|
"B": {"falcon": 345.0, "eagle": 200.0, "duck": 30.0},
|
|
}
|
|
)
|
|
pser1 = pdf.A
|
|
pser2 = pdf.B
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
|
|
self.assert_eq(
|
|
psser1.combine_first(psser2).sort_index(), pser1.combine_first(pser2).sort_index()
|
|
)
|
|
|
|
psser1.name = ("X", "A")
|
|
psser2.name = ("Y", "B")
|
|
pser1.name = ("X", "A")
|
|
pser2.name = ("Y", "B")
|
|
|
|
self.assert_eq(
|
|
psser1.combine_first(psser2).sort_index(), pser1.combine_first(pser2).sort_index()
|
|
)
|
|
|
|
def test_insert(self):
|
|
#
|
|
# Basic DataFrame
|
|
#
|
|
pdf = pd.DataFrame([1, 2, 3])
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
pser = pd.Series([4, 5, 6])
|
|
psser = ps.from_pandas(pser)
|
|
psdf.insert(1, "y", psser)
|
|
pdf.insert(1, "y", pser)
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
#
|
|
# DataFrame with Index different from inserting Series'
|
|
#
|
|
pdf = pd.DataFrame([1, 2, 3], index=[10, 20, 30])
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
pser = pd.Series([4, 5, 6])
|
|
psser = ps.from_pandas(pser)
|
|
psdf.insert(1, "y", psser)
|
|
pdf.insert(1, "y", pser)
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
#
|
|
# DataFrame with Multi-index columns
|
|
#
|
|
pdf = pd.DataFrame({("x", "a"): [1, 2, 3]})
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
pser = pd.Series([4, 5, 6])
|
|
psser = ps.from_pandas(pser)
|
|
pdf = pd.DataFrame({("x", "a", "b"): [1, 2, 3]})
|
|
psdf = ps.from_pandas(pdf)
|
|
psdf.insert(0, "a", psser)
|
|
pdf.insert(0, "a", pser)
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
psdf.insert(0, ("b", "c", ""), psser)
|
|
pdf.insert(0, ("b", "c", ""), pser)
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
def test_compare(self):
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.1"):
|
|
pser1 = pd.Series(["b", "c", np.nan, "g", np.nan])
|
|
pser2 = pd.Series(["a", "c", np.nan, np.nan, "h"])
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
self.assert_eq(
|
|
pser1.compare(pser2).sort_index(),
|
|
psser1.compare(psser2).sort_index(),
|
|
)
|
|
|
|
# `keep_shape=True`
|
|
self.assert_eq(
|
|
pser1.compare(pser2, keep_shape=True).sort_index(),
|
|
psser1.compare(psser2, keep_shape=True).sort_index(),
|
|
)
|
|
# `keep_equal=True`
|
|
self.assert_eq(
|
|
pser1.compare(pser2, keep_equal=True).sort_index(),
|
|
psser1.compare(psser2, keep_equal=True).sort_index(),
|
|
)
|
|
# `keep_shape=True` and `keep_equal=True`
|
|
self.assert_eq(
|
|
pser1.compare(pser2, keep_shape=True, keep_equal=True).sort_index(),
|
|
psser1.compare(psser2, keep_shape=True, keep_equal=True).sort_index(),
|
|
)
|
|
|
|
# MultiIndex
|
|
pser1.index = pd.MultiIndex.from_tuples(
|
|
[("a", "x"), ("b", "y"), ("c", "z"), ("x", "k"), ("q", "l")]
|
|
)
|
|
pser2.index = pd.MultiIndex.from_tuples(
|
|
[("a", "x"), ("b", "y"), ("c", "z"), ("x", "k"), ("q", "l")]
|
|
)
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
self.assert_eq(
|
|
pser1.compare(pser2).sort_index(),
|
|
psser1.compare(psser2).sort_index(),
|
|
)
|
|
|
|
# `keep_shape=True` with MultiIndex
|
|
self.assert_eq(
|
|
pser1.compare(pser2, keep_shape=True).sort_index(),
|
|
psser1.compare(psser2, keep_shape=True).sort_index(),
|
|
)
|
|
# `keep_equal=True` with MultiIndex
|
|
self.assert_eq(
|
|
pser1.compare(pser2, keep_equal=True).sort_index(),
|
|
psser1.compare(psser2, keep_equal=True).sort_index(),
|
|
)
|
|
# `keep_shape=True` and `keep_equal=True` with MultiIndex
|
|
self.assert_eq(
|
|
pser1.compare(pser2, keep_shape=True, keep_equal=True).sort_index(),
|
|
psser1.compare(psser2, keep_shape=True, keep_equal=True).sort_index(),
|
|
)
|
|
else:
|
|
psser1 = ps.Series(["b", "c", np.nan, "g", np.nan])
|
|
psser2 = ps.Series(["a", "c", np.nan, np.nan, "h"])
|
|
expected = ps.DataFrame(
|
|
[["b", "a"], ["g", None], [None, "h"]], index=[0, 3, 4], columns=["self", "other"]
|
|
)
|
|
self.assert_eq(expected, psser1.compare(psser2).sort_index())
|
|
|
|
# `keep_shape=True`
|
|
expected = ps.DataFrame(
|
|
[["b", "a"], [None, None], [None, None], ["g", None], [None, "h"]],
|
|
index=[0, 1, 2, 3, 4],
|
|
columns=["self", "other"],
|
|
)
|
|
self.assert_eq(
|
|
expected,
|
|
psser1.compare(psser2, keep_shape=True).sort_index(),
|
|
)
|
|
# `keep_equal=True`
|
|
expected = ps.DataFrame(
|
|
[["b", "a"], ["g", None], [None, "h"]], index=[0, 3, 4], columns=["self", "other"]
|
|
)
|
|
self.assert_eq(
|
|
expected,
|
|
psser1.compare(psser2, keep_equal=True).sort_index(),
|
|
)
|
|
# `keep_shape=True` and `keep_equal=True`
|
|
expected = ps.DataFrame(
|
|
[["b", "a"], ["c", "c"], [None, None], ["g", None], [None, "h"]],
|
|
index=[0, 1, 2, 3, 4],
|
|
columns=["self", "other"],
|
|
)
|
|
self.assert_eq(
|
|
expected,
|
|
psser1.compare(psser2, keep_shape=True, keep_equal=True).sort_index(),
|
|
)
|
|
|
|
# MultiIndex
|
|
psser1 = ps.Series(
|
|
["b", "c", np.nan, "g", np.nan],
|
|
index=pd.MultiIndex.from_tuples(
|
|
[("a", "x"), ("b", "y"), ("c", "z"), ("x", "k"), ("q", "l")]
|
|
),
|
|
)
|
|
psser2 = ps.Series(
|
|
["a", "c", np.nan, np.nan, "h"],
|
|
index=pd.MultiIndex.from_tuples(
|
|
[("a", "x"), ("b", "y"), ("c", "z"), ("x", "k"), ("q", "l")]
|
|
),
|
|
)
|
|
expected = ps.DataFrame(
|
|
[["b", "a"], [None, "h"], ["g", None]],
|
|
index=pd.MultiIndex.from_tuples([("a", "x"), ("q", "l"), ("x", "k")]),
|
|
columns=["self", "other"],
|
|
)
|
|
self.assert_eq(expected, psser1.compare(psser2).sort_index())
|
|
|
|
# `keep_shape=True`
|
|
expected = ps.DataFrame(
|
|
[["b", "a"], [None, None], [None, None], [None, "h"], ["g", None]],
|
|
index=pd.MultiIndex.from_tuples(
|
|
[("a", "x"), ("b", "y"), ("c", "z"), ("q", "l"), ("x", "k")]
|
|
),
|
|
columns=["self", "other"],
|
|
)
|
|
self.assert_eq(
|
|
expected,
|
|
psser1.compare(psser2, keep_shape=True).sort_index(),
|
|
)
|
|
# `keep_equal=True`
|
|
expected = ps.DataFrame(
|
|
[["b", "a"], [None, "h"], ["g", None]],
|
|
index=pd.MultiIndex.from_tuples([("a", "x"), ("q", "l"), ("x", "k")]),
|
|
columns=["self", "other"],
|
|
)
|
|
self.assert_eq(
|
|
expected,
|
|
psser1.compare(psser2, keep_equal=True).sort_index(),
|
|
)
|
|
# `keep_shape=True` and `keep_equal=True`
|
|
expected = ps.DataFrame(
|
|
[["b", "a"], ["c", "c"], [None, None], [None, "h"], ["g", None]],
|
|
index=pd.MultiIndex.from_tuples(
|
|
[("a", "x"), ("b", "y"), ("c", "z"), ("q", "l"), ("x", "k")]
|
|
),
|
|
columns=["self", "other"],
|
|
)
|
|
self.assert_eq(
|
|
expected,
|
|
psser1.compare(psser2, keep_shape=True, keep_equal=True).sort_index(),
|
|
)
|
|
|
|
# Different Index
|
|
with self.assertRaisesRegex(
|
|
ValueError, "Can only compare identically-labeled Series objects"
|
|
):
|
|
psser1 = ps.Series(
|
|
[1, 2, 3, 4, 5],
|
|
index=pd.Index([1, 2, 3, 4, 5]),
|
|
)
|
|
psser2 = ps.Series(
|
|
[2, 2, 3, 4, 1],
|
|
index=pd.Index([5, 4, 3, 2, 1]),
|
|
)
|
|
psser1.compare(psser2)
|
|
# Different MultiIndex
|
|
with self.assertRaisesRegex(
|
|
ValueError, "Can only compare identically-labeled Series objects"
|
|
):
|
|
psser1 = ps.Series(
|
|
[1, 2, 3, 4, 5],
|
|
index=pd.MultiIndex.from_tuples(
|
|
[("a", "x"), ("b", "y"), ("c", "z"), ("x", "k"), ("q", "l")]
|
|
),
|
|
)
|
|
psser2 = ps.Series(
|
|
[2, 2, 3, 4, 1],
|
|
index=pd.MultiIndex.from_tuples(
|
|
[("a", "x"), ("b", "y"), ("c", "a"), ("x", "k"), ("q", "l")]
|
|
),
|
|
)
|
|
psser1.compare(psser2)
|
|
|
|
def test_different_columns(self):
|
|
psdf1 = self.psdf1
|
|
psdf4 = self.psdf4
|
|
pdf1 = self.pdf1
|
|
pdf4 = self.pdf4
|
|
|
|
self.assert_eq((psdf1 + psdf4).sort_index(), (pdf1 + pdf4).sort_index(), almost=True)
|
|
|
|
# Multi-index columns
|
|
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b")])
|
|
psdf1.columns = columns
|
|
pdf1.columns = columns
|
|
columns = pd.MultiIndex.from_tuples([("z", "e"), ("z", "f")])
|
|
psdf4.columns = columns
|
|
pdf4.columns = columns
|
|
|
|
self.assert_eq((psdf1 + psdf4).sort_index(), (pdf1 + pdf4).sort_index(), almost=True)
|
|
|
|
def test_assignment_series(self):
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
pdf = self.pdf1
|
|
psser = psdf.a
|
|
pser = pdf.a
|
|
psdf["a"] = self.psdf2.a
|
|
pdf["a"] = self.pdf2.a
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
self.assert_eq(psser, pser)
|
|
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
pdf = self.pdf1
|
|
psser = psdf.a
|
|
pser = pdf.a
|
|
psdf["a"] = self.psdf2.b
|
|
pdf["a"] = self.pdf2.b
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
self.assert_eq(psser, pser)
|
|
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
pdf = self.pdf1
|
|
psdf["c"] = self.psdf2.a
|
|
pdf["c"] = self.pdf2.a
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
# Multi-index columns
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
pdf = self.pdf1
|
|
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b")])
|
|
psdf.columns = columns
|
|
pdf.columns = columns
|
|
psdf[("y", "c")] = self.psdf2.a
|
|
pdf[("y", "c")] = self.pdf2.a
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
pdf = pd.DataFrame({"a": [1, 2, 3], "Koalas": [0, 1, 2]}).set_index("Koalas", drop=False)
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
psdf.index.name = None
|
|
psdf["NEW"] = ps.Series([100, 200, 300])
|
|
|
|
pdf.index.name = None
|
|
pdf["NEW"] = pd.Series([100, 200, 300])
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
def test_assignment_frame(self):
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
pdf = self.pdf1
|
|
psser = psdf.a
|
|
pser = pdf.a
|
|
psdf[["a", "b"]] = self.psdf1
|
|
pdf[["a", "b"]] = self.pdf1
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
self.assert_eq(psser, pser)
|
|
|
|
# 'c' does not exist in `psdf`.
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
pdf = self.pdf1
|
|
psser = psdf.a
|
|
pser = pdf.a
|
|
psdf[["b", "c"]] = self.psdf1
|
|
pdf[["b", "c"]] = self.pdf1
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
self.assert_eq(psser, pser)
|
|
|
|
# 'c' and 'd' do not exist in `psdf`.
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
pdf = self.pdf1
|
|
psdf[["c", "d"]] = self.psdf1
|
|
pdf[["c", "d"]] = self.pdf1
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
# Multi-index columns
|
|
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b")])
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
pdf = self.pdf1
|
|
psdf.columns = columns
|
|
pdf.columns = columns
|
|
psdf[[("y", "c"), ("z", "d")]] = self.psdf1
|
|
pdf[[("y", "c"), ("z", "d")]] = self.pdf1
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
pdf = self.pdf1
|
|
psdf1 = ps.from_pandas(self.pdf1)
|
|
pdf1 = self.pdf1
|
|
psdf1.columns = columns
|
|
pdf1.columns = columns
|
|
psdf[["c", "d"]] = psdf1
|
|
pdf[["c", "d"]] = pdf1
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
def test_assignment_series_chain(self):
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
pdf = self.pdf1
|
|
psdf["a"] = self.psdf1.a
|
|
pdf["a"] = self.pdf1.a
|
|
|
|
psdf["a"] = self.psdf2.b
|
|
pdf["a"] = self.pdf2.b
|
|
|
|
psdf["d"] = self.psdf3.c
|
|
pdf["d"] = self.pdf3.c
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
def test_assignment_frame_chain(self):
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
pdf = self.pdf1
|
|
psdf[["a", "b"]] = self.psdf1
|
|
pdf[["a", "b"]] = self.pdf1
|
|
|
|
psdf[["e", "f"]] = self.psdf3
|
|
pdf[["e", "f"]] = self.pdf3
|
|
|
|
psdf[["b", "c"]] = self.psdf2
|
|
pdf[["b", "c"]] = self.pdf2
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
def test_multi_index_arithmetic(self):
|
|
psdf5 = self.psdf5
|
|
psdf6 = self.psdf6
|
|
pdf5 = self.pdf5
|
|
pdf6 = self.pdf6
|
|
|
|
# Series
|
|
self.assert_eq((psdf5.c - psdf6.e).sort_index(), (pdf5.c - pdf6.e).sort_index())
|
|
|
|
self.assert_eq((psdf5["c"] / psdf6["e"]).sort_index(), (pdf5["c"] / pdf6["e"]).sort_index())
|
|
|
|
# DataFrame
|
|
self.assert_eq((psdf5 + psdf6).sort_index(), (pdf5 + pdf6).sort_index(), almost=True)
|
|
|
|
def test_multi_index_assignment_series(self):
|
|
psdf = ps.from_pandas(self.pdf5)
|
|
pdf = self.pdf5
|
|
psdf["x"] = self.psdf6.e
|
|
pdf["x"] = self.pdf6.e
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
psdf = ps.from_pandas(self.pdf5)
|
|
pdf = self.pdf5
|
|
psdf["e"] = self.psdf6.e
|
|
pdf["e"] = self.pdf6.e
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
psdf = ps.from_pandas(self.pdf5)
|
|
pdf = self.pdf5
|
|
psdf["c"] = self.psdf6.e
|
|
pdf["c"] = self.pdf6.e
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
def test_multi_index_assignment_frame(self):
|
|
psdf = ps.from_pandas(self.pdf5)
|
|
pdf = self.pdf5
|
|
psdf[["c"]] = self.psdf5
|
|
pdf[["c"]] = self.pdf5
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
psdf = ps.from_pandas(self.pdf5)
|
|
pdf = self.pdf5
|
|
psdf[["x"]] = self.psdf5
|
|
pdf[["x"]] = self.pdf5
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
psdf = ps.from_pandas(self.pdf6)
|
|
pdf = self.pdf6
|
|
psdf[["x", "y"]] = self.psdf6
|
|
pdf[["x", "y"]] = self.pdf6
|
|
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
def test_frame_loc_setitem(self):
|
|
pdf_orig = pd.DataFrame(
|
|
[[1, 2], [4, 5], [7, 8]],
|
|
index=["cobra", "viper", "sidewinder"],
|
|
columns=["max_speed", "shield"],
|
|
)
|
|
psdf_orig = ps.DataFrame(pdf_orig)
|
|
|
|
pdf = pdf_orig.copy()
|
|
psdf = psdf_orig.copy()
|
|
pser1 = pdf.max_speed
|
|
pser2 = pdf.shield
|
|
psser1 = psdf.max_speed
|
|
psser2 = psdf.shield
|
|
|
|
another_psdf = ps.DataFrame(pdf_orig)
|
|
|
|
psdf.loc[["viper", "sidewinder"], ["shield"]] = -another_psdf.max_speed
|
|
pdf.loc[["viper", "sidewinder"], ["shield"]] = -pdf.max_speed
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(psser1, pser1)
|
|
self.assert_eq(psser2, pser2)
|
|
|
|
pdf = pdf_orig.copy()
|
|
psdf = psdf_orig.copy()
|
|
pser1 = pdf.max_speed
|
|
pser2 = pdf.shield
|
|
psser1 = psdf.max_speed
|
|
psser2 = psdf.shield
|
|
psdf.loc[another_psdf.max_speed < 5, ["shield"]] = -psdf.max_speed
|
|
pdf.loc[pdf.max_speed < 5, ["shield"]] = -pdf.max_speed
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(psser1, pser1)
|
|
self.assert_eq(psser2, pser2)
|
|
|
|
pdf = pdf_orig.copy()
|
|
psdf = psdf_orig.copy()
|
|
pser1 = pdf.max_speed
|
|
pser2 = pdf.shield
|
|
psser1 = psdf.max_speed
|
|
psser2 = psdf.shield
|
|
psdf.loc[another_psdf.max_speed < 5, ["shield"]] = -another_psdf.max_speed
|
|
pdf.loc[pdf.max_speed < 5, ["shield"]] = -pdf.max_speed
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(psser1, pser1)
|
|
self.assert_eq(psser2, pser2)
|
|
|
|
def test_frame_iloc_setitem(self):
|
|
pdf = pd.DataFrame(
|
|
[[1, 2], [4, 5], [7, 8]],
|
|
index=["cobra", "viper", "sidewinder"],
|
|
columns=["max_speed", "shield"],
|
|
)
|
|
psdf = ps.DataFrame(pdf)
|
|
another_psdf = ps.DataFrame(pdf)
|
|
|
|
psdf.iloc[[0, 1, 2], 1] = -another_psdf.max_speed
|
|
pdf.iloc[[0, 1, 2], 1] = -pdf.max_speed
|
|
self.assert_eq(psdf, pdf)
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError,
|
|
"shape mismatch",
|
|
):
|
|
psdf.iloc[[1, 2], [1]] = -another_psdf.max_speed
|
|
|
|
psdf.iloc[[0, 1, 2], 1] = 10 * another_psdf.max_speed
|
|
pdf.iloc[[0, 1, 2], 1] = 10 * pdf.max_speed
|
|
self.assert_eq(psdf, pdf)
|
|
|
|
with self.assertRaisesRegex(ValueError, "shape mismatch"):
|
|
psdf.iloc[[0], 1] = 10 * another_psdf.max_speed
|
|
|
|
def test_series_loc_setitem(self):
|
|
pdf = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}, index=["cobra", "viper", "sidewinder"])
|
|
psdf = ps.from_pandas(pdf)
|
|
pser = pdf.x
|
|
psery = pdf.y
|
|
psser = psdf.x
|
|
pssery = psdf.y
|
|
|
|
pser_another = pd.Series([1, 2, 3], index=["cobra", "viper", "sidewinder"])
|
|
psser_another = ps.from_pandas(pser_another)
|
|
|
|
psser.loc[psser % 2 == 1] = -psser_another
|
|
pser.loc[pser % 2 == 1] = -pser_another
|
|
self.assert_eq(psser, pser)
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(pssery, psery)
|
|
|
|
pdf = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}, index=["cobra", "viper", "sidewinder"])
|
|
psdf = ps.from_pandas(pdf)
|
|
pser = pdf.x
|
|
psery = pdf.y
|
|
psser = psdf.x
|
|
pssery = psdf.y
|
|
psser.loc[psser_another % 2 == 1] = -psser
|
|
pser.loc[pser_another % 2 == 1] = -pser
|
|
self.assert_eq(psser, pser)
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(pssery, psery)
|
|
|
|
pdf = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}, index=["cobra", "viper", "sidewinder"])
|
|
psdf = ps.from_pandas(pdf)
|
|
pser = pdf.x
|
|
psery = pdf.y
|
|
psser = psdf.x
|
|
pssery = psdf.y
|
|
psser.loc[psser_another % 2 == 1] = -psser
|
|
pser.loc[pser_another % 2 == 1] = -pser
|
|
self.assert_eq(psser, pser)
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(pssery, psery)
|
|
|
|
pdf = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}, index=["cobra", "viper", "sidewinder"])
|
|
psdf = ps.from_pandas(pdf)
|
|
pser = pdf.x
|
|
psery = pdf.y
|
|
psser = psdf.x
|
|
pssery = psdf.y
|
|
psser.loc[psser_another % 2 == 1] = -psser_another
|
|
pser.loc[pser_another % 2 == 1] = -pser_another
|
|
self.assert_eq(psser, pser)
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(pssery, psery)
|
|
|
|
pdf = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}, index=["cobra", "viper", "sidewinder"])
|
|
psdf = ps.from_pandas(pdf)
|
|
pser = pdf.x
|
|
psery = pdf.y
|
|
psser = psdf.x
|
|
pssery = psdf.y
|
|
psser.loc[["viper", "sidewinder"]] = -psser_another
|
|
pser.loc[["viper", "sidewinder"]] = -pser_another
|
|
self.assert_eq(psser, pser)
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(pssery, psery)
|
|
|
|
pdf = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}, index=["cobra", "viper", "sidewinder"])
|
|
psdf = ps.from_pandas(pdf)
|
|
pser = pdf.x
|
|
psery = pdf.y
|
|
psser = psdf.x
|
|
pssery = psdf.y
|
|
psser.loc[psser_another % 2 == 1] = 10
|
|
pser.loc[pser_another % 2 == 1] = 10
|
|
self.assert_eq(psser, pser)
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(pssery, psery)
|
|
|
|
def test_series_iloc_setitem(self):
|
|
pdf = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}, index=["cobra", "viper", "sidewinder"])
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
pser = pdf.x
|
|
psery = pdf.y
|
|
psser = psdf.x
|
|
pssery = psdf.y
|
|
|
|
pser1 = pser + 1
|
|
psser1 = psser + 1
|
|
|
|
pser_another = pd.Series([1, 2, 3], index=["cobra", "viper", "sidewinder"])
|
|
psser_another = ps.from_pandas(pser_another)
|
|
|
|
psser.iloc[[0, 1, 2]] = -psser_another
|
|
pser.iloc[[0, 1, 2]] = -pser_another
|
|
self.assert_eq(psser, pser)
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(pssery, psery)
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError,
|
|
"cannot set using a list-like indexer with a different length than the value",
|
|
):
|
|
psser.iloc[[1, 2]] = -psser_another
|
|
|
|
psser.iloc[[0, 1, 2]] = 10 * psser_another
|
|
pser.iloc[[0, 1, 2]] = 10 * pser_another
|
|
self.assert_eq(psser, pser)
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(pssery, psery)
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError,
|
|
"cannot set using a list-like indexer with a different length than the value",
|
|
):
|
|
psser.iloc[[0]] = 10 * psser_another
|
|
|
|
psser1.iloc[[0, 1, 2]] = -psser_another
|
|
pser1.iloc[[0, 1, 2]] = -pser_another
|
|
self.assert_eq(psser1, pser1)
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(pssery, psery)
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError,
|
|
"cannot set using a list-like indexer with a different length than the value",
|
|
):
|
|
psser1.iloc[[1, 2]] = -psser_another
|
|
|
|
pdf = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}, index=["cobra", "viper", "sidewinder"])
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
pser = pdf.x
|
|
psery = pdf.y
|
|
psser = psdf.x
|
|
pssery = psdf.y
|
|
|
|
piloc = pser.iloc
|
|
kiloc = psser.iloc
|
|
|
|
kiloc[[0, 1, 2]] = -psser_another
|
|
piloc[[0, 1, 2]] = -pser_another
|
|
self.assert_eq(psser, pser)
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(pssery, psery)
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError,
|
|
"cannot set using a list-like indexer with a different length than the value",
|
|
):
|
|
kiloc[[1, 2]] = -psser_another
|
|
|
|
kiloc[[0, 1, 2]] = 10 * psser_another
|
|
piloc[[0, 1, 2]] = 10 * pser_another
|
|
self.assert_eq(psser, pser)
|
|
self.assert_eq(psdf, pdf)
|
|
self.assert_eq(pssery, psery)
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError,
|
|
"cannot set using a list-like indexer with a different length than the value",
|
|
):
|
|
kiloc[[0]] = 10 * psser_another
|
|
|
|
def test_update(self):
|
|
pdf = pd.DataFrame({"x": [1, 2, 3], "y": [10, 20, 30]})
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
pser = pdf.x
|
|
psser = psdf.x
|
|
pser.update(pd.Series([4, 5, 6]))
|
|
psser.update(ps.Series([4, 5, 6]))
|
|
self.assert_eq(psser.sort_index(), pser.sort_index())
|
|
self.assert_eq(psdf.sort_index(), pdf.sort_index())
|
|
|
|
def test_where(self):
|
|
pdf1 = pd.DataFrame({"A": [0, 1, 2, 3, 4], "B": [100, 200, 300, 400, 500]})
|
|
pdf2 = pd.DataFrame({"A": [0, -1, -2, -3, -4], "B": [-100, -200, -300, -400, -500]})
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
self.assert_eq(pdf1.where(pdf2 > 100), psdf1.where(psdf2 > 100).sort_index())
|
|
|
|
pdf1 = pd.DataFrame({"A": [-1, -2, -3, -4, -5], "B": [-100, -200, -300, -400, -500]})
|
|
pdf2 = pd.DataFrame({"A": [-10, -20, -30, -40, -50], "B": [-5, -4, -3, -2, -1]})
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
self.assert_eq(pdf1.where(pdf2 < -250), psdf1.where(psdf2 < -250).sort_index())
|
|
|
|
# multi-index columns
|
|
pdf1 = pd.DataFrame({("X", "A"): [0, 1, 2, 3, 4], ("X", "B"): [100, 200, 300, 400, 500]})
|
|
pdf2 = pd.DataFrame(
|
|
{("X", "A"): [0, -1, -2, -3, -4], ("X", "B"): [-100, -200, -300, -400, -500]}
|
|
)
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
self.assert_eq(pdf1.where(pdf2 > 100), psdf1.where(psdf2 > 100).sort_index())
|
|
|
|
def test_mask(self):
|
|
pdf1 = pd.DataFrame({"A": [0, 1, 2, 3, 4], "B": [100, 200, 300, 400, 500]})
|
|
pdf2 = pd.DataFrame({"A": [0, -1, -2, -3, -4], "B": [-100, -200, -300, -400, -500]})
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
self.assert_eq(pdf1.mask(pdf2 < 100), psdf1.mask(psdf2 < 100).sort_index())
|
|
|
|
pdf1 = pd.DataFrame({"A": [-1, -2, -3, -4, -5], "B": [-100, -200, -300, -400, -500]})
|
|
pdf2 = pd.DataFrame({"A": [-10, -20, -30, -40, -50], "B": [-5, -4, -3, -2, -1]})
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
self.assert_eq(pdf1.mask(pdf2 > -250), psdf1.mask(psdf2 > -250).sort_index())
|
|
|
|
# multi-index columns
|
|
pdf1 = pd.DataFrame({("X", "A"): [0, 1, 2, 3, 4], ("X", "B"): [100, 200, 300, 400, 500]})
|
|
pdf2 = pd.DataFrame(
|
|
{("X", "A"): [0, -1, -2, -3, -4], ("X", "B"): [-100, -200, -300, -400, -500]}
|
|
)
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
self.assert_eq(pdf1.mask(pdf2 < 100), psdf1.mask(psdf2 < 100).sort_index())
|
|
|
|
def test_multi_index_column_assignment_frame(self):
|
|
pdf = pd.DataFrame({"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0]})
|
|
pdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
|
|
psdf = ps.DataFrame(pdf)
|
|
|
|
psdf["c"] = ps.Series([10, 20, 30, 20])
|
|
pdf["c"] = pd.Series([10, 20, 30, 20])
|
|
|
|
psdf[("d", "x")] = ps.Series([100, 200, 300, 200], name="1")
|
|
pdf[("d", "x")] = pd.Series([100, 200, 300, 200], name="1")
|
|
|
|
psdf[("d", "y")] = ps.Series([1000, 2000, 3000, 2000], name=("1", "2"))
|
|
pdf[("d", "y")] = pd.Series([1000, 2000, 3000, 2000], name=("1", "2"))
|
|
|
|
psdf["e"] = ps.Series([10000, 20000, 30000, 20000], name=("1", "2", "3"))
|
|
pdf["e"] = pd.Series([10000, 20000, 30000, 20000], name=("1", "2", "3"))
|
|
|
|
psdf[[("f", "x"), ("f", "y")]] = ps.DataFrame(
|
|
{"1": [100000, 200000, 300000, 200000], "2": [1000000, 2000000, 3000000, 2000000]}
|
|
)
|
|
pdf[[("f", "x"), ("f", "y")]] = pd.DataFrame(
|
|
{"1": [100000, 200000, 300000, 200000], "2": [1000000, 2000000, 3000000, 2000000]}
|
|
)
|
|
|
|
self.assert_eq(repr(psdf.sort_index()), repr(pdf))
|
|
|
|
with self.assertRaisesRegex(KeyError, "Key length \\(3\\) exceeds index depth \\(2\\)"):
|
|
psdf[("1", "2", "3")] = ps.Series([100, 200, 300, 200])
|
|
|
|
def test_series_dot(self):
|
|
pser = pd.Series([90, 91, 85], index=[2, 4, 1])
|
|
psser = ps.from_pandas(pser)
|
|
pser_other = pd.Series([90, 91, 85], index=[2, 4, 1])
|
|
psser_other = ps.from_pandas(pser_other)
|
|
|
|
self.assert_eq(psser.dot(psser_other), pser.dot(pser_other))
|
|
|
|
psser_other = ps.Series([90, 91, 85], index=[1, 2, 4])
|
|
pser_other = pd.Series([90, 91, 85], index=[1, 2, 4])
|
|
|
|
self.assert_eq(psser.dot(psser_other), pser.dot(pser_other))
|
|
|
|
# length of index is different
|
|
psser_other = ps.Series([90, 91, 85, 100], index=[2, 4, 1, 0])
|
|
with self.assertRaisesRegex(ValueError, "matrices are not aligned"):
|
|
psser.dot(psser_other)
|
|
|
|
# for MultiIndex
|
|
midx = pd.MultiIndex(
|
|
[["lama", "cow", "falcon"], ["speed", "weight", "length"]],
|
|
[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
|
|
)
|
|
pser = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], index=midx)
|
|
psser = ps.from_pandas(pser)
|
|
pser_other = pd.Series([-450, 20, 12, -30, -250, 15, -320, 100, 3], index=midx)
|
|
psser_other = ps.from_pandas(pser_other)
|
|
self.assert_eq(psser.dot(psser_other), pser.dot(pser_other))
|
|
|
|
pser = pd.Series([0, 1, 2, 3])
|
|
psser = ps.from_pandas(pser)
|
|
|
|
# DataFrame "other" without Index/MultiIndex as columns
|
|
pdf = pd.DataFrame([[0, 1], [-2, 3], [4, -5], [6, 7]])
|
|
psdf = ps.from_pandas(pdf)
|
|
self.assert_eq(psser.dot(psdf), pser.dot(pdf))
|
|
|
|
# DataFrame "other" with Index as columns
|
|
pdf.columns = pd.Index(["x", "y"])
|
|
psdf = ps.from_pandas(pdf)
|
|
self.assert_eq(psser.dot(psdf), pser.dot(pdf))
|
|
pdf.columns = pd.Index(["x", "y"], name="cols_name")
|
|
psdf = ps.from_pandas(pdf)
|
|
self.assert_eq(psser.dot(psdf), pser.dot(pdf))
|
|
|
|
pdf = pdf.reindex([1, 0, 2, 3])
|
|
psdf = ps.from_pandas(pdf)
|
|
self.assert_eq(psser.dot(psdf), pser.dot(pdf))
|
|
|
|
# DataFrame "other" with MultiIndex as columns
|
|
pdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("b", "y")])
|
|
psdf = ps.from_pandas(pdf)
|
|
self.assert_eq(psser.dot(psdf), pser.dot(pdf))
|
|
pdf.columns = pd.MultiIndex.from_tuples(
|
|
[("a", "x"), ("b", "y")], names=["cols_name1", "cols_name2"]
|
|
)
|
|
psdf = ps.from_pandas(pdf)
|
|
self.assert_eq(psser.dot(psdf), pser.dot(pdf))
|
|
|
|
psser = ps.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}).b
|
|
pser = psser.to_pandas()
|
|
psdf = ps.DataFrame({"c": [7, 8, 9]})
|
|
pdf = psdf.to_pandas()
|
|
self.assert_eq(psser.dot(psdf), pser.dot(pdf))
|
|
|
|
def test_frame_dot(self):
|
|
pdf = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
pser = pd.Series([1, 1, 2, 1])
|
|
psser = ps.from_pandas(pser)
|
|
self.assert_eq(psdf.dot(psser), pdf.dot(pser))
|
|
|
|
# Index reorder
|
|
pser = pser.reindex([1, 0, 2, 3])
|
|
psser = ps.from_pandas(pser)
|
|
self.assert_eq(psdf.dot(psser), pdf.dot(pser))
|
|
|
|
# ser with name
|
|
pser.name = "ser"
|
|
psser = ps.from_pandas(pser)
|
|
self.assert_eq(psdf.dot(psser), pdf.dot(pser))
|
|
|
|
# df with MultiIndex as column (ser with MultiIndex)
|
|
arrays = [[1, 1, 2, 2], ["red", "blue", "red", "blue"]]
|
|
pidx = pd.MultiIndex.from_arrays(arrays, names=("number", "color"))
|
|
pser = pd.Series([1, 1, 2, 1], index=pidx)
|
|
pdf = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]], columns=pidx)
|
|
psdf = ps.from_pandas(pdf)
|
|
psser = ps.from_pandas(pser)
|
|
self.assert_eq(psdf.dot(psser), pdf.dot(pser))
|
|
|
|
# df with Index as column (ser with Index)
|
|
pidx = pd.Index([1, 2, 3, 4], name="number")
|
|
pser = pd.Series([1, 1, 2, 1], index=pidx)
|
|
pdf = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]], columns=pidx)
|
|
psdf = ps.from_pandas(pdf)
|
|
psser = ps.from_pandas(pser)
|
|
self.assert_eq(psdf.dot(psser), pdf.dot(pser))
|
|
|
|
# df with Index
|
|
pdf.index = pd.Index(["x", "y"], name="char")
|
|
psdf = ps.from_pandas(pdf)
|
|
self.assert_eq(psdf.dot(psser), pdf.dot(pser))
|
|
|
|
# df with MultiIndex
|
|
pdf.index = pd.MultiIndex.from_arrays([[1, 1], ["red", "blue"]], names=("number", "color"))
|
|
psdf = ps.from_pandas(pdf)
|
|
self.assert_eq(psdf.dot(psser), pdf.dot(pser))
|
|
|
|
pdf = pd.DataFrame([[1, 2], [3, 4]])
|
|
psdf = ps.from_pandas(pdf)
|
|
self.assert_eq(psdf.dot(psdf[0]), pdf.dot(pdf[0]))
|
|
self.assert_eq(psdf.dot(psdf[0] * 10), pdf.dot(pdf[0] * 10))
|
|
self.assert_eq((psdf + 1).dot(psdf[0] * 10), (pdf + 1).dot(pdf[0] * 10))
|
|
|
|
def test_to_series_comparison(self):
|
|
psidx1 = ps.Index([1, 2, 3, 4, 5])
|
|
psidx2 = ps.Index([1, 2, 3, 4, 5])
|
|
|
|
self.assert_eq((psidx1.to_series() == psidx2.to_series()).all(), True)
|
|
|
|
psidx1.name = "koalas"
|
|
psidx2.name = "koalas"
|
|
|
|
self.assert_eq((psidx1.to_series() == psidx2.to_series()).all(), True)
|
|
|
|
def test_series_repeat(self):
|
|
pser1 = pd.Series(["a", "b", "c"], name="a")
|
|
pser2 = pd.Series([10, 20, 30], name="rep")
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
|
|
self.assert_eq(psser1.repeat(psser2).sort_index(), pser1.repeat(pser2).sort_index())
|
|
|
|
def test_series_ops(self):
|
|
pser1 = pd.Series([1, 2, 3, 4, 5, 6, 7], name="x", index=[11, 12, 13, 14, 15, 16, 17])
|
|
pser2 = pd.Series([1, 2, 3, 4, 5, 6, 7], name="x", index=[11, 12, 13, 14, 15, 16, 17])
|
|
pidx1 = pd.Index([10, 11, 12, 13, 14, 15, 16], name="x")
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
psidx1 = ps.from_pandas(pidx1)
|
|
|
|
self.assert_eq(
|
|
(psser1 + 1 + 10 * psser2).sort_index(), (pser1 + 1 + 10 * pser2).sort_index()
|
|
)
|
|
self.assert_eq(
|
|
(psser1 + 1 + 10 * psser2.rename()).sort_index(),
|
|
(pser1 + 1 + 10 * pser2.rename()).sort_index(),
|
|
)
|
|
self.assert_eq(
|
|
(psser1.rename() + 1 + 10 * psser2).sort_index(),
|
|
(pser1.rename() + 1 + 10 * pser2).sort_index(),
|
|
)
|
|
self.assert_eq(
|
|
(psser1.rename() + 1 + 10 * psser2.rename()).sort_index(),
|
|
(pser1.rename() + 1 + 10 * pser2.rename()).sort_index(),
|
|
)
|
|
|
|
self.assert_eq(psser1 + 1 + 10 * psidx1, pser1 + 1 + 10 * pidx1)
|
|
self.assert_eq(psser1.rename() + 1 + 10 * psidx1, pser1.rename() + 1 + 10 * pidx1)
|
|
self.assert_eq(psser1 + 1 + 10 * psidx1.rename(None), pser1 + 1 + 10 * pidx1.rename(None))
|
|
self.assert_eq(
|
|
psser1.rename() + 1 + 10 * psidx1.rename(None),
|
|
pser1.rename() + 1 + 10 * pidx1.rename(None),
|
|
)
|
|
|
|
self.assert_eq(psidx1 + 1 + 10 * psser1, pidx1 + 1 + 10 * pser1)
|
|
self.assert_eq(psidx1 + 1 + 10 * psser1.rename(), pidx1 + 1 + 10 * pser1.rename())
|
|
self.assert_eq(psidx1.rename(None) + 1 + 10 * psser1, pidx1.rename(None) + 1 + 10 * pser1)
|
|
self.assert_eq(
|
|
psidx1.rename(None) + 1 + 10 * psser1.rename(),
|
|
pidx1.rename(None) + 1 + 10 * pser1.rename(),
|
|
)
|
|
|
|
pidx2 = pd.Index([11, 12, 13])
|
|
psidx2 = ps.from_pandas(pidx2)
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError, "operands could not be broadcast together with shapes"
|
|
):
|
|
psser1 + psidx2
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError, "operands could not be broadcast together with shapes"
|
|
):
|
|
psidx2 + psser1
|
|
|
|
def test_index_ops(self):
|
|
pidx1 = pd.Index([1, 2, 3, 4, 5], name="x")
|
|
pidx2 = pd.Index([6, 7, 8, 9, 10], name="x")
|
|
psidx1 = ps.from_pandas(pidx1)
|
|
psidx2 = ps.from_pandas(pidx2)
|
|
|
|
self.assert_eq(psidx1 * 10 + psidx2, pidx1 * 10 + pidx2)
|
|
self.assert_eq(psidx1.rename(None) * 10 + psidx2, pidx1.rename(None) * 10 + pidx2)
|
|
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.0"):
|
|
self.assert_eq(psidx1 * 10 + psidx2.rename(None), pidx1 * 10 + pidx2.rename(None))
|
|
else:
|
|
self.assert_eq(
|
|
psidx1 * 10 + psidx2.rename(None), (pidx1 * 10 + pidx2.rename(None)).rename(None)
|
|
)
|
|
|
|
pidx3 = pd.Index([11, 12, 13])
|
|
psidx3 = ps.from_pandas(pidx3)
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError, "operands could not be broadcast together with shapes"
|
|
):
|
|
psidx1 + psidx3
|
|
|
|
pidx1 = pd.Index([1, 2, 3, 4, 5], name="a")
|
|
pidx2 = pd.Index([6, 7, 8, 9, 10], name="a")
|
|
pidx3 = pd.Index([11, 12, 13, 14, 15], name="x")
|
|
psidx1 = ps.from_pandas(pidx1)
|
|
psidx2 = ps.from_pandas(pidx2)
|
|
psidx3 = ps.from_pandas(pidx3)
|
|
|
|
self.assert_eq(psidx1 * 10 + psidx2, pidx1 * 10 + pidx2)
|
|
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.0"):
|
|
self.assert_eq(psidx1 * 10 + psidx3, pidx1 * 10 + pidx3)
|
|
else:
|
|
self.assert_eq(psidx1 * 10 + psidx3, (pidx1 * 10 + pidx3).rename(None))
|
|
|
|
def test_align(self):
|
|
pdf1 = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}, index=[10, 20, 30])
|
|
pdf2 = pd.DataFrame({"a": [4, 5, 6], "c": ["d", "e", "f"]}, index=[10, 11, 12])
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
for join in ["outer", "inner", "left", "right"]:
|
|
for axis in [None, 0]:
|
|
psdf_l, psdf_r = psdf1.align(psdf2, join=join, axis=axis)
|
|
pdf_l, pdf_r = pdf1.align(pdf2, join=join, axis=axis)
|
|
self.assert_eq(psdf_l.sort_index(), pdf_l.sort_index())
|
|
self.assert_eq(psdf_r.sort_index(), pdf_r.sort_index())
|
|
|
|
pser1 = pd.Series([7, 8, 9], index=[10, 11, 12])
|
|
pser2 = pd.Series(["g", "h", "i"], index=[10, 20, 30])
|
|
psser1 = ps.from_pandas(pser1)
|
|
psser2 = ps.from_pandas(pser2)
|
|
|
|
for join in ["outer", "inner", "left", "right"]:
|
|
psser_l, psser_r = psser1.align(psser2, join=join)
|
|
pser_l, pser_r = pser1.align(pser2, join=join)
|
|
self.assert_eq(psser_l.sort_index(), pser_l.sort_index())
|
|
self.assert_eq(psser_r.sort_index(), pser_r.sort_index())
|
|
|
|
psdf_l, psser_r = psdf1.align(psser1, join=join, axis=0)
|
|
pdf_l, pser_r = pdf1.align(pser1, join=join, axis=0)
|
|
self.assert_eq(psdf_l.sort_index(), pdf_l.sort_index())
|
|
self.assert_eq(psser_r.sort_index(), pser_r.sort_index())
|
|
|
|
psser_l, psdf_r = psser1.align(psdf1, join=join)
|
|
pser_l, pdf_r = pser1.align(pdf1, join=join)
|
|
self.assert_eq(psser_l.sort_index(), pser_l.sort_index())
|
|
self.assert_eq(psdf_r.sort_index(), pdf_r.sort_index())
|
|
|
|
# multi-index columns
|
|
pdf3 = pd.DataFrame(
|
|
{("x", "a"): [4, 5, 6], ("y", "c"): ["d", "e", "f"]}, index=[10, 11, 12]
|
|
)
|
|
psdf3 = ps.from_pandas(pdf3)
|
|
pser3 = pdf3[("y", "c")]
|
|
psser3 = psdf3[("y", "c")]
|
|
|
|
for join in ["outer", "inner", "left", "right"]:
|
|
psdf_l, psdf_r = psdf1.align(psdf3, join=join, axis=0)
|
|
pdf_l, pdf_r = pdf1.align(pdf3, join=join, axis=0)
|
|
self.assert_eq(psdf_l.sort_index(), pdf_l.sort_index())
|
|
self.assert_eq(psdf_r.sort_index(), pdf_r.sort_index())
|
|
|
|
psser_l, psser_r = psser1.align(psser3, join=join)
|
|
pser_l, pser_r = pser1.align(pser3, join=join)
|
|
self.assert_eq(psser_l.sort_index(), pser_l.sort_index())
|
|
self.assert_eq(psser_r.sort_index(), pser_r.sort_index())
|
|
|
|
psdf_l, psser_r = psdf1.align(psser3, join=join, axis=0)
|
|
pdf_l, pser_r = pdf1.align(pser3, join=join, axis=0)
|
|
self.assert_eq(psdf_l.sort_index(), pdf_l.sort_index())
|
|
self.assert_eq(psser_r.sort_index(), pser_r.sort_index())
|
|
|
|
psser_l, psdf_r = psser3.align(psdf1, join=join)
|
|
pser_l, pdf_r = pser3.align(pdf1, join=join)
|
|
self.assert_eq(psser_l.sort_index(), pser_l.sort_index())
|
|
self.assert_eq(psdf_r.sort_index(), pdf_r.sort_index())
|
|
|
|
self.assertRaises(ValueError, lambda: psdf1.align(psdf3, axis=None))
|
|
self.assertRaises(ValueError, lambda: psdf1.align(psdf3, axis=1))
|
|
|
|
def test_pow_and_rpow(self):
|
|
pser = pd.Series([1, 2, np.nan])
|
|
psser = ps.from_pandas(pser)
|
|
pser_other = pd.Series([np.nan, 2, 3])
|
|
psser_other = ps.from_pandas(pser_other)
|
|
|
|
self.assert_eq(pser.pow(pser_other), psser.pow(psser_other).sort_index())
|
|
self.assert_eq(pser ** pser_other, (psser ** psser_other).sort_index())
|
|
self.assert_eq(pser.rpow(pser_other), psser.rpow(psser_other).sort_index())
|
|
|
|
def test_shift(self):
|
|
pdf = pd.DataFrame(
|
|
{
|
|
"Col1": [10, 20, 15, 30, 45],
|
|
"Col2": [13, 23, 18, 33, 48],
|
|
"Col3": [17, 27, 22, 37, 52],
|
|
},
|
|
index=np.random.rand(5),
|
|
)
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
self.assert_eq(
|
|
pdf.shift().loc[pdf["Col1"] == 20].astype(int), psdf.shift().loc[psdf["Col1"] == 20]
|
|
)
|
|
self.assert_eq(
|
|
pdf["Col2"].shift().loc[pdf["Col1"] == 20].astype(int),
|
|
psdf["Col2"].shift().loc[psdf["Col1"] == 20],
|
|
)
|
|
|
|
def test_diff(self):
|
|
pdf = pd.DataFrame(
|
|
{
|
|
"Col1": [10, 20, 15, 30, 45],
|
|
"Col2": [13, 23, 18, 33, 48],
|
|
"Col3": [17, 27, 22, 37, 52],
|
|
},
|
|
index=np.random.rand(5),
|
|
)
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
self.assert_eq(
|
|
pdf.diff().loc[pdf["Col1"] == 20].astype(int), psdf.diff().loc[psdf["Col1"] == 20]
|
|
)
|
|
self.assert_eq(
|
|
pdf["Col2"].diff().loc[pdf["Col1"] == 20].astype(int),
|
|
psdf["Col2"].diff().loc[psdf["Col1"] == 20],
|
|
)
|
|
|
|
def test_rank(self):
|
|
pdf = pd.DataFrame(
|
|
{
|
|
"Col1": [10, 20, 15, 30, 45],
|
|
"Col2": [13, 23, 18, 33, 48],
|
|
"Col3": [17, 27, 22, 37, 52],
|
|
},
|
|
index=np.random.rand(5),
|
|
)
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
self.assert_eq(pdf.rank().loc[pdf["Col1"] == 20], psdf.rank().loc[psdf["Col1"] == 20])
|
|
self.assert_eq(
|
|
pdf["Col2"].rank().loc[pdf["Col1"] == 20], psdf["Col2"].rank().loc[psdf["Col1"] == 20]
|
|
)
|
|
|
|
|
|
class OpsOnDiffFramesDisabledTest(PandasOnSparkTestCase, SQLTestUtils):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
super().setUpClass()
|
|
set_option("compute.ops_on_diff_frames", False)
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
reset_option("compute.ops_on_diff_frames")
|
|
super().tearDownClass()
|
|
|
|
@property
|
|
def pdf1(self):
|
|
return pd.DataFrame(
|
|
{"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]},
|
|
index=[0, 1, 3, 5, 6, 8, 9, 9, 9],
|
|
)
|
|
|
|
@property
|
|
def pdf2(self):
|
|
return pd.DataFrame(
|
|
{"a": [9, 8, 7, 6, 5, 4, 3, 2, 1], "b": [0, 0, 0, 4, 5, 6, 1, 2, 3]},
|
|
index=list(range(9)),
|
|
)
|
|
|
|
@property
|
|
def psdf1(self):
|
|
return ps.from_pandas(self.pdf1)
|
|
|
|
@property
|
|
def psdf2(self):
|
|
return ps.from_pandas(self.pdf2)
|
|
|
|
def test_arithmetic(self):
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
self.psdf1.a - self.psdf2.b
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
self.psdf1.a - self.psdf2.a
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
self.psdf1["a"] - self.psdf2["a"]
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
self.psdf1 - self.psdf2
|
|
|
|
def test_assignment(self):
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psdf = ps.from_pandas(self.pdf1)
|
|
psdf["c"] = self.psdf1.a
|
|
|
|
def test_frame_loc_setitem(self):
|
|
pdf = pd.DataFrame(
|
|
[[1, 2], [4, 5], [7, 8]],
|
|
index=["cobra", "viper", "sidewinder"],
|
|
columns=["max_speed", "shield"],
|
|
)
|
|
psdf = ps.DataFrame(pdf)
|
|
another_psdf = ps.DataFrame(pdf)
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psdf.loc[["viper", "sidewinder"], ["shield"]] = another_psdf.max_speed
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psdf.loc[another_psdf.max_speed < 5, ["shield"]] = -psdf.max_speed
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psdf.loc[another_psdf.max_speed < 5, ["shield"]] = -another_psdf.max_speed
|
|
|
|
def test_frame_iloc_setitem(self):
|
|
pdf = pd.DataFrame(
|
|
[[1, 2], [4, 5], [7, 8]],
|
|
index=["cobra", "viper", "sidewinder"],
|
|
columns=["max_speed", "shield"],
|
|
)
|
|
psdf = ps.DataFrame(pdf)
|
|
another_psdf = ps.DataFrame(pdf)
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psdf.iloc[[1, 2], [1]] = another_psdf.max_speed.iloc[[1, 2]]
|
|
|
|
def test_series_loc_setitem(self):
|
|
pser = pd.Series([1, 2, 3], index=["cobra", "viper", "sidewinder"])
|
|
psser = ps.from_pandas(pser)
|
|
|
|
pser_another = pd.Series([1, 2, 3], index=["cobra", "viper", "sidewinder"])
|
|
psser_another = ps.from_pandas(pser_another)
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psser.loc[psser % 2 == 1] = -psser_another
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psser.loc[psser_another % 2 == 1] = -psser
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psser.loc[psser_another % 2 == 1] = -psser_another
|
|
|
|
def test_series_iloc_setitem(self):
|
|
pser = pd.Series([1, 2, 3], index=["cobra", "viper", "sidewinder"])
|
|
psser = ps.from_pandas(pser)
|
|
|
|
pser_another = pd.Series([1, 2, 3], index=["cobra", "viper", "sidewinder"])
|
|
psser_another = ps.from_pandas(pser_another)
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psser.iloc[[1]] = -psser_another.iloc[[1]]
|
|
|
|
def test_where(self):
|
|
pdf1 = pd.DataFrame({"A": [0, 1, 2, 3, 4], "B": [100, 200, 300, 400, 500]})
|
|
pdf2 = pd.DataFrame({"A": [0, -1, -2, -3, -4], "B": [-100, -200, -300, -400, -500]})
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psdf1.where(psdf2 > 100)
|
|
|
|
pdf1 = pd.DataFrame({"A": [-1, -2, -3, -4, -5], "B": [-100, -200, -300, -400, -500]})
|
|
pdf2 = pd.DataFrame({"A": [-10, -20, -30, -40, -50], "B": [-5, -4, -3, -2, -1]})
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psdf1.where(psdf2 < -250)
|
|
|
|
def test_mask(self):
|
|
pdf1 = pd.DataFrame({"A": [0, 1, 2, 3, 4], "B": [100, 200, 300, 400, 500]})
|
|
pdf2 = pd.DataFrame({"A": [0, -1, -2, -3, -4], "B": [-100, -200, -300, -400, -500]})
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psdf1.mask(psdf2 < 100)
|
|
|
|
pdf1 = pd.DataFrame({"A": [-1, -2, -3, -4, -5], "B": [-100, -200, -300, -400, -500]})
|
|
pdf2 = pd.DataFrame({"A": [-10, -20, -30, -40, -50], "B": [-5, -4, -3, -2, -1]})
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psdf1.mask(psdf2 > -250)
|
|
|
|
def test_align(self):
|
|
pdf1 = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}, index=[10, 20, 30])
|
|
pdf2 = pd.DataFrame({"a": [4, 5, 6], "c": ["d", "e", "f"]}, index=[10, 11, 12])
|
|
psdf1 = ps.from_pandas(pdf1)
|
|
psdf2 = ps.from_pandas(pdf2)
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psdf1.align(psdf2)
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psdf1.align(psdf2, axis=0)
|
|
|
|
def test_pow_and_rpow(self):
|
|
pser = pd.Series([1, 2, np.nan])
|
|
psser = ps.from_pandas(pser)
|
|
pser_other = pd.Series([np.nan, 2, 3])
|
|
psser_other = ps.from_pandas(pser_other)
|
|
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psser.pow(psser_other)
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psser ** psser_other
|
|
with self.assertRaisesRegex(ValueError, "Cannot combine the series or dataframe"):
|
|
psser.rpow(psser_other)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pyspark.pandas.tests.test_ops_on_diff_frames 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)
|