def tearDownClass(cls):
super(DistributedDefaultIndexTest, cls).tearDownClass()
reset_option('compute.default_index_type')
def tearDownClass(cls):
reset_option('plotting.max_rows')
reset_option('plotting.sample_ratio')
super(DataFramePlotTest, cls).tearDownClass()
def tearDownClass(cls):
reset_option("compute.default_index_type")
super(DistributedOneByOneDefaultIndexTest, cls).tearDownClass()
def tearDownClass(cls):
super(OneByOneDefaultIndexTest, cls).tearDownClass()
reset_option('compute.default_index_type')
def tearDownClass(cls):
reset_option("compute.ops_on_diff_frames")
super(OpsOnDiffFramesGroupByTest, cls).tearDownClass()
def tearDownClass(cls):
reset_option("compute.ops_on_diff_frames")
super().tearDownClass()
def tearDownClass(cls):
reset_option("plotting.max_rows")
reset_option("plotting.sample_ratio")
super().tearDownClass()
def tearDownClass(cls):
reset_option("display.max_rows")
def tearDownClass(cls):
reset_option('compute.ops_on_diff_frames')
super(OpsOnDiffFramesDisabledTest, cls).tearDownClass()
def tearDownClass(cls):
reset_option('plotting.max_rows')
super(SeriesPlotTest, cls).tearDownClass()
def tearDownClass(cls):
if LooseVersion(pd.__version__) >= LooseVersion("0.25"):
pd.reset_option("plotting.backend")
reset_option("plotting.backend")
reset_option("plotting.max_rows")
super().tearDownClass()
def test_transform(self):
pdf = pd.DataFrame(
{
'a': [1, 2, 3, 4, 5, 6],
'b': [1, 1, 2, 3, 5, 8],
'c': [1, 4, 9, 16, 25, 36]
},
columns=['a', 'b', 'c'])
kdf = koalas.DataFrame(pdf)
self.assert_eq(
kdf.groupby("b").transform(lambda x: x + 1).sort_index(),
pdf.groupby("b").transform(lambda x: x + 1).sort_index())
self.assert_eq(
kdf.groupby(['a', 'b']).transform(lambda x: x * x).sort_index(),
pdf.groupby(['a', 'b']).transform(lambda x: x * x).sort_index())
self.assert_eq(
kdf.groupby(['b'])['a'].transform(lambda x: x).sort_index(),
pdf.groupby(['b'])['a'].transform(lambda x: x).sort_index())
# multi-index columns
columns = pd.MultiIndex.from_tuples([('x', 'a'), ('x', 'b'),
('y', 'c')])
pdf.columns = columns
kdf.columns = columns
self.assert_eq(
kdf.groupby(("x", "b")).transform(lambda x: x + 1).sort_index(),
pdf.groupby(("x", "b")).transform(lambda x: x + 1).sort_index())
self.assert_eq(
kdf.groupby([('x', 'a'),
('x', 'b')]).transform(lambda x: x * x).sort_index(),
pdf.groupby([('x', 'a'),
('x', 'b')]).transform(lambda x: x * x).sort_index())
set_option('compute.shortcut_limit', 1000)
try:
pdf = pd.DataFrame(
{
'a': [1, 2, 3, 4, 5, 6] * 300,
'b': [1, 1, 2, 3, 5, 8] * 300,
'c': [1, 4, 9, 16, 25, 36] * 300
},
columns=['a', 'b', 'c'])
kdf = koalas.DataFrame(pdf)
self.assert_eq(
kdf.groupby("b").transform(lambda x: x + 1).sort_index(),
pdf.groupby("b").transform(lambda x: x + 1).sort_index())
self.assert_eq(
kdf.groupby(['a',
'b']).transform(lambda x: x * x).sort_index(),
pdf.groupby(['a',
'b']).transform(lambda x: x * x).sort_index())
self.assert_eq(
kdf.groupby(['b'])['a'].transform(lambda x: x).sort_index(),
pdf.groupby(['b'])['a'].transform(lambda x: x).sort_index())
with self.assertRaisesRegex(
TypeError, "<class 'int'> object is not callable"):
kdf.groupby("b").transform(1)
# multi-index columns
columns = pd.MultiIndex.from_tuples([('x', 'a'), ('x', 'b'),
('y', 'c')])
pdf.columns = columns
kdf.columns = columns
self.assert_eq(
kdf.groupby(
("x", "b")).transform(lambda x: x + 1).sort_index(),
pdf.groupby(
("x", "b")).transform(lambda x: x + 1).sort_index())
self.assert_eq(
kdf.groupby([('x', 'a'), ('x', 'b')
]).transform(lambda x: x * x).sort_index(),
pdf.groupby([('x', 'a'), ('x', 'b')
]).transform(lambda x: x * x).sort_index())
finally:
reset_option('compute.shortcut_limit')
def tearDownClass(cls):
reset_option("display.max_rows")
super(ReprTest, cls).tearDownClass()
def test_apply(self):
pdf = pd.DataFrame(
{
'a': [1, 2, 3, 4, 5, 6],
'b': [1, 1, 2, 3, 5, 8],
'c': [1, 4, 9, 16, 25, 36]
},
columns=['a', 'b', 'c'])
kdf = koalas.DataFrame(pdf)
self.assert_eq(
kdf.groupby("b").apply(lambda x: x + 1).sort_index(),
pdf.groupby("b").apply(lambda x: x + 1).sort_index())
self.assert_eq(
kdf.groupby(['a', 'b']).apply(lambda x: x * x).sort_index(),
pdf.groupby(['a', 'b']).apply(lambda x: x * x).sort_index())
self.assert_eq(
kdf.groupby(['b'])['a'].apply(lambda x: x).sort_index(),
pdf.groupby(['b'])['a'].apply(lambda x: x).sort_index())
# multi-index columns
columns = pd.MultiIndex.from_tuples([('x', 'a'), ('x', 'b'),
('y', 'c')])
pdf.columns = columns
kdf.columns = columns
self.assert_eq(
kdf.groupby(("x", "b")).apply(lambda x: x + 1).sort_index(),
pdf.groupby(("x", "b")).apply(lambda x: x + 1).sort_index())
self.assert_eq(
kdf.groupby([('x', 'a'),
('x', 'b')]).apply(lambda x: x * x).sort_index(),
pdf.groupby([('x', 'a'),
('x', 'b')]).apply(lambda x: x * x).sort_index())
# Less than 'compute.shortcut_limit' will execute a shortcut
# by using collected pandas dataframe directly.
# now we set the 'compute.shortcut_limit' as 1000 explicitly
set_option('compute.shortcut_limit', 1000)
try:
pdf = pd.DataFrame(
{
'a': [1, 2, 3, 4, 5, 6] * 300,
'b': [1, 1, 2, 3, 5, 8] * 300,
'c': [1, 4, 9, 16, 25, 36] * 300
},
columns=['a', 'b', 'c'])
kdf = koalas.DataFrame(pdf)
self.assert_eq(
kdf.groupby("b").apply(lambda x: x + 1).sort_index(),
pdf.groupby("b").apply(lambda x: x + 1).sort_index())
self.assert_eq(
kdf.groupby(['a', 'b']).apply(lambda x: x * x).sort_index(),
pdf.groupby(['a', 'b']).apply(lambda x: x * x).sort_index())
self.assert_eq(
kdf.groupby(['b'])['a'].apply(lambda x: x).sort_index(),
pdf.groupby(['b'])['a'].apply(lambda x: x).sort_index())
with self.assertRaisesRegex(
TypeError, "<class 'int'> object is not callable"):
kdf.groupby("b").apply(1)
# multi-index columns
columns = pd.MultiIndex.from_tuples([('x', 'a'), ('x', 'b'),
('y', 'c')])
pdf.columns = columns
kdf.columns = columns
self.assert_eq(
kdf.groupby(("x", "b")).apply(lambda x: x + 1).sort_index(),
pdf.groupby(("x", "b")).apply(lambda x: x + 1).sort_index())
self.assert_eq(
kdf.groupby([('x', 'a'),
('x', 'b')]).apply(lambda x: x * x).sort_index(),
pdf.groupby([('x', 'a'),
('x', 'b')]).apply(lambda x: x * x).sort_index())
finally:
reset_option('compute.shortcut_limit')
def tearDownClass(cls):
super(DataFramePlotTest, cls).tearDownClass()
reset_option('plotting.max_rows')
