def test_kv_transform__aggregate():
actual = pipe(
[(0, {
'v': 1
}), (1, {
'v': 2
}), (0, {
'v': 3
}), (1, {
'v': 4
}), (0, {
'v': 5
})],
kv_transform(aggregate({'v': [min, max, sum]})),
)
expected = [
(0, {
('v', 'max'): 5,
('v', 'min'): 1,
('v', 'sum'): 9
}),
(1, {
('v', 'max'): 4,
('v', 'min'): 2,
('v', 'sum'): 6
}),
]
assert actual == expected
def test_dfply__example():
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
actual = pipe(df, transmute(c=X.a + X.b), rewrites=[add_dfply_support])
expected = pd.DataFrame({'c': [5, 7, 9]})
pdt.assert_frame_equal(actual, expected)
def test_kv_aggregateby__no_perpartition():
actual = pipe(
[(0, {
'v': 1
}), (1, {
'v': 2
}), (0, {
'v': 3
}), (1, {
'v': 4
}), (0, {
'v': 5
})],
kv_aggregateby({'v': [min, max, sum]}),
)
expected = [
(0, {
('v', 'max'): 5,
('v', 'min'): 1,
('v', 'sum'): 9
}),
(1, {
('v', 'max'): 4,
('v', 'min'): 2,
('v', 'sum'): 6
}),
]
assert actual == expected
def test_aggregate__no_perpartion():
actual = pipe(
[{'value': 1}, {'value': 2}, {'value': 3}],
aggregate({'value': [sum, min, max]}),
)
expected = {('value', 'min'): 1, ('value', 'sum'): 6, ('value', 'max'): 3}
assert actual == expected
def test_dfply__map():
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
actual = pipe([df],
map(transmute(c=X.a + X.b)),
list,
rewrites=[add_dfply_support])
expected = pd.DataFrame({'c': [5, 7, 9]})
assert len(actual) == 1
pdt.assert_frame_equal(actual[0], expected)
def test_kv_transform__aggregate():
actual = pipe(
[(0, {'v': 1}), (1, {'v': 2}), (0, {'v': 3}), (1, {'v': 4}), (0, {'v': 5})],
kv_transform(aggregate({'v': [min, max, sum]})),
)
expected = [
(0, {('v', 'max'): 5, ('v', 'min'): 1, ('v', 'sum'): 9}),
(1, {('v', 'max'): 4, ('v', 'min'): 2, ('v', 'sum'): 6}),
]
assert actual == expected
def test_kv_aggregateby__no_perpartition():
actual = pipe(
[(0, {'v': 1}), (1, {'v': 2}), (0, {'v': 3}), (1, {'v': 4}), (0, {'v': 5})],
kv_aggregateby({'v': [min, max, sum]}),
)
expected = [
(0, {('v', 'max'): 5, ('v', 'min'): 1, ('v', 'sum'): 9}),
(1, {('v', 'max'): 4, ('v', 'min'): 2, ('v', 'sum'): 6}),
]
assert actual == expected
def test_dfply__example():
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
actual = pipe(
df,
transmute(c=X.a + X.b),
rewrites=[add_dfply_support]
)
expected = pd.DataFrame({
'c': [5, 7, 9]
})
pdt.assert_frame_equal(actual, expected)
def test_aggregate__no_perpartion():
actual = pipe(
[{
'value': 1
}, {
'value': 2
}, {
'value': 3
}],
aggregate({'value': [sum, min, max]}),
)
expected = {('value', 'min'): 1, ('value', 'sum'): 6, ('value', 'max'): 3}
assert actual == expected
def test_dfply__map():
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
actual = pipe(
[df],
map(transmute(c=X.a + X.b)),
list,
rewrites=[add_dfply_support]
)
expected = pd.DataFrame({
'c': [5, 7, 9]
})
assert len(actual) == 1
pdt.assert_frame_equal(actual[0], expected)
def test_apply():
assert pipe(5, lambda x: 2 * x, lambda x: x - 3) == 7
assert pipe(5, lambda x: 2 * x, rewrites=[lambda f: lambda x: x + 5]) == 10
def test_apply():
assert pipe(5, lambda x: 2 * x, lambda x: x - 3) == 7
assert pipe(5, lambda x: 2 * x, rewrites=[lambda f: lambda x: x + 5]) == 10