def test_merge_numeric():
d = {
'col1': [1, 2, 3, 4.0, 10],
'col2': [3, 4, 5, 6, 0],
'col3': ['q', '2', 'c', '4', 'x'],
'date':
['05-09-1988', '22-12-1994', '21-11-1995', '22-06-1994', '12-12-2012']
}
f = data.Frame(d)
op = ReplaceValues()
assert op.getOptions() == {'table': dict(), 'inverted': False}
op.addInputShape(f.shape, 0)
tOps = {
1: {
'values': '1.0 3.0 4.0; 6 0.0',
'replace': '-1.0;-2.0'
},
0: {
'values': '1.0 4.0',
'replace': '7.0'
}
}
op.setOptions(table=tOps, inverted=False)
dOps = op.getOptions()
assert dOps == {
'table': {
1: {
'values': '1.0 3.0 4.0; 6.0 0.0',
'replace': '-1.0; -2.0'
},
0: {
'values': '1.0 4.0',
'replace': '7.0'
}
},
'inverted': False
}
assert isDictDeepCopy(tOps, dOps['table'])
s = f.shape.clone()
assert op.getOutputShape() == s
g = op.execute(f)
assert g != f and g.shape == s
assert g.to_dict() == {
'col1': [7.0, 2.0, 3.0, 7.0, 10.0],
'col2': [-1.0, -1.0, 5.0, -2.0, -2.0],
'col3': ['q', '2', 'c', '4', 'x'],
'date':
['05-09-1988', '22-12-1994', '21-11-1995', '22-06-1994', '12-12-2012']
}
def test_exception():
d = {
'col1': [1, 2, 3, 4.0, 10],
'col2': [3, 4, 5, 6, 0],
'col3': ['q', '2', 'c', '4', 'x'],
'date':
['05-09-1988', '22-12-1994', '21-11-1995', '22-06-1994', '12-12-2012']
}
f = data.Frame(d)
op = ReplaceValues()
op.addInputShape(f.shape, 0)
with pytest.raises(exp.OptionValidationError):
op.setOptions(table={1: {
'replace': '7; h',
'values': '3 4 5; 2'
}},
inverted=False)
with pytest.raises(exp.OptionValidationError):
op.setOptions(
table={1: {
'replace': '7; 8;1',
'values': '3 4 5; 2'
}},
inverted=False)
def test_merge_index_val():
d = {
'cowq': [1, 2, 3, 4.0, 10],
'col2':
pd.Categorical(["3", "4", "5", "6", "0"]),
'col3': ['q', '2', 'c', '4', 'x'],
'date':
pd.Series([
'05-09-1988', '22-12-1994', '21-11-1995', '22-06-1994',
'12-12-2012'
],
dtype='datetime64[ns]')
}
f = data.Frame(d)
op = ReplaceValues()
op.addInputShape(f.shape, 0)
op.setOptions(table={1: {
'values': '3 4; 6 0',
'replace': 'h; nan'
}},
inverted=False)
s = f.shape.clone()
os = op.getOutputShape()
assert f.shape.colTypes[1] == Types.Nominal == os.colTypes[1]
assert os == s
g = op.execute(f)
assert g.shape == f.shape
assert nan_to_None(data.Frame(g.getRawFrame()['col2']).to_dict()) == \
{'col2': ["h", "h", "5", None, None]}
def test_merge_from_nan():
d = {
'cowq': [1, 2, None, 4.0, None],
'col2': pd.Categorical(["3", "4", "5", "6", "0"]),
'col3': ['q', '2', 'c', '4', 'x']
}
f = data.Frame(d)
op = ReplaceValues()
op.addInputShape(f.shape, 0)
op.setOptions(table={0: {
'values': 'Nan 2.0;4.0',
'replace': '-1;-2'
}},
inverted=False)
s = f.shape.clone()
assert f.shape.colTypes[1] == Types.Nominal
assert op.getOutputShape() == s
g = op.execute(f)
assert g.shape == f.shape
ff = {
'cowq': [1.0, -1.0, -1.0, -2.0, -1.0],
'col2': ["3", "4", "5", "6", "0"],
'col3': ['q', '2', 'c', '4', 'x']
}
assert nan_to_None(g.to_dict()) == ff
def test_merge_nan():
d = {
'cowq': [1, 2, 3, 4.0, 10],
'col2': pd.Categorical(["3", "4", "5", "6", "0"]),
'col3': ['q', '2', 'c', '4', 'x']
}
f = data.Frame(d)
op = ReplaceValues()
op.addInputShape(f.shape, 0)
op.setOptions(table={
1: {
'values': 'hello 2 6 0; 3',
'replace': 'NAN; nan'
},
0: {
'values': '2 4 10',
'replace': 'naN'
}
},
inverted=False)
s = f.shape.clone()
assert f.shape.colTypes[1] == Types.Nominal
assert op.getOutputShape() == s
g = op.execute(f)
assert g.shape == f.shape
ff = {
'cowq': [1, None, 3, None, None],
'col2': [None, "4", "5", None, None],
'col3': ['q', '2', 'c', '4', 'x']
}
assert nan_to_None(g.to_dict()) == ff
def test_merge_category_inverted():
d = {
'col1': [1, 2, 3, 4.0, 10],
'col2':
pd.Categorical(["3", "4", "5", "6", "0"]),
'col3': ['q', '2', 'c', '4', 'x'],
'date':
['05-09-1988', '22-12-1994', '21-11-1995', '22-06-1994', '12-12-2012']
}
f = data.Frame(d)
op = ReplaceValues()
op.addInputShape(f.shape, 0)
op.setOptions(table={1: {
'values': '4 0; 0',
'replace': 'val; NAN'
}},
inverted=True)
s = f.shape.clone()
assert op.getOutputShape() == s
assert s.colTypes[1] == Types.Nominal
g = op.execute(f)
assert nan_to_None(g.to_dict()) == {
'col1': [1, 2, 3, 4.0, 10],
'col2': [None, None, None, None, "0"],
'col3': ['q', '2', 'c', '4', 'x'],
'date':
['05-09-1988', '22-12-1994', '21-11-1995', '22-06-1994', '12-12-2012']
}
assert g != f and g.shape == s
def test_merge_string():
d = {'cowq': [1, 2, None, 4.0, None], 'col3': ['q', '2', 'c', '4', 'q']}
f = data.Frame(d)
op = ReplaceValues()
op.addInputShape(f.shape, 0)
op.setOptions(table={1: {
'values': 'q 2; nAn',
'replace': '-1;-2'
}},
inverted=False)
s = f.shape.clone()
assert op.getOutputShape() == s
g = op.execute(f)
assert g.shape == f.shape
ff = {
'cowq': [1.0, 2.0, None, 4.0, None],
'col3': ["-1", "-1", "c", "4", "-1"]
}
assert nan_to_None(g.to_dict()) == ff
def test_merge_numeric_inverted():
d = {
'col1': [1, 2, 3, 4.0, 10],
'col2': [3, 4, 5, 6, 0],
'col3': ['q', '2', 'c', '4', 'x'],
'date':
['05-09-1988', '22-12-1994', '21-11-1995', '22-06-1994', '12-12-2012']
}
f = data.Frame(d)
op = ReplaceValues()
op.addInputShape(f.shape, 0)
# Substitution doesn't make sense, but it's test
op.setOptions(table={
1: {
'values': '1.0 3.0 4.0; 6 0',
'replace': '-1;-2'
},
0: {
'values': '1 4',
'replace': '7'
}
},
inverted=True)
s = f.shape.clone()
assert op.getOutputShape() == s
g = op.execute(f)
assert g != f and g.shape == s
assert g.to_dict() == {
'col1': [1.0, 7.0, 7.0, 4.0, 7.0],
'col2': [-2.0, -2.0, -2.0, -2.0, -2.0],
'col3': ['q', '2', 'c', '4', 'x'],
'date':
['05-09-1988', '22-12-1994', '21-11-1995', '22-06-1994', '12-12-2012']
}