def test_repr_on_expands(self):
t = Table([('A', int), ('B', int)])
t.append([1, 2])
e = t.expand('C', ['A', 'B'], lambda *args: sum(args), int)
self.assertEqual(repr(e),
"\n".join([
"| A (int) | B (int) | C (int) |",
"| 1 | 2 | 3 |"
])
)
class TestExport(unittest.TestCase, TableTestMixin):
def setUp(self):
self.s = [
('A', int),
('B', float),
('C', str),
]
self.t = Table(self.s)
self.t.extend([
[1, 0.0, 'x'],
[2, 5.0, 'y'],
[3, 10.0, 'z'],
])
def test_export_csv(self):
output_file = StringIO()
self.t.to_csv(output_file=output_file)
output_file.seek(0)
reader = csv.DictReader(output_file)
row = next(reader)
self.assertEqual(row, {"A":"1", "B":"0.0", "C":"x"})
def test_export_csv_with_dialect(self):
output_file = StringIO()
self.t.to_csv(output_file=output_file, dialect="excel")
output_file.seek(0)
reader = csv.DictReader(output_file, dialect="excel")
row = next(reader)
self.assertEqual(row, {"A":"1", "B":"0.0", "C":"x"})
def test_export_csv_with_dialect_types(self):
output_file = StringIO()
self.t.to_csv(output_file=output_file, dialect="excel", descriptions=True)
output_file.seek(0)
reader = csv.DictReader(output_file, dialect="excel")
self.assertEqual(next(reader), {"A (int)":"1", "B (float)":"0.0", "C (str)":"x"})
def test_simple_expand(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C)
)
self.assertEqual(list(t.D), [1, 1, 1])
def test_derived_columns_are_iterable(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C)
)
for _ in t.D:
pass
def test_derived_columns_can_be_printed(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C)
)
str(t.D)
repr(t.D)
def test_derived_columns_have_descriptions(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C)
)
self.assertEqual(t.D.description, "D (int)")
def test_that_float_columns_have_descriptions(self):
t = self.t.expand(
name='D',
input_columns=['A', 'B', 'C'],
fn=lambda a,b,c: float(len(c) + a + b),
col_type=float
)
self.assertEqual(t.D.description, "D (float)")
def test_simple_expand_and_materialize(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C) + 1
).copy()
def test_export_csv_with_dialect_tab(self):
output_file = StringIO()
self.t.to_csv(output_file=output_file, dialect="excel-tab")
output_file.seek(0)
reader = csv.DictReader(output_file, dialect="excel-tab")
row = next(reader)
self.assertEqual(row, {"A":"1", "B":"0.0", "C":"x"})
def test_simple_expand_and_slice(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C) + 1
)[1:2]
expected = [
(2, 5.0, 'y', 2),
]
self.assertEqual(list(t), expected)
class TestExpandTable(unittest.TestCase):
def setUp(self):
self.s = [
('A', int),
('B', float),
('C', str),
]
self.t = Table(self.s)
self.t.extend([
[1, 1.1, 'hello'],
[2, 2.2, 'yello'],
])
def test_expand_const(self):
t = self.t.expand_const(
name='D',
value='X',
type=str
)
self.assertEqual(
t.column_names,
['A', 'B', 'C', 'D']
)
self.assertEqual(
t[0],
(1, 1.1, 'hello', 'X')
)
def test_expand_const_does_not_affect_length(self):
t = self.t.expand_const(
name='D',
type=str,
value='X'
)
self.assertEqual(len(t), len(self.t))
def test_simple_expand(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C)
)
self.assertEqual(list(t.D), [5, 5])
def test_derived_columns_are_iterable(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C)
)
for _ in t.D:
pass
def test_derived_columns_can_be_printed(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C)
)
str(t.D)
repr(t.D)
def test_derived_columns_have_descriptions(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C)
)
self.assertEqual(t.D.description, "D (int)")
def test_that_float_columns_have_descriptions(self):
t = self.t.expand(
name='D',
input_columns=['A', 'B', 'C'],
fn=lambda a,b,c: float(len(c) + a + b),
col_type=float
)
self.assertEqual(t.D.description, "D (float)")
def test_simple_expand_and_materialize(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C) + 1
).copy()
expected = [
(1, 1.1, 'hello', 6),
(2, 2.2, 'yello', 6),
]
self.assertEqual(list(t), expected)
self.assertIsInstance(t, Table)
def test_simple_expand_and_slice(self):
t = self.t.expand(
name='D',
col_type=int,
input_columns=['C'],
fn=lambda C: len(C) + 1
)[1:]
expected = [
(2, 2.2, 'yello', 6),
]
self.assertEqual(list(t), expected)
class TestNormalize(unittest.TestCase, TableTestMixin):
def setUp(self):
self.s = [("A", int), ("B", float), ("C", str)]
self.t = Table(self.s)
self.t.extend([[1, 0.0, "x"], [2, 5.0, "y"], [3, 10.0, "z"]])
def test_basic_normalize(self):
t = self.t.normalize({"B": 1.0})
self.assertEqual(list(t.B), [0, 0.5, 1])
def test_basic_standardization(self):
t = self.t.standardize({"B": 1.0})
result = list(t.B)
self.assertEqual(result[1], 0)
self.assertEqual(result[0], result[2] * -1)
def test_whole_of_normalized_table(self):
tn = self.t.normalize({"B": 1.0})
expected = table_literal(
"""
| A (int) | B (float) | C (str) |
| 1 | 0 | x |
| 2 | 0.5 | y |
| 3 | 1.0 | z |
"""
)
def test_whole_of_normalized_table_100(self):
tn = self.t.normalize({"B": 100})
expected = table_literal(
"""
| A (int) | B (float) | C (str) |
| 1 | 0 | x |
| 2 | 50 | y |
| 3 | 100 | z |
"""
)
self.assertTablesEqual(tn, expected)
def test_expand_of_normalized_table(self):
tn = self.t.normalize({"B": 1.0}).expand(
name="D", col_type=float, input_columns=["A", "B"], fn=lambda A, B: A * B
)
expected = table_literal(
"""
| A (int) | B (float) | C (str) | D (float) |
| 1 | 0 | x | 0.0 |
| 2 | 0.5 | y | 1.0 |
| 3 | 1.0 | z | 3.0 |
"""
)
self.assertTablesEqual(tn, expected)
def test_simple_expand(self):
t = self.t.expand(name="D", col_type=int, input_columns=["C"], fn=lambda C: len(C))
self.assertEqual(list(t.D), [1, 1, 1])
def test_derived_columns_are_iterable(self):
t = self.t.expand(name="D", col_type=int, input_columns=["C"], fn=lambda C: len(C))
for _ in t.D:
pass
def test_derived_columns_can_be_printed(self):
t = self.t.expand(name="D", col_type=int, input_columns=["C"], fn=lambda C: len(C))
str(t.D)
repr(t.D)
def test_derived_columns_have_descriptions(self):
t = self.t.expand(name="D", col_type=int, input_columns=["C"], fn=lambda C: len(C))
self.assertEqual(t.D.description, "D (int)")
def test_that_float_columns_have_descriptions(self):
t = self.t.expand(
name="D", input_columns=["A", "B", "C"], fn=lambda a, b, c: float(len(c) + a + b), col_type=float
)
self.assertEqual(t.D.description, "D (float)")
def test_simple_expand_and_materialize(self):
t = self.t.expand(name="D", col_type=int, input_columns=["A", "C"], fn=lambda A, C: len(C) + A).copy()
expected = [(1, 0.0, "x", 2), (2, 5.0, "y", 3), (3, 10.0, "z", 4)]
self.assertEqual(list(t), expected)
self.assertIsInstance(t, Table)
def test_simple_expand_and_slice(self):
t = self.t.expand(name="D", col_type=int, input_columns=["C"], fn=lambda C: len(C) + 2)[1:2]
expected = [(2, 5.0, "y", 3)]
self.assertEqual(list(t), expected)
