def test_extend_structured(self):
with filetext('1,1.0\n2,2.0\n') as fn:
csv = CSV(fn, 'r+', schema='{x: int32, y: float32}',
delimiter=',')
csv.extend([(3, 3)])
assert (list(csv) == [[1, 1.0], [2, 2.0], [3, 3.0]]
or list(csv) == [{'x': 1, 'y': 1.0},
{'x': 2, 'y': 2.0},
{'x': 3, 'y': 3.0}])
def test_a_mode(self):
text = ("id, name, balance\n1, Alice, 100\n2, Bob, 200\n"
"3, Charlie, 300\n4, Denis, 400\n5, Edith, 500")
with filetext(text) as fn:
csv = CSV(fn, 'a')
csv.extend([(6, 'Frank', 600),
(7, 'Georgina', 700)])
assert 'Georgina' in set(csv.py[:, 'name'])
def reconstructCsv(fuzzed: dict) -> bytes:
csv = []
count = 0
for i in fuzzed['values']:
if not isinstance(i, bytes):
i = str(i).encode() # bad?
if count == fuzzed['cpl']:
count = 0
csv.extend([i, b"\n"])
else:
csv.extend([i, b","])
count += 1
return b''.join(csv)
def test_extend_structured(self):
with filetext('1,1.0\n2,2.0\n') as fn:
csv = CSV(fn, 'r+', schema='{x: int32, y: float32}', delimiter=',')
csv.extend([(3, 3)])
assert (list(csv) == [[1, 1.0], [2, 2.0], [3, 3.0]]
or list(csv) == [{
'x': 1,
'y': 1.0
}, {
'x': 2,
'y': 2.0
}, {
'x': 3,
'y': 3.0
}])
def test_extend_structured(self):
with filetext('1,1.0\n2,2.0\n') as fn:
csv = CSV(fn, 'r+', schema='{x: int32, y: float32}',
delimiter=',')
csv.extend([(3, 3)])
assert tuplify(tuple(csv)) == ((1, 1.0), (2, 2.0), (3, 3.0))