def test_csv_json(self):
with filetext('1,1\n2,2\n') as csv_fn:
with filetext('') as json_fn:
schema = '2 * int'
csv = CSV(csv_fn, schema=schema)
json = JSON_Streaming(json_fn, mode='r+', schema=schema)
json.extend(csv)
self.assertEquals(list(json), [[1, 1], [2, 2]])
def test_csv_json_chunked(self):
with filetext('1,1\n2,2\n') as csv_fn:
with filetext('') as json_fn:
schema = '{a: int32, b: int32}'
csv = CSV(csv_fn, schema=schema)
json = JSON_Streaming(json_fn, mode='r+', schema=schema)
into(json, csv)
self.assertEquals(tuplify(tuple(json)), ((1, 1), (2, 2)))
def test_csv_json(self):
with filetext('1,1\n2,2\n') as csv_fn:
with filetext('') as json_fn:
schema = '2 * int'
csv = CSV(csv_fn, schema=schema)
json = JSON_Streaming(json_fn, mode='r+', schema=schema)
json.extend(csv)
self.assertEquals(tuple(map(tuple, json)), ((1, 1), (2, 2)))
def test_csv_json_chunked(self):
with filetext('1,1\n2,2\n') as csv_fn:
with filetext('') as json_fn:
schema = '{a: int32, b: int32}'
csv = CSV(csv_fn, schema=schema)
json = JSON_Streaming(json_fn, mode='r+', schema=schema)
into(json, csv)
self.assertEquals(tuplify(tuple(json)), ((1, 1), (2, 2)))
def test_json_csv_chunked(self):
data = [{'x': 1, 'y': 1}, {'x': 2, 'y': 2}]
text = '\n'.join(map(json.dumps, data))
schema = '{x: int, y: int}'
with filetext(text) as json_fn:
with filetext('') as csv_fn:
js = JSON_Streaming(json_fn, schema=schema)
csv = CSV(csv_fn, mode='r+', schema=schema)
copy(js, csv)
self.assertEquals(list(csv), data)
def test_json_csv_structured(self):
data = [{'x': 1, 'y': 1}, {'x': 2, 'y': 2}]
text = '\n'.join(map(json.dumps, data))
schema = '{x: int, y: int}'
with filetext(text) as json_fn:
with filetext('') as csv_fn:
js = JSON_Streaming(json_fn, schema=schema)
csv = CSV(csv_fn, mode='r+', schema=schema)
csv.extend(js)
self.assertEquals(tuple(map(tuple, (csv))), ((1, 1), (2, 2)))
def test_json_csv_chunked(self):
data = [{'x': 1, 'y': 1}, {'x': 2, 'y': 2}]
tuples = ((1, 1), (2, 2))
text = '\n'.join(map(json.dumps, data))
schema = '{x: int, y: int}'
with filetext(text) as json_fn:
with filetext('') as csv_fn:
js = JSON_Streaming(json_fn, schema=schema)
csv = CSV(csv_fn, mode='r+', schema=schema)
into(csv, js)
self.assertEquals(tuple(csv), tuples)
def test_json_csv_structured(self):
data = [{'x': 1, 'y': 1}, {'x': 2, 'y': 2}]
text = '\n'.join(map(json.dumps, data))
schema = '{x: int, y: int}'
with filetext(text) as json_fn:
with filetext('') as csv_fn:
js = JSON_Streaming(json_fn, schema=schema)
csv = CSV(csv_fn, mode='r+', schema=schema)
csv.extend(js)
self.assertEquals(tuple(map(tuple, (csv))),
((1, 1), (2, 2)))
def test_json_csv_chunked(self):
data = [{'x': 1, 'y': 1}, {'x': 2, 'y': 2}]
tuples = ((1, 1), (2, 2))
text = '\n'.join(map(json.dumps, data))
schema = '{x: int, y: int}'
with filetext(text) as json_fn:
with filetext('') as csv_fn:
js = JSON_Streaming(json_fn, schema=schema)
csv = CSV(csv_fn, mode='r+', schema=schema)
into(csv, js)
self.assertEquals(tuple(csv), tuples)
def test_unused_datetime_columns():
ds = dshape('2 * {val: string, when: datetime}')
with filetext("val,when\na,2000-01-01\nb,2000-02-02") as fn:
csv = CSV(fn, has_header=True)
s = symbol('s', discover(csv))
assert into(list, compute(s.val, csv)) == ['a', 'b']
def test_init(self):
with filetext(self.text) as fn:
dd = JSON_Streaming(fn, schema=self.schema)
self.assertEquals(tuple(dd), self.tuples)
assert dd.dshape in set((
datashape.dshape('var * {name: string, amount: int32}'),
datashape.dshape('5 * {name: string, amount: int32}')))
def test_chunks(self):
with filetext(self.text) as fn:
dd = JSON_Streaming(fn, schema=self.schema)
chunks = list(dd.chunks(blen=2))
assert isinstance(chunks[0], nd.array)
self.assertEquals(len(chunks), 3)
self.assertEquals(nd.as_py(chunks[0]), self.dicts[:2])
def test_init(self):
with filetext(self.text) as fn:
dd = JSON_Streaming(fn, schema=self.schema)
self.assertEquals(list(dd), self.data)
assert dd.dshape in set((
datashape.dshape('var * {name: string, amount: int32}'),
datashape.dshape('5 * {name: string, amount: int32}')))
def test_resource_gz(self):
with filetext(b'1,1\n2,2\n', extension='.csv.gz', open=gzip.open,
mode='wb') as fn:
dd = resource(fn, schema='{x: int, y: int}')
assert isinstance(dd, CSV)
assert dd.open == gzip.open
assert into(list, dd) == [(1, 1), (2, 2)]
def test_into(self):
with filetext('1,1\n2,2', extension='.csv') as a:
with tmpfile(extension='.csv') as b:
A = resource(a, schema='{x: int, y: int}')
B = resource(b, schema='{x: int, y: int}', mode='a')
B = into(B, A)
assert into(list, B) == [(1, 1), (2, 2)]
def test_into(self):
with filetext('1,1\n2,2', extension='.csv') as a:
with tmpfile(extension='.csv') as b:
A = resource(a, schema='{x: int, y: int}')
B = resource(b, schema='{x: int, y: int}', mode='a')
B = into(B, A)
assert into(list, B) == [(1, 1), (2, 2)]
def test_into_filename_filename():
with filetext('1,2\n3,4', extension='csv') as source_fn:
with tmpfile('csv') as target_fn:
into(target_fn, source_fn)
csv = CSV(target_fn)
assert into(list, csv) == [(1, 2), (3, 4)]
def test_chunks(self):
with filetext(self.text) as fn:
dd = JSON_Streaming(fn, schema=self.schema)
chunks = list(dd.chunks(blen=2))
assert isinstance(chunks[0], nd.array)
self.assertEquals(len(chunks), 3)
self.assertEquals(nd.as_py(chunks[0]), self.data[:2])
def test_into(self):
with filetext('1,1\n2,2', extension='.csv') as a:
with tmpfile(extension='.csv') as b:
A = resource(a, schema='2 * int')
B = resource(b, schema='2 * int', mode='a')
B = into(B, A)
assert tuplify(list(B)) == ((1, 1), (2, 2))
def test_unused_datetime_columns():
ds = dshape('2 * {val: string, when: datetime}')
with filetext("val,when\na,2000-01-01\nb,2000-02-02") as fn:
csv = CSV(fn, has_header=True)
s = symbol('s', discover(csv))
assert into(list, compute(s.val, csv)) == ['a', 'b']
def test_copy(self):
with filetext('1,1\n2,2', extension='.csv') as a:
with tmpfile(extension='.csv') as b:
A = resource(a, schema='2 * int')
B = resource(b, schema='2 * int', mode='a')
copy(A, B)
assert list(B) == [[1, 1], [2, 2]]
def test_into_filename_filename():
with filetext('1,2\n3,4', extension='csv') as source_fn:
with tmpfile('csv') as target_fn:
into(target_fn, source_fn)
csv = CSV(target_fn)
assert into(list, csv) == [(1, 2), (3, 4)]
def test_delayed_bad_datashape():
text = 'a,b\n' + '\n'.join(['1,2'] * 20) + '\n1,3.14'
with filetext(text) as fn:
csv = CSV(fn, nrows_discovery=2)
assert csv.schema == dshape('{a: int64, b: int64}')
with pytest.raises(ValueError):
list(csv)
def test_csv_gzip_into_sql():
from blaze.data.csv import CSV
engine, sql = single_table_engine()
with filetext(b'Alice,2\nBob,4', extension='csv.gz',
open=gzip.open, mode='wb') as fn:
csv = CSV(fn, schema=sql.schema)
into(sql, csv)
assert into(list, sql) == into(list, csv)
def test_delayed_bad_datashape():
text = 'a,b\n' + '\n'.join(['1,2']*20) + '\n1,3.14'
with filetext(text) as fn:
csv = CSV(fn, nrows_discovery=2)
assert csv.schema == dshape('{a: int64, b: int64}')
with pytest.raises(ValueError):
list(csv)
def test_extend_structured_many_newlines(self):
inan = np.array([np.nan]).astype('int32').item()
with filetext('1,1.0\n2,2.0\n\n\n\n') as fn:
csv = CSV(fn, 'r+', schema='{x: int32, y: float32}', delimiter=',')
csv.extend([(3, 3)])
result = tuplify(tuple(csv))
expected = ((1, 1.0), (2, 2.0), (inan, np.nan), (inan, np.nan),
(inan, np.nan), (3, 3.0))
assert np.isclose(result, expected, equal_nan=True).all()
def test_a_mode():
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)])
result = set(csv[:, 'name'])
assert 'Georgina' in result
def test_resource_gz(self):
with filetext(b'1,1\n2,2\n',
extension='.csv.gz',
open=gzip.open,
mode='wb') as fn:
dd = resource(fn, schema='{x: int, y: int}')
assert isinstance(dd, CSV)
assert dd.open == gzip.open
assert into(list, dd) == [(1, 1), (2, 2)]
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_re_dialect(self):
dialect1 = {'delimiter': ',', 'lineterminator': '\n'}
dialect2 = {'delimiter': ';', 'lineterminator': '--'}
text = '1,1\n2,2\n'
schema = '2 * int32'
with filetext(text) as source_fn:
with filetext('') as dest_fn:
src = CSV(source_fn, schema=schema, **dialect1)
dst = CSV(dest_fn, mode='w', schema=schema, **dialect2)
# Perform copy
dst.extend(src)
with open(dest_fn) as f:
self.assertEquals(f.read(), '1;1--2;2--')
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 test_DataFrame_CSV():
with filetext('1,2\n3,4\n') as fn:
csv = CSV(fn, schema='{a: int64, b: float64}')
df = into(DataFrame, csv)
expected = DataFrame([[1, 2.0], [3, 4.0]], columns=['a', 'b'])
assert str(df) == str(expected)
assert list(df.dtypes) == [np.int64, np.float64]
def test_re_dialect(self):
dialect1 = {'delimiter': ',', 'lineterminator': '\n'}
dialect2 = {'delimiter': ';', 'lineterminator': '--'}
text = '1,1\n2,2\n'
schema = '2 * int32'
with filetext(text) as source_fn:
with filetext('') as dest_fn:
src = CSV(source_fn, schema=schema, **dialect1)
dst = CSV(dest_fn, mode='w', schema=schema, **dialect2)
# Perform copy
dst.extend(src)
with open(dest_fn) as f:
self.assertEquals(f.read(), '1;1--2;2--')
def test_tuple_types():
"""
CSVs with uniform types still create record types with names
"""
with filetext('1,1\n2,2\n') as fn:
csv = CSV(fn, 'r+', delimiter=',')
assert csv[0] == (1, 1)
assert isinstance(csv.schema[0], Record)
assert len(csv.schema[0].types) == 2
assert len(set(csv.schema[0].types)) == 1
def test_DataFrame_CSV():
with filetext('1,2\n3,4\n') as fn:
csv = CSV(fn, schema='{a: int64, b: float64}')
df = into(DataFrame, csv)
expected = DataFrame([[1, 2.0], [3, 4.0]],
columns=['a', 'b'])
assert str(df) == str(expected)
assert list(df.dtypes) == [np.int64, np.float64]
def test_tuple_types():
"""
CSVs with uniform types still create record types with names
"""
with filetext('1,1\n2,2\n') as fn:
csv = CSV(fn, 'r+', delimiter=',')
assert csv[0] == (1, 1)
assert isinstance(csv.schema[0], Record)
assert len(csv.schema[0].types) == 2
assert len(set(csv.schema[0].types)) == 1
def test_append(self):
with filetext('') as fn:
dd = JSON_Streaming(fn, mode='w', schema=self.schema)
dd.extend([self.tuples[0]])
with open(fn) as f:
self.assertEquals(json.loads(f.read().strip()), self.dicts[0])
self.assertRaises(ValueError, lambda : dd.extend([5.5]))
self.assertRaises(ValueError,
lambda : dd.extend([{'name': 5, 'amount': 1.3}]))
def test_append(self):
with filetext('') as fn:
dd = JSON_Streaming(fn, mode='w', schema=self.schema)
dd.extend([self.data[0]])
with open(fn) as f:
self.assertEquals(json.loads(f.read().strip()), self.data[0])
self.assertRaises(ValueError, lambda : dd.extend([5.5]))
self.assertRaises(ValueError,
lambda : dd.extend([{'name': 5, 'amount': 1.3}]))
def test_csv_sql_json(self):
data = [('Alice', 100), ('Bob', 200)]
text = '\n'.join(','.join(map(str, row)) for row in data)
schema = '{name: string, amount: int}'
engine = create_engine('sqlite:///:memory:')
with filetext(text) as csv_fn:
with filetext('') as json_fn:
csv = CSV(csv_fn, mode='r', schema=schema)
sql = SQL(engine, 'testtable', schema=schema)
json = JSON_Streaming(json_fn, mode='r+', schema=schema)
copy(csv, sql)
self.assertEqual(list(sql), data)
copy(sql, json)
with open(json_fn) as f:
assert 'Alice' in f.read()
def test_csv_sql_json(self):
data = [('Alice', 100), ('Bob', 200)]
text = '\n'.join(','.join(map(str, row)) for row in data)
schema = '{name: string, amount: int}'
with filetext(text) as csv_fn:
with filetext('') as json_fn:
with tmpfile('db') as sqldb:
csv = CSV(csv_fn, mode='r', schema=schema)
sql = SQL('sqlite:///' + sqldb, 'testtable', schema=schema)
json = JSON_Streaming(json_fn, mode='r+', schema=schema)
into(sql, csv)
self.assertEqual(into(list, sql), data)
into(json, sql)
with open(json_fn) as f:
assert 'Alice' in f.read()
def test_csv_hdf5(self):
import h5py
from dynd import nd
with tmpfile('hdf5') as hdf5_fn:
with filetext('1,1\n2,2\n') as csv_fn:
csv = CSV(csv_fn, schema='2 * int')
hdf5 = HDF5(hdf5_fn, '/data', mode='a', schema='2 * int')
copy(csv, hdf5)
self.assertEquals(nd.as_py(hdf5.as_dynd()), [[1, 1], [2, 2]])
def test_csv_sql_json(self):
data = [('Alice', 100), ('Bob', 200)]
text = '\n'.join(','.join(map(str, row)) for row in data)
schema = '{name: string, amount: int}'
with filetext(text) as csv_fn:
with filetext('') as json_fn:
with tmpfile('db') as sqldb:
csv = CSV(csv_fn, mode='r', schema=schema)
sql = SQL('sqlite:///' + sqldb, 'testtable', schema=schema)
json = JSON_Streaming(json_fn, mode='r+', schema=schema)
into(sql, csv)
self.assertEqual(into(list, sql), data)
into(json, sql)
with open(json_fn) as f:
assert 'Alice' in f.read()
def test_csv_sql_json(self):
data = [('Alice', 100), ('Bob', 200)]
text = '\n'.join(','.join(map(str, row)) for row in data)
schema = '{name: string, amount: int}'
engine = create_engine('sqlite:///:memory:')
with filetext(text) as csv_fn:
with filetext('') as json_fn:
csv = CSV(csv_fn, mode='r', schema=schema)
sql = SQL(engine, 'testtable', schema=schema)
json = JSON_Streaming(json_fn, mode='r+', schema=schema)
copy(csv, sql)
self.assertEqual(list(sql), data)
copy(sql, json)
with open(json_fn) as f:
assert 'Alice' in f.read()
def test_csv_hdf5(self):
from dynd import nd
with tmpfile('hdf5') as hdf5_fn:
with filetext('1,1\n2,2\n') as csv_fn:
csv = CSV(csv_fn, schema='{a: int32, b: int32}')
hdf5 = resource(hdf5_fn + '::/data', dshape='var * {a: int32, b: int32}')
into(hdf5, csv)
self.assertEquals(hdf5[:].tolist(),
[(1, 1), (2, 2)])
def test_csv_hdf5(self):
import h5py
from dynd import nd
with tmpfile('hdf5') as hdf5_fn:
with filetext('1,1\n2,2\n') as csv_fn:
csv = CSV(csv_fn, schema='2 * int')
hdf5 = HDF5(hdf5_fn, '/data', schema='2 * int')
copy(csv, hdf5)
self.assertEquals(nd.as_py(hdf5.as_dynd()),
[[1, 1], [2, 2]])
def test_csv_hdf5(self):
from dynd import nd
with tmpfile('hdf5') as hdf5_fn:
with filetext('1,1\n2,2\n') as csv_fn:
csv = CSV(csv_fn, schema='{a: int32, b: int32}')
hdf5 = HDF5(hdf5_fn, '/data', schema='{a: int32, b: int32}')
into(hdf5, csv)
self.assertEquals(nd.as_py(hdf5.as_dynd()),
[{'a': 1, 'b': 1},
{'a': 2, 'b': 2}])
def test_csv_gzip_into_sql():
from blaze.data.csv import CSV
from blaze.data.sql import into
engine = sa.create_engine('sqlite:///:memory:')
sql = SQL(engine,
'accounts',
schema='{name: string, amount: int32}')
with filetext(b'Alice,2\nBob,4', extension='csv.gz',
open=gzip.open, mode='wb') as fn:
csv = CSV(fn, schema=sql.schema)
into(sql, csv)
assert list(sql) == list(csv)
def test_csv_gzip_into_sql():
from blaze.data.csv import CSV
from blaze.data.sql import into
engine = sa.create_engine('sqlite:///:memory:')
sql = SQL(engine,
'accounts',
schema='{name: string, amount: int32}')
with filetext(b'Alice,2\nBob,4', extension='csv.gz',
open=gzip.open, mode='wb') as fn:
csv = CSV(fn, schema=sql.schema)
into(sql, csv)
assert list(sql) == list(csv)
def test_schema_detection_modifiers():
text = "name amount date\nAlice 100 20120101\nBob 200 20120102"
with filetext(text) as fn:
assert (CSV(fn).schema ==
dshape('{name: string, amount: ?int64, date: ?int64}'))
assert (CSV(fn, columns=['NAME', 'AMOUNT', 'DATE']).schema ==
dshape('{NAME: string, AMOUNT: ?int64, DATE: ?int64}'))
assert (str(CSV(fn, types=['string', 'int32', 'date']).schema) ==
str(dshape('{name: string, amount: int32, date: date}')))
a = CSV(fn, typehints={'date': 'date'}).schema
b = dshape('{name: string, amount: ?int64, date: date}')
assert str(a) == str(b)
def test_schema_detection_modifiers():
text = "name amount date\nAlice 100 20120101\nBob 200 20120102"
with filetext(text) as fn:
assert (CSV(fn).schema == dshape(
'{name: string, amount: ?int64, date: ?int64}'))
assert (CSV(fn, columns=[
'NAME', 'AMOUNT', 'DATE'
]).schema == dshape('{NAME: string, AMOUNT: ?int64, DATE: ?int64}'))
assert (str(CSV(fn, types=['string', 'int32', 'date']).schema) == str(
dshape('{name: string, amount: int32, date: date}')))
a = CSV(fn, typehints={'date': 'date'}).schema
b = dshape('{name: string, amount: ?int64, date: date}')
assert str(a) == str(b)
def test_hdf5_csv(self):
import h5py
with tmpfile('hdf5') as hdf5_fn:
with filetext('') as csv_fn:
with h5py.File(hdf5_fn, 'w') as f:
d = f.create_dataset('data', (3, 3), dtype='i8')
d[:] = 1
csv = CSV(csv_fn, mode='r+', schema='3 * int')
hdf5 = HDF5(hdf5_fn, '/data')
copy(hdf5, csv)
self.assertEquals(list(csv), [[1, 1, 1], [1, 1, 1], [1, 1, 1]])
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_hdf5_csv(self):
import h5py
with tmpfile('hdf5') as hdf5_fn:
with filetext('') as csv_fn:
with h5py.File(hdf5_fn, 'w') as f:
d = f.create_dataset('data', (3, 3), dtype='i8')
d[:] = 1
csv = CSV(csv_fn, mode='r+', schema='3 * int')
hdf5 = HDF5(hdf5_fn, '/data')
copy(hdf5, csv)
self.assertEquals(tuple(map(tuple, csv)),
((1, 1, 1), (1, 1, 1), (1, 1, 1)))
def test_csv_hdf5(self):
from dynd import nd
with tmpfile('hdf5') as hdf5_fn:
with filetext('1,1\n2,2\n') as csv_fn:
csv = CSV(csv_fn, schema='{a: int32, b: int32}')
hdf5 = HDF5(hdf5_fn, '/data', schema='{a: int32, b: int32}')
into(hdf5, csv)
self.assertEquals(nd.as_py(hdf5.as_dynd()), [{
'a': 1,
'b': 1
}, {
'a': 2,
'b': 2
}])
def test_hdf5_csv(self):
import h5py
with tmpfile('hdf5') as hdf5_fn:
with filetext('') as csv_fn:
with h5py.File(hdf5_fn, 'w') as f:
d = f.create_dataset('data', (3,),
dtype=np.dtype([(c, 'i4')
for c in 'abc']))
d[:] = np.array(1)
csv = CSV(csv_fn, mode='r+', schema='{a: int32, b: int32, c: int32}')
hdf5 = HDF5(hdf5_fn, '/data', schema=csv.schema)
into(csv, hdf5)
self.assertEquals(tuple(map(tuple, csv)),
((1, 1, 1), (1, 1, 1), (1, 1, 1)))
def test_json_into_mongodb(empty_collec):
with filetext(json.dumps(les_mis_data)) as filename:
dd = JSON(filename)
coll = empty_collec
into(coll, dd)
mongo_data = list(coll.find())
last = mongo_data[0]['nodes'][-1]
first = mongo_data[0]['nodes'][0]
first = (first['group'], first['name'])
last = (last['group'], last['name'])
assert dd.as_py()[1][-1] == last
assert dd.as_py()[1][0] == first
def test_hdf5_csv(self):
import h5py
with tmpfile('hdf5') as hdf5_fn:
with filetext('') as csv_fn:
with h5py.File(hdf5_fn, 'w') as f:
d = f.create_dataset('data', (3, ),
dtype=np.dtype([(c, 'i4')
for c in 'abc']))
d[:] = np.array(1)
csv = CSV(csv_fn,
mode='r+',
schema='{a: int32, b: int32, c: int32}')
hdf5 = HDF5(hdf5_fn, '/data', schema=csv.schema)
into(csv, hdf5)
self.assertEquals(tuple(map(tuple, csv)),
((1, 1, 1), (1, 1, 1), (1, 1, 1)))
def test_into_list_Column():
with filetext('Alice,1\nBob,2') as fn:
csv = CSV(fn, columns=['name', 'id'])
t = Data(csv)
assert into(list, t.name) == ['Alice', 'Bob']
def test_as_py(self):
with filetext(self.text) as fn:
dd = JSON_Streaming(fn, mode='r', schema=self.schema)
assert dd.as_py() == self.data
