def variety(db):
for q in variety_queries['create']:
iquery(db, q)
variety = variety_schema.split('<')[0].strip()
yield variety
for q in variety_queries['clean']:
iquery(db, q)
def setup(db):
for q in types_queries['create']:
iquery(db, q)
yield True
for q in types_queries['clean']:
iquery(db, q)
def setup(db):
for q in types_queries['create']:
iquery(db, q)
yield True
for q in types_queries['clean']:
iquery(db, q)
def test_fetch_dataframe(self, db, type_name, schema):
# Pandas DataFrame
ar = iquery(db,
'build(<val:{}>[i=1:10,10,0], random())'.format(type_name),
fetch=True,
schema=schema)
assert ar.shape == (10, 2)
assert ar.ndim == 2
def test_fetch_dataframe(self, db, type_name, schema):
# Pandas DataFrame
ar = iquery(
db,
'build(<val:{}>[i=1:10,10,0], random())'.format(type_name),
fetch=True,
schema=schema)
assert ar.shape == (10, 2)
assert ar.ndim == 2
def test_types_dataframe_no_promo(self, db, setup, mode, schema):
# Pandas DataFrame, atts_only
ar = iquery(db,
'scan(types_{})'.format(mode),
fetch=True,
dataframe_promo=False)
assert ar.shape == types_shape[mode][0]
assert ar.ndim == 2
assert ar[0:1].to_records(index=False) == types_array_obj[mode][0]
assert ar[4:5].to_records(index=False) == types_array_obj[mode][1]
assert ar[8:9].to_records(index=False) == types_array_obj[mode][2]
def test_types_dataframe_no_promo(self, db, setup, mode, schema):
# Pandas DataFrame, atts_only
ar = iquery(db,
'scan(types_{})'.format(mode),
fetch=True,
dataframe_promo=False)
assert ar.shape == types_shape[mode][0]
assert ar.ndim == 2
assert ar[0:1].to_records(index=False) == types_array_obj[mode][0]
assert ar[4:5].to_records(index=False) == types_array_obj[mode][1]
assert ar[8:9].to_records(index=False) == types_array_obj[mode][2]
def test_variety_dataframe_no_promo(self, db, variety, schema):
# Pandas DataFrame, atts_only
ar = iquery(db,
'scan({})'.format(variety),
fetch=True,
dataframe_promo=False)
assert ar.shape == (12, 16)
assert ar.ndim == 2
assert ar[0:1].to_records(index=False) == variety_array_obj[0]
assert ar[4:5].to_records(index=False) == variety_array_obj[1]
assert ar[8:9].to_records(index=False) == variety_array_obj[2]
def test_types_numpy(self, db, setup, mode, schema):
# NumPy array
ar = iquery(db,
'scan(types_{})'.format(mode),
fetch=True,
as_dataframe=False,
schema=schema)
assert ar.shape == (12,)
assert ar.ndim == 1
assert ar[0] == types_array_struct[mode][0]
assert ar[4] == types_array_struct[mode][1]
assert ar[8] == types_array_struct[mode][2]
def test_types_numpy(self, db, setup, mode, schema):
# NumPy array
ar = iquery(db,
'scan(types_{})'.format(mode),
fetch=True,
as_dataframe=False,
schema=schema)
assert ar.shape == (12, )
assert ar.ndim == 1
assert ar[0] == types_array_struct[mode][0]
assert ar[4] == types_array_struct[mode][1]
assert ar[8] == types_array_struct[mode][2]
def test_variety_numpy(self, db, variety, schema):
# NumPy array
ar = iquery(db,
'scan({})'.format(variety),
fetch=True,
as_dataframe=False,
schema=schema)
assert ar.shape == (12, )
assert ar.ndim == 1
assert ar[0] == variety_array_struct[0]
assert ar[4] == variety_array_struct[1]
assert ar[8] == variety_array_struct[2]
def test_fetch_numpy(self, db, type_name, atts_only, schema):
# NumPy array
ar = iquery(db,
'build(<val:{}>[i=1:10,10,0], random())'.format(type_name),
fetch=True,
atts_only=atts_only,
as_dataframe=False,
schema=schema)
if not atts_only:
for i in range(10):
assert ar[i][0] == i + 1
assert ar.shape == (10, )
assert ar.ndim == 1
def test_fetch_dataframe_datetime(self, db, type_name, schema):
# Pandas DataFrame
type_conv = 'datetime(i)'
ar = iquery(
db,
'build(<val:{}>[i=1:10,10,0], {})'.format(
type_name,
type_conv if type_name == 'datetime'
else 'apply_offset({}, 0)'.format(type_conv)),
fetch=True,
schema=schema)
assert ar.shape == (10, 2)
assert ar.ndim == 2
def test_fetch_numpy(self, db, type_name, atts_only, schema):
# NumPy array
ar = iquery(
db,
'build(<val:{}>[i=1:10,10,0], random())'.format(type_name),
fetch=True,
atts_only=atts_only,
as_dataframe=False,
schema=schema)
if not atts_only:
for i in range(10):
assert ar[i][0] == i + 1
assert ar.shape == (10,)
assert ar.ndim == 1
def test_variety_dataframe_atts(self, db, variety, schema):
# Pandas DataFrame, atts_only
ar = iquery(db, 'scan({})'.format(variety), fetch=True, atts_only=True)
assert ar.shape == (12, 13)
assert ar.ndim == 2
assert numpy.all(ar[0:1].values == variety_array_promo[0][3:])
# Values which differ have to be NAN
ln = ar[4:5]
assert numpy.all(
numpy.isnan(
ln[ar.columns[(ln.values != variety_array_promo[1][3:])[0]]]))
ln = ar[8:9]
assert numpy.all(
numpy.isnan(
ln[ar.columns[(ln.values != variety_array_promo[2][3:])[0]]]))
def test_types_dataframe(self, db, setup, mode, schema):
# Pandas DataFrame, atts_only
ar = iquery(db, 'scan(types_{})'.format(mode), fetch=True)
assert ar.shape == types_shape[mode][0]
assert ar.ndim == 2
assert numpy.all(ar[0:1].values[0] == types_array_promo[mode][0])
# Values which differ have to be NAN
ln = ar[4:5]
assert numpy.all(
pandas.isnull(ln[ar.columns[(
ln.values[0] != types_array_promo[mode][1]).tolist()]]))
ln = ar[8:9]
assert numpy.all(
pandas.isnull(ln[ar.columns[(
ln.values[0] != types_array_promo[mode][2]).tolist()]]))
def test_fetch_numpy_datetime(self, db, type_name, atts_only, schema):
# NumPy array
type_conv = 'datetime(i)'
ar = iquery(
db,
'build(<val:{}>[i=1:10,10,0], {})'.format(
type_name,
type_conv if type_name == 'datetime'
else 'apply_offset({}, 0)'.format(type_conv)),
fetch=True,
atts_only=atts_only,
as_dataframe=False,
schema=schema)
if not atts_only:
for i in range(10):
assert ar[i][0] == i + 1
assert ar.shape == (10,)
assert ar.ndim == 1
def test_types_dataframe(self, db, setup, mode, schema):
# Pandas DataFrame, atts_only
ar = iquery(db,
'scan(types_{})'.format(mode),
fetch=True)
assert ar.shape == types_shape[mode][0]
assert ar.ndim == 2
assert numpy.all(ar[0:1].values[0] == types_array_promo[mode][0])
# Values which differ have to be NAN
ln = ar[4:5]
assert pandas.isnull(
ln[
ar.columns[
(ln.values[0] != types_array_promo[mode][1]).tolist()
]]).all().all()
ln = ar[8:9]
assert pandas.isnull(
ln[
ar.columns[
(ln.values[0] != types_array_promo[mode][2]).tolist()
]]).all().all()
