def test_can_convert_to_records_mixed_object_column_string_nan(fast_serializable_check):
with FastCheckSerializable(fast_serializable_check):
serializer = anr.DataFrameSerializer()
df = pd.DataFrame({'a': [1, 3, 4], 'b': [1.2, 8.0, 0.2]})
assert serializer.can_convert_to_records_without_objects(df, 'my_symbol')
df = pd.DataFrame({'a': [1, 3, 4], 'b': [1, 8.0, 2]})
assert serializer.can_convert_to_records_without_objects(df, 'my_symbol')
df = pd.DataFrame({'a': [1, 3, 4], 'b': [1.2, 8.0, np.NaN]})
assert serializer.can_convert_to_records_without_objects(df, 'my_symbol')
df = pd.DataFrame({'a': ['abc', 'cde', 'def'], 'b': [1.2, 8.0, np.NaN]})
assert serializer.can_convert_to_records_without_objects(df, 'my_symbol')
df = pd.DataFrame({'a': [u'abc', u'cde', 'def'], 'b': [1.2, 8.0, np.NaN]})
assert serializer.can_convert_to_records_without_objects(df, 'my_symbol')
df = pd.DataFrame({'a': [u'abc', u'cde', 'def'], 'b': [1.2, '8.0', np.NaN]})
assert not serializer.can_convert_to_records_without_objects(df, 'my_symbol')
# Do not serialize and force-stringify None
df = pd.DataFrame({'a': ['abc', None, 'def'], 'b': [1.2, 8.0, np.NaN]})
assert not serializer.can_convert_to_records_without_objects(df, 'my_symbol')
# Do not serialize and force-stringify np.NaN among strings, rather pickle
df = pd.DataFrame({'a': ['abc', np.NaN, 'def'], 'b': [1.2, 8.0, np.NaN]})
assert not serializer.can_convert_to_records_without_objects(df, 'my_symbol')
import time
import arctic.serialization.numpy_records as anr
from tests.unit.serialization.serialization_test_data import _mixed_test_data as input_test_data
df_serializer = anr.DataFrameSerializer()
def _bench(rounds, input_df, fast):
fast = bool(fast)
anr.set_fast_check_df_serializable(fast)
start = time.time()
for i in range(rounds):
df_serializer.can_convert_to_records_without_objects(input_df, 'symA')
print("Time per iteration (fast={}): {}".format(fast, (time.time() - start)/rounds))
# Results suggest significant speed improvements for
# (1) large df with objects
# Time per iteration (fast=False): 0.0281402397156
# Time per iteration (fast=True): 0.00866063833237
# (2) large multi-column df
# Time per iteration (fast=False): 0.00556221961975
# Time per iteration (fast=True): 0.00276621818542
# (3) large multi-index df
# Time per iteration (fast=False): 0.00640722036362
# Time per iteration (fast=True): 0.00154552936554
def assess_speed(df_kind):
rounds = 100
input_df = input_test_data()[df_kind][0]
orig_config = anr._FAST_CHECK_DF_SERIALIZABLE