def test_acceptance_columnsTypes_int(self):
# Log to file
self.logger.debug("\n\n### Test Acceptance Columns Int Type\n")
# Seleziono le colonne dalle info del DB
default_cols_int = {
i: col
for i, col in self.default_columns_type.items()
if 'int' in str(col)
}
test_columns_types = dict()
cast_result = dict()
# seleziono le colonne tipo int
for num_col in default_cols_int.keys():
selected_cols_df = self.anagrafica.df.iloc[:, num_col].to_frame()
# casting
try:
col_casted = selected_cols_df.fillna(0).astype('int32')
cast_result[num_col] = True
except:
cast_result[num_col] = False
col_casted = selected_cols_df
test_columns_types[num_col] = getColumnsTypes(
col_casted).popitem()[1]
# LOG
super().logDifferences_types(self.anagrafica.df, cast_result)
self.assertEqual(
test_columns_types, default_cols_int,
"Selected columns [26, 28] must be np.dtype('int32')")
def test_acceptance_columnsTypes_obj(self):
# Log to file
self.logger.debug("\n\n### Test Acceptance Columns Object Type\n")
# Seleziono le colonne dalle info del DB
default_cols_obj = {
i: col
for i, col in self.default_columns_type.items()
if 'obj' in str(col)
}
# seleziono le colonne tipo object
selected_cols_df = self.anagrafica.df.iloc[:,
list(default_cols_obj.keys(
))]
test_columns_types = dict()
cast_result = dict()
for num_col in default_cols_obj.keys():
selected_cols_df = self.anagrafica.df.iloc[:, num_col].to_frame()
# casting
try:
col_casted = selected_cols_df.astype('object')
cast_result[num_col] = True
except:
cast_result[num_col] = False
col_casted = selected_cols_df
test_columns_types[num_col] = getColumnsTypes(
col_casted).popitem()[1]
# LOG
super().logDifferences_types(self.anagrafica.df, cast_result)
self.assertEqual(test_columns_types, default_cols_obj,
"Selected columns must be np.dtype('object')")
def test_getColumnsTypes_right(self):
datetimes = [
datetime(2019, 9, 30),
datetime(2020, 2, 29),
datetime(2018, 8, 30)
]
test_data = {
'col0': ['casa', '000342', 'abcABC0123lk#'],
'col1': [0, 123, 9999999],
'col2': [0.2, 0.0002, 123.456],
'col3': [
np.datetime64(datetimes[0]),
np.datetime64(datetimes[1]),
np.datetime64(datetimes[2])
]
}
test_df = pd.DataFrame(test_data)
self.assertDictEqual(
getColumnsTypes(test_df),
{
0: np.dtype('O'), # object type
1: np.dtype('int64'), # signed integer
2: np.dtype('float64'), # float
3: np.dtype('<M8[ns]') # datetime
},
"Error test getColumnsTypes")