def test_combine_subregions_nodate():
test_data = pd.DataFrame(create_test_data())
test_data.loc[:, 'end_date'] = pd.to_datetime(test_data['end_date'])
test_data_subregion = test_data[['end_date', 'value']]
test_data_no_date = test_data_subregion.drop('end_date', axis=1)
with pytest.raises(KeyError):
get_transform_data.combine_subregions(test_data_no_date)
def time_series(client, data_series, initial_date, final_date):
"""
retrieves a sub-time series of a time series associated with a gro data_series
:param client: GroClient
:param data_series: A dictionary of Gro data_series
:param final_date: 'YYYY-MM-DD'
:param initial_date: 'YYYY-MM-DD'
:return: A dataframe with data from the relevant dates
"""
logger = client.get_logger()
entities = {'metric_id', 'item_id', 'region_id', 'source_id', 'frequency_id', 'start_date'}
discard = []
for entity_type in data_series:
if entity_type not in entities:
discard.append(entity_type)
for entity_type in discard:
data_series.pop(entity_type)
data = get_transform_data.get_data(client, **data_series)
consolidated_data = get_transform_data.combine_subregions(data)
try:
ts = get_transform_data.extract_time_periods_by_dates(consolidated_data,
initial_date,
final_date)
return ts
except Exception as e:
message = ('Please check availability of data for {}'.format(client.lookup(
'items', data_series['item_id'])['name']))
logger.warning(message)
raise e
def test_combine_subregions_with_subregion():
test_data = pd.DataFrame(create_test_data())
test_data.loc[:, 'end_date'] = pd.to_datetime(test_data['end_date'])
test_data_subregion = test_data[['end_date', 'value']]
expected_subregion = pd.DataFrame({'end_date': ['2000-03-01T00:00:00.000Z',
'2005-08-28T00:00:00.000Z',
'2019-07-31T00:00:00.000Z'],
'value': [2.39664378851418,
2.28192643351167,
0.13002748115958]})
utc_tz = False
if pd.to_datetime(expected_subregion['end_date'][0]).tzinfo:
utc_tz = True
# Depending on the version of pandas, the data type of the datetime object
# can vary between 'datetime64[ns, UTC]' and 'datetime64[ns]'
expected_subregion.loc[:, 'end_date'] = pd.to_datetime(expected_subregion['end_date'],
utc=utc_tz)
# The order of the columns after applying consolidation function is the following
expected_subregion = expected_subregion[['value', 'end_date']]
# The dataframes index columns is same as 'end_date' column
expected_subregion.index = expected_subregion['end_date']
# Test the equality of frames
# expected_subregion.index = pd.to_datetime(expected_subregion.index)
expected_subregion.loc[:, 'end_date'] = expected_subregion.index
assert_frame_equal(get_transform_data.combine_subregions(test_data_subregion),
expected_subregion)
def test_combine_subregions_with_nosubregion():
test_data = pd.DataFrame(create_test_data())
test_data.loc[:, 'end_date'] = pd.to_datetime(test_data['end_date'])
test_data_subregion = test_data[['end_date', 'value']]
test_data_nosubregion = test_data_subregion.drop(
test_data_subregion.index[-1])
expected_nosubregion = pd.DataFrame({
'end_date': [
'2019-07-31T00:00:00.000Z', '2005-08-28T00:00:00.000Z',
'2000-03-01T00:00:00.000Z'
],
'value': [0.13002748115958, 1.17640700229636, 2.39664378851418]
})
utc_tz = False
if pd.to_datetime(expected_nosubregion['end_date'][0]).tzinfo:
utc_tz = True
# Depending on the version of pandas, the data type of the datetime object
# can vary between 'datetime64[ns, UTC]' and 'datetime64[ns]'
expected_nosubregion.index = expected_nosubregion['end_date']
expected_nosubregion.loc[:, 'end_date'] = pd.to_datetime(
expected_nosubregion['end_date'], utc=utc_tz)
expected_nosubregion.index = pd.to_datetime(expected_nosubregion.index,
utc=utc_tz)
expected_nosubregion = expected_nosubregion.resample('D').pad()
expected_nosubregion.loc[:, 'end_date'] = pd.to_datetime(
expected_nosubregion.index, utc=utc_tz)
assert_frame_equal(
get_transform_data.combine_subregions(test_data_nosubregion),
expected_nosubregion)
