def test_multiple_metrics_reversed(self):
result = CSV(mock_dataset.fields.wins, mock_dataset.fields.votes) \
.transform(dimx0_metricx2_df, [], [])
expected = dimx0_metricx2_df.copy()[[f('wins'), f('votes')]]
expected.columns = ['Wins', 'Votes']
expected = expected.applymap(format_float_raw)
self.assertEqual(expected.to_csv(**csv_options), result)
def test_int_dim(self):
result = CSV(mock_dataset.fields.wins) \
.transform(dimx1_num_df, [mock_dataset.fields['candidate-id']], [])
expected = dimx1_num_df.copy()[[f('wins')]]
expected.index = pd.Index(list(range(1, 12)), name='Candidate ID')
expected.columns = ['Wins']
self.assertEqual(expected.to_csv(**csv_options), result)
def test_str_dim(self):
result = CSV(mock_dataset.fields.wins) \
.transform(dimx1_str_df, mock_dataset, [mock_dataset.fields.political_party], [])
expected = dimx1_str_df.copy()[[f('wins')]]
expected.index = pd.Index(['Democrat', 'Independent', 'Republican'], name='Party')
expected.columns = ['Wins']
self.assertEqual(expected.to_csv(**csv_options), result)
def test_single_metric(self):
result = CSV(mock_dataset.fields.votes) \
.transform(dimx0_metricx1_df, [], [])
expected = dimx0_metricx1_df.copy()[[f('votes')]]
expected.columns = ['Votes']
expected = expected.applymap(format_float_raw)
self.assertEqual(expected.to_csv(**csv_options), result)
def test_time_series_dim(self):
result = CSV(mock_dataset.fields.wins) \
.transform(dimx1_date_df, [mock_dataset.fields.timestamp], [])
expected = dimx1_date_df.copy()[[f('wins')]]
expected.index.names = ['Timestamp']
expected.columns = ['Wins']
expected = expected.applymap(format_float_raw)
self.assertEqual(expected.to_csv(**csv_options), result)
def test_multi_dimx2_date_str(self):
query_dimensions = [mock_dataset.fields.timestamp, mock_dataset.fields.political_party]
result = CSV(mock_dataset.fields.wins) \
.transform(dimx2_date_str_df, mock_dataset, query_dimensions, [])
expected = dimx2_date_str_df.copy()[[f('wins')]]
expected.index.names = ['Timestamp', 'Party']
expected.columns = ['Wins']
self.assertEqual(expected.to_csv(**csv_options), result)
def test_time_series_dim_with_operation(self):
query_dimensions = [mock_dataset.fields.timestamp]
result = CSV(CumSum(mock_dataset.fields.votes)) \
.transform(dimx1_date_operation_df, query_dimensions, [])
expected = dimx1_date_operation_df.copy()[[f('cumsum(votes)')]]
expected.index.names = ['Timestamp']
expected.columns = ['CumSum(Votes)']
expected = expected.applymap(format_float_raw)
self.assertEqual(expected.to_csv(**csv_options), result)
def test_pivoted_single_dimension_transposes_data_frame(self):
result = CSV(mock_dataset.fields.wins, pivot=[mock_dataset.fields.political_party]) \
.transform(dimx1_str_df, mock_dataset, [mock_dataset.fields.political_party], [])
expected = dimx1_str_df.copy()[[f('wins')]]
expected.index = pd.Index(['Democrat', 'Independent', 'Republican'], name='Party')
expected.columns = ['Wins']
expected.columns.names = ['Metrics']
expected = expected.transpose()
self.assertEqual(expected.to_csv(**csv_options), result)
def test_pivoted_multi_dimx2_date_str(self):
result = CSV(mock_dataset.fields.wins, pivot=[mock_dataset.fields.political_party]) \
.transform(dimx2_date_str_df,
[mock_dataset.fields.timestamp, mock_dataset.fields.political_party], [])
expected = dimx2_date_str_df.copy()[[f('wins')]]
expected = expected.unstack(level=[1])
expected.index.names = ['Timestamp']
expected.columns = ['Democrat', 'Independent', 'Republican']
expected = expected.applymap(format_float_raw)
self.assertEqual(expected.to_csv(**csv_options), result)
def test_time_series_multi_ref(self):
query_dimensions = [mock_dataset.fields.timestamp, mock_dataset.fields.political_party]
query_references = [ElectionOverElection(mock_dataset.fields.timestamp)]
result = CSV(mock_dataset.fields.votes, mock_dataset.fields.wins) \
.transform(dimx2_date_str_ref_df, mock_dataset, query_dimensions, query_references)
expected = dimx2_date_str_ref_df.copy()[[f('votes'), f('votes_eoe'), f('wins'), f('wins_eoe')]]
expected.index.names = ['Timestamp', 'Party']
expected.columns = ['Votes', 'Votes EoE', 'Wins', 'Wins EoE']
expected = expected.applymap(_format_float)
self.assertEqual(expected.to_csv(**csv_options), result)
def test_pivoted_multi_dimx2_date_num(self):
query_dimensions = [mock_dataset.fields.timestamp, mock_dataset.fields['candidate-id']]
result = CSV(mock_dataset.fields.votes, pivot=[mock_dataset.fields['candidate-id']]) \
.transform(dimx2_date_num_df, mock_dataset, query_dimensions, [])
expected = dimx2_date_num_df.copy()[[f('votes')]]
expected = expected.unstack(level=1)
expected.index.names = ['Timestamp']
expected.columns = list(range(1, 12))
expected = expected.applymap(_format_float)
self.assertEqual(expected.to_csv(**csv_options), result)
def test_hidden_dimx2_date_str(self):
dimensions = [
mock_dataset.fields.timestamp, mock_dataset.fields.political_party
]
result = CSV(mock_dataset.fields.wins, hide=[mock_dataset.fields.political_party]) \
.transform(dimx2_date_str_df, dimensions, [])
expected = dimx2_date_str_df.copy()[[f('wins')]]
expected.reset_index('$political_party', inplace=True, drop=True)
expected.index.names = ['Timestamp']
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
self.assertEqual(expected.to_csv(**csv_options), result)
def test_hidden_ref_dimx2_date_str(self):
dimensions = [
mock_dataset.fields.timestamp, mock_dataset.fields.political_party
]
references = [ElectionOverElection(mock_dataset.fields.timestamp)]
result = CSV(mock_dataset.fields.votes,
hide=['votes_eoe']).transform(dimx2_date_str_ref_df,
dimensions, references)
expected = dimx2_date_str_ref_df.copy()[[f('votes')]]
expected.index.names = ['Timestamp', 'Party']
expected.columns = ['Votes']
expected.columns.name = 'Metrics'
expected = expected.applymap(format_float_raw)
self.assertEqual(expected.to_csv(**csv_options), result)
