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_dimx1_str(self):
result = Pandas(mock_dataset.fields.wins).transform(dimx1_str_df, [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.name = 'Metrics'
expected = expected.applymap(format_float)
pandas.testing.assert_frame_equal(expected, result)
def test_dimx1_int(self):
result = Pandas(mock_dataset.fields.wins) \
.transform(dimx1_str_df, mock_dataset, [mock_dataset.fields['candidate-id']], [])
expected = dimx1_str_df.copy()[[f('wins')]]
expected.index.names = ['Candidate ID']
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
expected = expected.applymap(_format_float)
pandas.testing.assert_frame_equal(expected, 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_neginf_in_metrics(self):
cat_dim_df_with_nan = dimx1_str_df.copy()
cat_dim_df_with_nan['$wins'] = cat_dim_df_with_nan['$wins'].apply(float)
cat_dim_df_with_nan.iloc[2, 1] = np.inf
result = Pandas(mock_dataset.fields.wins) \
.transform(cat_dim_df_with_nan, mock_dataset, [mock_dataset.fields.political_party], [])
expected = cat_dim_df_with_nan.copy()[[f('wins')]]
expected.index = pd.Index(['Democrat', 'Independent', 'Republican'], name='Party')
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
expected = expected.applymap(_format_float)
pandas.testing.assert_frame_equal(expected, result)
def test_inf_in_metrics_with_precision_zero(self):
cat_dim_df_with_nan = dimx1_str_df.copy()
cat_dim_df_with_nan['$wins'] = cat_dim_df_with_nan['$wins'].apply(float)
cat_dim_df_with_nan.iloc[2, 1] = np.inf
slicer_modified = copy.deepcopy(mock_dataset)
slicer_modified.fields.wins.precision = 0
result = Pandas(slicer_modified.fields.wins) \
.transform(cat_dim_df_with_nan, slicer_modified, [slicer_modified.fields.political_party], [])
expected = cat_dim_df_with_nan.copy()[[f('wins')]]
expected.index = pd.Index(['Democrat', 'Independent', 'Republican'], name='Party')
expected['$wins'] = ['6', '0', 'Inf']
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
