def test_multi_dims_time_series_and_uni(self): result = CSV(slicer.metrics.wins) \ .transform(cont_uni_dim_df, slicer, [slicer.dimensions.timestamp, slicer.dimensions.state], []) expected = cont_uni_dim_df.copy() \ .set_index(fd('state_display'), append=True) \ .reset_index(fd('state'), drop=False)[[fm('wins')]] expected.index.names = ['Timestamp', 'State'] expected.columns = ['Wins'] self.assertEqual(expected.to_csv(), result)
def test_multi_dims_time_series_and_uni(self): result = CSV(slicer.metrics.wins) \ .transform(cont_uni_dim_df, slicer, [slicer.dimensions.timestamp, slicer.dimensions.state], []) expected = cont_uni_dim_df.copy() \ .set_index(fd('state_display'), append=True) \ .reset_index(fd('state'), drop=False)[[fm('wins')]] expected.index.names = ['Timestamp', 'State'] expected.columns = ['Wins'] self.assertEqual(expected.to_csv(), result)
def test_pivoted_multi_dims_time_series_and_uni(self): result = CSV(slicer.metrics.votes, pivot=[slicer.dimensions.state]) \ .transform(cont_uni_dim_df, slicer, [slicer.dimensions.timestamp, slicer.dimensions.state], []) expected = cont_uni_dim_df.copy() \ .set_index(fd('state_display'), append=True) \ .reset_index(fd('state'), drop=True)[[fm('votes')]] expected = expected.unstack(level=[1]) expected.index.names = ['Timestamp'] expected.columns = ['California', 'Texas'] self.assertEqual(expected.to_csv(), result)
def test_pivoted_multi_dims_time_series_and_uni(self): result = CSV(slicer.metrics.votes, pivot=[slicer.dimensions.state]) \ .transform(cont_uni_dim_df, slicer, [slicer.dimensions.timestamp, slicer.dimensions.state], []) expected = cont_uni_dim_df.copy() \ .set_index(fd('state_display'), append=True) \ .reset_index(fd('state'), drop=True)[[fm('votes')]] expected = expected.unstack(level=[1]) expected.index.names = ['Timestamp'] expected.columns = ['California', 'Texas'] self.assertEqual(expected.to_csv(), result)
def test_pivoted_multi_dims_time_series_and_uni_with_sort_index_desc(self): result = Pandas(slicer.metrics.votes, pivot=[slicer.dimensions.state], sort=[0], ascending=[False]) \ .transform(cont_uni_dim_df, slicer, [slicer.dimensions.timestamp, slicer.dimensions.state], []) expected = cont_uni_dim_df.copy() \ .set_index(fd('state_display'), append=True) \ .reset_index(fd('state'), drop=True)[[fm('votes')]] expected = expected.unstack(level=[1]) expected.index.names = ['Timestamp'] expected.columns = ['California', 'Texas'] expected.columns.names = ['State'] expected = expected.sort_index(ascending=False) pandas.testing.assert_frame_equal(expected, result)
def test_pivoted_multi_dims_time_series_and_uni_with_sort_index_desc(self): result = Pandas(slicer.metrics.votes, pivot=[slicer.dimensions.state], sort=[0], ascending=[False]) \ .transform(cont_uni_dim_df, slicer, [slicer.dimensions.timestamp, slicer.dimensions.state], []) expected = cont_uni_dim_df.copy() \ .set_index(fd('state_display'), append=True) \ .reset_index(fd('state'), drop=True)[[fm('votes')]] expected = expected.unstack(level=[1]) expected.index.names = ['Timestamp'] expected.columns = ['California', 'Texas'] expected.columns.names = ['State'] expected = expected.sort_index(ascending=False) pandas.testing.assert_frame_equal(expected, result)
def test_multi_dims_time_series_and_cat_sort_index_level_0_asc(self): result = Pandas(slicer.metrics.wins, sort=[0]) \ .transform(cont_uni_dim_df, slicer, [slicer.dimensions.timestamp, slicer.dimensions.state], []) expected = cont_uni_dim_df.copy() \ .set_index(fd('state_display'), append=True) \ .reset_index(fd('state'), drop=False)[[fm('wins')]] expected.index.names = ['Timestamp', 'State'] expected.columns = ['Wins'] expected.columns.name = 'Metrics' expected = expected.reset_index() expected = expected.sort_values(['Timestamp']) expected = expected.set_index(['Timestamp', 'State']) pandas.testing.assert_frame_equal(expected, result)
def test_pivoted_multi_dims_time_series_and_cat_sort_index_and_values(self): result = Pandas(slicer.metrics.wins, sort=[0, 2], ascending=[False, True]) \ .transform(cont_uni_dim_df, slicer, [slicer.dimensions.timestamp, slicer.dimensions.state], []) expected = cont_uni_dim_df.copy() \ .set_index(fd('state_display'), append=True) \ .reset_index(fd('state'), drop=False)[[fm('wins')]] expected.index.names = ['Timestamp', 'State'] expected.columns = ['Wins'] expected.columns.name = 'Metrics' expected = expected.reset_index() expected = expected.sort_values(['Timestamp', 'Wins'], ascending=[False, True]) expected = expected.set_index(['Timestamp', 'State']) pandas.testing.assert_frame_equal(expected, result)