def test_multiple_metrics_reversed(self):
result = Pandas(slicer.metrics.wins, slicer.metrics.votes) \
.transform(multi_metric_df, slicer, [], [])
expected = multi_metric_df.copy()[[fm('wins'), fm('votes')]]
expected.columns = ['Wins', 'Votes']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_single_metric(self):
result = Pandas(slicer.metrics.votes) \
.transform(single_metric_df, slicer, [], [])
expected = single_metric_df.copy()[[fm('votes')]]
expected.columns = ['Votes']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_sort_with_no_index(self):
result = Pandas(slicer.metrics.wins, sort=[0]) \
.transform(no_index_df, slicer, [slicer.dimensions.timestamp], [])
expected = no_index_df.copy()[[fm('wins')]]
expected.index.names = ['Timestamp']
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_time_series_dim_with_operation(self):
result = Pandas(CumSum(slicer.metrics.votes)) \
.transform(cont_dim_operation_df, slicer, [slicer.dimensions.timestamp], [])
expected = cont_dim_operation_df.copy()[[fm('cumsum(votes)')]]
expected.index.names = ['Timestamp']
expected.columns = ['CumSum(Votes)']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_time_series_dim(self):
result = Pandas(slicer.metrics.wins) \
.transform(cont_dim_df, slicer, [slicer.dimensions.timestamp], [])
expected = cont_dim_df.copy()[[fm('wins')]]
expected.index.names = ['Timestamp']
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_sort_value_greater_than_number_of_columns_is_ignored(self):
result = Pandas(slicer.metrics.wins, sort=[5]) \
.transform(cont_dim_df, slicer, [slicer.dimensions.timestamp], [])
expected = cont_dim_df.copy()[[fm('wins')]]
expected.index.names = ['Timestamp']
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_cat_dim(self):
result = Pandas(slicer.metrics.wins) \
.transform(cat_dim_df, slicer, [slicer.dimensions.political_party], [])
expected = cat_dim_df.copy()[[fm('wins')]]
expected.index = pd.Index(['Democrat', 'Independent', 'Republican'],
name='Party')
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_pivoted_multi_dims_time_series_and_cat(self):
result = Pandas(slicer.metrics.wins, pivot=[slicer.dimensions.political_party]) \
.transform(cont_cat_dim_df, slicer, [slicer.dimensions.timestamp, slicer.dimensions.political_party], [])
expected = cont_cat_dim_df.copy()[[fm('wins')]]
expected = expected.unstack(level=[1]).fillna(value='')
expected.index.names = ['Timestamp']
expected.columns = ['Democrat', 'Independent', 'Republican']
expected.columns.names = ['Party']
pandas.testing.assert_frame_equal(expected, result)
def test_multi_dims_time_series_and_uni(self):
result = Pandas(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']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_multiple_metrics_sort_value_desc(self):
result = Pandas(slicer.metrics.wins, sort=[1], ascending=[False]) \
.transform(cont_dim_df, slicer, [slicer.dimensions.timestamp], [])
expected = cont_dim_df.copy()[[fm('wins')]]
expected.index.names = ['Timestamp']
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
expected = expected.sort_values(['Wins'], ascending=False)
pandas.testing.assert_frame_equal(expected, result)
def test_pivoted_multi_dims_time_series_and_uni(self):
result = Pandas(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']
expected.columns.names = ['State']
pandas.testing.assert_frame_equal(expected, result)
def test_uni_dim(self):
result = Pandas(slicer.metrics.wins) \
.transform(uni_dim_df, slicer, [slicer.dimensions.candidate], [])
expected = uni_dim_df.copy() \
.set_index(fd('candidate_display'), append=True) \
.reset_index(fd('candidate'), drop=True) \
[[fm('wins')]]
expected.index.names = ['Candidate']
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_neginf_in_metrics(self):
cat_dim_df_with_nan = cat_dim_df.copy()
cat_dim_df_with_nan['$m$wins'] = cat_dim_df_with_nan['$m$wins'].apply(
float)
cat_dim_df_with_nan.iloc[2, 1] = np.inf
result = Pandas(slicer.metrics.wins) \
.transform(cat_dim_df_with_nan, slicer, [slicer.dimensions.political_party], [])
expected = cat_dim_df_with_nan.copy()[[fm('wins')]]
expected.index = pd.Index(['Democrat', 'Independent', 'Republican'],
name='Party')
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_uni_dim_no_display_definition(self):
import copy
candidate = copy.copy(slicer.dimensions.candidate)
uni_dim_df_copy = uni_dim_df.copy()
del uni_dim_df_copy[fd(slicer.dimensions.candidate.display.key)]
del candidate.display
result = Pandas(slicer.metrics.wins) \
.transform(uni_dim_df_copy, slicer, [candidate], [])
expected = uni_dim_df_copy.copy()[[fm('wins')]]
expected.index.names = ['Candidate']
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_pivoted_multi_dims_time_series_and_cat_sort_index_level_1_desc(
self):
result = Pandas(slicer.metrics.wins, sort=[1], 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=False)[[fm('wins')]]
expected.index.names = ['Timestamp', 'State']
expected.columns = ['Wins']
expected.columns.name = 'Metrics'
expected = expected.reset_index()
expected = expected.sort_values(['State'], ascending=[False])
expected = expected.set_index(['Timestamp', 'State'])
pandas.testing.assert_frame_equal(expected, result)
def test_time_series_ref(self):
result = Pandas(slicer.metrics.votes) \
.transform(cont_uni_dim_ref_df, slicer,
[
slicer.dimensions.timestamp,
slicer.dimensions.state
], [
ElectionOverElection(slicer.dimensions.timestamp)
])
expected = cont_uni_dim_ref_df.copy() \
.set_index(fd('state_display'), append=True) \
.reset_index(fd('state'), drop=True)[[fm('votes'), fm('votes_eoe')]]
expected.index.names = ['Timestamp', 'State']
expected.columns = ['Votes', 'Votes (EoE)']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)
def test_metric_format(self):
import copy
votes = copy.copy(slicer.metrics.votes)
votes.prefix = '$'
votes.suffix = '€'
votes.precision = 2
# divide the data frame by 3 to get a repeating decimal so we can check precision
result = Pandas(votes) \
.transform(cont_dim_df / 3, slicer, [slicer.dimensions.timestamp], [])
expected = cont_dim_df.copy()[[fm('votes')]]
expected[fm('votes')] = [
'${0:,.2f}€'.format(x) for x in expected[fm('votes')] / 3
]
expected.index.names = ['Timestamp']
expected.columns = ['Votes']
expected.columns.name = 'Metrics'
pandas.testing.assert_frame_equal(expected, result)