def test_time_series_dim(self):
result = CSV(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']
self.assertEqual(expected.to_csv(), result)
def test_time_series_dim(self):
result = CSV(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']
self.assertEqual(expected.to_csv(), 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_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_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_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_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_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)
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)