def test_copy():
from eva.dataframe import DataFrame
df = DataFrame(
DATA,
dimensions=['id', 'type', 'code'],
metrics=['value_1', 'value_2'],
)
assert isinstance(df.copy(), DataFrame)
assert (df.copy().values == df.values).all().all()
assert df.copy().dimensions == df.dimensions
assert df.copy().metrics == df.metrics
def test_values():
from eva.dataframe import DataFrame
df = DataFrame(
DATA,
dimensions=['id', 'type', 'code'],
metrics=['value_1', 'value_2'],
)
df.metrics = 'value_1'
assert df.metrics == ['value_1']
df.metrics = ['value_2']
assert df.metrics == ['value_2']
def test_import_data():
from eva.dataframe import DataFrame
with pytest.raises(AssertionError,
message='Dimensions/values is not well-defined.'):
DataFrame(DATA)
with pytest.raises(AssertionError,
message='Dimensions/values is not well-defined.'):
DataFrame(DATA, dimensions='id', metrics='value_1')
DataFrame(
DATA,
dimensions=['id', 'type', 'code'],
metrics=['value_1', 'value_2'],
)
def test_native_dataframe():
from eva.dataframe import DataFrame
df = DataFrame(
DATA,
dimensions=['id', 'type', 'code'],
metrics=['value_1', 'value_2'],
)
native_dataframe = pd.DataFrame(DATA)
assert (df.index == native_dataframe.index).all()
assert (df.columns == native_dataframe.columns).all()
assert df.shape == native_dataframe.shape
def test_table_view():
from eva.dataframe import DataFrame
df = DataFrame(
DATA,
dimensions=['id', 'type', 'code'],
metrics=['value_1', 'value_2'],
)
table_view = df.table_view
assert set(table_view.columns) == {
'id', 'type', 'code', 'timestamp', 'delta', 'value_1', 'value_2'
}
assert (table_view.index == pd.RangeIndex(len(table_view))).all()
def test_matrix_view():
from eva.dataframe import DataFrame
df = DataFrame(
DATA,
dimensions=['id', 'type', 'code'],
metrics=['value_1', 'value_2'],
)
matrix_view = df.matrix_view
assert list(
matrix_view.columns.names) == [None, 'id', 'type', 'code', 'delta']
assert matrix_view.index.name == 'timestamp'
assert set(
matrix_view.columns.get_level_values(0)) == {'value_1', 'value_2'}