def test_should_include_columns_also_for_empty_dataframe(self):
df = pd.DataFrame({
'group': [],
'value': []
})
result_df = groupby_agg_droplevel(df, ['group'], {
'value': {
'sum': sum,
'count': pd.np.size
}
})
assert set(result_df.columns) == {'group', 'sum', 'count'}
assert len(result_df) == 0
def test_should_apply_simple_aggregation_with_function(self):
df = pd.DataFrame({
'g': ['g1', 'g1', 'g2'],
'v': [1, 2, 3]
})
result_df = groupby_agg_droplevel(df, ['g'], {
'v': sum
})
assert list(result_df.columns) == ['g', 'v']
assert list(result_df.values.tolist()) == [
['g1', 3],
['g2', 3]
]
def test_should_apply_nested_aggregation_with_functions(self):
df = pd.DataFrame({
'group': ['g1', 'g1', 'g2'],
'value': [1, 2, 3]
})
result_df = groupby_agg_droplevel(df, ['group'], {
'value': {
'sum': sum,
'count': pd.np.size
}
})
assert set(result_df.columns) == {'group', 'sum', 'count'}
# ensure the order of the columns is as expected
result_df = result_df[['group', 'sum', 'count']]
assert result_df.values.tolist() == [
['g1', 3, 2],
['g2', 3, 1]
]
