atoti.function.filter module#
- atoti.filter(measure, condition)#
Return a filtered measure.
The new measure is equal to the passed one where the condition is
True
and toNone
elsewhere.Example
>>> from datetime import date >>> data = pd.DataFrame( ... { ... "Date": [date(2021, 1, 13), date(2021, 7, 5), date(2021, 7, 6)], ... "City": ["Paris", "Paris", "London"], ... "Age": [18, 25, 8], ... "Quantity": [200, 500, 100], ... } ... ) >>> table = session.read_pandas( ... data, ... table_name="City date table", ... hierarchized_columns=["Date", "City", "Age"], ... ) >>> table.head() Date City Age Quantity 0 2021-01-13 Paris 18 200 1 2021-07-05 Paris 25 500 2 2021-07-06 London 8 100 >>> cube = session.create_cube(table) >>> l, m = cube.levels, cube.measures >>> # Levels compared to literals of the same type: >>> m["London Quantity.SUM"] = tt.filter( ... m["Quantity.SUM"], l["City"] == "London" ... ) >>> m["Quantity.SUM before July"] = tt.filter( ... m["Quantity.SUM"], l["Date"] < date(2021, 7, 1) ... ) >>> m["Quantity.SUM for age under 18"] = tt.filter( ... m["Quantity.SUM"], l["Age"] <= 18 ... ) >>> # A conjunction of conditions using the ``&`` operator: >>> m["July Quantity.SUM in Paris"] = tt.filter( ... m["Quantity.SUM"], ... ( ... (l["City"] == "Paris") ... & ((l["Date"]) >= date(2021, 7, 1)) ... & (l["Date"] <= date(2021, 7, 31)) ... ), ... ) >>> cube.query( ... m["Quantity.SUM"], ... m["London Quantity.SUM"], ... m["Quantity.SUM before July"], ... m["Quantity.SUM for age under 18"], ... m["July Quantity.SUM in Paris"], ... ) Quantity.SUM London Quantity.SUM Quantity.SUM before July Quantity.SUM for age under 18 July Quantity.SUM in Paris 0 800 100 200 300 500 >>> cube.query( ... m["Quantity.SUM"], ... m["London Quantity.SUM"], ... m["Quantity.SUM before July"], ... m["Quantity.SUM for age under 18"], ... m["July Quantity.SUM in Paris"], ... levels=[l["Date"], l["Age"], l["City"]], ... ) Quantity.SUM London Quantity.SUM Quantity.SUM before July Quantity.SUM for age under 18 July Quantity.SUM in Paris Date Age City 2021-01-13 18 Paris 200 200 200 2021-07-05 25 Paris 500 500 2021-07-06 8 London 100 100 100
- Parameters
- Return type