atoti.where()#
- atoti.where(condition: ColumnCondition, true_value: ColumnConvertible, false_value: ColumnConvertible | None = None, /) Operation[ColumnIdentifier] #
- atoti.where(condition: VariableMeasureConvertible, true_value: MeasureConvertible, false_value: MeasureConvertible | None = None, /) MeasureDescription
- atoti.where(condition_to_value: Mapping[VariableMeasureConvertible, MeasureConvertible], /, *, default: MeasureConvertible | None = None) MeasureDescription
Return a conditional measure.
This function is like an if-then-else statement:
Where the condition is
True
, the new measure will be equal to true_value.Where the condition is
False
, the new measure will be equal to false_value.
If false_value is not
None
, true_value and false_value must either be both numerical, both boolean or both objects.If one of the values compared in the condition is
None
, the condition will be consideredFalse
.Different types of conditions are supported:
Measures compared to anything measure-like:
m["Test"] == 20
Levels compared to levels, (if the level is not expressed, it is considered
None
):l["source"] == l["destination"]
Levels compared to constants of the same type:
l["city"] == "Paris" l["date"] > datetime.date(2020, 1, 1) l["age"] <= 18
A conjunction or disjunction of conditions using the
&
operator or|
operator:(m["Test"] == 20) & (l["city"] == "Paris") (l["Country"] == "USA") | (l["Currency"] == "USD")
Example
>>> df = pd.DataFrame( ... columns=["Id", "City", "Value"], ... data=[ ... (0, "Paris", 1.0), ... (1, "Paris", 2.0), ... (2, "London", 3.0), ... (3, "London", 4.0), ... (4, "Paris", 5.0), ... ], ... ) >>> table = session.read_pandas(df, keys=["Id"], table_name="filter example") >>> cube = session.create_cube(table) >>> l, m = cube.levels, cube.measures >>> m["Paris value"] = tt.where(l["City"] == "Paris", m["Value.SUM"], 0) >>> cube.query(m["Paris value"], levels=[l["City"]]) Paris value City London .00 Paris 8.00
When a mapping of condition to value is passed, the resulting value is the one of the first condition evaluating to
True
:>>> m["Value.RECAP"] = tt.where( ... { ... m["Value.SUM"] < 3: "less than 3", ... m["Value.SUM"] <= 3: "less than or equal to 3", ... m["Value.SUM"] ... == 3: "equal to 3", # never used because of the broader condition before ... }, ... default="more than 3", ... ) >>> cube.query(m["Value.SUM"], m["Value.RECAP"], levels=[l["Id"]]) Value.SUM Value.RECAP Id 0 1.00 less than 3 1 2.00 less than 3 2 3.00 less than or equal to 3 3 4.00 more than 3 4 5.00 more than 3
See also