atoti.date_shift()#
- atoti.date_shift(measure, on, /, *, offset, method='exact')#
Return a measure equal to the passed measure shifted to another date.
- Parameters:
measure (VariableMeasureConvertible) – The measure to shift.
on (Hierarchy) – The hierarchy to shift on. Only hierarchies with their last level of type date (or datetime) are supported. If one of the member of the hierarchy is
N/A
their shifted value will always beNone
.offset (str) – The period to shift by as specified by Java’s Period.parse().
method (Literal['exact', 'previous', 'next', 'interpolate']) –
Determine the value to use when there is no member at the shifted date:
exact
:None
.previous
: Value at the previous existing date.next
: Value at the next existing date.interpolate
: Linear interpolation of the values at the previous and next existing dates.
- Return type:
MeasureDescription
Example
>>> from datetime import date >>> df = pd.DataFrame( ... columns=["Date", "Price"], ... data=[ ... (date(2020, 8, 1), 5), ... (date(2020, 8, 15), 7), ... (date(2020, 8, 30), 15), ... (date(2020, 8, 31), 15), ... (date(2020, 9, 1), 10), ... (date(2020, 9, 30), 21), ... (date(2020, 10, 1), 9), ... (date(2020, 10, 31), 8), ... ], ... ) >>> table = session.read_pandas( ... df, ... table_name="date_shift example", ... ) >>> cube = session.create_cube(table) >>> h, l, m = cube.hierarchies, cube.levels, cube.measures >>> cube.create_date_hierarchy( ... "Date parts", column=table["Date"], levels={"Year": "y", "Month": "M"} ... ) >>> h["Date"] = {**h["Date parts"], "Date": table["Date"]} >>> m["Exact"] = tt.date_shift( ... m["Price.SUM"], h["Date"], offset="P1M", method="exact" ... ) >>> m["Exact in the past"] = tt.date_shift( ... m["Price.SUM"], h["Date"], offset="-P1M", method="exact" ... ) >>> m["Previous"] = tt.date_shift( ... m["Price.SUM"], h["Date"], offset="P1M", method="previous" ... ) >>> m["Next"] = tt.date_shift( ... m["Price.SUM"], h["Date"], offset="P1M", method="next" ... ) >>> m["Interpolate"] = tt.date_shift( ... m["Price.SUM"], h["Date"], offset="P1M", method="interpolate" ... ) >>> cube.query( ... m["Price.SUM"], ... m["Exact"], ... m["Exact in the past"], ... m["Previous"], ... m["Next"], ... m["Interpolate"], ... levels=[l["Date"]], ... include_totals=True, ... ) Price.SUM Exact Exact in the past Previous Next Interpolate Year Month Date Total 90 2020 90 8 42 2020-08-01 5 10 10 10 10.00 2020-08-15 7 10 21 15.31 2020-08-30 15 21 21 21 21.00 2020-08-31 15 21 21 21 21.00 9 31 2020-09-01 10 9 5 9 9 9.00 2020-09-30 21 15 9 8 8.03 10 17 2020-10-01 9 10 8 2020-10-31 8 21 8
Explanations for values:
- Exact
The value for
2020-08-31
is taken from2020-09-30
because there is no 31st of September.
- Exact in the past
The value for
2020-10-31
is taken from2020-09-30
for the same reason.
- Interpolate
10.00, 21.00, 21.00, 9.00
: no interpolation is required since there is an exact match.15.31
: linear interpolation of2020-09-01
’s10
and2020-09-30
’s21
at2020-08-15
.8.03
: linear interpolation of2020-10-01
’s9
and2020-10-31
’s8
at2020-09-30
.∅: no interpolation possible because there are no records after
2020-10-31
.