atoti.function.date_shift module

atoti.date_shift(measure, on, /, *, offset, method='exact')

Return a measure equal to the passed measure shifted to another date.

Parameters

• measure (NonConstantMeasureConvertible) – 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 be None.

• 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"].levels, "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 from 2020-09-30 because there is no 31st of September.

• Exact in the past

  • The value for 2020-10-31 is taken from 2020-09-30 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 of 2020-09-01’s 10 and 2020-09-30’s 21 at 2020-08-15.

  • 8.03: linear interpolation of 2020-10-01’s 9 and 2020-10-31’s 8 at 2020-09-30.

  • ∅: no interpolation possible because there are no records after 2020-10-31.