atoti.Cube.query()#

Cube.query(*measures: Measure, context: Mapping[str, object] = {}, explain: Literal[False] = False, filter: QueryFilter | None = None, include_empty_rows: bool = False, include_totals: bool = False, levels: Sequence[Level] = (), mode: Literal['pretty'] = 'pretty', scenario: str = None, timeout: timedelta = datetime.timedelta(seconds=30)) → MdxQueryResult#

Cube.query(*measures: Measure, context: Mapping[str, object] = {}, explain: Literal[False] = False, filter: QueryFilter | None = None, include_empty_rows: bool = False, include_totals: bool = False, levels: Sequence[Level] = (), mode: Literal['pretty', 'raw'] = 'pretty', scenario: str = None, timeout: timedelta = datetime.timedelta(seconds=30)) → DataFrame

Cube.query(*measures: Measure, context: Mapping[str, object] = {}, explain: Literal[True], filter: QueryFilter | None = None, include_empty_rows: bool = False, include_totals: bool = False, levels: Sequence[Level] = (), mode: Literal['pretty', 'raw'] = 'pretty', scenario: str = None, timeout: timedelta = datetime.timedelta(seconds=30)) → Sequence[QueryPlan]

Execute and MDX query.

In JupyterLab with atoti-jupyterlab installed, query results can be converted to interactive widgets with the Convert to Widget Below action available in the command palette or by right clicking on the representation of the returned Dataframe.

Parameters:

measures – The measures to query.
context – Context values to use when executing the query.
explain – When True, execute the query but, instead of returning its result, return an explanation of how it was executed containing a summary, global timings, and the query plan and all its retrievals.

filter –

The filtering condition.

Example

>>> df = pd.DataFrame(
...     columns=["Continent", "Country", "Currency", "Year", "Month", "Price"],
...     data=[
...         ("Europe", "France", "EUR", 2023, 10, 200.0),
...         ("Europe", "Germany", "EUR", 2024, 2, 150.0),
...         ("Europe", "United Kingdom", "GBP", 2022, 10, 120.0),
...         ("America", "United states", "USD", 2020, 5, 240.0),
...         ("America", "Mexico", "MXN", 2021, 3, 270.0),
...     ],
... )
>>> table = session.read_pandas(
...     df,
...     keys={"Continent", "Country", "Currency", "Year", "Month"},
...     table_name="Prices",
... )
>>> cube = session.create_cube(table)
>>> del cube.hierarchies["Continent"]
>>> del cube.hierarchies["Country"]
>>> cube.hierarchies["Geography"] = [
...     table["Continent"],
...     table["Country"],
... ]
>>> del cube.hierarchies["Year"]
>>> del cube.hierarchies["Month"]
>>> cube.hierarchies["Date"] = [
...     table["Year"],
...     table["Month"],
... ]
>>> cube.measures["American Price"] = tt.where(
...     cube.levels["Continent"] == "America",
...     cube.measures["Price.SUM"],
... )
>>> h, l, m = cube.hierarchies, cube.levels, cube.measures

Single equality condition:

>>> cube.query(
...     m["Price.SUM"],
...     levels=[l["Country"]],
...     filter=l["Continent"] == "Europe",
... )
                         Price.SUM
Continent Country
Europe    France            200.00
          Germany           150.00
          United Kingdom    120.00

Combined equality condition:

>>> cube.query(
...     m["Price.SUM"],
...     levels=[l["Country"], l["Currency"]],
...     filter=((l["Continent"] == "Europe") & (l["Currency"] == "EUR")),
... )
                           Price.SUM
Continent Country Currency
Europe    France  EUR         200.00
          Germany EUR         150.00

Hierarchy condition:

>>> cube.query(
...     m["Price.SUM"],
...     levels=[l["Country"]],
...     filter=h["Geography"].isin(("America",), ("Europe", "Germany")),
... )
                        Price.SUM
Continent Country
America   Mexico           270.00
          United states    240.00
Europe    Germany          150.00

Inequality condition:

>>> cube.query(
...     m["Price.SUM"],
...     levels=[l["Country"], l["Currency"]],
...     # Equivalent to `filter=(l["Currency"] != "GBP") & (l["Currency"] != "MXN")`
...     filter=~l["Currency"].isin("GBP", "MXN"),
... )
                                 Price.SUM
Continent Country       Currency
America   United states USD         240.00
Europe    France        EUR         200.00
          Germany       EUR         150.00
>>> cube.query(
...     m["Price.SUM"],
...     levels=[l["Year"]],
...     filter=l["Year"] >= 2022,
... )
     Price.SUM
Year
2022    120.00
2023    200.00
2024    150.00

Deep level of a multilevel hierarchy condition:

>>> cube.query(
...     m["Price.SUM"],
...     levels=[l["Month"]],
...     filter=l["Month"] == 10,
... )
           Price.SUM
Year Month
2022 10       120.00
2023 10       200.00

include_empty_rows –

Whether to keep the rows where all the requested measures have no value.

Example

>>> cube.query(
...     m["American Price"],
...     levels=[l["Continent"]],
...     include_empty_rows=True,
... )
          American Price
Continent
America           510.00
Europe

include_totals –

Whether to query the grand total and subtotals and keep them in the returned DataFrame. Totals can be useful but they make the DataFrame harder to work with since its index will have some empty values.

Example

>>> cube.query(
...     m["Price.SUM"],
...     levels=[l["Country"], l["Currency"]],
...     include_totals=True,
... )
                                  Price.SUM
Continent Country        Currency
Total                                980.00
America                              510.00
          Mexico                     270.00
                         MXN         270.00
          United states              240.00
                         USD         240.00
Europe                               470.00
          France                     200.00
                         EUR         200.00
          Germany                    150.00
                         EUR         150.00
          United Kingdom             120.00
                         GBP         120.00

levels – The levels to split on. If None, the value of the measures at the top of the cube is returned.

mode –

The query mode.

"pretty" is best for queries returning small results.

Example:

>>> cube.query(
...     m["Price.SUM"],
...     levels=[l["Continent"]],
...     mode="pretty",
... )
          Price.SUM
Continent
America      510.00
Europe       470.00

"raw" is best for benchmarks or large exports:
- A faster and more efficient endpoint reducing the data transfer from Java to Python will be used.
- The Convert to Widget Below action provided by atoti-jupyterlab will not be available.
Example:
>>> cube.query( ... m["Price.SUM"], ... levels=[l["Continent"]], ... mode="raw", ... ) Continent Price.SUM 0 America 510.0 1 Europe 470.0

scenario – The name of the scenario to query.
timeout – The duration the query execution can take before being aborted.