atoti.Cube.query()

@overload
Cube.query(
    *measures: Measure,
    context: Mapping[str, bool | int | float | str] = frozendict({}),
    explain: Literal[False] = False,
    filter: CubeQueryFilterCondition | None = None,
    include_empty_rows: bool = False,
    include_totals: bool = False,
    levels: Sequence[Level] = (),
    mode: Literal[‘pretty’] = 'pretty',
    scenario: str | None = None,
) → MdxQueryResult

@overload
Cube.query(
    *measures: Measure,
    context: Mapping[str, bool | int | float | str] = frozendict({}),
    explain: Literal[False] = False,
    filter: CubeQueryFilterCondition | None = None,
    include_empty_rows: bool = False,
    include_totals: bool = False,
    levels: Sequence[Level] = (),
    mode: Literal[‘pretty’, ‘raw’] = 'pretty',
    scenario: str | None = None,
) → DataFrame

@overload
Cube.query(
    *measures: Measure,
    context: Mapping[str, bool | int | float | str] = frozendict({}),
    explain: Literal[True],
    filter: CubeQueryFilterCondition | None = None,
    include_empty_rows: bool = False,
    include_totals: bool = False,
    levels: Sequence[Level] = (),
    mode: Literal[‘pretty’, ‘raw’] = 'pretty',
    scenario: str | None = None,
) → object

Execute an 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. See shared_context for some of the available context values.

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.

>>> 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)
>>> h, l, m = cube.hierarchies, cube.levels, cube.measures
>>> h["Geography"] = [table["Continent"], table["Country"]]
>>> for name in h["Geography"]:
...     del h[name]
>>> h["Date"] = [table["Year"], table["Month"]]
>>> for name in h["Date"]:
...     del h[name]

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"]],
...     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

Measure condition:

>>> cube.query(
...     m["Price.SUM"],
...     levels=[l["Month"]],
...     filter=m["Price.SUM"] >= 123,
... )
           Price.SUM
Year Month
2020 5        240.00
2021 3        270.00
2023 10       200.00
2024 2        150.00

>>> cube.query(m["Price.SUM"], filter=m["Price.SUM"] > 123)
  Price.SUM
0    980.00

>>> cube.query(m["Price.SUM"], filter=m["Price.SUM"] < 123)
Empty DataFrame
Columns: []
Index: []

include_empty_rows

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

>>> m["American price"] = tt.where(
...     l["Continent"] == "America", m["Price.SUM"]
... )
>>> 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.

>>> 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.

>>> 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.

>>> 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.

See also: atoti.Session.query_mdx()

Releases and upgrades

Getting started

Guides

API reference

Cube.query()

Parameters

*measures

context

explain

filter

include_empty_rows

include_totals

levels

mode

scenario

​Parameters

​*measures

​context

​explain

​filter

​include_empty_rows

​include_totals

​levels

​mode

​scenario

Parameters

*measures

context

explain

filter

include_empty_rows

include_totals

levels

mode

scenario