atoti.Session.query_mdx()#
- Session.query_mdx(mdx: str, /, *, context: Mapping[str, object] = {}, explain: Literal[False] = False, keep_totals: bool = False, mode: Literal['pretty'] = 'pretty', timeout: timedelta = datetime.timedelta(seconds=30)) MdxQueryResult #
- Session.query_mdx(mdx: str, /, *, context: Mapping[str, object] = {}, explain: Literal[False] = False, keep_totals: bool = False, mode: Literal['pretty', 'raw'] = 'pretty', timeout: timedelta = datetime.timedelta(seconds=30)) DataFrame
- Session.query_mdx(mdx: str, /, *, context: Mapping[str, object] = {}, explain: Literal[True], keep_totals: bool = False, mode: Literal['pretty', 'raw'] = 'pretty', timeout: timedelta = datetime.timedelta(seconds=30)) Sequence[QueryPlan]
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:
mdx –
The MDX
SELECT
query to execute.Regardless of the axes on which levels and measures appear in the MDX, the returned DataFrame will have all levels on rows and measures on columns.
Example
>>> from datetime import date >>> df = pd.DataFrame( ... columns=["Country", "Date", "Price"], ... data=[ ... ("China", date(2020, 3, 3), 410.0), ... ("France", date(2020, 1, 1), 480.0), ... ("France", date(2020, 2, 2), 500.0), ... ("France", date(2020, 3, 3), 400.0), ... ("India", date(2020, 1, 1), 360.0), ... ("India", date(2020, 2, 2), 400.0), ... ("UK", date(2020, 2, 2), 960.0), ... ], ... ) >>> table = session.read_pandas( ... df, keys=["Country", "Date"], table_name="Prices" ... ) >>> cube = session.create_cube(table)
This MDX:
>>> mdx = ( ... "SELECT" ... " NON EMPTY Hierarchize(" ... " DrilldownLevel(" ... " [Prices].[Country].[ALL].[AllMember]" ... " )" ... " ) ON ROWS," ... " NON EMPTY Crossjoin(" ... " [Measures].[Price.SUM]," ... " Hierarchize(" ... " DrilldownLevel(" ... " [Prices].[Date].[ALL].[AllMember]" ... " )" ... " )" ... " ) ON COLUMNS" ... " FROM [Prices]" ... )
Returns this DataFrame:
>>> session.query_mdx(mdx, keep_totals=True) Price.SUM Date Country Total 3,510.00 2020-01-01 840.00 2020-02-02 1,860.00 2020-03-03 810.00 China 410.00 2020-01-01 China 2020-02-02 China 2020-03-03 China 410.00 France 1,380.00 2020-01-01 France 480.00 2020-02-02 France 500.00 2020-03-03 France 400.00 India 760.00 2020-01-01 India 360.00 2020-02-02 India 400.00 2020-03-03 India UK 960.00 2020-01-01 UK 2020-02-02 UK 960.00 2020-03-03 UK
But, if it was displayed into a pivot table, would look like this:
Country
Price.sum
Total
2020-01-01
2020-02-02
2020-03-03
Total
3,510.00
840.00
1,860.00
810.00
China
410.00
410.00
France
1,380.00
480.00
500.00
400.00
India
760.00
360.00
400.00
UK
960.00
960.00
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.keep_totals – Whether the resulting DataFrame should contain, if they are present in the query result, the grand total and subtotals. Totals can be useful but they make the DataFrame harder to work with since its index will have some empty values.
mode –
The query mode.
"pretty"
is best for queries returning small results."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.
timeout – The duration the query execution can take before being aborted.
See also