atoti.query.session module¶
-
class
atoti.query.session.QuerySession(url, *, auth=None, name=None)¶ Bases:
atoti._base._base_session.BaseSession[atoti.query.cubes.QueryCubes]Used to query an existing session.
Query sessions are considered immutable: the structure of their cubes is not expected to change.
-
property
cubes¶ Cubes of the session.
- Return type
-
query_mdx(mdx, *, keep_totals=False, timeout=30, **kwargs)¶ Execute an MDX query and return its result as a pandas DataFrame.
- Parameters
mdx (
str) – The MDXSELECTquery to execute.keep_totals (
bool) – Whether the returned DataFrame should contain, if they are present in the query result, the grand total and subtotals.timeout (
int) – The query timeout in seconds.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.
Example
An MDX query that would be displayed as this pivot table:
Country
Total Price.SUM
2018-01-01
2019-01-01
2019-01-02
2019-01-05
Price.SUM
Price.SUM
Price.SUM
Price.SUM
Total Country
2,280.00
840.00
1,860.00
810.00
770.00
China
760.00
410.00
350.00
France
1,800.00
480.00
500.00
400.00
420.00
India
760.00
360.00
400.00
UK
960.00
960.00
will return this DataFrame:
Date
Country
Price.SUM
2019-01-02
China
410.0
2019-01-05
China
350.0
2018-01-01
France
480.0
2019-01-01
France
500.0
2019-01-02
France
400.0
2019-01-05
France
420.0
2018-01-01
India
360.0
2019-01-01
India
400.0
2019-01-01
UK
960.0
- Return type
-
visualize(name=None)¶ Display an atoti widget to explore the session interactively.
Note
This method requires the
atoti-jupyterlabplugin.The widget state will be stored in the cell metadata. This state should not have to be edited but, if desired, it can be found in JupyterLab by opening the “Notebook tools” sidebar and expanding the the “Advanced Tools” section.
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property