python-tabular 0.0.8

Connect to your tabular model and perform operations programmatically

PyTabular

What Does It Do?

PyTabular allows for programmatic execution on your tabular models... In Python!

How Does It Work?

I basically took my two favorite things Python and Tabular Models and connected the two. Thanks to Pythonnet (3.0.0a2) and Microsoft's .Net APIs on Azure Analysis Services. The package should have the dll files included when you import the package.

Getting Started

Importing package

python3 -m pip install python_tabular

In your python environment, import pytabular and call the main Tabular Class. Only parameter needed is a solid connection string.

    import pytabular
    model = pytabular.Tabular(CONNECTION_STR)

DAX Query

    model.Query(DAX_QUERY)
    # Returns a Pandas DataFrame

Refresh Tables and Partitions

    #filter down the collection to what you want to refresh
    tables_to_refresh = [table for table in model.Tables if table.get_Name() in ['Table1','Table2','Table3']]
    
    #Queue up the tables and partitions that you want to refresh.
    model.Refresh(tables_to_refresh)

    #Once you are ready, update to execute the refresh
    model.Update()

Built In Dax Query Helpers

    #Query Every Column
    model.Query_Every_Column() #Will return pd.DataFrame()

    #Query Every Table
    model.Query_Every_Table() #Will return pd.DataFrame()
    
    '''
    NOTE, notice the default values for the query_function argument. 
    Query_Every_Column will get COUNTROWS(VALUES(_))
    and Query_Every_Table() will get COUNTROWS(_)
    with '_' being replaced with the dax identifier to the table or column in question.
    You can replace this str with anything you want. For example output the MIN(_) or MAX(_) of each column rather than the default queries.
    '''