Feature request: The function power_bi.execute_view should internally use the optimized version for getting data, but still retain the same shape

[Cubewise-gtejeda]

When comparing cells.execute_mdx_csv(use_iterative_json=True) to power_bi.execute_view on a larger dataset, it shows an around 85% memory improvement when using execute_mdx_csv.
The two dimensional cube used for this test had one measure on the column and 5 million rows. There was no data in the cube and no zero suppression had been applied.

power_bi.execute_view:

execute_mdx_csv(use_iterative_json=True):

[MariusWirtz]

#893 solves this.
When working with very large data sets, you can now use execute_view_dataframe(..., shaped=True) instead of execute_view_dataframe_shaped.

You can also pass use_iterative_json=True or use_blob=True to the existingtm1.cells.execute_view_dataframe_shaped and tm1.power_bi.execute_view function.
Please note that in this case, the resulting data frame will be similar but slightly different:

• types are inherited slightly differently
• empty rows are omitted by default (unless skip_zeros=False is passed)
• 0 values in cells instead of nan

Sample:

from TM1py import TM1Service

params = {
    "address": "",
    "port": 8010,
    "user": "admin",
    "password": "apple",
    "ssl": True
}

with TM1Service(**params) as tm1:
    df = tm1.power_bi.execute_view("Sales Cube", "Default", private=False)
    print(df.to_markdown())

    df = tm1.power_bi.execute_view("Sales Cube", "Default", private=False, use_iterative_json=True)
    print(df.to_markdown())

    df = tm1.power_bi.execute_view("Sales Cube", "Default", private=False, use_blob=True)
    print(df.to_markdown())

    df = tm1.power_bi.execute_view("Sales Cube", "Default", private=False, use_blob=True, skip_zeros=False)
    print(df.to_markdown())