This article explains three methods of loading data into a TM1 cube with TM1py.

The first method is to create a dictionary of cells and assign a value to each set

cells[element_name, "Numeric"] = 1

Once the cells set has been created you just need to use the TM1py function cells.write_values as below:

from TM1py import TM1Servicewith TM1Service(address=ADDRESS, port=PORT, user=USER, password=PWD, ssl=SSL) as tm1:    cells = dict()    for i in range(500_000):        element_name = str(i).zfill(6)        cells[element_name, "Numeric"] = 1    tm1.cells.write_values(        cube_name="Big Cube",        cellset_as_dict=cells,        dimensions=["Big Dimension", "Big Cube Measure"],        deactivate_transaction_log=True,        reactivate_transaction_log=True)

Another way to update cells in a cube with the TM1 REST API is to create a TM1 cellset and then update this cellset. To do that with TM1py, you will need to use the function cells.write_values_through_cellset:

from TM1py.Services import TM1Servicefrom mdxpy import MdxBuilder, Memberwith TM1Service(address=ADDRESS, port=PORT, user=USER, password=PWD, ssl=SSL) as tm1:    query = MdxBuilder.from_cube("Big Cube")    for i in range(500_000):        element_name = str(i).zfill(6)        query.add_member_tuple_to_rows(Member.of("Big Dimension", element_name))    query.add_member_tuple_to_columns(Member.of("Big Cube Measure", "Numeric"))    tm1.cells.write_values_through_cellset(        mdx=query.to_mdx(),        values=[1] * 500_000)

Instead of writing a MDX, this script is leveragin the MDXpy package.

The third method is to use an hybrid approach, using TM1py to export the data into a csv file and then executing a TM1 process to load the data into the cube:

from TM1py.Services import TM1Servicefrom mdxpy import MdxBuilder, MemberCSV_FILE = r"C:ProjectsTM1py_ChampionsSession5data.csv"with TM1Service(address=ADDRESS, port=PORT, user=USER, password=PWD, ssl=SSL) as tm1:    query = MdxBuilder.from_cube("Big Cube")    with open(CSV_FILE, "w") as file:        lines = list()        for i in range(500_000):            element_name = str(i).zfill(6)            lines.append(",".join([element_name, "Numeric", "1", "n"]))        file.writelines(lines)    process_name = "import_csv"    success, status, error_log_file = tm1.processes.execute_with_return(process_name)    if not success:        raise RuntimeError(f"Process '{process_name}' failed with status: {status}")

When working with large dimensions (100,000+elements), it is recommended to use this approach as it is the fastest. However its main downside is that it cannot be used if TM1 is hosted on the IBM Cloud.