tm1py

By wrapping the IBM Planning Analytics (TM1) REST API in a concise Python framework, TM1py facilitates Python developments for TM1.

Interacting with TM1 programmatically has never been easier.

with TM1Service(address='localhost', port=8001, user='admin', password='apple', ssl=True) as tm1:
    subset = Subset(dimension_name='Month', subset_name='Q1', elements=['Jan', 'Feb', 'Mar'])
    tm1.subsets.create(subset, private=True)

Features

TM1py offers handy features to interact with TM1 from Python, such as

Read data from cubes through cube views and MDX Queries
Write data into cubes
Execute processes and chores
Execute loose statements of TI
CRUD features for TM1 objects (cubes, dimensions, subsets, etc.)
Query and kill threads
Query MessageLog, TransactionLog and AuditLog
Generate MDX Queries from existing cube views

Requirements

python (3.7 or higher)
requests
requests_negotiate_sspi
TM1 11

Optional Requirements

pandas

Install

without pandas

pip install tm1py

with pandas

pip install "tm1py[pandas]"

Usage

on-premise

from TM1py.Services import TM1Service

with TM1Service(address='localhost', port=8001, user='admin', password='apple', ssl=True) as tm1:
    for chore in tm1.chores.get_all():
        chore.reschedule(hours=-1)
        tm1.chores.update(chore)

IBM cloud

with TM1Service(
        base_url='https://mycompany.planning-analytics.ibmcloud.com/tm1/api/tm1/',
        user="non_interactive_user",
        namespace="LDAP",
        password="U3lSn5QLwoQZY2",
        ssl=True,
        verify=True,
        async_requests_mode=True) as tm1:
    for chore in tm1.chores.get_all():
        chore.reschedule(hours=-1)
        tm1.chores.update(chore)

Samples: https://github.com/cubewise-code/TM1py-samples