TM1py v1.9 is now available

A new version of TM1py is now available to download from the TM1py project on GitHub. We would like to thank Vinoth Kumar Ravi, Josh Robinson, gbryant-dev, Ray Price, Andrew Scheel, and Jordan Jeremy for their contributions to this release.

The way TM1py retrieves values has been optimized using iterative JSON parsing. By using this technique, TM1py requires a significantly smaller memory footprint (down to ~ 20% of the original value) at an almost negligible cost of performance (single percentage digit). To turn it on, just add use_iterative_json = True when retrieving values:

This optional argument to the write/write_dataframe functions asks TM1py to filter out cells that can not be updated before attempting to write. If not used, TM1py will fail with an error when attempting to write to rule-derived or consolidated cells.

This is very helpful, as it saves the TM1py user from verifying the validity of the cell updates when not working with flawless data sources.

New search functions have been added to each object. For example, use the code below to get all processes containing the string “Sales”:

A complete list of all enhancements and fixes can be found in the TM1py 1.9 Release Notes available on GitHub.

• Getting started with TM1py

• Benefits of IBM PA Cloud

• Simplifying MDX with Arc and TM1py