Changelog Terality #3: Export dataframes into multiple files

November 11, 2021
Terality team
Terality team

You can now export dataframes into multiple files, using to_csv_folder / to_parquet_folder.

Exporting your data in one huge file is often a big problem for the next steps in your workflow. Terality now offers the possibility to export a dataframe into multiple files, with two extra methods to the pandas API: to_csv_folder and to_parquet_folder. 

The number of files can be specified in several ways:: the number of files, the number of rows per file, or the in-memory size per file. 

More information on our documentation:



Terality user dashboard:

  • You can now go to to signup and manage your Terality account. The dashboard includes a quick start section to get started with Terality in about one minute and allows you to monitor your data usage. Expect more improvements to this app over time.
    If you already created a Terality account before this dashboard was available, simply create a new account on the dashboard with the email of your existing account, and the accounts will get automatically merged.


Terality’s documentation

  • We have updated our documentation to add a lot of information about how Terality works and how to use it. Here.


Pandas functions

  • Implementation of the functions nlargest / nsmallest
  • Series/Index support string accessor and Series supports Datetime/Period/Timedelta accessor
  • Apply now accepts functions with any return type.


Performance improvements

  • Switched our internal serialization format from json to protobuf, resulting in massive improvements at scale


Added support for Apple Silicon Macs

  • The Terality client can now be installed on Apple Silicon Macs using pip.


Improvements & Fixes

  • We fixed a spurious error message when running the Terality CLI on a system that did not already have a Terality credentials file.
  • We fixed an internal server error that could occur during a long Terality session.
  • We Fixed some occurrences of internal server errors under load.
  • We improved error messages when requests from users are invalid for better visibility and UX
  • We improved our internal performance tracking system: fixed AWS lambda cold start unexplained delay

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