Welcome to the TStore documentation!#
The TStore is a Python package designed to make your life easier when dealing with time series data. Our goal is to empower you to focus more on what you can discover and create with the data, rather than getting bogged down by the process of handling it.
With TStore, you can:
Efficiently store (probably almost) any form time series data to disk. Thanks to its hierarchically-structured approach based on Apache Parquet and GeoParquet, you can choose how samples and variables are stored and temporally partitioned.
Process complex time series data structures thanks to the TSDF object, which by encapsulating univariate or multivariate time series data into an Apache Arrow structure TS. Thus, you can zero-copy transform TS objects into any of the supported data frame backends (pandas, dask, polars, pyarrow). This enables distributed, lazy and and parallel processing. Additionally, TSDF have seamless integration with geopandas and xvec to perform spatial operations.
See the key concepts page for more details on the TStore’s core functionalities, and/or jump in to explore the various tutorials available in the documentation.
We’re excited to see how you’ll use this tool to push the boundaries of what’s possible, all while making your workflow smoother, enjoyable, and more productive.
Ready to jump in?
Consider joining our Slack Workspace to say hi or ask questions. It’s a great place to connect with others and get support.
Let’s get started and unlock the full potential of TSTORE archiving together!