Performance

Because pipescript is implemented with instrumentation (see How it works), using its syntax incurs some run-time overhead. Two facts keep this from mattering in practice.

Overhead is only paid when pipescript syntax is used. Vanilla Python runs at full speed even with the extension loaded in your session – there is no penalty for code that does not use any pipescript operators or macros.

For top-level notebook code, the overhead is generally insignificant. pipescript targets interactive, scratchpad-style work, where a pipeline’s cost is dwarfed by the data-intensive dataframe operations and SQL queries typical of data-science workloads. For code written directly in a Jupyter cell (without deep indentation), the added overhead tends not to be noticeable.

If you are writing performance-critical inner loops, that is exactly the kind of production code pipescript is not aimed at – reach for plain Python there. See Installation for the intended scope.