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peppy is a Python package that provides an API for handling standardized project and sample metadata. If you define your project in Portable Encapsulated Project (PEP) format, you can use the peppy package to instantiate an in-memory representation of your project and sample metadata. You can then use peppy for interactive analysis, or to develop Python tools so you don't have to handle sample processing. peppy is useful to tool developers and data analysts who want a standard way of representing sample-intensive research project metadata.

What is a PEP?

A PEP is a collection of metadata files conforming to a standardized structure. These files are written using the simple YAML and TSV/CSV formats, and they can be read by a variety of tools in the pep toolkit, including peppy. If you don't already understand why the PEP concept is useful to you, start by reading the PEP specification, where you can also find example projects.

Why use peppy?

peppy provides an API with which to interact from Python with PEP metadata. This is often useful on its own, but the big wins include:

  • Portability between computing environments
  • Reusability among different tools and project stages
  • Durability with respect to data movement

Who should use peppy?

There are two main kinds of user that may have interest:

  • A tool developer
  • A data analyst

If you neither of those describes you, you may be interested in pepr (R package), which provides an R interface to PEP objects, or looper (command-line application), which lets you run any command-line tool or pipeline on samples in a project.


As a tool developer, you should import peppy in your Python tool and read PEP projects as its input.

This will simplify use of your tool, because users may already have PEP-formatted projects for other tools.


peppy provides an easy way to read project metadata into Python. You will have access to an API to access samples and their attributes, facilitating downstream analysis.