A small library that can clump sequences of data together.

Part of a video series on

Early Notice

This package is quite new and not a whole lot of users have been able to find all the edge cases yet. Each verb in our library has at least one test, but there may be edges.


  • This library has no dependencies besides a modern version of python.
  • The library offers a pattern of verbs that are very expressive.
  • You can write code from top to bottom, left to right.
  • You can read in many json/yaml/csv files by using a wildcard *.
  • MIT License


You can install this package via pip.

pip install clumper

It may be safer however to install via;

python -m pip install clumper

For details on why, check out this resource.


Make sure you check out the issue list beforehand. New features should be discussed first and we also want to prevent that two people are working on the same thing. To get started locally, you can clone the repo and quickly get started using the Makefile.

git clone
cd clumper
make install-dev


If you encounter a bug, we'd love to hear about it! We would appreciate though if you could add a reproducible example when you submit an issue on github.

We've included some methods to our library to make this relatively easy. Here's an example of a reproducible code-block.

from clumper import Clumper

data = [{"a": 1}, {"a": 2}]

clump = Clumper(data)
expected = [{"a": 1}, {"a": 2}]
assert clump.equals(expected)

Note how this block uses .equals() to demonstrate what the expected output is. This is great for maintainers because they can just copy the code and work on a fix.

Origin Stories

Why the name?

Sometimes you just want something to "clump" together in the right way. So we turned the word "clump" into a verb and into a python package.

How did it get started?

The origin of this package was educational. It got started as free educational content on to demonstrate how to make your own package. If you're interested in learning how this package got made you can watch a small documented series of the lessons learned.