Cacha

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The simplest Python cache for Data Scientist:

• cache on disk, use between runs,
• use at function call, not definition.

Example

If you don't want to wait for a given compute() function to complete each time you run the script, you can cache it:

import cacha

result = compute(data) # regular usage, slow

result = cacha.cache(compute, (data, ))  # usage with cache

The cached data is stored in $HOME/.cacha/. Each time you run the function with identical input arguments, the output data will be loaded, instead of being computed.

It can be easily used with popular data structures like pandas.DataFrame or numpy.array. In case of complicated python objects that can't be easily hashed, you can use an additional key parameter that forces saving the cache based on the key value.

import cacha

result = cacha.cache(compute, (data, ), key="compute-v3")

FAQ

How is it different other caching packages?

In contrary to many other tools, cacha:

• is used at the function call, not definition. Many packages implement the @cache decorator that has to be used before definition of a function that is not easy enough to use.
• it stores the cache on disk which means you can use cache between runs. This is convenient in data science work.

How can I clear the cache?

Just delete the $HOME/.cacha/ directory. You can also call cacha.clean() which has the same effect.

Why does it require the pandas, numpy and other libraries?

To properly cache the objects from specific packages, it is necessary to have access to the functions they provide in that regard.

The main goal of cache is not to be lightweight but to provide the best developer experience.

However most of the required packages are usually used in Machine Learning projects anyway.