Welcome to bamboo-stash’s documentation!#

Overview#

bamboo-stash is a Python library for automatically caching function calls, similar to functools.cache(). You can use it in a similar way:

Using functools.cache

from functools import cache

@cache
def my_function():
    ...

Using bamboo-stash

from bamboo_stash import stash

@stash
def my_function():
    ...

The key difference is that bamboo-stash is primarily designed for iterating on pandas-based code in a Jupyter notebook. To support this, bamboo-stash has the following features:

Function calls are cached into files. This means that function calls will still be cached even if the kernel is restarted
Cache lookup is based not only on the function arguments, but also on the function source code itself. This means that if you modify the source code of a decorated function, subsequent calls will use a new cache that is separate from the old code’s cache. This lets you continue to iterate on your code without accidentally using cached data from an older version of your code. This also means that if you then revert back to an older version of your code, it will automatically switch back to the previous cache it was using!
Supports Series and DataFrame arguments, in addition to any hashable argument. pandas arguments are hashed using pandas.util.hash_pandas_object(). This has reasonable performance even for large inputs. From a casual benchmark, hashing a 1 million row dataframe with mixes of numeric and string columns takes about 0.5 seconds. If you’re reaching for this library, 0.5 seconds is probably much faster than recomputing your function result.

Contents:

Welcome to bamboo-stash’s documentation!#

Overview#

Indices and tables#