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A Python library for creating fast, repeatable and self-documenting data analysis pipelines.
Hello, Would be great if there was a built-in way to define that a process will be the last one executed. Something like: ## works as expected... ``` python analysis...
This would make it easier to use Proof to load and clean your data, then to switch to exploratory analysis with another tool, such as a Jupyter notebook.
Hello, Instead to use a function, I prefer to write a class aiming a better description of my analysis rules. So, I defined a callable: ``` python class MyCallable(object): def...
I like the idea behind proof, but find the step chaining cumbersome in some cases. I know this isn't very constructive feedback and I'm still working out exactly which cases...
There are often cases where multiple reporters, with different skillsets, will be working on analysis in parallel. Often the common language is a SQL database. It would be cool to...
I think this is a proof issue? Sample code: ``` import agate import proof def load_data(data): data['responses'] = agate.Table.from_csv('responses.csv') def unweighted_totals(data): data['first'] = data['responses'].group_by('first') if __name__ == '__main__': data_loaded =...
Can I switch to something like JSON and keep it generic? Is there a method I can implement on my agate objects to ensure they serialize correctly? http://www.benfrederickson.com/dont-pickle-your-data/ https://kovshenin.com/2010/pickle-vs-json-which-is-faster/ http://matthewrocklin.com/blog/work/2015/03/16/Fast-Serialization/
Hi If there will be a feature that let us add argument to the process would be good. for example: ``` python proof.arguments({'file_path': "path/to/file", "lr": .1}) ``` So the arguments...
In `broca` there's support for [branching pipelines](https://github.com/ftzeng/broca#multi-pipelines) so you can combine pipelines which have many redundant components - I was thinking of breaking that out into a separate package but...
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A Python library for creating fast, repeatable and self-documenting data analysis pipelines.