alexeybaran

`ZipFile()` seems to have compression parameter, which is defaulted to no compression. Here is one of the options making it compress: `compression=zipfile.ZIP_DEFLATED`

I don't think it is that useful to have such a change tracking mechanism explicitly implemented around data object. I'm pretty happy with storing df in a cell (`foo` in...

Another argument here is the need to maintain consistency between data items and overwrites of the formula driven cells. I built model around operations with pandas objects: pandas objects are...

This is about appropriate usage of high level programming language. It will of course be a problem if multiple copies of the object are created with minor modifications for no...

Btw. `modelx` doesn't create copies of pandas objects, when it isn't specifically forced to do it. It was something I had to get used to and explicitly use `df.copy()` in...

Why can't in your example df be stored as a cell value and the other cells referencing it?

Won't this work: ``` import pandas as pd import modelx as mx df = pd.DataFrame() space = mx.new_space() @mx.defcells def foo(): pass @mx.defcells def bar(): pass foo[()]= df bar[()]= [foo()]...

Sorry I wasn't precise. I meant this ``` import pandas as pd import modelx as mx df = pd.DataFrame() space = mx.new_space() @mx.defcells def foo(): pass foo[()]= df @mx.defcells def...

It looks complex and introduces inconsistency. It also doesn't allow to track changes in specific data entries. My actual use case is as follows: ``` @mx.defcells def foo(t): pass foo[1]=...

I assume you are talking about the case, when there is actually something different between 2 dfs - not just that they are stored separately in memory, but MD5 of...