redframes VS conda

I'm not GP, but I find the pandas API incredibly inconsistent and difficult to remember how to do simple transformations. For example, it sometimes overloads operators because it doesn't use built in language features like lambdas. There are reasons for the inconsistency, but using the alternatives like R's tidyverse or Julia's DataFramess.jl is like night and day for me.

I found RedFrames [1] recently which wraps Pandas dataframes with a more consistent interface, it's probably what I'd use if I had to write data transformations that had to be compatible with Pandas.

[1] https://github.com/maxhumber/redframes

[2022-10-23 14:11:15]: Question []: should we use Red Frames (https://github.com/maxhumber/redframes) in addition to Pandas? Criteria for decision? @me #projectLion

If you like writing chain-able pandas, you should check out: https://github.com/maxhumber/redframes

I intend to use this pattern in my redframes library to hijack some pd.DataFrame methods.

I'm dog-fooding my new Python data manipulation library, redframes: https://github.com/maxhumber/redframes

To help me prep for my Fantasy Hockey Draft next week!

Python's venv module is officially recommended for creating virtual environments since Python 3.5 comes packaged with your Python installation. While there still are additional older tools available, such as conda and virtualenv, if you are new to virtual environments, it is best to use venv now.

conda's dependency solver solves a harder problem than pip's. This quote alludes to it "Conda will never be as fast as pip, so long as we're doing real environment solves and pip satisfies itself only for the current operation." (from https://github.com/conda/conda/issues/7239). Thus mamba was created to improve performance and now conda is bringing in that performance boost.

The other challenge I ran into sometimes was that if I was running a lower memory/storage Codespace instance, when I tried to use Conda from the command line to modify environments, the process would be killed after a few seconds. This turns out to be related to some performance issues Conda has that make it consume a lot of memory when trying to work with the conda-forge installation channel. You can always then just increase the size of the Codespace your are working with (just go to your Codespaces list and use the triple dots to change the settings for a Codespace).

It's worth noting that [ana]conda isn't even fully compatible yet with 3.11 (you can use it to create 3.11 environments--and you really should rather than waiting on relying on the system python--but conda itself can only run on 3.10.

It took some time but as great Christmas present Miniconda was finally released with Python 3.10!

The September update broke it.

According to this this issue is high on their priority list (whatever that means).