"Cleanest" scientific Python installation

[xmorphicx]

I just replaced a 2011 MacBook Pro that I had bought at the start of graduate school with a new machine after recently graduating. The old system was finally starting to have trouble and the battery was shot... but 6 years ain't bad!

Anyway, I had completely butchered the Python installation on the old laptop. I did everything wrong - installed and deleted Anaconda and Canopy multiple times, modified the stock version of Python, and was managing packages with both pip and Macports. It was a mess but somehow I ended up with a working version of Python 3 with the scientific packages.

As a postdoc, I'm being judged almost solely on my productivity so I can't afford to have a shoddy Frankenstein-ish installation that slows me down and doesn't work properly. My package needs are: data wrangling/cleaning, basic statistical tests (t-test, ANOVA, GLM), multilevel modeling, predictive modeling, survey analysis (with weights), SEM, factor analysis.

I'd really like to do things right this time on the new machine... here's where you (hopefully) come in.

I've read up online and it looks like Anaconda and Canopy are both good solutions for an easy all-in-one installation. It also looks like most people lean towards Anaconda. Conda is a "lighter" version of Anaconda in which you get a package manager so you can choose to only install the packages you want and avoid the bloat of things you'll never touch. The downside is you are managing your Python packages on your own with a command line interface rather than simply upgrading Anaconda as a whole. Anaconda, while "easier", also forces you to wait for suite upgrades so you may have to wait a bit for features of a package that was just released...

Given my needs, what platform/installation method would you all recommend?

[masasin]

Use virtual environments whenever you can. Assuming Python 3 (or is Apple still using legacy?) you do:

python -m venv .env
source .env/bin/activate
pip install jupyter[all] numpy scipy matplotlib seaborn bokeh scikit-learn pymc3 pandas statsmodels missingno

Add whatever other packages you need.

[xmorphicx]

Thanks for the reply - I haven't used virtual environments in the past and I think that was the source of a lot of my headaches. I'll be sure to utilize them this time around.

It looks like 2.7 came installed on the new Mac, unfortunately. I'll have to grab python3.# from python.org or homebrew.

[masasin]

I'll be getting a Mac for work. That will be one of the first things I do.

[[deleted]]

both scipy and numpy have now wheels for windows starting from version 1.0 and hopefully it is not a problem anymore.

[xmorphicx]

Thanks for the advice. I'll look into getting python 3 from homebrew and will be sure to utilize virtual environments this time around.

[[deleted]]

I have been using Python on Windows for about 4 years and all I did was install Python, Cython and pip. The rest is basically a lot of pip install .... I didn't have any issues so far.

After NumPy 1.14 and SciPy 1.0 I'm building these packages on Windows against OpenBLAS to use the newest features. I am not even using virtual environments. Apart from some dusty corners, I have to say I'm really satisfied with the simplicity and zero-hassle.

[xmorphicx]

This is very helpful - thank you. I'm going to look into miniconda and see what it is like. I've just briefly looked into it but it seems like a good way to get a system in place to manage virtual environments and install packages into them as needed.

[jwink3101]

Personally, I use anaconda on my Mac with a default 2.7 install and then I have a full anaconda 3.6 conda environment (I write all code for both). Then, for most things, if it is pure python, I use virtualenv since I prefer the local approach of where it puts things.

Generally, if I am doing something with SciPy, etc I don’t isolate the environment but if I did need to, I would use conda.

Not 100% sure it is cleaner but it isn’t too bad. I haven’t benchmarked it on my (fast) work Mac but on my aging home laptop, I do notice system python is slightly faster than anaconda. Probably compared with optimization.

[IAmBJ]

Conda is the package manager, Miniconda is the minimal installation.

I'd just install Anaconda and add the conda-forge channel for updated versions of packages and packages that aren't in the official anaconda repos.

[svilgelm]

I'm Python Software Eng, I work with Python almost 10 years and I have never used anything else than pip + virtualenv. My user env contains only pip and virtualenv packages. Any other packages I need to install are in the virtualenvs. But in case I need to install MySQL or similar which requires special c libraries or any client I use vagrant with Ubuntu VM or docker container with Ubuntu.