mech VS plotnine

You should take a look at the language I’m developing, Mech: https://github.com/mech-lang/mech

I'm working on a language called Mech (github.com/mech-lang/mech) that is semantically parallel and asynchronous first. You can write something like this:

> HN readers - do you have an "up and coming" language that you think has better structured the fundamentals from R, that you hope will someday have enough capabilities you can use it instead of R?

Hope is the operative word here!

I'm writing a language to compete in this area. It's called Mech and I'll be releasing the first beta in October. You can think of it like Matlab + Excel. It's very fast, has default-parallel semantics for operators and functions, and supports full interactive coding with no startup/compilation latency issues. It's meant for robots, but I've also designed it to be a better Matlab, and I think it should take on R handily. Fair warning, it's public alpha now so error messages are sparse and the happy path is narrow.

https://github.com/mech-lang/mech

> Python doesn’t yet have anything remotely close to ggplot for rapidly making exploratory graphics, for example.

Plug for plotnine (https://plotnine.readthedocs.io/en/stable/). I don't know R but use ggplot indirectly through this library for exploratory data analysis, and comparing the experience to any other python plotting library, I understand why R folks are usually so sad to be using Python.

Python is my daily driver, but I briefly experimented with R and had a delightful experience with ggplot2. The ‘grammar of graphics’ was hard to leave behind when I switched back to Python, until I heard about Plotnine [1], which brings much of the same grammar and functionality to Python. It’s built on Matplotlib and a few other common libraries like Pandas.

[1] https://plotnine.readthedocs.io/en/stable/

The best one I've found is plotnine, which is just a reimplementation of the ggplot API.

I will separate my answer on two parts: (1) fun stuff and (2) useful work-related things. As it relates to (1), it is obviously a function on what you're mainly interested in. Nonetheless, I definitely recommend reading parts of the book Think Python (available for free here), which includes many different examples on how to use Python for creating your own functions, and I've used it to build my own tweet scraper using Tweepy, create budget planners, etc. As it relates to (2), I believe it is useful to learn about data manipulation and visualization libraries (I have a job related to business development). For instance, knowing how to use Pandas to take a database of customers, group them by some useful variables (such as location, spending potential, etc.), and use visualization libraries (such as MatplotLib or Plotnine) to display your analysis can help you build sales report much more quickly. Hope this helps.

I think plotnine is one solution. It is implemented based on matplotlib, but it provides an almost complete ggplot syntax for matplotlib. The other solution is a next-generation seaborn interface. It is also `build on matplotlib and still in progress; however, the API would be really useful! And I have personally also developed a few libraries to solve the complex syntax of matplotlib. As an example, patchworkllib allows dynamic subplot layout on Jupyter-lab. Maybe the library can support handling matplotlib and seaborn plots.

Check out plotnine. Really good clone of ggplot for python.

https://plotnine.readthedocs.io/en/stable/

Just gonna leave this here https://plotnine.readthedocs.io/en/stable/

If you're working in python but don't want to give up ggplot there is plotnine which deserves more attention than it gets IMO.

plotnine is a Python port of ggplot.

There are Python ports of ggplot (e.g. plotnine (https://github.com/has2k1/plotnine)), but agreed, Python is behind here. I'm not the best at data viz, but I can usually piece together a way to make ggplot do what I want it to do without that much trouble or looking at documentation.

Matplotlib, though ... that's a harder beast to internalize. I know it's possible to make high-quality matplotlib plots, but it's much harder for me. Like pandas, it's a library that I don't want to denigrate because I know people put lots of effort into it, but I can't lie -- I'm not a fan.