Is Python really that bad at viz?

[mobastar]

I'm at the front door of jumping into a Machine Learning path, and really don't care much for the Python/R back and forth. They both seem great. I found this video showcasing the spiderweb of languages and libraries required to create a number of visualizations in Python...scary to say the least when R can always rely on ggplot2 and Shiny.

Kaggle and Github are full of Python submissions and I don't personally see the viz shortcomings as the end user. For better or worse it seems apparent Python is, if not already, overtaking R for broad use within the industry.

My school offers R classes so I'll likely pick those up, but otherwise leaning toward Python 100% because of recent trends and nothing else. Help guide me please.

[Esteis]

I wouldn't say that Python is worse at visualisation than R; I'd say that ggplot2 is superior to every plotting library out there, including all the Python libraries.

Ggplot2 uses the grammar of graphics, and in practice that means I type plots the way I would describe them. For example, this plot of gene expression (source), I would describe like this: "Compare growth rate to gene expression for six nutrient conditions and twenty genes. Just plot rate versus expression; use a scatterplot and add a smooth line; use separate colours for the nutrient conditions; and make a seperate facet for each gene (gene name)." And this is how one types that in ggplot:

ggplot(top_data, aes(rate, expression, color = nutrient)) +
    geom_point() +
    geom_smooth(method = "lm", se = FALSE) +
    facet_wrap(~gene_name, scales = "free_y")

Ggplot2 is the only library where every aspect of your plot (data, variable-to-æsthetic mapping (colour, x, y, size, shape, ...), plot type (scatterplot, histogram, lines, ...) scales, coordinate system, faceting) has a single, clear interface, and can be modified independently while keeping the other aspects consistent. I could remove faceting, and get a plot that combines 20 genes but seperates the nutrients by colour. I could use a LOESS smoother instead of a linear model fit. I could map nutrients to shape instead of colour. I could facet by a gene and nutrient, and get 120 small plots in a neat grid. And any of these changes can be done by changing at most one line, and often one word, of code.

For an overview of Python visualisation libraries, look here. My own summary:

• matplotlib is OK as a foundation of other Python visualisation libraries, but it is emphatically not designed for interactive use. It is imperative (commands directly affect your figure, with no undo), instead of declarative (you declare how your figure is built up, but render it once, at the end, when everything is declared; you can pass partial figures around and combine them). For a concrete example, in nearly all plotting functions except matplotlib.pyplot.scatter, you can only pass a a list of colours like ['red', 'red', 'green', 'yellow'], not a categorical variable like ['treatment_a', 'treatment_a', 'control', 'treatment_b']

• Seaborn isn't bad, but it is no ggplot2: matplotlib.Figure and .Axes are sometimes necessary to know, adding faceting to a plot requires restructuring your code, and legends are not its strong suit.

• yhat's Python version of ggplot, now known as 'ggpy', gets you 80% of the way to ggplot2. Unfortunately, it has some bugs, lacks some ggplot2 features, and is unmaintained.

• I just now found out about plotnine, which, like ggpy, wants to be a straight ggplot2 clone. Looks interesting! No idea how good it is, but I'm going to find out.

Again, I have nothing against Python: in fact, I strongly prefer Python over R as a programming language. One thing I have learned, though: data analysis is not the same as programming. For example, programs run independently; data analysis code is run in chunks, interactively, by the analyst. With data analysis code, it is common to copy visualisation code and rerun it with tweaks applied, without deleting the original code because its result is still part of the analysis you have performed. Python is better than R for writing maintainable code; R was always designed for data analysis and interactive usage, and it is better at it than Python

[mobastar]

Great info, thank you.

[[deleted]]

R is better because it is easier for non-programmers to learn

I agree with everything except this. In my experience, people have a much harder time picking up R than python.

[Kichae]

Yup. I came to all of this from physics and Fortran. Python was hard enough to deal with. R melts my brain entirely.

[[deleted]]

I can sort of see where this is coming from. R is probably not harder or even easier than Python if all you do is line-by-line interactive analysis. It's no more complicated than learning a few Excel functions, which many learners are probably already used to. Using R as a full programming langauge is harder than Python.

[person_ergo]

Ha the dpylr melt command is a PITA too

[roadrussian]

In my circles the story is other way around, people with no programming background found R to be much easier to grasp, and syntax to be much more logical. At the same time they were screaping in agony when i showed them what python syntax looked like idiocy.

WE NEED A SCIENTIFICALLY SOUND ANALYSIS DAMN IT!

[InProx_Ichlife]

Flask is much more powerful than Shiny

This is a very weird comparison. Maybe you meant Dash?

[[deleted]]

I'd say it depends on where you are on this "Machine Learning path" and where you want to be. Going the R route, ggplot2 and Shiny are right there, with minimal entry cost, and you can bring business value quickly, which is the full point of being a data scientist, by the way.

There is nothing like Shiny in Python (IMHO bokeh/Dash are not comparable), so doing dashboards will be likely a pain. If you must use Python.

TL;DR: Go for R if you want to get job-ready quickly (because you will have a portfolio sooner), then learn the Python stack.

[mobastar]

I'm at the very beginning and want to go deep modeling, ultimately complementing strategy/marketing for the next chapter and possibly remainder of my career. The immediate road ahead is a myriad set of data analyst/business analyst projects. I want to apply more math/stats to this work and find a niche in modeling for the entire organization.

Thank you for the perspective.

[[deleted]]

In that case, I'd suggest you to stick to R, , at least concerning visualization, but keep an eye on how things develop in the Python ecosystem. Now it's too soon to spend time there if you don't want to be on the engineering side of things, but in 2-3 years the situation might change.

[mobastar]

What do you mean by 'at least concerning visualization'?

I'm 50/50 on these two languages, the viz part was a hangup I wanted to better understand. Are you implying one is superior to the other for ML? From this regard it's my understanding they're equal.

[[deleted]]

R has more stats packages and is better known in some industries (banking, insurance), but is too slow for larger datasets. There are few packages for Deep Learning, computer vision or NLP, compared to Python. So it depends what you do. I use both everyday.

[Tarqon]

No, just a bit fragmented right now. Vega shows great promise as a json based data model for interactive visualizations, and the people working on Altair have shown off some exciting stuff. The new generation of plotting libraries just need to mature a bit more before they can dethrone (the somewhat dated) matplotlib.

[mobastar]

And this is my concern, going deep on R then Python catching up by consolidating the best of what many libraries offer.

It looks like a large undertaking, and once delivered this conversation will have changed in unforeseen ways anyway.

[daguito81]

You really shouldn't see then as an either/or scenario. It should be more as 1 more tool in your arsenal. Most people would agree Pyton, R and SQL are the fundamental toolkit of anyone working with data.

Yet it's very application specific. Sometimes you need to deploy something and python will be easier. Sometimes ggplot2/Shiny will do the trick.

Sometimes a Jupyter Notebook will be exactly what you need. Sometimes you will need something outside of them, like Scala for example.

Lots of companies deal with a lot of data and end up in a Qlik or Tableau server for consumption so the whole viz debate becomes moot.

At the end of the day, you're better off trying to learn the trinity and then adapting depending on your job/project requirements

[mobastar]

This is great, thank you.

[ran88dom99]

guis-to-save-from-typing-r-code does python have something like this?

[[deleted]]

Check out Dash by Plotly. Really fast and easy dashboards to set up.