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Python Data Visualization 2018: Why So Many Libraries?

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Re: Python Data Visualization 2018: Why So Many Libraries?

#21

Post by ExcaliburZero »

ObscureMind wrote: post #20
minimaxir wrote: post #4

I'm pretty good at Python and do most of my work in it nowadays, but if I need to make a data visualization, I still go to R just for ggplot2. Nothing currently in Python compares (not even the "ggplot2 port"), and it takes an order of magnitude longer to make a comparable viz.

Check plotnine, which is a decent reimplementation of ggplot2 in python: https://plotnine.readthedocs.io/en/latest/

Plotnine is quite nice. Its api is very similar to R's ggplot so it gives you the same level of expressiveness and flexibility while being able to stay the Python ecosystem to use numpy, pandas, etc..

Re: Python Data Visualization 2018: Why So Many Libraries?

#22

Post by ObscureMind »

ExcaliburZero wrote: post #21
ObscureMind wrote: post #20

Earlier quoted context omitted.

Check plotnine, which is a decent reimplementation of ggplot2 in python: https://plotnine.readthedocs.io/en/latest/

Plotnine is quite nice. Its api is very similar to R's ggplot so it gives you the same level of expressiveness and flexibility while being able to stay the Python ecosystem to use numpy, pandas, etc..

The authors also provide a reimplementation of dplyr in Python that integrates well with plotnine: https://github.com/has2k1/plydata

Re: Python Data Visualization 2018: Why So Many Libraries?

#24

Post by spott »

rsivapr wrote: post #11

A friend of mine created a page to show code snippets and comparisons between a few of the plotting libraries available in Python (and R) -- http://pythonplot.com/

I've run across that page before. It really needs altair, bokeh and holoviews support.

Re: Python Data Visualization 2018: Why So Many Libraries?

#25

Post by japhyr »

ms013 wrote: post #17
frankc wrote: post #16

There's like 15 comments in this post and 10 different suggestions on what visualization library to use. That's not great.

Yup. A likely result is that if you pick one and spend the time to learn it and use it for a project, there's a non-trivial chance that the choice you make will be join the ever growing collection of library abandon-ware in the not too distant future. This is why my favorite Python visualization tools are not Python - I've been burned too many times by libraries coming and going, and I just don't have the time to spe…

Then again, if you learn one well it's not too difficult to learn another one as well. And once you've worked with a couple different libraries, you start to understand the different data models and it's even easier to adapt to the next library.

Also, for many of the visualizations I've worked with I'm fine to use an older library that's reached a stable point of development. It feels like the whole conversation about whether to build your new project on the latest web framework, or use something old and established like Django or Rails. Boring, relatively stable software in any area is a pleasure to work with if you really want to focus on your real-world problem more than you want to focus on newer tech.

Re: Python Data Visualization 2018: Why So Many Libraries?

#27

Post by minimaxir »

ObscureMind wrote: post #20
minimaxir wrote: post #4

I'm pretty good at Python and do most of my work in it nowadays, but if I need to make a data visualization, I still go to R just for ggplot2. Nothing currently in Python compares (not even the "ggplot2 port"), and it takes an order of magnitude longer to make a comparable viz.

Check plotnine, which is a decent reimplementation of ggplot2 in python: https://plotnine.readthedocs.io/en/latest/

Huh, when I was looking into Python options I had thought plotnine was dead, but looking at the GitHub that's not the case: https://github.com/has2k1/plotnine

I'll give it a shot! :)

Re: Python Data Visualization 2018: Why So Many Libraries?

#29

Post by dnhz »

minimaxir wrote: post #4

I'm pretty good at Python and do most of my work in it nowadays, but if I need to make a data visualization, I still go to R just for ggplot2. Nothing currently in Python compares (not even the "ggplot2 port"), and it takes an order of magnitude longer to make a comparable viz.

How do you transfer data from Python to R? I like the look of ggplot2, but most of my data I keep as pickle files.

Re: Python Data Visualization 2018: Why So Many Libraries?

#30

Post by thicknavyrain »

cozzyd wrote: post #26

As a ROOT guru and occasional pythonist, I recommend PyROOT especially if you want to interactively explore what you're plotting.

Does anyone outside of High Energy Physics use ROOT? I'm not trying to be argumentative, I'm genuinely curious, I'm a physicist by day so seeing ROOT talked about on here amongst popular libraries is like someone recommending my local pub as the best in town.
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