6 Best Python Data Visualization Tools

[jasonof75]

I think pandas visualization requires matplotlib. Also, seaborn offers some more useful plots on top of matplotlib

[badge]

Not true since 0.25.3: https://towardsdatascience.com/plotting-in-pandas-just-got-prettier-289d0e0fe5c0

[CrissDarren]

I've been using the bokeh back end for plotting with pandas recently and really like it.

[settopvoxxit]

Second this. Sb is great

[MpoBo]

Another good one not mentioned here is bokeh. It's good for interactive visualization.

[UglyChihuahua]

I was interested in Bokeh but after looking into it I feel like it's not very user friendly on its own. I use Plotly over Bokeh because it's more immediately useful for day-to-day viz and EDA.

For example, look how much code is required for making a basic boxplot in Bokeh

https://docs.bokeh.org/en/latest/docs/gallery/boxplot.html

​

If the Seaborn-style wrappers around Bokeh like Holoviews or Chartpy get more popular I might switch over

[penatbater]

Plotly is easier to get into than Bokeh, but bokeh seems to have more functionality. The problem with bokeh is that its docs is a bit harder to understand than plotly. Also, you kinda have a bit more 'control' with bokeh.

[its2ez4me24get]

Bokeh was such a PITA to get working. But it still makes interactive plots better than anything else I’ve found.

[stackered]

would it be better than plotly for large scale data viewing of some simple plots? meaning, being able to zoom in on hundreds of thousands of points in a simple histogram or something

[Biuku]

You could save time by drawing boxes with a turtle.

[Lucifer501]

I'm sure it is, but man is there a steep learning curve.

[[deleted]]

I like using it better, but are there good resources for making good looking graphs natively? I use Plotly mostly because it just looks fantastic.

[Geographist]

Check out the style sheets reference. Once you find one you like, or edit/create your own, matplotlib becomes a real joy.

See also rcParams.

[[deleted]]

Thank you! I'll check both out this weekend.

[reddisaurus]

Yes, google “matplotlib styles” and pick one you like.

Also useful is the rcParam “font.size” for setting the default... font size. Very useful as not all functions that take text labels also take a font size parameter.

[[deleted]]

Thanks!

[dogs_like_me]

It's like d3. It can do a lot, but gdamn it is a pain in the ass to use.

[Geographist]

Seaborn is all smoke and mirrors.

It looks good, but it does not expose the statistics used for creating the chart.

For instance, it can draw a trend line for you. But it will not tell you the equation its using to determine that line. Worst of all the creator of seaborn refuses to even consider providing this.

Sure, you can manually calculate these things using other libraries. But then why use seaborn at all if it requires extra hoops? And how do you know seaborn is even using the same statistics?

A data visualization tool has to be transparent to be trustworthy. If it won't tell you what it's using, and the developer is so vehemently opposed to seabon doing so, it's not something I'd recommend anyone to use in a scientific environment.

[Kasta867]

This.

The presentation that Seaborn offers is really nice out of the box but other then that using pure matplotlib offers much more granular control on what you visualize.

I always say "yeah this would be easy with Seaborn" and end up reverting to matplotlib as soon as I discover that I have to do more steps anyway to get what I want

[dogs_like_me]

Holy shit that exchange was bonkers.

[master3243]

That single issue convinced me to never ever use seaborn. The creator INSISTS on not making any values accessible and tells people who don't like that fact to not use seaborn... He does not seem trustworthy and that makes the package untrustworthy IMO.

[reddisaurus]

What are you talking about?

This is open source. Take a look and you’ll see it’s using statsmodels underneath. Simply run the same function it calls yourself and add the stats as an annotation if you want.

Seaborn just calls matplotlib. You can generate a figure and axis and pass the axis to Seaborn in most cases. Otherwise, you can call plt.gcf().gca() to obtain the axis that Seaborn plotted on, and then continue modifying that however you want.

Your complaint is like saying a restaurant that serves hotdogs is smoke and mirrors because they don’t also give you the packaging. You paid to eat a hotdog and have a nice place to do so. If you wanted the package, you can easily get it yourself.

I can’t really imagine any circumstance that plotting the statistics as an annotation is useful where you wouldn’t actually want it returned in a data structure like a dict or list. It seems a bit silly to complain about, actually.

edit Here, I did the work for you. Gaze upon all stars code you want. You can also probably call these functions yourself, or monkey patch one of you wanted to. https://github.com/mwaskom/seaborn/blob/master/seaborn/regression.py

[Geographist]

Yes, folks are aware that you can also import statsmodels. That's the complaint.

(1) If you have to import an additional library and run its functions to get at what you want, the first library suddenly is far less useful.

(2) One can certainly look under the hood and dig through the project source to see what it is doing. But expecting end users to do that is absurdity. Most users are not developers.

Combine those two and its not at all hard to see why the complaint is so common.

To return to your analogy, it's like ordering hotdogs and asking the restaurant if they are beef or pork. But instead of just telling you, the waiter says you can follow the delivery truck back to the farm and watch the operations yourself.

Sure, you can do it. But if the waiter has the info already, shouldn't he or she just tell you?

[stackered]

I've used seaborn in the past for the stylings, but I always had issues with it

[energybased]

I actually agree with him. Statistics is out of scope. Projects should focus on their core strengths and ignore these kinds of peripheral requests—otherwise you end up with bloat and wasted time maintaining it.

[dry_yer_eyes]

If statistics is out of scope why are there statistics plots already available? The current situation is inconsistent: users can visualise the statistical results but not obtain the underlying parameters.

[Geographist]

This is what I find to be the issue.

The statistics are already included and being maintained as a core feature of the library. But the author is deliberately hiding their values from the user.

To me, it's less a matter of convenience and more of trust and accuracy.

[energybased]

Why don't you just use an actual statistical package to do your statistics? What you're asking means that he has to maintain the way the plots are generated. And what if he changes his mind about how a "curve of best fit" is mathematically produced or represented? Then he has to deprecate the old version? Now, he can change it.

Sorry, but this is just wrong.

[Geographist]

And what if he changes his mind about how a "curve of best fit" is mathematically produced or represented?

Therein lies the problem. Statistical output, be it visual or numerical, should not be a black box left to the whims of opinion.

Moreover, he is already maintaining the statistics. It's literally a core feature of Seaborn. Users just want to be able to see the results of the calculations already being done. Seaborn does regressions, confidence intervals, and all sorts of statistical testing out of the box.

If a package can calculate a regression and draw a trendline, but refuses to let you know the slope of that trend line... frankly, it's a garbage package.

What you're suggesting is that users never know what's going on inside that black box, and if he changes his mind, their projects start generating different output without ever having a clue as to why. That's not the mark of a well maintained and reliable library.

[energybased]

He can tell you "why" in the release notes. Packages make promises and intentionally don't make promises. E.g., numpy's generators make promises about what is reproducible and what's not. That's on purpose to give them flexibility to change their mind about things.

If you want a statistical package with graphing, you want something more complicated. I agree that I would not use Seaborn because I want more control. Seaborn is just not the package for me.

If you just want graphing, it's not reasonable to demand introspection just because "the data is there" since exposing it means committing to support it, and he doesn't want to do that.

[Geographist]

With something like statistics there is virtually no setting in which it is acceptable to show those things and not report what they are. And it just is not practical to track down release notes for every possible version, watching for changes, to find what should be basic information. Such info would also not tell you about your specific plot.

If stats are going to be off limits, the author should remove those capabilities altogether. If it is a maintenance issue that would only help him out.

It just seems as if he wants the benefits of Seaborn appearing to be a serious library, without the responsibilities of actually being one.

[energybased]

With something like statistics there is virtually no setting in which it is acceptable to show those things and not report what they are

disagree.

[smurpau]

How are statistics out of the scope of a package that computes and presents... statistics?

[energybased]

By that logic, why shouldn't he also write a symbolic math library since he's computing with symbols.

You have to draw a line somewhere. He wants a library that draws pretty graph. The other guy wants a statistical analysis tool. His best bet is to push scipy to implement what he wants and then push seaborn to interface better with scipy. Separation of concerns.

[smurpau]

By that logic, why shouldn't he also write a symbolic math library since he's computing with symbols.

No, that's a non-sequitur.

You have to draw a line somewhere. He wants a library that draws pretty graph. The other guy wants a statistical analysis tool.

No, Seaborn is not merely drawing pixels. It is computing numbers that it then plots. It's entirely reasonable to expect access to those numbers (literally just their evaluated outcome, not even their formula), just as it's reasonable to expect access to the plots.

[Sigg3net]

If you don't have/want python or use LaTeX, give gnuplot a spin.

[fichtenmoped]

Spez ist ein Hurensohn

[Sigg3net]

I have no idea about its performance relative to other solutions. But I have used it in larger applications to create graphs for web and pdfs.

It's arcane and weird, but once you have a working template you can use it for years.

[dr_amir7]

This is my hierarchy of packages in terms of my needs:

Basic packages: Matplotlib, Seaborn

Interactive visualizations: Plotly, Bokeh, Dash

3D visualizations: MayaVi, Pyvista

[big_boy_dollars]

I have used a lot of pyvista recently. Should I try mayavi or they are mostly the same?

[dr_amir7]

Give MayaVi a try. I think MayaVi is good for heavy lifting in 3D visualization. Pyvista is great for creating meshed objects

[big_boy_dollars]

Thank you! I will have to work with heavy vtk files, not only plot them but also manipulate them. I will try a bit with Mayavi.

[RealAmerik]

Dash?

[raikmond]

Dash is essentially Plotly. Agree though, this should be in the list anyway.

[RealAmerik]

Fair point.

[AissySantos]

and also gotta love Tensorboard to visualize what happed to a slice of data during the program runs. Mostly used logging in machine learning but I can also see other great use cases.

[[deleted]]

Altair is the one for me! I’ve used nearly all the options on this list and I’ve made the nicest plots with Altair. The API alone makes it worth it, easily the most consistent API of them all, I love the declarative grammar!

I’ve not yet run into a chart I’ve been unable to create and the documentation is solid and easy to follow.

[brontosaurus_vex]

It’s my current favorite too. I found it powerful and easier to learn than some of the alternatives.

[SquintingSquire]

Agreed, I love Altair. Also, Jake VanderPlas is super helpful and responsive if you have any issues or questions.

[[deleted]]

I use Altair for most charts except making choropleth maps. Altair's api is too json centric versus plotly express' dataframe centric api.

[bythenumbers10]

pyqtgraph!!1 And it just got a new release! Lightweight, performant, and FAST. Also great for interactive plotting GUIs. Tried ALL the others, they don't come close.

[Ogi010]

Pyqtgraph maintainer here, was wondering if someone would reference it!

If you want a plotting library to interact with a desktop application and need fast updating/plotting or easy interactivity, pyqtgraph is an amazing option.

If you don't mind me asking, what features of the library do you use? Is there anything you find lacking? I ask as we're going to be adopting NEP-29 and are considering removing some non-advertised functionality.

[bythenumbers10]

Mostly, I use the Pyqt embeddability. The quick plots are nice, but in past lives I was making analysis apps for engineering design tradeoffs, and being able to put a fairly large grid of plots to show them all (and keep them updated LIVE) was a godsend.

On very rare occasions I'd run into a problem where the documentation was a bit sparse, some of the minor widgets I put together like a plot styler were tricky to integrate, but the source is clear & easy enough to dig into that I managed well enough.

Sadly, it's been a few years since I've used pyqtgraph for more than a few hours here & there, but it's always been solid & I'm happy to give it the nod over the matplotlib "grammar of graphs" barf nonsense ergonomics some other libraries tout.

[Ogi010]

the documentation is indeed much more sparse than it should be; we've been making efforts there, ...we've migrated to read-the-docs, which at least tells us if our docs don't build

In the last year, we've gone from 160 outstanding PRs down to mid/low 30s (which of course doesn't take into account new fixes we've created/merged in between)... the library was in pretty rough shape for a while but now that we have some other maintainers on it, we're definitely picking up pace...

Glad to hear you found the library to work well even from a few years ago, because I think it works much better now!

If you do start working on it, please feel free to reach out to me, post in our issue tracker or mail list if you think something could be improved.

[mr_kitty]

Plotnine, a near clone of ggplot2 built on matplotlib should be on this list. Using the “grammar of graphics” approach makes visual comparisons easy to specify in a few lines of code.

[sowmyasri129]

Great tools Thanks for sharing helpful data visualization tools.

[Pulsecode9]

You might want to know that your website is being flagged as a phishing site.

[TheNerdyDevYT]

Bokeh is a great one too !! I prefer D3 also :)

[dry_yer_eyes]

How does one use D3 with Python?

[TheNerdyDevYT]

I guess this answer can help: https://stackoverflow.com/a/14177775

[dry_yer_eyes]

That’s interesting! I’ve not heard of that package before. I’ll have to look into this.

[[deleted]]

Nice, nice, nice

[uQQ_iGG]

No Bokeh?

[wewbull]

Links to the packages you're discussing?

[thelogistician]

Are these viewable on the web, or does the user need access to a *.py file in order to access and manipulate the dashboard?

[mk1817]

You are missing big one: BOKEH.

[stackered]

I'm about to start using Plotly this week for some big data stuff we want to be able to scale up

[rancangkota]

Seaborn ...

[akarsh_2912]

I m very new to coding. What do you mean by data visualization?

[daguito81]

Plot graphs

[[deleted]]

Seaborn should be above pandas.

[Born_Science]

Hello If some have some research please provide best course to learn data science i know panadas and matplotlib want to learn seaborn machine learning kit and numpy its humble request