On top of the beautiful styling for basic plots, Plot.ly comes with a fully-featured 3D visualization system that rivals

To add to the unreasonable power of Plot.ly, Plot.ly also supports several different types of statistical plots. Furthermore, Plot.ly’s statistical plotting is incredibly mature and makes things like plotting distributions relatively simple and easy.

One thing that has become surprising in the recent development of technology is geo-data. Geo-data has evolutionized with FIPS to such an extent primarily because of smart-phones and global positioning systems (GPS,) and this has given way to a strong foundation for data science to build on top of. This is of course because data is now more readily available than it ever has been for free with geo-data included.

The last great thing about Plot.ly is that it is open-source. While this isn’t as big of a benefit to some, being able to make changes if required or understand how code is being ran on your machine is always a plus. Being open-source software also means that Plot.ly can be used in practical business applications, making it valuable not only to those who program as a hobby, but also for real-world professionals.

It is hard to argue with Plot.ly’s beautiful approach to maps, as well. Plot.ly’s clorepleths in particular are an absolute joy to use and can make compelling visualizations in a matter of seconds.

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ob and decision. And it’s tricky when your emotions and attachment get in the way. Sometimes you even meet people who are manipulative and abusive without appearing so, making it almost impossible to make ruthless judgments and leave.

The second great tool I would recommend for data analysis with Python is GGPlot.py. Any scientist who has experience in the R programming language has likely used GGPlot or GGPlot2. Both the R and Python package are fantastic and make plotting a breeze and furthermore in-depth. While the R implementation is certainly worth checking out, the Pythonic version is certainly pretty awesome as well.

Another great thing about Plot.ly is that it is written in C. As a result, it uses the LLVM compiler libraries and is compatible with a whole array of programming languages with just a simple API. Not only is Plot.ly available for Python users, but also R users, C users, and any other language’s user base that is willing to implement it. There is even an implementation for the Julia language using the Plots.jl package.

Although the financial side of things is certainly not where most of my domain knowledge falls, Plot.ly has great support for several financial visualizations. This goes to show Plot.ly’s versatility as well, as visualizations rely both in and out of many scientist’s domain. Like everything in Plot.ly, financial charts are easy to use and beautiful — a while ago I worked on a project where we tracked the value of currencies and created a full web-app with an LSTM model that would retrain itself and predict new data based on what it had learnt. Plot.ly really came in handy with its easy to use and Javascript integrated visualizations for candlestick data in this circumstance.

Steering away from the world of visualization is a fantastic Python package called SymPy. MATLAB, Mathematica, and Julia programmers, hold on to your hats — SymPy is a Python module that allows Pythonic programmers and scientists to use more mathematical bindings. This allows the language to shift from a typically more software engineering approach to mathematics to the other end of the spectrum where code is frequently written as math.

Another significant thing to note about GGPlot is its approach to graphics and visualization. Generally, GGPlot is more geometrically modular. This means that compositions can often be added to or altered quite easily. This is further beneficial to data science, as often there is a certain idea or data point that might want to be emphasized more than others.

A significant difference between the GGPlot package and the Plot.ly package for Python is that GGPlot is much more statistically-focused. Plot.ly is a great tool for data visualization, but is also targeted at more typical data visualization, rather than specifically statistical plotting.

Bokeh as a visualization tool is incredibly similar to Plot.ly, and that isn’t a bad thing at all. This means that Bokeh can be used to create beautiful and interactive visualizations incredibly easily.

Many awesome implementations of Pythonic code and math use SymPy for mathematical calculations on the back-end. Here are some notable examples pulled straight from their homepage:

While Bokeh is similar to Plot.ly in a lot of ways, this is certainly not the case in what is possible with Bokeh. Not only is there support for all of the typical visualization techniques, but Bokeh also allows a user to create interesting and interactive visualizations of nearly anything. One relatively common use for Bokeh is the visualization of network graphs, which is really cool! On top of that, it does come with the geo-data visualizations that one might expect from the Plot.ly library.

Bokeh is another interactive plotting library that was built with modern web-browsing and computing in mind. Similarly to Plot.ly, Bokeh might not be as extendable as something like GGPlot, but presents a host of benefits over the former by being incredibly integrated with Javascript. Every data scientist loves interactive visualizations, and Bokeh often takes this to a very high-level and simple API with stunning results.