![]() ![]() If you are serious about data visualisation, you need to be at least aware of, if not proficient in, some of these. NodeBox is a quick, easy way for Python-savvy developers to create 2D visualisations (opens in new tab) Pro toolsĪt the opposite end of the spectrum from Excel are professional data-analysis tools. It is a desktop application, but can be run on all platforms, and given that it is now several years old, there are plenty of examples and code from the community. There is also a Processing.js project to make it easier for websites to use Processing without Java applets, plus a port to Objective-C so you can use it on iOS. ![]() It enables you to write much simpler code which is in turn compiled into Java. Processing has become the poster child for interactive visualisations. This could mean desktop applications and programming environments. If you're getting serious about data visualisations, you need to move beyond simple web-based widgets onto something more powerful. They have the usual problem of OpenType not being fully supported in all browsers, but they're something to consider in the near future. A few of these fonts, such as FF Chartwell and Chartjunk, have been specially crafted for the purpose of displaying charts and graphs. They scale and print perfectly, and look great on newer Retina devices too. One recent trend in web development is to merge symbol fonts with font embedding to create beautifully vectorised icons. This is a product of Stamen, Bloom and MapBox, so you know it has an interesting track record.ĬartoDB provides an unparalleled way to combine maps and tabular data to create visualisations (opens in new tab) Charting fonts This makes it very limited in its basic form – but don't let that fool you: with a few extensions, such as Wax, you can really make this library sing. Weighing in at only 10kB, it is the smallest of options discussed here. Sure, you can probably shoehorn everything you need into any of these maps, but it's best not to have a hammer and view every problem as a nail. There are now several options out there if you are looking to embed custom mapping solutions in your own data visualisation project, and knowing when to choose one over the others is a key business decision. Since then, the market has matured a great deal. Soon after, Google released its Maps API, which allowed any developer to embed maps in their own sites. Then Google Maps came along and blew away every preconceived notion of how an online map should work. Mapping used to be a really hard task on the web. This creates a real-time feedback loop, enabling you to understand complex equations in a more intuitive way (opens in new tab) Mapping Pulling on any one of the knobs affects data throughout all of the linked charts. Hopefully this gives some sort of reference to help answer your question.Īlso to help avoid closure flags and downvotes you should try and show some of what you have tried to do or find, this makes for a better question and helps to illicit responses.Tangle creates complex interactive graphics. There are a few web sources that I found concerning it using duckduckgo at this link.Īs far as an API like matplotlib, I cannot say for certain that one exists. The Tableau data extract API and some information about it can be found at this link. There is also a package called ggplot which is used in R alot, but is also made for Python, which you can find here ggplot for python. There is a Data Extract API that you could use to import your data into Python and do your visualizations there, so I do not know if this is going to answer your question entirely.Īs in the first comment you can use Matplotlib from Matplotlib website, or you could install Canopy from Enthought which has it available, there is also Pandas, which you could also use for data analysis and some visualizations. ![]() There is a Tablaeu API and you can use Python to use it, but maybe not in the sense that you think. ![]()
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