We are pleased to announce the first public release of HoloViews, a Python package for scientific and engineering data visualization: http://ioam.github.io/holoviews HoloViews provides composable, sliceable, declarative data structures for building even complex visualizations easily. It's designed to exploit the rich ecosystem of scientific Python tools already available, using Numpy for data storage, matplotlib and mpld3 as plotting backends, and integrating fully with IPython Notebook to make your data instantly visible. If you look at the website for just about any other visualization package, you'll see a long list of pretty pictures, each one of which has a page or two of code putting it together. There are pretty pictures in HoloViews too, but there is *no* hidden code -- *all* of the steps needed to build a given figure are shown right before the HoloViews plot, with just a few lines needed for nearly all of our examples, even complex multi-figure subplots and animations. This concise but flexible specification makes it practical to explore and analyze your data interactively, while leaving a full record for later reproducibility in the notebook. It may sound like magic, but it's not -- HoloViews simply lets you annotate your data with appropriate metadata, and then the data can display itself! HoloViews provides a set of general, compositional, multidimensional data structures suitable for both discrete and continuous real-world data, and pairs them with separate customizable plotting classes to visualize them without extensive coding. An large collection of continuously tested IPython Notebook tutorials accompanies HoloViews, showing you precisely the small number of steps required to generate any of the plots. Some of the most important features: - Freely available under a BSD license - Python 2 and 3 compatible - Minimal external dependencies -- easy to integrate into your workflow - Builds figures by slicing, sampling, and composing your data - Builds web-embeddable animations without any extra coding - Easily customizable without obscuring the underlying data objects - Includes interfaces to pandas and Seaborn - Winner of the 2015 UK Open Source Award For the rest, check out ioam.github.io/holoviews! Jean-Luc Stevens Philipp Rudiger James A. Bednar The University of Edinburgh School of Informatics -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.