# Vaex: Interactive visualization and exploration of more than a billion objects¶

## What is Vaex?¶

- Veax is a
**program**and**python library**to visualize and explore large tabular datasets. - It mainly renders
**histograms**,**density plots**and**volume rendering**plots for visualization in the order of a**billion**(10^{9}) objects in the order of**1 second**. *Statistics*such as mean, sum, count, standard deviation etc, can all be calculated on an*N-dimensional grid*.- For
**exploration**it support selection in 1 and 2d, but it can also analyse the columns (dimensions) to find subspaces which are richer in information than others. **Downloads**:*Standalone version or Python package***More**:*Gallery*|*Program documentation*|*Tutorial*|*API docs*|*Examples*

## Main features¶

The vaex graphical interface

- Visualize a
**billion**(10^{9}) rows**interactively**in a graphics interface in 1d (**histogram**), 2d (**density plot**) and 3d (**volume rendering**) - Overplot
**vectors**, for instance mean motions,**tensors**(for instance mean velocity dispersion tensor) **Custom expressions**, e.g. log(sqrt(x**2+y**2)), calculated on the fly- publish quality output (using
**matplotlib**) - Linked views: selecting in 1 view will also select it in different views
- data formats

**hdf5**: gadget, our own format (in the future: other formats can be supported with a few lines of code)- hdf5 from Amuse.
- fits bintable
- VOtable over SAMP
- gadget native format

- Ranking of subspaces: for 2 and 3 dimensional subspaces, a ranking can be calculated that indicates the relative richness of structure and/or correlation in them.
- Easily showing a fraction of the data: if the rows are uncorrelated in order (random order), a subset of the data can be shown using a slider (which can make the program more responsive)
- exporting data: the selected data can be exported for further analysis
- undo/redo: a mistake in selection or navigation can quickly be undone using undo

- Visualize a
The vaex library

- Generate the same plots and more as the graphical interface
- Integration with IPython notebook

## Getting started¶

If you want to try out vaex as a graphical tool only, download the binary and read the quickstart.

If you want to use vaex as a library, from your script or IPython notebook, install vaex as a library, and go through the tutorial.

## Links¶

Vaex uses several sites:

- Main page: http://www.astro.rug.nl/~breddels/vaex/
- Github for source, bugs, wiki, releases: https://github.com/maartenbreddels/vaex
- Python Package Index for installing the source in your Python tree: https://pypi.python.org/pypi/vaex/
- Documentation, similar to the homepage, but also has older versions: http://vaex.readthedocs.org/

## Guide¶

Contents: