Several python data visualization tools – some aimed at scientific work, and others with a more commercial touch.
Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
ggplot is a plotting system for Python based on R’s ggplot2 and the Grammar of Graphics. It is built for making profressional looking, plots quickly with minimal code. Making plots is a very repetetive: draw this line, add these colored points, then add these, etc. Instead of re-using the same code over and over, ggplot implements them using a high-level but very expressive API. The result is less time spent creating your charts, and more time interpreting what they mean.
matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell (ala MATLAB®* or Mathematica®†), web application servers, and six graphical user interface toolkits.
Mayavi2 is a general purpose, cross-platform tool for 3-D scientific data visualization. Its features include:
- Visualization of scalar, vector and tensor data in 2 and 3 dimensions.
- Easy scriptability using Python.
- Easy extendibility via custom sources, modules, and data filters.
- Reading several file formats: VTK (legacy and XML), PLOT3D, etc.
- Saving of visualizations.
- Saving rendered visualization in a variety of image formats.
- Convenient functionality for rapid scientific plotting via mlab
Pygal is a python SVG chart generator with support for bar charts, line charts, XY charts, pie charts, radar charts, box plots, dot charts, pyramid charts, funnel charts, gauge charts, worldmap charts and country charts. There are many options for chart customization.
Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. Some of the features that seaborn offers are:
- Several built-in themes that improve on the default matplotlib aesthetics
- Tools for choosing color palettes to make beautiful plots that reveal patterns in your data
- Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data
- Tools that fit and visualize linear regression models for different kinds of independent and dependent variables
- Functions that visualize matrices of data and use clustering algorithms to discover structure in those matrices
- A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate
- High-level abstractions for structuring grids of plots that let you easily build complex visualizations
Vispy is a high-performance interactive 2D/3D data visualization library. Vispy leverages the computational power of modern Graphics Processing Units (GPUs) through the OpenGL library to display very large datasets.
Applications of Vispy include:
- High-quality interactive scientific plots with millions of points.
- Direct visualization of real-time data.
- Fast interactive visualization of 3D models (meshes, volume rendering).
- OpenGL visualization demos.
- Scientific GUIs with fast, scalable visualization widgets (Qt or IPython notebook with WebGL).