How to: Data Visualization
_Libraries for visualizing data. Also see [awesome-javascript](https://github.com/sorrycc/awesome-javascript#data-visualization)._

- [altair](https://github.com/altair-viz/altair) - Declarative statistical visualization library for Python.
- [bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.
- [bqplot](https://github.com/bloomberg/bqplot) - Interactive Plotting Library for the Jupyter Notebook.
- [cartopy](https://github.com/SciTools/cartopy) - A cartographic python library with matplotlib support.
- [diagrams](https://github.com/mingrammer/diagrams) - Diagram as Code.
- [matplotlib](https://github.com/matplotlib/matplotlib) - A Python 2D plotting library.
- [plotnine](https://github.com/has2k1/plotnine) - A grammar of graphics for Python based on ggplot2.
- [pygal](https://github.com/Kozea/pygal) - A Python SVG Charts Creator.
- [pygraphviz](https://github.com/pygraphviz/pygraphviz/) - Python interface to [Graphviz](http://www.graphviz.org/).
- [pyqtgraph](https://github.com/pyqtgraph/pyqtgraph) - Interactive and realtime 2D/3D/Image plotting and science/engineering widgets.
- [seaborn](https://github.com/mwaskom/seaborn) - Statistical data visualization using Matplotlib.
- [UltraPlot](https://github.com/ultraplot/UltraPlot) - Matplotlib wrapper for publication-ready scientific figures with minimal code. Includes advanced subplot management, panel layouts, and batteries-included geoscience plotting.
- [vispy](https://github.com/vispy/vispy) - High-performance scientific visualization based on OpenGL.