А вот и 6 популярных библиотек для визуализации в Питоне с описанием на английском языке (по ссылкам есть примеры использования и красивые графики).
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.
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.
Seaborn offers:
- 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
matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code
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.
pygal is a dynamic SVG charting library. It features various graph types like bar charts, line charts, XY charts, pie charts, radar charts, dot charts, pyramid charts, funnel charts, gauge charts. It features css features with pre-defined themes.
igraph is a collection of network analysis tools with the emphasis on efficiency, portability and ease of use. python-igraph is a python interface to the igraph. graph plotting functionality is provided by the Cairo library.
Инфографику и дополнительные библиотеки можно посмотреть здесь или здесь.