R、Python和Julia中的7個數據可視化工具
Python
A full-purpose programming language, python has now also become a tool-of-choice for many in data science. Pandas and Scikit-learn provide many of necessary functions for data analysis and machine learning. Below is a list of some of the leading tools for creating visualizations in Python. See the following project for these examples, Python Visualization.
- Seaborn - A visualization library based upon matplotlib. Although not interactive, the visualizations can be very nice.
- Bokeh - Bokeh provides a bit more interaction than Seaborn, but it is still not fully interactive. Click on the image to see the plot in full size.
R
A very popular language for data science, originally built by/for statisticians but now very widely used.
- htmlwidgets - Previously discussed in the post, htmlwidgets for Rich Data Visualizations in R. Allows for tons of interaction and great for the web.
- ggplot2 - A very popular plotting system for R. It is widely used and can create just about every type of graph. However, the plots are not interactive. R visualization is a sample application that creates the graph below.
Julia
A more recent newcomer, Julia is quickly gaining popularity among data scientists. Due to its young age, the Julia visualization tools are less mature, but they are advancing quickly. A sample project for Julia can be seen at, Simple Julia Plots.
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Gadfly - A Julia library for visualizations. Inspired by ggplot2 for R. It is not really interactive, but it is a great start.
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Escher - Beautiful, interactive web UIs in Julia. Escher is rather new, so it is definitely a project to watch. It uses gadfly for graphics.
Other - Multiple Languages
- Plot.ly - An online collaboration platform for sharing visualizations and data. It includes supports for python, R, and matlab. Hopefully, more to come on this in a future blog post.