Seaborn is an open source Python visualization library. It provides a high-level interface for drawing alluring statistical graphics in an easy way without too much boilerplate code. It aims to make visualization a central part of exploring and understanding data.
Seaborn 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 seeks to complement matplotlib, rather than trying to replace it.
This library requires Python 2.7 or 3.4+.
Key Features
- Makes visually appealing graphics. Seaborn also offers an attractive collection of colour palettes and plot styles.
- Concentrates on statistical visualization and modelling.
- Several built-in themes for styling matplotlib graphics.
- High-level interface for controlling the look of matplotlib figures.
- Tools for choosing colour 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 time series 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.
- Reduces the amount of boilerplate code to create statistical data exploration.
- Keeps matplotlib as its backend but provides a new API.
- Good integration with pandas.
- Supports Python 2.7 and 3.4+.
Website: seaborn.pydata.org
Support: Tutorial, GitHub Code Repository
Developer: Michael L. Waskom and contributors
License: BSD 3-Clause “New” or “Revised” License
Seaborn is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Python Visualization Packages | |
|---|---|
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| Bokeh | Elegant, concise construction of versatile graphics |
| Dash | Python framework for building analytical web applications |
| seaborn | Python visualization library based on matplotlib |
| Plotly | Interactive, browser-based graphing library for Python |
| VisPy | Visualize massive datasets in real time |
| Vega-Altair | Declarative Visualization in Python |
| plotnine | Grammar of graphics for Python |
| PyQtGraph | Python graphics and GUI library built on PyQt4 / PySide and numpy |
| bqplot | Interactive Plotting Framework for the Jupyter Notebook |
| Vaex | Fast visualization of big data |
| PyVista | 3D plotting and mesh analysis |
| folium | Visualize data in a Leaflet map |
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| Datashader | Generates aggregate arrays and representations of them as images |
| yt | Multi-code Toolkit for Analyzing and Visualizing Volumetric Data |
| Glumpy | Intuitive interface between NumPy and modern OpenGL |
| GeoViews | Explore and visualize geographical, meteorological, and oceanographic datasets |
| Pygal | Dynamic SVG charting library |
| Glue | Multi-dimensional linked-data exploration |
Read our verdict in the software roundup.
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