Data visualization is a way of representing data or information in some form of visual formats like graphs and charts. Graphical representation helps in highlighting useful information and getting insights from the data. Here is the list of libraries and framework which helps in drawing or converting data to different types of charts and graphs.
Top data visualization libraries and frameworks
- Open source: Chart.js is a community-maintained project, contributions welcome!
- 8 Chart types: Visualize your data in 8 different ways, animated and customizable.
- HTML5 Canvas: Great rendering performance across all modern browsers.
- Responsive: Redraws charts on window resize for perfect scale granularity.
- Flexible Chart Types: Apache ECharts provides more than 20 chart types available out of the box, along with a dozen components, and each of them can be arbitrarily combined to use.
- Powerful Rendering Engine: Easily switch between Canvas and SVG rendering. Progressive rendering and stream loading make it possible to render 10 million data in real-time.
- Professional Data Analysis: Manage data through datasets, which support data transforms like filtering, clustering, and regression to help analyze the multi-dimensional analysis of the same data.
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib produces high-quality charts in various formats and interactive environments across platforms. Matplotlib can be used in Python scripts, Python/IPython shells, web application servers, and graphical user interface toolkits.
- Create high-quality plots.
- Make interactive figures that can zoom, pan, and update.
- Customize visual style and layout.
- Export to many file formats.
- Embed in JupyterLab and Graphical User Interfaces.
- Use a rich array of third-party packages built on Matplotlib.
Seaborn is a library for making statistical graphics in Python. It is a Python data visualization library based on matplotlib and integrates closely with pandas data structures. Seaborn helps you explore and understand your data. Behind the scenes, seaborn uses matplotlib to draw its plots. Its plotting functions operate on data frames and arrays to produce informative plots.
Bokeh is an interactive visualization library for modern web browsers. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.
- Flexible: Bokeh makes it simple to create common plots and handle custom or specialized use-cases.
- Interactive: Tools and widgets let you and your audience probe “what if” scenarios or drill down into the details of your data.
- Shareable: Plots, dashboards, and apps can be published in web pages or Jupyter notebooks.
- Productive: Work with PyData tools you are already familiar.
- Open Source: Everything, including the Bokeh server, is BSD licensed and available on GitHub.
Plotly is an interactive, open-source, and browser-based graphing library for Python. Built on top of plotly.js, it is a high-level, declarative charting library. Plotly ships with over 30charts, including scientific charts, 3D graphs, statistical charts, SVG maps, financial charts, etc.