Team at plotly excitingly announces the biggest release of plotly pythonic interface, plotly.py 3.0. This new release comes with great support to Jupyter and Jupyterlab environment, imperative manipulation techniques, animation transition, lots of performance improvements and bug fixes.
What’s new in plotly.py 3.0
- New widget support for Jupyter and Jupyterlab: New widget added called, FigureWidget that creates final object to be plotted with another dictionary-like object containing both data and layout objects. It is compatible with the widget frameworks. One can even hover around the plot and zoom in into regions.
- Manipulation Attributes: Specific and dedicated attributes added making it easier to edit your graphs in the jupyter environment. With this set of attributes, the figures can be manipulated and graphs can be explored more in detail.
- Docstring support: This new support adds informative docstrings for better documentation of your python codes, classes and functions. These docstrings are directly fetched from plotly.js schema and automatically updated to python interface plotly.py.
- Figure specs are now serialized and transferred to plotly.js over Jupyter comm protocol.
- Plotting speed of large data is now much faster, reducing the plotting time from 35 seconds to as low as 3 seconds for 1 million data points.
- Added direct support of Typed Arrays for faster access to raw data.
plotly.py is considered to be a high-performance charting library through which one can plot data across different charts and graphs such as 3D graphs, statistical charts, financial charts, scientific charts and more. To know more on its different styled charts and custom control options, read the official documentation.