Orca is a pipeline orchestration tool that allows you to define dynamic data sources and explicitly connect them to processing functions. Orca has many features for working with Pandas data structures, but it can be used with anything.
Orca has explit goals of flexibility, transparency, lazy execution, and encouraging good practices. Those goals are achieved by:
- Users may write and run any Python
- Dependencies between data and processing units are explicitly listed
- Your code is a record of everything that happens
- Orca only calls functions if they are explicitly needed
- Encourage small, functional units
- Encourage code re-use
import plotly.graph_objects as go
ValueError Traceback (most recent call last)
With the dependencies installed, install Orca with pip:
pip install orca
Orca may also be installed with conda:
conda install -c udst orca
Add the server option to include the optional server dependencies:
pip install orca[server]
Kaleido is a cross-platform library for generating static images (e.g. png, svg, pdf, etc.) for web-based visualization libraries, with a particular focus on eliminating external dependencies.
The project’s initial focus is on the export of plotly.js images from Python for use by plotly.py, but it is designed to be relatively straight-forward to extend to other web-based visualization libraries, and other programming languages.
The primary focus of Kaleido (at least initially) is to serve as a dependency of web-based visualization libraries like plotly.py. As such, the focus is on providing a programmatic-friendly, rather than user-friendly, API.
pip install kaleido
Install the kaleido wheel.
$ pip install kaleido
Install plotly as well
$ pip install plotly
from kaleido.scopes.plotly import PlotlyScope