Our technical teams have both
benefited from and contributed
to open source software
Like most data scientists and engineers, our technical
teams have benefited greatly from open source software.
And, as we continue to explore new ways to research,
develop, implement and maintain AI, we’ve developed
lots of software of our own, too.
If you’re a data scientist, you can use any of the
software below to help run your own AI projects.
Lens is our tool for summarising and exploring Pandas DataFrames. It automatically generates summary statistics, density plots and cross correlation plots for an input DataFrame, speeding up the initial stages of many data science projects.
Dash bootstrap components is our project providing Bootstrap components and layouts for Plotly Dash. With this library, you can quickly put together attractive and responsive apps with Dash that look as good on mobile as they do on desktop.
A Scala library we have written and use extensively for loading configuration from disk or remote sources and refreshing it at regular intervals.
marshmallow-dataframe helps you generate marshmallow Schemas for Pandas DataFrames. This makes it easier to write robust code that can validate the structure of data that includes DataFrames.
Python library for interacting with the Faculty Platform. Use this to build your own tools integrating with Faculty.
A command line interface to the Faculty Platform, allowing easy access to the workspace, servers, jobs and other features for those comfortable in the terminal.
Interact with Faculty datasets and reports using R.
Use Faculty in conjunction with your favourite editor. This command line tool keeps a local directory synchronised with your Faculty project’s workspace.
Labelling data is a common menial task that can take up a lot of a data scientist’s time. Superintendent provides interactive tools for Jupyter to make data labelling quick and easy.
With Jupyter widgets, you can add interactivity to your notebooks. Be it for data exploration, cleaning, visualisation or interactive reporting, widgets provide possibilities for rich views on your data.
gmaps is a Jupyter plugin for embedding Google Maps in your notebooks. It’s designed with data visualisation in mind, and supports a wide range of Google Maps features.
MLflow provides tooling for managing the data science workflow. It’s supported in Faculty Platform through our experiment tracking feature, and Faculty engineers have made extensive contributions back to the open source project.
A Python client for Apache Livy, allowing easy management of Spark sessions and running code on a remote Spark cluster.