Support for interactive data visualization and use of GUI toolkits. Interpreters to load into your own projects. Easy to use, high performance tools for parallel computing. To get started with IPython in the Jupyter Notebook, see our official example collection. To learn more about IPython, you can download our talks and presentations. Or read our extensive documentation.

Using IPython for interactive work. Guide for IPython core Developers. Welcome to the official IPython documentation. IPython provides a rich toolkit to help you make the most out of using Python interactively. A powerful interactive Python shell. Kernel to work with Python code in Jupyter notebooks and other interactive frontends. Input history, persistent across sessions.

Dask is a flexible parallel computing library for analytic computing. Airflow, Luigi, Celery, or Make. But optimized for interactive computational workloads. Like parallel arrays, dataframes, and lists that extend common interfaces like. NumPy, Pandas, or Python iterators. To larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.

Daskdistributed is a lightweight library for distributed computing in Python. APIs to moderate sized clusters. Serves to complement the existing PyData analysis stack. Each task suffers about 1ms of overhead. A small computation and network roundtrip can complete in less than 10ms.