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Jupyter Tutorial 1.1.0
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Jupyter Tutorial 1.1.0
  • Introduction
  • What’s new?
  • Notebook
    • Install Jupyter Notebook
    • Create notebook
    • Keyboard shortcuts
    • Jupyter paths and configuration
    • Parameterisation and scheduling
    • Testing
      • Unit tests
      • Doctests
      • Mock
      • unittest2
      • ipytest
      • Hypothesis
  • JupyterLab
    • Install JupyterLab
    • JupyterLab extensions
    • JupyterLab on JupyterHub
    • Real-time collaboration
    • Scheduler
  • JupyterHub
    • Installation
    • Configuration
    • systemdspawner
    • Create service nbviewer
    • ipyparallel
      • Installation
      • Overview
      • Check the installation
      • Configuration
      • IPython’s Direct interface
      • ipyparallel magics
      • Task interface
      • AsyncResult object
      • MPI
  • Binder
  • nbconvert
  • nbviewer
  • Kernels
    • Install, view and start the kernel
    • Python2
    • What’s new in Python 3.8?
    • What’s new in Python 3.9?
    • What’s New In Python 3.10
    • R-Kernel
  • ipywidgets
    • Examples
    • Widget list
    • Widget events
    • Custom widget
    • ipywidgets libraries
      • ipycanvas
      • ipywebrtc
      • ipysheet
      • qgrid
      • ipyvuetify
      • ipympl
    • Embed Jupyter widgets
  • nbextensions
    • Installation
    • List of extensions
    • Create plugin
    • setup.ipynb
    • ipylayout
  • Visualise data
  • Dashboards
    • Jupyter Dashboards
      • Installation of Jupyter dashboards
      • Create dashboard layouts
      • Matplotlib example
    • Appmode
      • app-example.ipynb
    • nbviewer
    • Panel
      • Installation
      • Overview
      • Interactions
      • Widgets
      • Parameterisation
      • Styling
      • Deploy and export
      • Pipelines
      • Templates
      • Running Panel in the browser with WASM
      • FastAPI integration
    • Voilà
      • Installation and use
      • Templating
      • bqplot_vuetify_example.ipynb
      • debug.ipynb
    • jupyter-flex
  • Sphinx
    • nbsphinx
    • Executable Books
  • Use cases
  • Index
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Use cases#

In some companies, Jupyter notebooks are used to explore the ever-increasing amounts of data. These include:

  • Netflix

    • Beyond Interactive: Notebook Innovation at Netflix

    • Part 2: Scheduling Notebooks at Netflix

  • Bloomberg BQuant platform

    • Bloomberg BQuant (BQNT)

  • PayPal

    • PayPal Notebooks: Data science and machine learning at scale, powered by Jupyter

  • Société Générale

    • Jupyter & Python in the corporate LAN

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Executable Books
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