Difference between revisions of "Jupyter"
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=== <span id="ipyWidgets"></span>ipyWidgets === | === <span id="ipyWidgets"></span>ipyWidgets === | ||
* [http://jupyter.org/widgets Notebook Widgets] | * [http://jupyter.org/widgets Notebook Widgets] | ||
| + | * [[Python#Matplotlib| Matplotlib]] - generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc | ||
* [[Python#Plotly| Plotly]] - graphing library makes interactive, publication-quality graphs | * [[Python#Plotly| Plotly]] - graphing library makes interactive, publication-quality graphs | ||
* [http://github.com/jupyter-widgets/tutorial The Jupyter Widget Ecosystem | M. Breddels, M. Craig, J. Grout, and M. Renou] Tutorial, SciPy 2019 | * [http://github.com/jupyter-widgets/tutorial The Jupyter Widget Ecosystem | M. Breddels, M. Craig, J. Grout, and M. Renou] Tutorial, SciPy 2019 | ||
Revision as of 06:23, 25 July 2019
Youtube search... ...Google search
- Colaboratory
- Git - GitHub and GitLab
- Jupyter Homepage
- JupyterHub
- NBviewer share Jupyter Notebooks
- Python
ipyWidgets
- Notebook Widgets
- Matplotlib - generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc
- Plotly - graphing library makes interactive, publication-quality graphs
- The Jupyter Widget Ecosystem | M. Breddels, M. Craig, J. Grout, and M. Renou Tutorial, SciPy 2019
Notebooks come alive when interactive widgets are used. Users can visualize and control changes in the data. Learning becomes an immersive, plus fun, experience. Researchers can easily see how changing inputs to a model impacts the results.
JupyterLab
