This page showcases a set of sample Jupyter notebooks that illustrate how to use Studio APIs.
These notebooks are designed to be copied, forked and reused, and are being made available as follows:

Service	Description
	All sample notebooks are provided in open source form under the MIT license on GitHub.
	All sample notebooks on this page can be run directly in your browser via the Binder service. (Note: It can take take a minute for the Jupyter environment on Binder to start a notebook, however once a notebook is loaded you can fully interact with it in the browser.)
	All sample notebooks on this page (except Plotly Cross-filtering) can be run directly in your browser via Google Colab.

Map SDK Samples

A number of sample notebooks that show how to work with Studio maps using the Map SDK:

Notebook	Preview
Introduction Create Studio map, add a dataset, and set the map view.
Local Maps Use local Studio maps to visualize Dataframes, GeoJSON, and CSV.
Layers Add and control map layers.
Filters Control filters and listen to filter change events.
Timeline Control map filters and listen to filter change events.
Event Handling Interactivity, click and hover events.

Integration Notebooks

The sample notebooks in this section illustrate how to integrate the Map SDK with other notebook packages and workflows.

Notebook	Preview
Plotly Cross-filtering Synchronize Studio map filters with `plotly` charts.
TensorFlow Prediction Visual prediction analysis using TensorFlow.
PyTorch Prediction Bike share trip duration prediction using PyTorch.
Kuwala Correlation Correlating against Kuwala data feeds.