This tutorial provides an example on how to access a Semantic Container from within a Jupyter notebook. Refer to the Tutorial-Overview for other aspects.
Accesing Semantic Containers in a Jupyter Notebook is as easy as accessing any other online resource. The following example reads data from a Semantic Container on https://vownyourdata.zamg.ac.at:9505 and stores it in the data frame df:
# Create URL to JSON file
url = "https://vownyourdata.zamg.ac.at:9505/api/data/plain"
# Load the JSON file into a data frame
df = pd.read_json(url, orient='records')
These Jupyter Notebook examples demonstrate different use case on how to work with data and meta-data provided by Semantic Containers:
- Visualizing seismic events: SEMCON-ZAMG.ipynb
- Checking location restrictions in Usage Policy: location_policy.ipynb
- Working wiht Provenance information: provenance_visualization.ipynb
Credit: Thanks a lot to user Peb for creating the Jupyter Notebooks!