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raw_df = c3.BlockInfo.fetch(spec={ 'limit': -1, 'filter': 'exists(prp_bf_lr)', 'order': 'descending(id)', 'include': 'pct_i_l,pct_t_l,prp_res_lr,pop10_ha_lr,hu10_ha_lr,eroom_ha_lr,med10_age,prp_bf_lr' }) |
More examples of fetch commands can be found in our examples here:
- https://github.com/c3aidti/casesExample
- https://github.com/c3aidti/genomicsExample
- https://github.com/c3aidti/HouseCoverageExample
- https://github.com/c3aidti/VaccineListing
- https://github.com/c3aidti/EpidemiologyExample
Here is a list of C3 documentation mentioning fetching:
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Converting Fetch results to usable forms in Jupyter Notebook
For most data analysis situations, Fetch Results need to be massaged a little bit
A Code Example in Jupyter Notebook:
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Users can operations like after you convert the c3 Dataset into the pandas dataframe.
ExpressionEngineFunctions
TODO:
Executing Metrics on Time series data
TODO:
Constructing, Training, and Scoring a Machine Learning Model:
Code example using both Python and JS: https://developer.c3.ai/docs/7.12.0/topic/mlpipe-code-examples#construct-tensorflowpipe