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In static console, 'c3Grid' displays the 'evaluate' method's result nicely: Note: the 'locationType' expression within the 'group' field is also within the 'projection' field. This is required.

var eval_result = OutbreakLocation.evaluate({
    'projection': 'avg(countryArea), locationType',
    'group': 'locationType',
    'filter': 'exists(countryArea) && exists(locationType)'
})
c3Grid(eval_result)

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Users can also run the 'evaluate' method in python. In this case, users often modify the 'evaluate' method's results for data analysis. To view and analyze the 'evaluate' method's results in Python, please use the helper function available in DTI's c3python module here: https://github.com/c3aidti/c3python NOTE: the 'locationType' expression within the 'group' field is also within the 'projection' field. This is required.

eval_spec = {
    'projection': 'avg(countryArea), locationType',
    'group': 'locationType',
    'filter': 'exists(countryArea) && exists(locationType)'
}
eval_res = c3.OutbreakLocation.evaluate(eval_spec)
df = c3python.EvaluateResultToPandas(result=eval_res, eval_spec=eval_spec)

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Here's another example of running the 'evaluate' method in Python, this time using the 'order' parameter as well: NOTE: the 'count(ethnicity)' expression within the 'order' field is also within the 'projection' field. This is required.

spec = c3.EvaluateSpec(
    projection="ethnicity, count(ethnicity)",
    order='descending(count(ethnicity))',
    group="ethnicity"
)
c3python.EvaluateResultToPandas(result=c3.SurveyData.evaluate(spec), eval_spec=spec)

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To learn more about the 'evaluate' method, please see the C3.ai resources here:

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