Can't handle for forecastio.utils.PropertyUnavailable keyError

[jm20122012]

I've got a script built to report daily data forecasts, but sometimes I get the forecastio.utils.PropertyUnavailable error. I understand that this means there is no data for the particular forecast item I'm looking for. What I'm asking is how do I handle for these errors? This error is causing my script not to finish retrieving the forecast because of this error.

Traceback (most recent call last):
  File "/home/user/.local/lib/python3.6/site-packages/forecastio/models.py", line 103, in __getattr__
    return self.d[name]
KeyError: 'precipType'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "DarkSky.py", line 100, in <module>
    main()
  File "DarkSky.py", line 97, in main
    getDaily(forecast_data)
  File "DarkSky.py", line 70, in getDaily
    'Precip_Type' : i.precipType,
  File "/home/user/.local/lib/python3.6/site-packages/forecastio/models.py", line 107, in __getattr__
    " or is not available for this forecast".format(name)
forecastio.utils.PropertyUnavailable: Property 'precipType' is not valid or is not available for this forecast

I'd be fine with being able to say something like:
'Precipitation Type: none or no precipitation type data exists for this forecast'

The problem is I can't figure out how to handle for this error.

I've tried

exception KeyError:
    print('No data exists for this forecast variable')

and

exception forecastio.utils.PropertyUnavailable:
    print('No data exists for this forecast variable')

But neither of these work. I noticed in the traceback that this ultimately results from a KeyError exception, but using this as an Exception raise doesn't work.

Can anyone help with how to handle for these errors? Thanks

[sjmccorm1993]

@Tesla-Nikola-2012 This example is for hourly data, but the same principle applies:

byHour = forecast.hourly()
series_list = []

    # List of all possible measurements of interest returned by Dark
    # Sky forecast, along with corresponding desired output name
    measurements = {
        "summary": "summary",
        "temperature": "temperature",
        "precipIntensity": "precip_intensity",
        "precipProbability": "precip_prob",
        "humidity": "humidity",
        "windSpeed": "wind_speed",
        "windGust": "wind_gust",
        "cloudCover": "cloud_cover",
        "uvIndex": "uv_index",
        "visibility": "visibility"
    }

    for data in byHour.data:
        s = pd.Series([])

        for key, value in data.d.items():
            if key in measurements.keys():
                s[measurements[key]] = value

        series_list.append(s)
return pd.DataFrame(series_list)

Then the missing measurements end up as NaN values in the resulting dataframe, and your script doesn't break.

[jm20122012]

@sjmccorm1993

So I'm not 100% sure what exactly is being done here to handle for the keyError. Are you suggesting using pd.Series() to initially hold the values, and then assign the values to the elements in the dict?