KeyError: 'parameter' for two different locations, 1 works, 1 doesn't.

[TheAnalystx]

HI, I try to get weather data for two different locations, one works another throws an exception, can anyone explain why this is happening?

KeyError: 'parameter'

from wetterdienst.provider.dwd.observation import DwdObservationRequest, DwdObservationDataset, \
    DwdObservationResolution, DwdObservationPeriod


stations = DwdObservationRequest(
    parameter=[
        DwdObservationDataset.TEMPERATURE_AIR,
        DwdObservationDataset.WIND,
        DwdObservationDataset.PRECIPITATION
    ],
    resolution=DwdObservationResolution.MINUTE_10,
    period=DwdObservationPeriod.NOW
)

# works
nearest_station = stations.filter_by_rank(rank=1, latitude=52.122050, longitude=11.619845)
df = nearest_station.values.all().df

#fails
nearest_station = stations.filter_by_rank(rank=1, latitude=52.384630, longitude=9.733908)
df = nearest_station.values.all().df  # <- fails here

[gutzbenj]

Dear @TheAnalystx ,

I'm on it and will draft a new release just now to resolve this error!

[TheAnalystx]

Dear @gutzbenj ,

First of all, thank you very much for this great library and your fast reply!
The new version seems to work and the exception is gone, but I noticed a lot of warnings occuring now:

D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:752: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, column] = self._coerce_floats(df[column])
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:583: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, Columns.QUALITY.value] = pd.to_numeric(df[Columns.QUALITY.value]).astype(float)
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:652: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, column] = self._coerce_dates(df[column], timezone_)
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:752: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, column] = self._coerce_floats(df[column])
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:583: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, Columns.QUALITY.value] = pd.to_numeric(df[Columns.QUALITY.value]).astype(float)
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:652: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, column] = self._coerce_dates(df[column], timezone_)
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:752: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, column] = self._coerce_floats(df[column])
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:583: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, Columns.QUALITY.value] = pd.to_numeric(df[Columns.QUALITY.value]).astype(float)
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:669: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, Columns.STATION_ID.value] = self._parse_station_id(df[Columns.STATION_ID.value]).astype("category")
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:670: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, Columns.DATASET.value] = self._coerce_strings(df[Columns.DATASET.value]).astype("category")
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:673: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, Columns.PARAMETER.value] = self._coerce_strings(df[Columns.PARAMETER.value]).astype("category")
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:791: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, Columns.PARAMETER.value] = (
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:669: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, Columns.STATION_ID.value] = self._parse_station_id(df[Columns.STATION_ID.value]).astype("category")
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:670: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, Columns.DATASET.value] = self._coerce_strings(df[Columns.DATASET.value]).astype("category")
D:\Anaconda3\envs\my_project\lib\site-packages\wetterdienst\core\scalar\values.py:673: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  df.loc[:, Columns.PARAMETER.value] = self._coerce_strings(df[Columns.PARAMETER.value]).astype("category")

Best wishes!,
Andreas