For the following dataframe:
StationID HoursAhead BiasTemp
SS0279 0 10
SS0279 1 20
KEOPS 0 0
KEOPS 1 5
BB 0 5
BB 1 5
I'd like to get something like:
StationID BiasTemp
SS0279 15
KEOPS 2.5
BB 5
I know I can script something like this to get the desired result:
def transform_DF(old_df,col):
list_stations = list(set(old_df['StationID'].values.tolist()))
header = list(old_df.columns.values)
header.remove(col)
header_new = header
new_df = pandas.DataFrame(columns = header_new)
for i,station in enumerate(list_stations):
general_results = old_df[(old_df['StationID'] == station)].describe()
new_row = []
for column in header_new:
if column in ['StationID']:
new_row.append(station)
continue
new_row.append(general_results[column]['mean'])
new_df.loc[i] = new_row
return new_df
But I wonder if there is something more straightforward in pandas.
You could groupby on StationID and then take mean() on BiasTemp. To output Dataframe, use as_index=False
In [4]: df.groupby('StationID', as_index=False)['BiasTemp'].mean()
Out[4]:
StationID BiasTemp
0 BB 5.0
1 KEOPS 2.5
2 SS0279 15.0
Without as_index=False, it returns a Series instead
In [5]: df.groupby('StationID')['BiasTemp'].mean()
Out[5]:
StationID
BB 5.0
KEOPS 2.5
SS0279 15.0
Name: BiasTemp, dtype: float64
Read more about groupby in this pydata tutorial.
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