Find Maximum Values by year in timeseries dataframe, keeping the date

df2=df1.ix[df1.groupby(df1.index.year).idxmax().iloc[:,0]]

Daily precipitation data to monthly and yearly

With pandas, we can group datetime values according to datetime units, as months and years.

If we have a pandas dataframe with 2 columns  - DATE and VALUE

Certify that the column DATE is recognized as datetime64 format - you can use, for example:

df1['DATE'] = pd.to_datetime(df1['DATE'])

and then you can group and make operations on this group.

Getting the average of total monthly precipitation:

monthly = df1.groupby(df1['DATE'].dt.month).sum() / len(pd.unique(df1['DATE'].dt.year))

(groups by month sum and then divide by the number of years)

Getting the average yearly precipitation:

PAnual = df1.groupby(df1[0].dt.year).sum().mean()