Maximum, minimum and average monthly precipitation

Below is shown some panda commands for retrieving maximum, minimum and average monthly precipitation from daily precipitation data.

The daily precipitation is assumed to be in a pandas DataFrame, with its index in Datetime index format.

1 - Daily to monthly precipitation
df_m=df1.resample('M').sum()

2 - Maximum monthly precipitation
p_max=df_m.groupby(df_m.index.month).max()

3 - Minimum monthly precipitation
p_min=df_m.groupby(df_m.index.month).min()

4 - Average monthly precipitation
p_avg=df_m.groupby(df_m.index.month).mean()

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()