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()

Accumulating Precipitation Data

If we have a pandas dataframe named df1 with a column '15min' containing 15 minute precipitation data, we can easily accumulate for other durations, using the rolling method as shown in the example below:

df1['01 h'] =df1['15min'].rolling(window=4,center=False).sum()

Note that the window=4 parameter means that it will accumulate 4 lines of 15 minute data, resulting in 1 hour precipitation. This parameter can be changed to whatever duration you want.

Numpy - Accumulated and Incremental series

In Hydrology, it is always needed to deal with time-series of variables, as flow series or precipitation series, with the variable being incremental or accumulated.

Numpy has a great way to transform between accumulated and incremental series.

To accumulate a incremental series use the method

   numpy.cumsum(incrementalSeries)

And to transform a accumulated array to a incremental one, use:

    numpy.diff(accumulatedSeries)