From the three datafiles we provided for this chapter we used the first one, solarwatts.dat, to set up a dataframe solarWatts; see Section 10.3.1, Creating a dataframe from imported data. In a similar way, we can create dataframes price and rates from the other two files.
We show now how to merge these three dataframes into one and to treat rows with missing data in the resulting dataframe.
First, we merge solarWatts with price. For this, we use the pandas command merge:
solar_all=pd.merge(solarWatts, price, how='outer', sort=True, on='Date')
solar_all=pd.merge(solar_all, rates, how='outer', sort=True, on='Date')
It sets the column Date, which exists in both dataframes as the index of the new frame. The parameter how defines how to set up the new index column. By specifying outer we decided to choose the union of both index columns. Finally, we want to sort the index.
As solarWatts has data for every minute and...