It is often required to resample the dataset at lower or higher frequencies. This resampling is done based on aggregation or grouping operations. For example, we can resample the data based on the weekly mean time series as follows:

1. We can use the code given here to resample our data:

columns = ['Consumption', 'Wind', 'Solar', 'Wind+Solar']

power_weekly_mean = df_power[columns].resample('W').mean()
power_weekly_mean

The output of the preceding code is given here:

As shown in the preceding screenshot, the first row, labeled 2006-01-01, includes the average of all the data. We can plot the daily and weekly time series to compare the dataset over the six-month period. 

2. Let's see the last six months of 2016. Let's start by initializing the variable:

start, end = '2016-01', '2016-06'

3. Next, let's plot the graph using the code given here:

fig, ax = plt.subplots()

ax.plot(df_power.loc[start:end, 'Solar'],
marker='.', linestyle='-', linewidth=0.5, label='Daily')
ax.plot(power_weekly_mean.loc[start:end, 'Solar'],
marker='o', markersize=8, linestyle='-', label='Weekly Mean Resample')
ax.set_ylabel('Solar Production in (GWh)')
ax.legend();

The output of the preceding code is given here:

The preceding screenshot shows that the weekly mean time series is increasing over time and is much smoother than the daily time series.