You may sometimes be required to display your data in a more aggregated form. For example, the co2_levels data contains weekly data, but you may need to display its values aggregated by month of year. In datasets such as the co2_levels DataFrame where the index is a datetime type, you can extract the year of each dates in the index:

# extract of the year in each dates of the df DataFrame
index_year = df.index.year

To extract the month or day of the dates in the indices of the df DataFrame, you would use df.index.month and df.index.day, respectively. You can then use the extracted year of each indices in the co2_levels DataFrame and the groupby function to compute the mean CO2 levels by year:

df_by_year = df.groupby(index_year).mean()

Summarize the values in your time series data

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