Data aggregation

As a final topic, we will look at ways to get a condensed view of data with aggregations. Pandas comes with a lot of aggregation functions built-in. We already saw the describe function in Chapter 3, Data Analysis with Pandas. This works on parts of the data as well. We start with some artificial data again, containing measurements about the number of sunshine hours per city and date:

>>> df.head()
   country     city        date  hours
0  Germany  Hamburg  2015-06-01      8
1  Germany  Hamburg  2015-06-02     10
2  Germany  Hamburg  2015-06-03      9
3  Germany  Hamburg  2015-06-04      7
4  Germany  Hamburg  2015-06-05      3

To view a summary per city, we use the describe function on the grouped data set:

>>> df.groupby("city").describe()
                      hours
city
Berlin     count  10.000000
           mean    6.000000
           std     3.741657
           min     0.000000
           25%     4.000000
           50%     6.000000
           75...