Grouping data

One typical workflow during data exploration looks as follows:

  • You find a criterion that you want to use to group your data. Maybe you have GDP data for every country along with the continent and you would like to ask questions about the continents. These questions usually lead to some function applications- you might want to compute the mean GDP per continent. Finally, you want to store this data for further processing in a new data structure.
  • We use a simpler example here. Imagine some fictional weather data about the number of sunny hours per day and city:
    >>> df
          date    city  value
0   2000-01-03  London      6
1   2000-01-04  London      3
2   2000-01-05  London      4
3   2000-01-03  Mexico      3
4   2000-01-04  Mexico      9
5   2000-01-05  Mexico      8
6   2000-01-03  Mumbai     12
7   2000-01-04  Mumbai      9
8   2000-01-05  Mumbai      8
9   2000-01-03   Tokyo      5
10  2000-01-04   Tokyo      5
11  2000-01-05   Tokyo      6

    The groups attributes return a dictionary containing the unique groups and the corresponding values as axis labels:

    >>> df.groupby("city").groups
{'London': [0, 1, 2],
'Mexico': [3, 4, 5],
'Mumbai': [6, 7, 8],
'Tokyo': [9, 10, 11]}
  • Although the result of a groupby is a GroupBy object, not a DataFrame, we can use the usual indexing notation to refer to columns:
    >>> grouped = df.groupby(["city", "value"])
>>> grouped["value"].max()
city
London     6
Mexico     9
Mumbai    12
Tokyo      6
Name: value, dtype: int64
>>> grouped["value"].sum()
city
London    13
Mexico    20
Mumbai    29
Tokyo     16
Name: value, dtype: int64
  • We see that, according to our data set, Mumbai seems to be a sunny city. An alternative – and more verbose – way to achieve the preceding code would be:
    >>> df['value'].groupby(df['city']).sum()
city
London    13
Mexico    20
Mumbai    29
Tokyo     16
Name: value, dtype: int64
Grouping data
Grouping data
Grouping data
Grouping data
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