1. Group By

1.1 split-apply-combine

By “group by” we are referring to a process involving one or more of the following steps

• Splitting the data into groups based on some criteria
• Applying a function to each group independently
• Combining the results into a data structure

Be quite familiar to those who have used a SQL-based tool, in which you can write code like:

SELECT Column1, Column2, mean(Column3), sum(Column4)
FROM SomeTable
GROUP BY Column1, Column2

1.2 splitting an object into groups

1.2.1 GroupBy sorting

In [13]: df2 = pd.DataFrame({'X' : ['B', 'B', 'A', 'A'], 'Y' : [1, 2, 3, 4]})

In [14]: df2.groupby(['X']).sum()
Out[14]: 
   Y
X   
A  7
B  3

In [15]: df2.groupby(['X'], sort=False).sum()
Out[15]: 
   Y
X   
B  3
A  7

1.3 Selecting a group

In [52]: grouped.get_group('bar')
Out[52]: 
     A      B         C         D
1  bar    one  0.254161  1.511763
3  bar  three  0.215897 -0.990582
5  bar    two -0.077118  1.211526
In [53]: df.groupby(['A', 'B']).get_group(('bar', 'one'))
Out[53]: 
     A    B         C         D
1  bar  one  0.254161  1.511763

1.4 Aggregation

In [54]: grouped = df.groupby('A')

In [55]: grouped.aggregate(np.sum)
Out[55]: 
            C         D
A                      
bar  0.392940  1.732707
foo -1.796421  2.824590

In [56]: grouped = df.groupby(['A', 'B'])

In [57]: grouped.aggregate(np.sum)
Out[57]: 
                  C         D
A   B                        
bar one    0.254161  1.511763
    three  0.215897 -0.990582
    two   -0.077118  1.211526
foo one   -0.983776  1.614581
    three -0.862495  0.024580
    two    0.049851  1.185429