Group by value of sum of columns with Pandas
dataframe, group-by, pandas, python
Solution
Here you go:
In [57]: df.groupby(df.sum(), axis=1).sum()
Out[57]:
1 9
0 2 2
1 1 3
2 0 4
[3 rows x 2 columns]
`df.sum()` is your grouper. It sums over the 0 axis (the index), giving you the two groups: `1` (columns `a`, `b`, and, `c`) and `9` (column `d`) . You want to group the columns (`axis=1`), and take the sum of each group.
Problem
I got lost in Pandas doc and features trying to figure out a way to `groupby` a `DataFrame` by the values of the sum of the columns. for instance, let say I have the following data : ``` In [2]: dat = {'a':[1,0,0], 'b':[0,1,0], 'c':[1,0,0], 'd':[2,3,4]} In [3]: df = pd.DataFrame(dat) In [4]: df Out[4]: a b c d 0 1 0 1 2 1 0 1 0 3 2 0 0 0 4 ``` I would like columns `a`, `b` and `c` to be grouped since they all have their sum equal to 1. The resulting DataFrame would have columns labels equals to the sum of the columns it summed. Like this : ``` 1 9 0 2 2 1 1 3 2 0 4 ``` Any idea to put me in the good direction ? Thanks in advance !