Use case for Pandas' groupby(axis=1) (now deprecated: pandas#51203), and 2 example workarounds (using 2 and 4 "transpose" operations, resp.)
- proj.py shows a simple example of using
groupby(axis=1)
- The
tt branch shows one workaround for the deprecation:
- Transpose before
.groupby
- Transpose logic in the function passed to DataFrameGroupBy.apply
- Transpose again after
.apply
- The
tttt branch shows a simpler workaround, but which introduces 4 transpose operations:
- Transpose before
.groupby
- Transpose at the start of the function passed to DataFrameGroupBy.apply
- Transpose at the end of the function passed to DataFrameGroupBy.apply
- Transpose again after
.apply
Input table
ytds.csv
|
crashes |
cyclist |
driver |
passenger |
pedestrian |
|
cur_ytd |
prv_end |
prv_ytd |
cur_ytd |
prv_end |
prv_ytd |
cur_ytd |
prv_end |
prv_ytd |
cur_ytd |
prv_end |
prv_ytd |
cur_ytd |
prv_end |
prv_ytd |
| county |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| Atlantic |
3 |
36 |
3 |
0 |
2 |
0 |
2 |
17 |
1 |
0 |
7 |
0 |
1 |
13 |
2 |
| Bergen |
5 |
36 |
2 |
0 |
1 |
0 |
2 |
21 |
0 |
0 |
4 |
0 |
3 |
12 |
2 |
| Camden |
3 |
41 |
3 |
0 |
5 |
0 |
0 |
19 |
1 |
0 |
7 |
2 |
3 |
11 |
0 |
| Cape May |
0 |
7 |
1 |
0 |
0 |
0 |
0 |
4 |
1 |
0 |
1 |
0 |
0 |
2 |
0 |
| Essex |
2 |
50 |
6 |
0 |
2 |
0 |
0 |
23 |
1 |
0 |
5 |
1 |
2 |
24 |
4 |
| Gloucester |
1 |
33 |
2 |
0 |
1 |
0 |
1 |
22 |
2 |
0 |
7 |
1 |
0 |
5 |
0 |
| Hudson |
2 |
25 |
2 |
0 |
3 |
0 |
1 |
11 |
1 |
0 |
3 |
0 |
1 |
10 |
1 |
| Hunterdon |
1 |
4 |
1 |
0 |
1 |
1 |
0 |
3 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
| Mercer |
2 |
31 |
1 |
0 |
0 |
0 |
2 |
16 |
0 |
0 |
3 |
0 |
0 |
12 |
1 |
| Middlesex |
6 |
62 |
8 |
0 |
2 |
0 |
2 |
32 |
5 |
2 |
9 |
0 |
4 |
21 |
3 |
| Monmouth |
5 |
38 |
5 |
0 |
4 |
1 |
1 |
18 |
0 |
3 |
7 |
0 |
2 |
9 |
4 |
| Morris |
0 |
22 |
2 |
0 |
0 |
0 |
0 |
14 |
1 |
0 |
4 |
0 |
0 |
4 |
1 |
| Ocean |
6 |
41 |
3 |
0 |
1 |
0 |
2 |
28 |
2 |
0 |
7 |
0 |
4 |
8 |
1 |
| Passaic |
1 |
24 |
1 |
0 |
0 |
0 |
0 |
15 |
1 |
0 |
1 |
0 |
1 |
9 |
0 |
| Union |
1 |
34 |
5 |
0 |
2 |
0 |
0 |
13 |
3 |
0 |
6 |
1 |
1 |
15 |
2 |
| Warren |
0 |
12 |
1 |
0 |
0 |
0 |
0 |
8 |
0 |
0 |
1 |
0 |
0 |
3 |
1 |
| Burlington |
3 |
34 |
0 |
0 |
1 |
0 |
2 |
26 |
0 |
1 |
3 |
0 |
0 |
6 |
0 |
| Cumberland |
1 |
20 |
0 |
0 |
0 |
0 |
0 |
13 |
0 |
0 |
5 |
0 |
1 |
4 |
0 |
| Salem |
0 |
11 |
0 |
0 |
0 |
0 |
0 |
8 |
0 |
0 |
2 |
0 |
0 |
2 |
0 |
| Somerset |
0 |
22 |
0 |
0 |
0 |
0 |
0 |
14 |
0 |
0 |
4 |
0 |
0 |
6 |
0 |
| Sussex |
0 |
6 |
0 |
0 |
0 |
0 |
0 |
6 |
0 |
0 |
2 |
0 |
0 |
1 |
0 |
Output table
proj.csv
|
crashes |
cyclist |
driver |
passenger |
pedestrian |
|
roy |
projected |
roy |
projected |
roy |
projected |
roy |
projected |
roy |
projected |
| county |
|
|
|
|
|
|
|
|
|
|
| Atlantic |
33 |
36 |
2 |
2 |
17 |
19 |
6 |
6 |
11 |
12 |
| Bergen |
39 |
44 |
1 |
1 |
21 |
23 |
4 |
4 |
10 |
13 |
| Camden |
38 |
41 |
5 |
5 |
16 |
16 |
5 |
5 |
13 |
16 |
| Cape May |
5 |
5 |
0 |
0 |
3 |
3 |
1 |
1 |
2 |
2 |
| Essex |
41 |
43 |
2 |
2 |
20 |
20 |
4 |
4 |
19 |
21 |
| Gloucester |
30 |
31 |
1 |
1 |
19 |
20 |
5 |
5 |
5 |
5 |
| Hudson |
23 |
25 |
3 |
3 |
10 |
11 |
3 |
3 |
9 |
10 |
| Hunterdon |
3 |
4 |
0 |
0 |
3 |
3 |
0 |
0 |
1 |
2 |
| Mercer |
33 |
35 |
0 |
0 |
16 |
18 |
3 |
3 |
10 |
10 |
| Middlesex |
53 |
59 |
2 |
2 |
26 |
28 |
10 |
12 |
19 |
23 |
| Monmouth |
33 |
38 |
3 |
3 |
17 |
18 |
9 |
12 |
5 |
7 |
| Morris |
18 |
18 |
0 |
0 |
12 |
12 |
4 |
4 |
3 |
3 |
| Ocean |
41 |
47 |
1 |
1 |
26 |
28 |
6 |
6 |
9 |
13 |
| Passaic |
23 |
24 |
0 |
0 |
13 |
13 |
1 |
1 |
9 |
10 |
| Union |
27 |
28 |
2 |
2 |
9 |
9 |
5 |
5 |
12 |
13 |
| Warren |
10 |
10 |
0 |
0 |
7 |
7 |
1 |
1 |
2 |
2 |
| Burlington |
34 |
37 |
1 |
1 |
26 |
28 |
4 |
5 |
5 |
5 |
| Cumberland |
19 |
20 |
0 |
0 |
12 |
12 |
5 |
5 |
5 |
6 |
| Salem |
10 |
10 |
0 |
0 |
7 |
7 |
2 |
2 |
2 |
2 |
| Somerset |
20 |
20 |
0 |
0 |
13 |
13 |
4 |
4 |
5 |
5 |
| Sussex |
5 |
5 |
0 |
0 |
5 |
5 |
2 |
2 |
1 |
1 |