My data set looks something like this
Faculty ID Measure ID Score
0 10001 EDV high
1 10001 IMM_3 99
2 10001 OP_18b 196
5 10001 OP_22 2
7 10001 OP_29 100
10 10001 PC_01 0
11 10001 SEP_1 56
12 10001 SEP_SH_3HR 89
14 10001 SEV_SEP_3HR 73
15 10001 SEV_SEP_6HR 84
16 10005 EDV high
17 10005 IMM_3 93
18 10005 OP_18b 141
19 10005 OP_18c 311
21 10005 OP_22 2
22 10005 OP_23 100
23 10005 OP_29 93
26 10005 PC_01 1
27 10005 SEP_1 61
28 10005 SEP_SH_3HR 83
29 10005 SEP_SH_6HR 74
30 10005 SEV_SEP_3HR 83
31 10005 SEV_SEP_6HR 97
32 10006 EDV medium
33 10006 IMM_3 82
34 10006 OP_18b 176
37 10006 OP_22 2
38 10006 OP_23 58
39 10006 OP_29 80
42 10006 PC_01 0
43 10006 SEP_1 37
44 10006 SEP_SH_3HR 80
46 10006 SEV_SEP_3HR 62
47 10006 SEV_SEP_6HR 74
48 10007 EDV low
49 10007 IMM_3 72
How would I use Pandas to rearrange the data so the unique ID is the Faculty ID, the headers are the values in the Measure ID column and the values are the scores? For example:
Faculty ID EDV IMM_3 OP_18b OP_22 OP_22 ...
0 10001 high 99 196 2 9 ...
1 10005 high 93 141 ...
make use of the pivot
df.pivot(index='Faculty_ID', columns='Measure_ID')
Score
Measure_ID EDV IMM_3 OP_18b OP_18c OP_22 OP_23 OP_29 PC_01 SEP_1 SEP_SH_3HR SEP_SH_6HR SEV_SEP_3HR SEV_SEP_6HR
Faculty_ID
10001 high 99 196 NaN 2 NaN 100 0 56 89 NaN 73 84
10005 high 93 141 311 2 100 93 1 61 83 74 83 97
10006 medium 82 176 NaN 2 58 80 0 37 80 NaN 62 74
10007 low 72 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.