I’m having trouble formatting a dataframe in a specific style. I want to have data pertaining to one S/N all clumped together. My ultimate goal with the dataset is to plot Dis vs Rate for all the S/Ns. I’ve tired iterating over rows to slice data but that hasnt worked. What would be the best(easiest) approach for this formatting. Thanks!
For example: S/N 332 has Dis 4.6 and Rate of 91.2 in the first row, immediately after that I want it to have S/N 332 with Dis 9.19 and Rate 76.2 and so on for all rows with S/N 332.
S/N Dis Rate
0 332 4.6030 91.204062
1 445 5.4280 60.233917
2 999 4.6030 91.474156
3 332 9.1985 76.212943
4 445 9.7345 31.902842
5 999 9.1985 76.212943
6 332 14.4405 77.664282
7 445 14.6015 36.261851
8 999 14.4405 77.664282
9 332 20.2005 76.725955
10 445 19.8630 40.705467
11 999 20.2005 76.725955
12 332 25.4780 31.597510
13 445 24.9050 4.897008
14 999 25.4780 31.597510
15 332 30.6670 74.096975
16 445 30.0550 35.217889
17 999 30.6670 74.096975
Edit: Tried using sort as @Ian Kenney suggested but that doesn’t help because now the Dis values are no longer in the ascending order:
0 332 4.6030 91.204062
15 332 30.6670 74.096975
3 332 9.1985 76.212943
6 332 14.4405 77.664282
9 332 20.2005 76.725955
12 332 25.4780 31.597510
1 445 5.4280 60.233917
4 445 9.7345 31.902842
7 445 14.6015 36.261851
16 445 30.0550 35.217889
10 445 19.8630 40.705467
13 445 24.9050 4.897008
>Solution :
Use sort_values, which can accept a list of sorting targets. In this case it sounds like you want to sort by S/N, then Dis, then Rate:
df = df.sort_values(['S/N', 'Dis', 'Rate'])
# S/N Dis Rate
# 0 332 4.6030 91.204062
# 3 332 9.1985 76.212943
# 6 332 14.4405 77.664282
# 9 332 20.2005 76.725955
# 12 332 25.4780 31.597510
# 15 332 30.6670 74.096975
# 1 445 5.4280 60.233917
# 4 445 9.7345 31.902842
# 7 445 14.6015 36.261851
# 10 445 19.8630 40.705467
# 13 445 24.9050 4.897008
# 16 445 30.0550 35.217889
# ...