Perhaps you're looking for `pd.cut` + `pd.get_dummies`?
pd.get_dummies(pd.cut( 100 - df.Error_Rate, bins=[0, 65, 80, 90, 100], labels=['Below 65%', '65% - 80%', '80% - 90%', 'Above 90%']) ) Below 65% 65% - 80% 80% - 90% Above 90% 0 0 0 0 1 1 0 0 1 0 2 0 1 0 0 3 0 0 1 0 4 0 0 1 0 5 0 1 0 0 6 1 0 0 0 7 0 1 0 0 8 0 1 0 0 9 1 0 0 0 10 1 0 0 0 11 1 0 0 0 12 1 0 0 0 13 1 0 0 0

Just out of curiosity I've taken a look at what happens under the hood, and I've used [dtruss/strace][1] on each test.
C++
./a.out < in Saw 6512403 lines in 8 seconds. Crunch speed: 814050
syscalls `sudo dtruss -c ./a.out < in`
CALL COUNT __mac_syscall 1 <snip> open 6 pread 8 mprotect 17 mmap 22 stat64 30 read_nocancel 25958

Python
./a.py < in Read 6512402 lines in 1 seconds. LPS: 6512402
syscalls `sudo dtruss -c ./a.py < in`
CALL COUNT __mac_syscall 1 <snip> open 5 pread 8 mprotect 17 mmap 21 stat64 29
[1]: http://en.wikipedia.org/wiki/Strace