Creating multiple observations from single observation depending on conditions

I have written a function in python with three loops which is time consuming. Is it possible to do the same operation in less time with some other way. Here are my code and sample data you can run at your end

#Expand files to minute-level
def expand_b34(data):
final_list = []
df_columns = list(data.columns.tolist())
for i in range(len(data)):
#print i
row_list = np.repeat(data.values[i][np.newaxis,:],     data['DURATION'].loc[i], axis=0).tolist()
start_end_list = range(data['START'].loc[i], data['END_MINUTE'].loc[i])
for j in range(len(row_list)):
row_list[j].extend([start_end_list[j]])
for k in row_list:
final_list.append(k)

data = pd.DataFrame(final_list)
data.columns = df_columns + ['START_MINUTE']
data = data.drop(['START','END_MINUTE'], axis=1)

return data

df = expand_b34(test)

Sample data
date  Id     LD GOOD_AP_ORIG ap_station JULIAN_DAY START DURATION END_MINUTE PLDS PL PLT PLAY
16080 4012007 1 G            5000       16081       0       60       60      0    0   0
16080 4012007 1 G            5000       16081      60       60      120      0    0   0
16080 4012007 1 G            5000       16081     120       60      180      0    0   0
16080 4012007 1 G            5000       16080     180       60      240      0    0   0
16080 4012007 1 G            5000       16080     240      120      360      0    0   0


Code Work flow:

1. calculate difference between START and END_MINUTE
2. expand the observations for the difference

So if the difference is 10, ten more lines of observations will be generated for the observation which is having the same data except for the START_MINUTE variable. START_MINUTE starts at 0 (because for that particular observation START == 0) and ends at 9.

For a single observation the loop creates 10 duplicate observations except for the start minute variable, which varies from 0 to 9.

Can someone help me to do the same observation expansion by optimizing my code?

• Welcome to Code Review , I hope you get good answers. – Siobhan Oct 14 '16 at 13:19
• Optimize for what? Speed or memory? Are either giving you issues? – user83808 Oct 14 '16 at 15:02
• @Bey The description of the tag performance answers your first two questions: "Performance is a subset of Optimization: performance is the goal when you want the execution time of your program or routine to be optimal." – Peilonrayz Oct 14 '16 at 15:13
• Have you tried using pypy instead of CPython, pypy excels at repeated calls because of the JIT (the only thing that might give it trouble is Pandas). See my answer here: codereview.stackexchange.com/a/144196/82612 ~10x perfomance increase only switching from CPython to pypy. – Dair Oct 14 '16 at 19:20
• How slow? I re-worked your script and got it down to 5 lines and one for loop with a couple of list comprehensions. While it may look more elegant than yours, your function is 5 times faster! However, your START_MINUTE is a running count of all records not just for each corresponding row of original df. – Parfait Oct 15 '16 at 1:47