Conditional forward fill in pandas
groupby + ffill and bfill
df.Q=df.groupby('code').Q.apply(lambda x : x.ffill().bfill())dfOut[755]: Date S E cp Last Q code30 2017-11-10 22500 2017-11-17 P 170.0 828.47 11/17/2017P2250032 2017-11-10 22625 2017-11-17 P 180.0 646.91 11/17/2017P2262535 2017-11-10 22750 2017-11-17 C 145.0 651.84 11/17/2017C2275036 2017-11-13 22500 2017-11-17 P 245.0 828.47 11/17/2017P2250038 2017-11-13 22625 2017-11-17 P 315.0 646.91 11/17/2017P2262541 2017-11-13 22750 2017-11-17 C 35.0 651.84 11/17/2017C2275042 2017-11-14 22500 2017-11-17 P 215.0 828.47 11/17/2017P2250044 2017-11-14 22625 2017-11-17 P 305.0 646.91 11/17/2017P2262547 2017-11-14 22750 2017-11-17 C 26.0 651.84 11/17/2017C2275048 2017-11-15 22500 2017-11-17 P 490.0 828.47 11/17/2017P2250050 2017-11-15 22625 2017-11-17 P 605.0 646.91 11/17/2017P2262553 2017-11-15 22750 2017-11-17 C 4.0 651.84 11/17/2017C2275054 2017-11-16 22500 2017-11-17 P 140.0 828.47 11/17/2017P2250056 2017-11-16 22625 2017-11-17 P 295.0 646.91 11/17/2017P2262559 2017-11-16 22750 2017-11-17 C 4.0 651.84 11/17/2017C2275060 2017-11-17 22250 2017-11-24 P 165.0 707.57 11/24/2017P2225061 2017-11-17 22375 2017-11-24 P 195.0 607.16 11/24/2017P2237565 2017-11-17 22500 2017-11-24 C 175.0 666.56 11/24/2017C2250066 2017-11-20 22250 2017-11-24 P 175.0 707.57 11/24/2017P2225067 2017-11-20 22375 2017-11-24 P 225.0 607.16 11/24/2017P2237571 2017-11-20 22500 2017-11-24 C 75.0 666.56 11/24/2017C2250072 2017-11-21 22250 2017-11-24 P 70.0 707.57 11/24/2017P2225073 2017-11-21 22375 2017-11-24 P 120.0 607.16 11/24/2017P2237577 2017-11-21 22500 2017-11-24 C 95.0 666.56 11/24/2017C2250078 2017-11-22 22250 2017-11-24 P 15.0 707.57 11/24/2017P2225079 2017-11-22 22375 2017-11-24 P 35.0 607.16 11/24/2017P2237583 2017-11-22 22500 2017-11-24 C 125.0 666.56 11/24/2017C2250084 2017-11-24 22375 2017-12-01 P 140.0 834.13 12/01/2017P2237585 2017-11-24 22500 2017-12-01 P 185.0 763.76 12/01/2017P2250089 2017-11-24 22625 2017-12-01 C 165.0 750.45 12/01/2017C22625
You can use transform on the groupby object.
df.loc[:, 'Q'] = df.groupby('code')['Q'].transform(lambda group: group.ffill())Timings
%timeit -n 1000 df.loc[:, 'Q'] = df.groupby('code')['Q'].transform(lambda group: group.ffill())# 1000 loops, best of 3: 2.41 ms per loop%timeit -n 1000 df.loc[:, 'Q'] = df.groupby('code')['Q'].ffill()# 1000 loops, best of 3: 3.66 ms per loop