Here, since .apply(pd.Series) is slow, so ,use DataFrame constructor:
# data sample
d1 = pd.DataFrame({'Airline':['A','B','C'],'Delays':[[],[1],[1,2]]})
d2 = (pd.DataFrame(d1['Delays'].values.tolist(), index=d1.index)
.rename(columns = lambda x: 'Delay{}'.format(x+1)))
df = d1.join(d2)
print (df)
Airline Delays Delay1 Delay2
0 A [] NaN NaN
1 B [1] 1.0 NaN
2 C [1, 2] 1.0 2.0
Use pop first if there is a need to remove column :
d2 = (pd.DataFrame(d1.pop('Delays').values.tolist(), index=d1.index)
.rename(columns = lambda x: 'Delay{}'.format(x+1)))
df = d1.join(d2)
print (df)
Airline Delay1 Delay2
0 A NaN NaN
1 B 1.0 NaN
2 C 1.0 2.0
If you wish to learn Pandas visit this Pandas Tutorial.