I have a pandas data frame that one of its columns is a list. Please see below:
>>> a.head
C1 C2
0 23 [2,4,5,8,1]
1 24 [1,2]
2 15 [-2]
3 19 [1,3,4,5,6,7,8,9,0]
I would like to find a row that contains 6 in the C2 and return the value in C1. I thought for something like
b = a["C1"][(6 in a["C2"])]
return(int(b))
but it is not working. Thanks.
I think you need apply and in for test value in list for creating boolean mask:
print (a.C2.apply(lambda x: 6 in x))
0 False
1 False
2 False
3 True
Name: C2, dtype: bool
Then use loc with boolean indexing for select by mask:
b = a.loc[a.C2.apply(lambda x: 6 in x), 'C1']
print (b)
3 19
Name: C1, dtype: int64
Last if need scalar output convert to numpy array and select first value by []:
print (b.values)
[19]
print (b.values[0])
19
print (b.iat[0])
19
print (b.iloc[0])
19