membership test in pandas data frame column

Question

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.

Answer 1

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

Or use iat or iloc:

print (b.iat[0])
19

print (b.iloc[0])
19