I have a dataframe like this:
RecID| A |B
----------------
1 |a | abc
2 |b | cba
3 |c | bca
4 |d | bac
5 |e | abc
And want to create another column, C, out of A and B such that for the same row, if the string in column A is contained in the string of column B, then C = True and if not then C = False.
The example output I am looking for is this:
RecID| A |B |C
--------------------
1 |a | abc |True
2 |b | cba |True
3 |c | bca |True
4 |d | bac |False
5 |e | abc |False
Is there a way to do this in pandas quickly and without using a loop? Thanks
You need apply with in:
df['C'] = df.apply(lambda x: x.A in x.B, axis=1)
print (df)
RecID A B C
0 1 a abc True
1 2 b cba True
2 3 c bca True
3 4 d bac False
4 5 e abc False
Another solution with list comprehension is faster, but there has to be no NaNs:
df['C'] = [x[0] in x[1] for x in zip(df['A'], df['B'])]
print (df)
RecID A B C
0 1 a abc True
1 2 b cba True
2 3 c bca True
3 4 d bac False
4 5 e abc False
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