Python Pandas：检查一列中的字符串是否包含在同一行中另一列的字符串中

Question

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

Answer 1

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 NaN s:

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

Answer 2

I could not get either answer @jezreal provided to handle None's in the first column. A slight alteration to the list comprehension is able to handle it:

[x[0] in x[1] if x[0] is not None else False for x in zip(df['A'], df['B'])]

Answer 3

如果您将字符串与字符串进行比较并获得类型错误，您可以这样编码：

df['C'] = df.apply(lambda x: str(x.A) in str(x.B), axis=1)