Clever Geek Handbook

Python Pandas: check if a row in one column is contained in a row of another column in the same row

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 from A and B so that for the same row, if the row in column A is contained in the row of column B, then C = True, and if not, then C = False.

The example output I'm looking for is the following:

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 to

+17


source to share


2 answers


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 shouldn't be 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
+24


source


I was unable to get any of @ jezreal's answer provided for handling None in the first column. A small change in list comprehension can handle this:



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


source






More articles:


All Articles