Assign value to new column if substring from a list appears in string

Question

I have a dataframe df:

tags
"a,b,c,d"
"c,q,k,t"

and a list of strings I need to search for:

searchList =  ["a", "b"]

I need to add a new column to my dataframe named "topic". If a string from searchList appears in column "tags" I need to set the value in that row to bool True, otherwise bool False.

Endresult:

tags      | topic
"a,b,c,d" | True
"c,q,k,t" | False

My code so far:

searchList =  ["a", "b"]
pattern = '|'.join(searchfor)
df["topic"] = df.loc[(df["tags"].str.contains('|'.join(pattern), na=False)), True] = True

But I get the error:

KeyError: 'cannot use a single bool to index into setitem'

?

Answer 1

You can assign mask to new column, also change pattern to searchList:

searchList =  ["a", "b"]
df["topic"] = df["tags"].str.contains('|'.join(searchList), na=False)
print (df)
      tags  topic
0  a,b,c,d   True
1  c,q,k,t  False

EDIT:

searchList =  ["a", "b"]
df["topic"] = df["tags"].str.split(',', expand=True).isin(searchList).sum(axis=1).eq(2)
print (df)
      tags  topic
0  a,b,c,d   True
1  c,q,k,t  False
2    a,c,d  False

Details:

First use Series.str.split with expand=True for new DataFrame:

print (df["tags"].str.split(',', expand=True))
   0  1  2     3
0  a  b  c     d
1  c  q  k     t
2  a  c  d  None

Then compare by DataFrame.isin for membership:

print (df["tags"].str.split(',', expand=True).isin(searchList))
       0      1      2      3
0   True   True  False  False
1  False  False  False  False
2   True  False  False  False

And count Trues values by sum:

print (df["tags"].str.split(',', expand=True).isin(searchList).sum(axis=1))
0    2
1    0
2    1
dtype: int64

Last compare by Series.eq, == for mask.