The Python Oracle

Check if String in List of Strings is in Pandas DataFrame Column

Become part of the top 3% of the developers by applying to Toptal https://topt.al/25cXVn

--

Music by Eric Matyas
https://www.soundimage.org
Track title: Dreamlands

--

Chapters
00:00 Question
01:39 Accepted answer (Score 6)
02:50 Thank you

--

Full question
https://stackoverflow.com/questions/5617...

Accepted answer links:
[Series.isin]: http://pandas.pydata.org/pandas-docs/sta...
[Series.str.contains]: http://pandas.pydata.org/pandas-docs/sta...

--

Content licensed under CC BY-SA
https://meta.stackexchange.com/help/lice...

--

Tags
#python #string #pandas

#avk47



ACCEPTED ANSWER

Score 9


If need match values in list, use Series.isin:

df['Match'] = df["Brand"].isin(search_for_these_values)
print (df)
            Brand  Price Liscence Plate  Match
0     Honda Civic  22000        ABC 123  False
1  Toyota Corolla  25000        XYZ 789  False
2      Ford Focus  27000        CBA 321   True
3         Audi A4  35000        ZYX 987  False
4             NaN  29000        DEF 456  False

Solution with match is used for check substrings, so different output.

Alternative solution for match substrings with Series.str.contains and parameter na=False:

df['Match'] = df["Brand"].str.contains(pattern, na=False)
print (df)
            Brand  Price Liscence Plate  Match
0     Honda Civic  22000        ABC 123   True
1  Toyota Corolla  25000        XYZ 789   True
2      Ford Focus  27000        CBA 321   True
3         Audi A4  35000        ZYX 987  False
4             NaN  29000        DEF 456  False

EDIT:

For test values in substrings is possible use list comprehension with loop by values in search_for_these_values and test match by in with any for return at least one True:

df['Match'] = [any(x in z for z in search_for_these_values) 
                                if x == x 
                                else False 
                                for x in df["Brand"]]
print (df)

            Brand  Price Liscence Plate  Match
0     Honda Civic  22000        ABC 123  False
1  Toyota Corolla  25000        XYZ 789  False
2      Ford Focus  27000        CBA 321   True
3         Audi A4  35000        ZYX 987   True
4             NaN  29000        DEF 456  False