How to test if a string contains one of the substrings in a list, in pandas?
You can use str.contains
alone with a regex pattern using OR (|)
:
s[s.str.contains('og|at')]
Or you could add the series to a dataframe
then use str.contains
:
df = pd.DataFrame(s)
df[s.str.contains('og|at')]
Output:
0 cat
1 hat
2 dog
3 fog
Here is a one line lambda that also works:
df["TrueFalse"] = df['col1'].apply(lambda x: 1 if any(i in x for i in searchfor) else 0)
Input:
searchfor = ['og', 'at']
df = pd.DataFrame([('cat', 1000.0), ('hat', 2000000.0), ('dog', 1000.0), ('fog', 330000.0),('pet', 330000.0)], columns=['col1', 'col2'])
col1 col2
0 cat 1000.0
1 hat 2000000.0
2 dog 1000.0
3 fog 330000.0
4 pet 330000.0
Apply Lambda:
df["TrueFalse"] = df['col1'].apply(lambda x: 1 if any(i in x for i in searchfor) else 0)
Output:
col1 col2 TrueFalse
0 cat 1000.0 1
1 hat 2000000.0 1
2 dog 1000.0 1
3 fog 330000.0 1
4 pet 330000.0 0
One option is just to use the regex |
character to try to match each of the substrings in the words in your Series s
(still using str.contains
).
You can construct the regex by joining the words in searchfor
with |
:
>>> searchfor = ['og', 'at']
>>> s[s.str.contains('|'.join(searchfor))]
0 cat
1 hat
2 dog
3 fog
dtype: object
As @AndyHayden noted in the comments below, take care if your substrings have special characters such as $
and ^
which you want to match literally. These characters have specific meanings in the context of regular expressions and will affect the matching.
You can make your list of substrings safer by escaping non-alphanumeric characters with re.escape
:
>>> import re
>>> matches = ['$money', 'x^y']
>>> safe_matches = [re.escape(m) for m in matches]
>>> safe_matches
['\\$money', 'x\\^y']
The strings with in this new list will match each character literally when used with str.contains
.