如何在大熊猫中测试字符串是否包含列表中的子字符串之一？

Is there any function that would be the equivalent of a combination of df.isin() and df[col].str.contains()?

For example, say I have the series s = pd.Series(['cat','hat','dog','fog','pet']), and I want to find all places where s contains any of ['og', 'at'], I would want to get everything but ‘pet’.

I have a solution, but it’s rather inelegant:

searchfor = ['og', 'at']
found = [s.str.contains(x) for x in searchfor]
result = pd.DataFrame[found]
result.any()

Is there a better way to do this?

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.

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