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?
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
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
Source: Stackoverflow.com