My pandas dataframe:
| ID | String | Pet |
|---|---|---|
| 1 | this is a cat | |
| 2 | hello dog | |
I would like to extract the pet from the 'String' column and fill the 'Pet' column accordingly.
The third row should be empty, and not filled by default.
My attempt:
df['Pet'] = np.where(df['String'].str.contains("cat"), "cat",
np.where(df['String'].str.contains("dog"), "dog", '0'))
Unfortunately the empty (third) row also gets filled in my attempt.
Thank you in advance for your help!
One approach is to, first, create a list with the strings that one see as pet, such as
pets = ['cat', 'dog', 'bird']
Then, using Python pandas.Series.str.extract and regular expressions (using re) one is able to do the work
import re
df['Pet'] = df['String'].str.extract(f'({"|".join(pets)})', flags=re.IGNORECASE, expand=False)
[Out]:
ID String Pet
0 1 this is a cat cat
1 2 hello dog dog
Note:
flags=re.IGNORECASE makes this approach case insensitive.Looks like you could use a regex with str.extract and fillna for your default value:
animals = ['cat', 'dog']
regex = '|'.join(animals)
df['Pet'] = df['String'].str.extract(f'(?i)({regex})', expand=False).fillna(0)
output:
ID String Pet
0 1 this is a cat cat
1 2 hello dog dog
'0'to''?