I have a column named "KL" with for example:
sem_0405M4209F2057_1.000
sem_A_0103M5836F4798_1.000
Now I want to extract the four digits after "M" and the four digits after "F". But with df["KL"].str.extract I can't get it to work.
Locations of M and F vary, thus just using the slice [9:13] won't work for the complete column.
If you want to use str.extract, here's how:
>>> df['KL'].str.extract(r'M(?P<M>[0-9]{4})F(?P<F>[0-9]{4})')
M F
0 4209 2057
1 5836 4798
Here, M(?P<M>[0-9]{4}) matches the character 'M' and then captures 4 digits following it (the [0-9]{4} part). This is put in the column M (specified with ?P<M> inside the capturing group). The same thing is done for F.
You could use split to achieve this, probably a better way exists:
In [147]:
s = pd.Series(['sem_0405M4209F2057_1.000','sem_A_0103M5836F4798_1.000'])
s
Out[147]:
0 sem_0405M4209F2057_1.000
1 sem_A_0103M5836F4798_1.000
dtype: object
In [153]:
m = s.str.split('M').str[1].str.split('F').str[0][:4]
f = s.str.split('M').str[1].str.split('F').str[1].str[:4]
print(m)
print(f)
0 4209
1 5836
dtype: object
0 2057
1 4798
dtype: object
You can also use regex:
import re
def get_data(x):
data = re.search( r'M(\d{4})F(\d{4})', x)
if data:
m = data.group(1)
f = data.group(2)
return m, f
df = pd.DataFrame(data={'a': ['sem_0405M4209F2057_1.000', 'sem_0405M4239F2027_1.000']})
df['data'] = df['a'].apply(lambda x: get_data(x))
>>
a data
0 sem_0405M4209F2057_1.000 (4209, 2057)
1 sem_0405M4239F2027_1.000 (4239, 2027)
