I have a Dataframe that stores aging value as below:
Aging
-84 days +11:36:15.000000000
-46 days +12:25:48.000000000
-131 days +20:53:45.000000000
-131 days +22:22:50.000000000
-130 days +01:02:03.000000000
-80 days +17:02:55.000000000
I am trying to extract the text before days in the above column. I tried the below:
df['new'] = df.Aging.split('days')[0]
The above returns
AttributeError: 'Series' object has no attribute 'split'
Expected output:
-84
-46
-131
-131
-130
-80
IMO, a better idea would be to convert to timedelta and extract the days component.
pd.to_timedelta(df.Aging, errors='coerce').dt.days
0 -84
1 -46
2 -131
3 -131
4 -130
5 -80
Name: Aging, dtype: int64
If you insist on using string methods, you can use str.extract.
pd.to_numeric(
df.Aging.str.extract('(.*?) days', expand=False),
errors='coerce')
0 -84
1 -46
2 -131
3 -131
4 -130
5 -80
Name: Aging, dtype: int32
Or, using str.split
pd.to_numeric(df.Aging.str.split(' days').str[0], errors='coerce')
0 -84
1 -46
2 -131
3 -131
4 -130
5 -80
Name: Aging, dtype: int64