Now i have some data looks like below :
song_id artist_id 0 days 1 days 2 days
1 0919b5ed4ce2649f61bcc6c21fadab12 0c80008b0a28d356026f4b1097041689 0 0 0
2 8a0777df37bf6a0f3384d63a47d4d21b 0c80008b0a28d356026f4b1097041689 0 1 0
3 b61bc45712ee40c3f4a37dd4d063ad52 0c80008b0a28d356026f4b1097041689 0 0 0
4 a2fbe29da3a760d7467b8a7b3247a9c8 0c80008b0a28d356026f4b1097041689 0 0 1
5 b5e92cb9ff2126189c19305cf148b25d 0c80008b0a28d356026f4b1097041689 0 0 0
And I want to group them by artist_id and aggregate the sum on 0 days , 1 days and 2 days , and get result like that .
artist_id 0 days 1 days 2 days
0 0c80008b0a28d356026f4b1097041689 0 1 1
I tried
df.groupby('artist_id').sum()
But it raise an error .
TypeError: Cannot compare type 'Timedelta' with type 'str'
df.info shows:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10842 entries, 0 to 10841
Columns: 185 entries, song_id to 182 days 00:00:00
dtypes: float64(183), object(2)
memory usage: 15.4+ MB
How can I solve it using pandas's way ?
Any help is welcomed .
You can use astype:
df.columns = df.columns.astype(str)
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