I have a dataframe which is monthly averages which looks like the following;
A B C D E
1 3 21 3 22 3
2 4 32 3 24 0
3 5 1 12 3 12
.
.
11 5 4 9 85 85 3
12 43 4 48 3 84 4
I'm looking to convert this data to a daily timeframe so that the dataframe would be a ten year timeseries and each value would correspond to its' monthly value. For example;
A B C D E
01/01/2010 3 21 3 22 3
02/01/2010 3 21 3 22 3
.
.
31/01/2010 3 21 3 22 3
.
.
.
30/12/2020 43 4 48 84 4
31/12/2020 43 4 48 84 4
Any help much appreciated!
Thanks
You can reindex:
idx = pd.date_range('2010-01-01', '2020-12-30', freq='D')
out = df.reindex(idx.month).set_axis(idx)
Output:
A B C D E
2010-01-01 3.0 21.0 3.0 22.0 3.0
2010-01-02 3.0 21.0 3.0 22.0 3.0
2010-01-03 3.0 21.0 3.0 22.0 3.0
2010-01-04 3.0 21.0 3.0 22.0 3.0
2010-01-05 3.0 21.0 3.0 22.0 3.0
... ... ... ... ... ...
2020-12-26 43.0 4.0 48.0 84.0 4.0
2020-12-27 43.0 4.0 48.0 84.0 4.0
2020-12-28 43.0 4.0 48.0 84.0 4.0
2020-12-29 43.0 4.0 48.0 84.0 4.0
2020-12-30 43.0 4.0 48.0 84.0 4.0
[4017 rows x 5 columns]