Python pandas dataframe - any way to set frequency programmatically?

Question

I\'m trying to process CSV files like this:

df = pd.read_csv(\"raw_hl.csv\", index_col=\'time\', parse_dates = True))
df.head(2)
                    high             


        

Answer 1

If you have a regular frequency it will be reported when you look at df.index.freq

In [20]: df = DataFrame({'A' : np.arange(5)},index=pd.date_range('20130101 09:00:00',freq='3T',periods=5))

In [21]: df
Out[21]: 
                     A
2013-01-01 09:00:00  0
2013-01-01 09:03:00  1
2013-01-01 09:06:00  2
2013-01-01 09:09:00  3
2013-01-01 09:12:00  4

In [22]: df.index.freq
Out[22]: <3 * Minutes>

Have an irregular frequency will return None

In [23]: df.index = df.index[0:2].tolist() + [Timestamp('20130101 09:05:00')] + df.index[-2:].tolist()

In [24]: df
Out[24]: 
                     A
2013-01-01 09:00:00  0
2013-01-01 09:03:00  1
2013-01-01 09:05:00  2
2013-01-01 09:09:00  3
2013-01-01 09:12:00  4

In [25]: df.index.freq

You can recover a regular frequency by doing this. Downsampling to a lower freq (where you don't have overlapping values), forward filling, then reindexing to the desired frequency and end-points).

In [31]: df.resample('T').ffill().reindex(pd.date_range(df.index[0],df.index[-1],freq='3T'))
Out[31]: 
                     A
2013-01-01 09:00:00  0
2013-01-01 09:03:00  1
2013-01-01 09:06:00  2
2013-01-01 09:09:00  3
2013-01-01 09:12:00  4