I have a normal df.index that I would like to add some hours to it.
In [1]: test[1].index Out[2]: <class 'pandas.tseries.index.DatetimeIndex'> [2010-03-11, ..., 2014-08-14] Length: 52, Freq: None, Timezone: None
This is how the first element looks like:
In [1]: test[1].index[0] Out[2]: Timestamp('2010-03-11 00:00:00')
So I try this to add the hours:
In [1]: test[1].index[0] + pd.tseries.timedeltas.to_timedelta(16, unit='h')
However I get this:
Out[2]: Timestamp('2010-03-11 00:00:00.000000016')
But I would like to get this:
Out[2]: Timestamp('2010-03-11 16:00:00')
What I am missing?. The enviroment is Anaconda (latest) Python 2.7.7, iPython 2.2
Thanks a lot
pandas contains extensive capabilities and features for working with time series data for all domains. Using the NumPy datetime64 and timedelta64 dtypes, pandas has consolidated a large number of features from other Python libraries like scikits.
reset_index() function to reset the index of the given series object and also we will be dropping the original index labels. Output : As we can see in the output, the Series. reset_index() function has reset the index of the given Series object to default.
You can use pd.DateOffset:
test[1].index + pd.DateOffset(hours=16)
pd.DateOffset
accepts the same keyword arguments as dateutil.relativedelta.
The problem you encountered was due to this bug which has been fixed in Pandas version 0.14.1:
In [242]: pd.to_timedelta(16, unit='h') Out[242]: numpy.timedelta64(16,'ns')
If you upgrade, your original code should work.
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