Change timezone of date-time column in pandas and add as hierarchical index
I have data with a time-stamp in UTC. I'd like to convert the timezone of this timestamp to 'US/Pacific' and add it as a hierarchical index to a pandas DataFrame. I've been able to convert the timestamp as an Index, but it loses the timezone formatting when I try to add it back into the DataFrame, either as a column or as an index.
>>> import pandas as pd
>>> dat = pd.DataFrame({'label':['a', 'a', 'a', 'b', 'b', 'b'], 'datetime':['2011-07-19 07:00:00', '2011-07-19 08:00:00', '2011-07-19 09:00:00', '2011-07-19 07:00:00', '2011-07-19 08:00:00', '2011-07-19 09:00:00'], 'value':range(6)})
>>> dat.dtypes
#datetime object
#label object
#value int64
#dtype: object
Now if I try to convert the Series directly I run into an error.
>>> times = pd.to_datetime(dat['datetime'])
>>> times.tz_localize('UTC')
#Traceback (most recent call last):
# File "<stdin>", line 1, in <module>
# File "/Users/erikshilts/workspace/schedule-detection/python/pysched/env/lib/python2.7/site-packages/pandas/core/series.py", line 3170, in tz_localize
# raise Exception('Cannot tz-localize non-time series')
#Exception: Cannot tz-localize non-time series
If I convert it to an Index then I can manipulate it as a timeseries. Notice that the index now has the Pacific timezone.
>>> times_index = pd.Index(times)
>>> times_index_pacific = times_index.tz_localize('UTC').tz_convert('US/Pacific')
>>> times_index_pacific
#<class 'pandas.tseries.index.DatetimeIndex'>
#[2011-07-19 00:00:00, ..., 2011-07-19 02:00:00]
#Length: 6, Freq: None, Timezone: US/Pacific
However, now I run into problems adding the index back to the dataframe as it loses its timezone formatting:
>>> dat_index = dat.set_index([dat['label'], times_index_pacific])
>>> dat_index
# datetime label value
#label
#a 2011-07-19 07:00:00 2011-07-19 07:00:00 a 0
# 2011-07-19 08:00:00 2011-07-19 08:00:00 a 1
# 2011-07-19 09:00:00 2011-07-19 09:00:00 a 2
#b 2011-07-19 07:00:00 2011-07-19 07:00:00 b 3
# 2011-07-19 08:00:00 2011-07-19 08:00:00 b 4
# 2011-07-19 09:00:00 2011-07-19 09:00:00 b 5
You'll notice the index is back on the UTC timezone instead of the converted Pacific timezone.
How can I change the timezone and add it as an index to a DataFrame?
Answer
If you set it as the index, it's automatically converted to an Index:
In [11]: dat.index = pd.to_datetime(dat.pop('datetime'), utc=True)
In [12]: dat
Out[12]:
label value
datetime
2011-07-19 07:00:00 a 0
2011-07-19 08:00:00 a 1
2011-07-19 09:00:00 a 2
2011-07-19 07:00:00 b 3
2011-07-19 08:00:00 b 4
2011-07-19 09:00:00 b 5
Then do the tz_localize:
In [12]: dat.index = dat.index.tz_localize('UTC').tz_convert('US/Pacific')
In [13]: dat
Out[13]:
label value
datetime
2011-07-19 00:00:00-07:00 a 0
2011-07-19 01:00:00-07:00 a 1
2011-07-19 02:00:00-07:00 a 2
2011-07-19 00:00:00-07:00 b 3
2011-07-19 01:00:00-07:00 b 4
2011-07-19 02:00:00-07:00 b 5
And then you can append the label column to the index:
Hmmm this is definitely a bug!
In [14]: dat.set_index('label', append=True).swaplevel(0, 1)
Out[14]:
value
label datetime
a 2011-07-19 07:00:00 0
2011-07-19 08:00:00 1
2011-07-19 09:00:00 2
b 2011-07-19 07:00:00 3
2011-07-19 08:00:00 4
2011-07-19 09:00:00 5
A hacky workaround is to convert the (datetime) level directly (when it's already a MultiIndex):
In [15]: dat.index.levels[1] = dat.index.get_level_values(1).tz_localize('UTC').tz_convert('US/Pacific')
In [16]: dat1
Out[16]:
value
label datetime
a 2011-07-19 00:00:00-07:00 0
2011-07-19 01:00:00-07:00 1
2011-07-19 02:00:00-07:00 2
b 2011-07-19 00:00:00-07:00 3
2011-07-19 01:00:00-07:00 4
2011-07-19 02:00:00-07:00 5