I have pandas Series where index is a list of integer (timestamp), how can I convert them to datetime.datetime (with timezone) more efficient than below raw conversion?
pd.Series(data=s.values, index=map(lambda x:datetime.datetime.fromtimestamp(x,tz=utc), s.index))
In [49]: s = Series(range(10))
Using to_datetime
, you can supply a unit to select what the meaning of the integers.
In [50]: pd.to_datetime(s,unit='s')
Out[50]:
0 1970-01-01 00:00:00
1 1970-01-01 00:00:01
2 1970-01-01 00:00:02
3 1970-01-01 00:00:03
4 1970-01-01 00:00:04
5 1970-01-01 00:00:05
6 1970-01-01 00:00:06
7 1970-01-01 00:00:07
8 1970-01-01 00:00:08
9 1970-01-01 00:00:09
dtype: datetime64[ns]
In [51]: pd.to_datetime(s,unit='ms')
Out[51]:
0 1970-01-01 00:00:00
1 1970-01-01 00:00:00.001000
2 1970-01-01 00:00:00.002000
3 1970-01-01 00:00:00.003000
4 1970-01-01 00:00:00.004000
5 1970-01-01 00:00:00.005000
6 1970-01-01 00:00:00.006000
7 1970-01-01 00:00:00.007000
8 1970-01-01 00:00:00.008000
9 1970-01-01 00:00:00.009000
dtype: datetime64[ns]
In [52]: pd.to_datetime(s,unit='D')
Out[52]:
0 1970-01-01
1 1970-01-02
2 1970-01-03
3 1970-01-04
4 1970-01-05
5 1970-01-06
6 1970-01-07
7 1970-01-08
8 1970-01-09
9 1970-01-10
dtype: datetime64[ns]
Creating a Series is then straightforward
In [54]: Series(s.values,index=pd.to_datetime(s,unit='s'))
Out[54]:
1970-01-01 00:00:00 0
1970-01-01 00:00:01 1
1970-01-01 00:00:02 2
1970-01-01 00:00:03 3
1970-01-01 00:00:04 4
1970-01-01 00:00:05 5
1970-01-01 00:00:06 6
1970-01-01 00:00:07 7
1970-01-01 00:00:08 8
1970-01-01 00:00:09 9
dtype: int64
In [63]: s = pd.Series(range(10))
In [64]: s.index = pd.DatetimeIndex(s.index.asi8*10**9, tz='utc')
In [65]: s
Out[65]:
1970-01-01 00:00:00+00:00 0
1970-01-01 00:00:01+00:00 1
1970-01-01 00:00:02+00:00 2
1970-01-01 00:00:03+00:00 3
1970-01-01 00:00:04+00:00 4
1970-01-01 00:00:05+00:00 5
1970-01-01 00:00:06+00:00 6
1970-01-01 00:00:07+00:00 7
1970-01-01 00:00:08+00:00 8
1970-01-01 00:00:09+00:00 9
dtype: int64