I have a csv file with a time column representing POSIX timestamps in milliseconds. When I read it in pandas, it correctly reads it as Int64 but I would like to convert it to a DatetimeIndex. Right now I first convert it to datetime object and then cast it to a DatetimeIndex.

In [20]: df.time.head()Out[20]: 
0    1283346000062
1    1283346000062
2    1283346000062
3    1283346000062
4    1283346000300
Name: timeIn [21]: map(datetime.fromtimestamp, df.time.head()/1000.)
Out[21]: 
[datetime.datetime(2010, 9, 1, 9, 0, 0, 62000),datetime.datetime(2010, 9, 1, 9, 0, 0, 62000),datetime.datetime(2010, 9, 1, 9, 0, 0, 62000),datetime.datetime(2010, 9, 1, 9, 0, 0, 62000),datetime.datetime(2010, 9, 1, 9, 0, 0, 300000)]In [22]: pandas.DatetimeIndex(map(datetime.fromtimestamp, df.time.head()/1000.))
Out[22]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2010-09-01 09:00:00.062000, ..., 2010-09-01 09:00:00.300000]
Length: 5, Freq: None, Timezone: None

Is there an idiomatic way of doing this? And more importantly is this the recommended way of storing non-unique timestmaps in pandas?

You can use a converter in combination with read_csv.

In [423]: d = """\
timestamp data
1283346000062 a
1283346000062 b
1283346000062 c
1283346000062 d
1283346000300 e
"""In [424]: fromtimestamp = lambda x:datetime.fromtimestamp(int(x) / 1000.)In [425]: df = pandas.read_csv(StringIO(d), sep='\s+', converters={'timestamp': fromtimestamp}).set_index('timestamp')In [426]: df.index
Out[426]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2010-09-01 15:00:00.062000, ..., 2010-09-01 15:00:00.300000]
Length: 5, Freq: None, Timezone: NoneIn [427]: df
Out[427]:data
timestamp
2010-09-01 15:00:00.062000    a
2010-09-01 15:00:00.062000    b
2010-09-01 15:00:00.062000    c
2010-09-01 15:00:00.062000    d
2010-09-01 15:00:00.300000    e