Change Pandas index from integer to datetime format

Question

I have a huge size DataFrame that contains index in integer form for date time representation, for example, 20171001. What I'm going to do is to change the form, for example, 20171001, to the datetime format, '2017-10-01'.

For simplicity, I generate such a dataframe.

>>> df = pd.DataFrame(np.random.randn(3,2), columns=list('ab'), index=
[20171001,20171002,20171003])
>>> df
             a         b
20171001  2.205108  0.926963
20171002  1.104884 -0.445450
20171003  0.621504 -0.584352
>>> df.index
Int64Index([20171001, 20171002, 20171003], dtype='int64')

If we apply 'to_datetime' to df.index, we have the weird result:

>>> pd.to_datetime(df.index)
DatetimeIndex(['1970-01-01 00:00:00.020171001',
           '1970-01-01 00:00:00.020171002',
           '1970-01-01 00:00:00.020171003'],
          dtype='datetime64[ns]', freq=None)

What I want is DatetimeIndex(['2017-10-01', '2017-10-02', '2017-10--3'], ...) How can I manage this problem? Note that the file is given.

Bharath M Shetty · Accepted Answer · 2017-10-07 13:11:48Z

11

Use format %Y%m%d in pd.to_datetime i.e

pd.to_datetime(df.index, format='%Y%m%d')
DatetimeIndex(['2017-10-01', '2017-10-02', '2017-10-03'], dtype='datetime64[ns]', freq=None)

To assign df.index = pd.to_datetime(df.index, format='%Y%m%d')

answered Oct 7, 2017 at 13:11

Bharath M Shetty

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1 Comment

gnoejh Over a year ago

Thanks Bharath, it works. The format seems to mean the input data. Perfect.

Anton vBR · Accepted Answer · 2017-10-07 13:53:34Z

1

pd.to_datetime is the panda way of doing it. But here are two alternatives:

import datetime
df.index = (datetime.datetime.strptime(str(i),"%Y%m%d") for i in df.index)

or

import datetime
df.index = df.index.map(lambda x: datetime.datetime.strptime(str(x),"%Y%m%d"))

edited Oct 7, 2017 at 13:53

answered Oct 7, 2017 at 13:48

Anton vBR

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