Slice by date in pandas without re-indexing

Question

I have a pandas dataframe where one of the columns is made up of strings representing dates, which I then convert to python timestamps by using pd.to_datetime().

How can I select the rows in my dataframe that meet conditions on date.

I know you can use the index (like in this question) but my timestamps are not unique.

How can I select the rows where the 'Date' field is say, after 2015-03-01?

jakevdp · Accepted Answer · 2015-11-06 03:17:21Z

2

You can use a mask on the date, e.g.

df[df['date'] > '2015-03-01']

Here is a full example:

>>> df = pd.DataFrame({'date': pd.date_range('2015-02-15', periods=5, freq='W'),
                       'val': np.random.random(5)})
>>> df
        date       val
0 2015-02-15  0.638522
1 2015-02-22  0.942384
2 2015-03-01  0.133111
3 2015-03-08  0.694020
4 2015-03-15  0.273877

>>> df[df.date > '2015-03-01']
        date       val
3 2015-03-08  0.694020
4 2015-03-15  0.273877

answered Nov 6, 2015 at 3:17

jakevdp

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1 Comment

Alexis Eggermont Over a year ago

Thanks. I did not realize you could simply use a string as a condition on a timestamp column.

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