Python pandas select rows by list of dates

Question

How to select multiple rows of a dataframe by list of dates

dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))

In[1]: df

Out[1]: 
                   A         B         C         D
2013-01-01  0.084393 -2.460860 -0.118468  0.543618
2013-01-02 -0.024358 -1.012406 -0.222457  1.906462
2013-01-03 -0.305999 -0.858261  0.320587  0.302837
2013-01-04  0.527321  0.425767 -0.994142  0.556027
2013-01-05  0.411410 -1.810460 -1.172034 -1.142847
2013-01-06 -0.969854  0.469045 -0.042532  0.699582

myDates = ["2013-01-02", "2013-01-04", "2013-01-06"]

So the output should be

                   A         B         C         D
2013-01-02 -0.024358 -1.012406 -0.222457  1.906462
2013-01-04  0.527321  0.425767 -0.994142  0.556027
2013-01-06 -0.969854  0.469045 -0.042532  0.699582

akuiper · Accepted Answer · 2016-11-01 15:35:05Z

9

You can use index.isin() method to create a logical index for subsetting:

df[df.index.isin(myDates)]

answered Nov 1, 2016 at 15:35

akuiper

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1 Comment

Pat Over a year ago

Thanks. That was too easy.

Zeugma · Accepted Answer · 2016-11-01 16:52:21Z

4

Convert your entry into a DateTimeIndex:

df.loc[pd.to_datetime(myDates)]

                   A         B         C         D
2013-01-02 -0.047710 -1.827593 -0.944548 -0.149460
2013-01-04  1.437924  0.126788  0.641870  0.198664
2013-01-06  0.408820 -1.842112 -0.287346  0.071397

answered Nov 1, 2016 at 16:52

Zeugma

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Comments

snatchysquid · Accepted Answer · 2022-09-06 11:29:15Z

0

If you have a timeseries containing hours and minutes in the index (e.g. 2022-03-07 09:03:00+00:00 instead of 2022-03-07), and you want to filter by dates (without hours, minutes, etc.), you can use the following:

df.loc[np.isin(df.index.date, myDates)]

If you try df.loc[df.index.date.isin(myDates)] it might not work and python will throw an error saying 'numpy.ndarray' object has no attribute 'isin', and this is why we use np.isin.

This is an old post but I think this can be useful to a lot of people (such as myself).

answered Sep 6, 2022 at 11:29

snatchysquid

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