df.at_time NotImplementedError {asof]

[hdascapital]

The asof functionality int the at_time function gives a NotImplemented Error

df1 = df.at_time(time(15, 0), asof=True)

asof is very useful. If implemented it would allow us to use the at_time functionality and work with data that might not be complete, or don't have homogeneous time stamps. It would enable us to get the "closest" observation to the desired time for subsetting.

Thanks

Hernan

NotImplementedError Traceback (most recent call last) <ipython-input-28-3c10b7e963c9> in <module>() ----> 1 df2 = df1.at_time(time(15, 0), asof=True) C:\Users\Hernan\AppData\Local\Enthought\Canopy\User\lib\site-packages\pandas\core\generic.pyc in at_time(self, time, asof) 2767 """ 2768 try: -> 2769 indexer = self.index.indexer_at_time(time, asof=asof) 2770 return self.take(indexer, convert=False) 2771 except AttributeError: C:\Users\Hernan\AppData\Local\Enthought\Canopy\User\lib\site-packages\pandas\tseries\index.pyc in indexer_at_time(self, time, asof) 1689 1690 if asof: -> 1691 raise NotImplementedError 1692 1693 if isinstance(time, compat.string_types): NotImplementedError:

[jreback]

this is a dupe of #3004.

[jreback]

pls give a complete copy-pastable example

[jreback]

@hdascapital
you can easily do this by:

• reindex to the full range (create whatever freq you want with date_range)
• ffill
• indexer_between_time

something like

df = DataFrame({'value' : [1,2,3],index=pd.DatetimeIndex(['20130101 2:00','20130101 4:00', '20130101 6:00']))
df = df.reindex(index=date_range('20130101',periods=24,freq='H').ffill()
df[df.index.indexer_between_time('3:00','3:00')]

[hdascapital]

If I reindex I will destroy the value of the timestamps, nevermind the connection I have with other data liked to that index.

Let me complement my request. Sorry for the form, but this is my first time here.

Let's think of the dataframe
df = DataFrame({'value' : [1,2,3,4,5,6],index=pd.DatetimeIndex(['20130101 2:00', '20130101 3:00','20130102 2:00', '20130102 3:00', '20130103 1:58', '20130104 2:00']))

Let's say you want to get the the values at 2 pm for each day

df1 = df.at_time(time(2, 0), asof=False)

Then you would get 1, 3, 6,

However, imagine that you have a dataframe like the one in the example, but with all the data with the minutes in between, except for some reason you don't have the timestamp '20130103 2:00'. I was thinking that asof=True would help me get the closest non NaN value to the timestamp I was looking for. In this silly example, let's say the function would have picked up '20130103 1:58' as the closest and the answer would have been

1, 3, 5, 6.

Thanks for your help

[jreback]

not sure what you mean by reindex destroys the timestamps

In [14]: df
Out[14]: 
                     value
2013-01-01 02:00:00      1
2013-01-01 03:00:00      2
2013-01-02 02:00:00      3
2013-01-02 03:00:00      4
2013-01-03 01:58:00      5
2013-01-04 02:00:00      6

In [15]: x = df.reindex(date_range('20130101','20130105',freq='T')).ffill()

In [16]: x.iloc[x.index.indexer_between_time('2:00','2:00')]
Out[16]: 
                     value
2013-01-01 02:00:00      1
2013-01-02 02:00:00      3
2013-01-03 02:00:00      5
2013-01-04 02:00:00      6

[hdascapital]

I got it. Amazing. Thanks a lot.

[jreback]

that said, this could be implemented (essentially like that)

[hdascapital]

@jreback One consideration, if you are going to implement this the way you wrote it. If you are handling financial data, some of it is business days, others is business days + sunday afternoon. When you do df.reindex, it doesn't take that into account and that distorts your data. I don't know if this is clear. Let me know. Thanks