convert a Numpy Array to datetime.date pandas DataFrame

Question

I have a numpy.ndarray thats values are pandas._libs.tslib.Timestamp

Example:

In: type(closedDate)

Out: numpy.ndarray

In: type(closedDate[0])

Out: pandas._libs.tslib.Timestamp

I would like to convert the contents of closedDate into a list of datetime.date

I have tried the following:

for i in closedDate:
    closedDate[i].to_datetime()

But get this error:

IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

What to do? :/

closedDate is a numpy array not a pandas df so closedDate.values would raise an error @Ami Tavory also the existing question of this deals with pandas df at first, whereas mine is a numpy array, so it is not a duplicate :P — cool_beans
– cool_beans, Commented May 17, 2018 at 19:32
ValueError: non convertible value 2017-09-25 14:39:00with the unit 's' i get this error @Ami Tavory — cool_beans
– cool_beans, Commented May 17, 2018 at 20:04
It would help if you'd post a sample of the input in the question. — Ami Tavory
– Ami Tavory, Commented May 17, 2018 at 20:09
Just to address your error message (which doesn't have anything to do with your type casting): you mixed iterating over indices and iterating over elements. Either for cd in closedDate: cd.to_datetime() or for i in range(len(closedDate)): closedDate[i].to_datetime(). However, none oft these loops would be meaningful as the values aren't saved in a list vor wherever, just calculated for nothing. And besides: such loops are best written with one thing I really linke in Python: for i, cd in enumerate(closedDate): ... - this provides you both the elements and the indices in your loop. — SpghttCd
– SpghttCd, Commented May 17, 2018 at 22:55

Kodiologist · Accepted Answer · 2018-05-17 22:31:19Z

0

[x.to_pydatetime().date() for x in closedDate]

Note, however, that you've probably made some kind of mistake to end up with a numpy array containing a pandas datatype. pandas Series and DataFrames are better equipped to handle pandas-specific types than numpy arrays are.

answered May 17, 2018 at 22:31

Kodiologist

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SpghttCd · Accepted Answer · 2018-05-18 04:34:56Z

0

I'd expect numpy to provide this functionality via astype, so you would not even have to loop over the array explicitely:

dt_arr = closedDate.astype(np.datetime64)

check:

closedDate
Out: 
array([Timestamp('2017-09-25 14:39:00'), Timestamp('2017-09-26 14:39:00'),
   Timestamp('2017-09-27 14:39:00')], dtype=object)

type(closedDate)
Out: numpy.ndarray

type(closedDate[0])
Out: pandas._libs.tslib.Timestamp

type(dt_arr)
Out: numpy.ndarray

type(dt_arr[0])
Out: numpy.datetime64

edited May 18, 2018 at 4:34

answered May 17, 2018 at 22:30

SpghttCd

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