Convert string to NumPy datetime64 dtype

Question

I program gets a string of current time every minute as date = '201711081750'

I want to store these strings as np.datetime64 into an array.

I think I could convert this kind of strings as

>>> date = '201711081750'

>>> np.datetime64( date[:4] +'-'+date[4:6]+'-'+date[6:8]+' ' +date[8:10]+':'+date[10:]  , 'm' )
numpy.datetime64('2017-11-08T17:50')

But it looks complicated and I think it might engender errors later.

Are there simpler ways to do this?

cs95 · Accepted Answer · 2019-01-13 06:58:13Z

15

`pd.to_datetime`

import pandas as pd

pd.to_datetime(date, format='%Y%m%d%H%M')
Timestamp('2017-11-08 17:50:00')

The important bit here is the format string '%Y%m%d%H%M'.

`datetime.datetime` _{equivalent in python.}

from datetime import datetime as dt

dt.strptime(date, '%Y%m%d%H%M')
datetime.datetime(2017, 11, 8, 17, 50)

edited Jan 13, 2019 at 6:58

answered Nov 8, 2017 at 11:15

cs95

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4 Comments

ifly6 Over a year ago

This doesn't yield the possibility of using np.datetime with base units like days, since Pandas is set up to cast to nanosecond resolution. Does there exist a similarly easy way to parse np.datetime objects from string?

Seymour Over a year ago

The work-around I found is to use the pd.to_datetime function as suggested by @cs95 and then use strftime function to customize the format extracted. For example, dt.strftime('%Y-%m') to keep only Year, Month.

cs95 Over a year ago

@ifly6 You can always use pd.to_datetime(date).to_numpy().

ifly6 Over a year ago

Doesn't solve the problem. Pandas will throw out of bounds exception when parsing date

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`pd.to_datetime`

`datetime.datetime` _{equivalent in python.}