I tried to build a structured array with a datetime coloumn
import numpy as np
na_trades = np.zeros(2, dtype = 'datetime64,i4')
na_trades[0] = (np.datetime64('1970-01-01 00:00:00'),0)
TypeError: Cannot cast NumPy timedelta64 scalar from metadata [s] to according to the rule 'same_kind'
Is there a way to fix this?
You have to specify that the datetime64 is in seconds when you create the array because the one you parse and try to assign is a datetime64[s]:
na_trades = np.zeros(2, dtype='datetime64[s],i4')
na_trades[0] = (np.datetime64('1971-01-01 00:00:00'), 0)
The error you get means that the datetime64 object that you specified is not same_kind as the one you try to assing. You try to assign a seconds resolution one, and you created a different one when you constructed the array (by default I think it's nanoseconds).
Try following:
>>> na_trades = np.zeros(2, dtype=[('dt', 'datetime64[s]'), ('vol', 'i4')])
>>> na_trades
array([(datetime.datetime(1970, 1, 1, 0, 0), 0),
(datetime.datetime(1970, 1, 1, 0, 0), 0)],
dtype=[('dt', ('<M8[s]', {})), ('vol', '<i4')])
>>> na_trades[0] = (np.datetime64('1970-01-02 00:00:00'),1)
>>> na_trades
array([(datetime.datetime(4707, 11, 29, 0, 0), 1),
(datetime.datetime(1970, 1, 1, 0, 0), 0)],
dtype=[('dt', ('<M8[s]', {})), ('vol', '<i4')])
TypeError: Cannot cast NumPy timedelta64 scalar from metadata [s] to according to the rule 'same_kind' You should update to a new version because the old style of managing datetimes is obsolete in the 1.7.x version of NumPy docs