Get year, month or day from numpy datetime64

Question

I have an array of datetime64 type:

dates = np.datetime64([\'2010-10-17\', \'2011-05-13\', \"2012-01-15\"])

Is there a better way than loop

Answer 1

This is how I do it.

import numpy as np

def dt2cal(dt):
    """
    Convert array of datetime64 to a calendar array of year, month, day, hour,
    minute, seconds, microsecond with these quantites indexed on the last axis.

    Parameters
    ----------
    dt : datetime64 array (...)
        numpy.ndarray of datetimes of arbitrary shape

    Returns
    -------
    cal : uint32 array (..., 7)
        calendar array with last axis representing year, month, day, hour,
        minute, second, microsecond
    """

    # allocate output 
    out = np.empty(dt.shape + (7,), dtype="u4")
    # decompose calendar floors
    Y, M, D, h, m, s = [dt.astype(f"M8[{x}]") for x in "YMDhms"]
    out[..., 0] = Y + 1970 # Gregorian Year
    out[..., 1] = (M - Y) + 1 # month
    out[..., 2] = (D - M) + 1 # dat
    out[..., 3] = (dt - D).astype("m8[h]") # hour
    out[..., 4] = (dt - h).astype("m8[m]") # minute
    out[..., 5] = (dt - m).astype("m8[s]") # second
    out[..., 6] = (dt - s).astype("m8[us]") # microsecond
    return out

It's vectorized across arbitrary input dimensions, it's fast, its intuitive, it works on numpy v1.15.4, it doesn't use pandas.

I really wish numpy supported this functionality, it's required all the time in application development. I always get super nervous when I have to roll my own stuff like this, I always feel like I'm missing an edge case.

Answer 2

Use dates.tolist() to convert to native datetime objects, then simply access year. Example:

>>> dates = np.array(['2010-10-17', '2011-05-13', '2012-01-15'], dtype='datetime64')
>>> [x.year for x in dates.tolist()]
[2010, 2011, 2012]

This is basically the same idea exposed in https://stackoverflow.com/a/35281829/2192272, but using simpler syntax.

Tested with python 3.6 / numpy 1.18.

Answer 3

Another possibility is:

np.datetime64(dates,'Y') - returns - numpy.datetime64('2010')

or

np.datetime64(dates,'Y').astype(int)+1970 - returns - 2010

but works only on scalar values, won't take array

Answer 4

Using numpy version 1.10.4 and pandas version 0.17.1,

dates = np.array(['2010-10-17', '2011-05-13', '2012-01-15'], dtype=np.datetime64)
pd.to_datetime(dates).year

I get what you're looking for:

array([2010, 2011, 2012], dtype=int32)