Numpy date utilities

This library provides utility functions to perform conversions and get information about numpy dates quickly.

import datetime
import random

import numpy

import pyinterp


def make_date(samples=10000):
    """Generates random dates."""
    epoch = datetime.datetime(1970, 1, 1)
    delta = datetime.datetime.now() - datetime.datetime(1970, 1, 1)

    pydates = [epoch + random.random() * delta for _ in range(samples)]
    npdates = numpy.array(pydates).astype('datetime64[ns]')

    return npdates

dates = make_date()
print(dates)

['1994-11-24T21:26:53.551494000' '1970-01-19T14:11:44.598902000'
 '2016-01-06T20:38:19.204420000' ... '2013-02-09T17:36:58.341425000'
 '2011-01-30T16:23:31.879411000' '1975-05-18T07:59:36.177413000']

Get the date part as a structured numpy array of three fields: year, month and day:

pyinterp.dateutils.date(dates)

array([(1994, 11, 24), (1970,  1, 19), (2016,  1,  6), ...,
       (2013,  2,  9), (2011,  1, 30), (1975,  5, 18)],
      shape=(10000,), dtype=[('year', '<i4'), ('month', '<u4'), ('day', '<u4')])

Get the time part as a structured numpy array of three fields: hour, minute and second:

pyinterp.dateutils.time(dates)

array([(21, 26, 53), (14, 11, 44), (20, 38, 19), ..., (17, 36, 58),
       (16, 23, 31), ( 7, 59, 36)],
      shape=(10000,), dtype=[('hour', '<u4'), ('minute', '<u4'), ('second', '<u4')])

Get the ISO calendar of the date as a structured numpy array of three fields: year, weekday and week:

pyinterp.dateutils.isocalendar(dates)

array([(1994, 47, 4), (1970,  4, 1), (2016,  1, 3), ..., (2013,  6, 6),
       (2011,  4, 7), (1975, 20, 7)],
      shape=(10000,), dtype=[('year', '<i4'), ('week', '<u4'), ('weekday', '<u4')])

Get the week day of the dates (Sunday is 0 … Saturday is 6):

pyinterp.dateutils.weekday(dates)

array([4, 1, 3, ..., 6, 0, 0], shape=(10000,), dtype=uint32)

Get the timedelta from since January

pyinterp.dateutils.timedelta_since_january(dates)

array([28330013551494000,  1606304598902000,   506299204420000, ...,
        3433018341425000,  2564611879411000, 11865576177413000],
      shape=(10000,), dtype='timedelta64[ns]')

Get the dates as datetime.datetime array

pyinterp.dateutils.datetime(dates)

array([datetime.datetime(1994, 11, 24, 21, 26, 53, 551494),
       datetime.datetime(1970, 1, 19, 14, 11, 44, 598902),
       datetime.datetime(2016, 1, 6, 20, 38, 19, 204420), ...,
       datetime.datetime(2013, 2, 9, 17, 36, 58, 341425),
       datetime.datetime(2011, 1, 30, 16, 23, 31, 879411),
       datetime.datetime(1975, 5, 18, 7, 59, 36, 177413)],
      shape=(10000,), dtype=object)