Numpy (time date and time increment)

datetime64 Basics

stay numpy in , We can easily convert a string to a time date type datetime64（datetime Has been python Occupied by the included date time library ）.

datatime64 Is a date time type with units , Its units are as follows ：

【 example 】 Create from a string datetime64 Type , By default ,numpy The corresponding unit will be automatically selected according to the string .

import numpy as np

a = np.datetime64('2020-03-01')
print(a, a.dtype)  # 2020-03-01 datetime64[D]

a = np.datetime64('2020-03')
print(a, a.dtype)  # 2020-03 datetime64[M]

a = np.datetime64('2020-03-08 20:00:05')
print(a, a.dtype)  # 2020-03-08T20:00:05 datetime64[s]

a = np.datetime64('2020-03-08 20:00')
print(a, a.dtype)  # 2020-03-08T20:00 datetime64[m]

a = np.datetime64('2020-03-08 20')
print(a, a.dtype)  # 2020-03-08T20 datetime64[h]

【 example 】 Create from a string datetime64 Type , You can force the units to be used .

import numpy as np

a = np.datetime64('2020-03', 'D')
print(a, a.dtype)  # 2020-03-01 datetime64[D]

a = np.datetime64('2020-03', 'Y')
print(a, a.dtype)  # 2020 datetime64[Y]

print(np.datetime64('2020-03') == np.datetime64('2020-03-01'))  # True
print(np.datetime64('2020-03') == np.datetime64('2020-03-02'))  #False

As can be seen from the above example ,2020-03 and 2020-03-01 It means the same time . in fact , If two datetime64 Objects have different units , They may still represent the same moment . And from the larger units （ Such as month ） Convert to smaller units （ Such as days ） Is safe .

【 example 】 Create from a string datetime64 Array time , If the units are not uniform , Then it will all be converted into the smallest unit .

import numpy as np

a = np.array(['2020-03', '2020-03-08', '2020-03-08 20:00'], dtype='datetime64')
print(a, a.dtype)
# ['2020-03-01T00:00' '2020-03-08T00:00' '2020-03-08T20:00'] datetime64[m]

【 example 】 Use arange() establish datetime64 Array , Used to generate date range .

import numpy as np

a = np.arange('2020-08-01', '2020-08-10', dtype=np.datetime64)
print(a)
# ['2020-08-01' '2020-08-02' '2020-08-03' '2020-08-04' '2020-08-05'
# '2020-08-06' '2020-08-07' '2020-08-08' '2020-08-09']
print(a.dtype)  # datetime64[D]

a = np.arange('2020-08-01 20:00', '2020-08-10', dtype=np.datetime64)
print(a)
# ['2020-08-01T20:00' '2020-08-01T20:01' '2020-08-01T20:02' ...
# '2020-08-09T23:57' '2020-08-09T23:58' '2020-08-09T23:59']
print(a.dtype)  # datetime64[m]

a = np.arange('2020-05', '2020-12', dtype=np.datetime64)
print(a)
# ['2020-05' '2020-06' '2020-07' '2020-08' '2020-09' '2020-10' '2020-11']
print(a.dtype)  # datetime64[M]

datetime64 and timedelta64 operation

【 example 】timedelta64 Two datetime64 The difference between .timedelta64 Also with units , And two of the sum and subtraction operations datetime64 The smaller units in the .

import numpy as np

a = np.datetime64('2020-03-08') - np.datetime64('2020-03-07')
b = np.datetime64('2020-03-08') - np.datetime64('202-03-07 08:00')
c = np.datetime64('2020-03-08') - np.datetime64('2020-03-07 23:00', 'D')

print(a, a.dtype)  # 1 days timedelta64[D]
print(b, b.dtype)  # 956178240 minutes timedelta64[m]
print(c, c.dtype)  # 1 days timedelta64[D]

a = np.datetime64('2020-03') + np.timedelta64(20, 'D')
b = np.datetime64('2020-06-15 00:00') + np.timedelta64(12, 'h')
print(a, a.dtype)  # 2020-03-21 datetime64[D]
print(b, b.dtype)  # 2020-06-15T12:00 datetime64[m]

【 example 】 Generate timedelta64 when , Pay attention to the year （‘Y’） He Yue （‘M’） These two units cannot operate with other units （ How many days a year ？ There are several hours in a month ？ These are all uncertain ）.

import numpy as np

a = np.timedelta64(1, 'Y')
b = np.timedelta64(a, 'M')
print(a)  # 1 years
print(b)  # 12 months

c = np.timedelta64(1, 'h')
d = np.timedelta64(c, 'm')
print(c)  # 1 hours
print(d)  # 60 minutes

print(np.timedelta64(a, 'D'))
# TypeError: Cannot cast NumPy timedelta64 scalar from metadata [Y] 
# to [D] according to the rule 'same_kind'

print(np.timedelta64(b, 'D'))
# TypeError: Cannot cast NumPy timedelta64 scalar from metadata [M] 
# to [D] according to the rule 'same_kind'

【 example 】timedelta64 Arithmetic .

import numpy as np

a = np.timedelta64(1, 'Y')
b = np.timedelta64(6, 'M')
c = np.timedelta64(1, 'W')
d = np.timedelta64(1, 'D')
e = np.timedelta64(10, 'D')

print(a)  # 1 years
print(b)  # 6 months
print(a + b)  # 18 months
print(a - b)  # 6 months
print(2 * a)  # 2 years
print(a / b)  # 2.0
print(c / d)  # 7.0
print(c % e)  # 7 days

【 example 】numpy.datetime64 And datetime.datetime transformation

import numpy as np
import datetime

dt = datetime.datetime(year=2020, month=6, day=1, hour=20, minute=5, second=30)
dt64 = np.datetime64(dt, 's')
print(dt64, dt64.dtype)
# 2020-06-01T20:05:30 datetime64[s]

dt2 = dt64.astype(datetime.datetime)
print(dt2, type(dt2))
# 2020-06-01 20:05:30 <class 'datetime.datetime'>

datetime64 Application

To allow datetime to be used in a context where only certain days of the week are valid ,NumPy Contains a set of “busday”（ Working day ） function .

• numpy.busday_offset(dates, offsets, roll=‘raise’, weekmask=‘1111100’,holidays=None, busdaycal=None, out=None)

  First, adjust the date to belong to the effective date according to the rolling rule , The offset is then applied to the given date calculated in the effective date .

Parameters roll：{‘raise’, ‘nat’, ‘forward’, ‘following’, ‘backward’, ‘preceding’, ‘modifiedfollowing’, ‘modifiedpreceding’}

• ‘raise’ means to raise an exception for an invalid day.
• ‘nat’ means to return a NaT (not-a-time) for an invalid day.
• ‘forward’ and ‘following’ mean to take the first valid day later in
  time.
• ‘backward’ and ‘preceding’ mean to take the first valid day earlier
  in time.
  【 example 】 Applies the specified offset to the working day , Unit day （‘D’）. Calculate the next working day , If the current date is not a working day , Default error . You can specify forward or backward Rules to avoid error reporting .（ One is to take the first valid working day forward , One is to take back the first valid working day ）

import numpy as np

# 2020-07-10  Friday 
a = np.busday_offset('2020-07-10', offsets=1)
print(a)  # 2020-07-13

a = np.busday_offset('2020-07-11', offsets=1)
print(a)
# ValueError: Non-business day date in busday_offset

a = np.busday_offset('2020-07-11', offsets=0, roll='forward')
b = np.busday_offset('2020-07-11', offsets=0, roll='backward')
print(a)  # 2020-07-13
print(b)  # 2020-07-10

a = np.busday_offset('2020-07-11', offsets=1, roll='forward')
b = np.busday_offset('2020-07-11', offsets=1, roll='backward')
print(a)  # 2020-07-14
print(b)  # 2020-07-13

You can specify an offset of 0 To get the most recent working day forward or backward of the current date , Of course , If the current date itself is a working day , The current date is returned directly .

• numpy.is_busday(dates, weekmask=‘1111100’, holidays=None,busdaycal=None, out=None) Calculates which of the given dates are valid days, and which are not.

【 example 】 Returns whether the specified date is a working day .

import numpy as np

# 2020-07-10  Friday 
a = np.is_busday('2020-07-10')
b = np.is_busday('2020-07-11')
print(a)  # True
print(b)  # False

【 example 】 Count one datetime64[D] Number of working days in the array .

import numpy as np

# 2020-07-10  Friday 
begindates = np.datetime64('2020-07-10')
enddates = np.datetime64('2020-07-20')
a = np.arange(begindates, enddates, dtype='datetime64')
b = np.count_nonzero(np.is_busday(a))
print(a)
# ['2020-07-10' '2020-07-11' '2020-07-12' '2020-07-13' '2020-07-14'
# '2020-07-15' '2020-07-16' '2020-07-17' '2020-07-18' '2020-07-19']
print(b)  # 6

【 example 】 Custom week mask value , That is, specify which weeks of the week are working days .

import numpy as np

# 2020-07-10  Friday 
a = np.is_busday('2020-07-10', weekmask=[1, 1, 1, 1, 1, 0, 0])
b = np.is_busday('2020-07-10', weekmask=[1, 1, 1, 1, 0, 0, 1])
print(a)  # True
print(b)  # False

• numpy.busday_count(begindates, enddates,weekmask=‘1111100’,holidays=[], busdaycal=None,out=None)Counts the number of valid days between begindates and enddates, not including the day of enddates.

【 example 】 Returns the number of working days between two dates .

import numpy as np

# 2020-07-10  Friday 
begindates = np.datetime64('2020-07-10')
enddates = np.datetime64('2020-07-20')
a = np.busday_count(begindates, enddates)
b = np.busday_count(enddates, begindates)
print(a)  # 6
print(b)  # -6