python3数字、日期和时间

1、对数值进行取整

#使用内建的round(value,ndigits)函数来取整,ndigits指定保留的位数，在取整时会取值在偶数上，如1.25取一位会取整1.2,1.26会取整1.3
In [1]: round(1.23,1)
Out[1]: 1.2

In [2]: round(1.25,1)
Out[2]: 1.2

In [3]: round(1.26,1)
Out[3]: 1.3

In [4]: round(1.2645,3)
Out[4]: 1.264
#如果参数ndigits为负数的话会相应的取整到十位、白位和千位
In [1]: a = 1234567

In [2]: round(a,-1)
Out[2]: 1234570

In [3]: round(a,-3)
Out[3]: 1235000

#通过格式化操作取小数精度
In [4]: x = 1.23456

In [5]: format(x,'0.2f')
Out[5]: '1.23'

In [6]: 'value is {:0.3f}'.format(x)
Out[6]: 'value is 1.235'

2、执行精确的小数计算

#在数学计算中由于CPU的浮点运算单元特性导致会引入微小的误差
In [11]: a = 4.2

In [12]: b = 2.1

In [13]: a + b
Out[13]: 6.300000000000001

In [14]: (a + b) == 6.3
Out[14]: False

#可以通过Decimal模块来将数字以字符串的形式来指定，但它支持所有常见的数学操作
In [27]: from decimal import Decimal

In [28]: a = Decimal('4.2')

In [29]: b = Decimal('2.1')

In [30]: a + b
Out[30]: Decimal('6.3')

In [31]: print(a + b)
6.3

In [32]: print(type(a + b))
<class 'decimal.Decimal'>

In [33]: (a + b) == Decimal('6.3')
Out[33]: True
#decimal模块的主要功能是允许控制计算过程中的各个方面，包括数字位数的四舍五入，可以通过创建本地的上下文环境来修改其设定
In [34]: from decimal import localcontext

In [35]: a = Decimal('1.3')

In [36]: b = Decimal('1.7')

In [37]: a / b
Out[37]: Decimal('0.7647058823529411764705882353')

In [38]: with localcontext() as ctx:
    ...:     ctx.prec = 3  #指定精确位数
    ...:     print(a / b)
    ...:     
0.765

In [39]: with localcontext() as ctx:
    ...:     ctx.prec = 30
    ...:     print(a / b)
    ...:     
0.764705882352941176470588235294

#如果在数字进行运算时可以使用math.fsum()精确误差
In [41]: nums = [1.23,10,1,-10,-1.23]

In [42]: sum(nums)
Out[42]: 1.0000000000000004

In [44]: import math
In [46]: math.fsum(nums)
Out[46]: 1.0

3、对数值做格式化输出

In [47]: x = 1234.56789
#格式化时精确2位小数
In [48]: format(x,'0.2f')
Out[48]: '1234.57'
#右对齐宽度20精确小数3位格式化
In [49]: 'value is{:>20.3f}'.format(x)
Out[49]: 'value is            1234.568'
#左对齐宽度20精确小数位3位格式化
In [50]: 'value is{:<20.3f}'.format(x)
Out[50]: 'value is1234.568            '
#剧中对齐20宽度精确3位小数位格式化
In [51]: 'value is{:^20.3f}'.format(x)
Out[51]: 'value is      1234.568      '
#指定逗号为千位分隔符
In [52]: 'value is{:^20,.1f}'.format(x)
Out[52]: 'value is      1,234.6       '
#使用科学计算法输出
In [53]: 'value is{:^20,.4e}'.format(x)
Out[53]: 'value is     1.2346e+03     '

In [54]: 'value is{:^20,.4E}'.format(x)
Out[54]: 'value is     1.2346E+03     '

4、同二进制、八进制和十六进制数打交道

In [55]: num = 12345
#转换为二进制
In [56]: bin(num)
Out[56]: '0b11000000111001'
#转换为八进制
In [57]: oct(num)
Out[57]: '0o30071'
#转换为十六进制
In [58]: hex(num)
Out[58]: '0x3039'

#通过format()函数也可以转换，它会省去前面的标识0b\0o\0x
In [59]: format(num,'b')
Out[59]: '11000000111001'

In [60]: format(num,'o')
Out[60]: '30071'

In [61]: format(num,'x')
Out[61]: '3039'

#处理负数
In [62]: x = -1234

In [63]: format(x,'b')
Out[63]: '-10011010010'

In [64]: format(x,'o')
Out[64]: '-2322'

In [65]: format(x,'x')
Out[65]: '-4d2'
#通过字符串回转只需要通过int函数转换为数字并指定进制即可
In [66]: int('-4d2',16)
Out[66]: -1234

In [67]: int('-2322',8)
Out[67]: -1234

In [68]: int('-10011010010',2)
Out[68]: -1234

#在python中指定八进制的语法是在添加前缀0o，如修改文件权限时，不加上前缀将会报错
In [1]: import os

In [2]: os.chmod('test.py',0777)
  File "<ipython-input-2-ddababe9874c>", line 1
    os.chmod('test.py',0777)
                          ^
SyntaxError: invalid token

#指定数据为八进制
In [3]: os.chmod('test.py',0o0777)

5、从字节串中打包和解包大整数

In [4]: x = 23**23

In [5]: x
Out[5]: 20880467999847912034355032910567
#将大整数转换为字节串，使用int.to_bytes()方法，指定字节数和字节序即可
In [8]: x.to_bytes(16,'big')
Out[8]: b'\x00\x00\x01\x07\x8cnO}uE\x0b\x1f\xb3\xecj\xe7'

#将字节转换为整数，使用int.from_bytes()方法，指定字节序即可
In [9]: data = b'\x00\x00\x01\x07\x8cnO}uE\x0b\x1f\xb3\xecj\xe7'

In [10]: len(data)
Out[10]: 16

In [11]: int.from_bytes(data,'big')
Out[11]: 20880467999847912034355032910567
#指定从小到大的字节序
In [12]: int.from_bytes(data,'little')
Out[12]: 307606851333435471716003534337847918592

#如果指定的字节数位数不够将会报错，可以使用int.bit_length()方法来确定需要多少位的值才能保存这个值
In [13]: xx = 523 ** 23
In [14]: xx
Out[14]: 335381300113661875107536852714019056160355655333978849017944067

In [15]: xx.to_bytes(16,'little')
---------------------------------------------------------------------------
OverflowError                             Traceback (most recent call last)
<ipython-input-15-2f3e88637b10> in <module>()
----> 1 xx.to_bytes(16,'little')

OverflowError: int too big to convert

In [17]: xx.bit_length()
Out[17]: 208

In [18]: x.to_bytes(208,'little')
Out[18]: b'\xe7j\xec\xb3\x1f\x0bEu}On\x8c\x07\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00...'

6、复数运算

#创建复数
In [19]: a = complex(2,4)
In [20]: b = 3 - 5j
In [22]: a,b
Out[22]: ((2+4j), (3-5j))
#取实属部分
In [23]: a.real
Out[23]: 2.0
#取虚数部分
In [24]: a.imag
Out[24]: 4.0
#取共取值
In [25]: a.conjugate()
Out[25]: (2-4j)
#复数运算操作
In [26]: a + b
Out[26]: (5-1j)

In [27]: a - b
Out[27]: (-1+9j)

In [28]: a * b
Out[28]: (26+2j)

In [29]: a / b
Out[29]: (-0.4117647058823529+0.6470588235294118j)

In [30]: abs(a)
Out[30]: 4.47213595499958
#复数的函数操作正弦、余弦和平方根，可以使用cmath模块
In [31]: import cmath
#正弦
In [32]: cmath.sin(a)
Out[32]: (24.83130584894638-11.356612711218173j)
#余弦
In [33]: cmath.cos(a)
Out[33]: (-11.36423470640106-24.814651485634183j)
#平方根
In [34]: cmath.exp(a)
Out[34]: (-4.829809383269385-5.5920560936409816j)

#使用numpy模块直接创建复数数组，并对他们执行操作
In [1]: import numpy as np

In [2]: a = np.array([2+3j,4+5j,6-7j,8+9j])

In [3]: a
Out[3]: array([2.+3.j, 4.+5.j, 6.-7.j, 8.+9.j])

In [4]: a + 2
Out[4]: array([ 4.+3.j,  6.+5.j,  8.-7.j, 10.+9.j])

In [5]: np.sin(a)
Out[5]: 
array([   9.15449915  -4.16890696j,  -56.16227422 -48.50245524j,
       -153.20827755-526.47684926j, 4008.42651446-589.49948373j])

7、处理无穷大和NaN

#无穷大、负无穷大和NaN可以通过float()函数来创建
In [6]: a = float('inf')
In [7]: b = float('-inf')
In [8]: c = float('nan')
In [9]: a,b,c
Out[9]: (inf, -inf, nan)

#通过math.isinf()和math.isnan()函数来检测是否出现这些值
In [11]: import math

In [12]: math.isinf(a)
Out[12]: True

In [13]: math.isnan(c)
Out[13]: True

In [14]: math.isinf(b)
Out[14]: True
#无穷大在数学计算中应用
In [15]: a + 100
Out[15]: inf

In [16]: a * 100000
Out[16]: inf

In [17]: 10 / a
Out[17]: 0.0
#特定的操作会产生NaN结果
In [18]: a/a
Out[18]: nan

In [19]: a + b
Out[19]: nan

In [20]: c + 2345
Out[20]: nan

In [21]: c / 2222222
Out[21]: nan

In [23]: c * 33323333333
Out[23]: nan

In [24]: math.sqrt(c)
Out[24]: nan
#NaN在做比较时从不会判定为相等
In [25]: x = float('nan')

In [26]: y = float('nan')

In [27]: x == y
Out[27]: False

In [28]: x is y
Out[28]: False
#唯一能检测是否为NaN的办法只有math.isnan()方法
In [29]: math.isnan(x)
Out[29]: True

8、分数的计算

#fractions模块可以用来处理涉及分数的数学计算
In [1]: from fractions import Fraction

In [2]: a = Fraction(3,4)

In [3]: b = Fraction(4,8)

In [4]: print(a+b)
5/4

In [5]: print(a-b)
1/4

In [6]: print(a*b)
3/8

In [7]: c = a * b

In [8]: c
Out[8]: Fraction(3, 8)
#显示分数
In [9]: c.numerator
Out[9]: 3
#显示母数
In [10]: c.denominator
Out[10]: 8
#将分数转换为浮点数
In [11]: float(c)
Out[11]: 0.375
#限制分母
In [12]: print(c.limit_denominator(4))
1/3
#将浮点数转换为分数
In [13]: x = 3.75

In [14]: y = Fraction(*x.as_integer_ratio())

In [15]: y
Out[15]: Fraction(15, 4)

In [16]: print(y)
15/4

9、处理大型数组的计算

#大型数组的计算可以使用numpy库来运算
In [17]: import numpy as np

In [18]: ax = np.array([1,2,3,4])

In [19]: ay = np.array([5,6,7,8])

In [20]: ax * 3
Out[20]: array([ 3,  6,  9, 12])

In [21]: ax / 2
Out[21]: array([0.5, 1. , 1.5, 2. ])

In [22]: ax - ay
Out[22]: array([-4, -4, -4, -4])

In [23]: ax + ay
Out[23]: array([ 6,  8, 10, 12])

In [24]: ax * ay
Out[24]: array([ 5, 12, 21, 32])
#多计算组合
In [25]: def f(x):
    ...:     return 2 * x + 10
    ...: 
    ...: 
In [26]: f(ax)
Out[26]: array([12, 14, 16, 18])

In [27]: f(ay)
Out[27]: array([20, 22, 24, 26])
#计算数组的平方根
In [28]: np.sqrt(ax)
Out[28]: array([1.        , 1.41421356, 1.73205081, 2.        ])
#余弦数
In [29]: np.cos(ax)
Out[29]: array([ 0.54030231, -0.41614684, -0.9899925 , -0.65364362])
#正弦数
In [30]: np.sin(ax)
Out[30]: array([ 0.84147098,  0.90929743,  0.14112001, -0.7568025 ])

#通过numpy创建二维浮点数组
In [31]: grid = np.zeros(shape=(1000,1000),dtype=float)
In [32]: grid
Out[32]: 
array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])
#数组计算
In [33]: grid += 11

In [34]: grid
Out[34]: 
array([[11., 11., 11., ..., 11., 11., 11.],
       [11., 11., 11., ..., 11., 11., 11.],
       [11., 11., 11., ..., 11., 11., 11.],
       ...,
       [11., 11., 11., ..., 11., 11., 11.],
       [11., 11., 11., ..., 11., 11., 11.],
       [11., 11., 11., ..., 11., 11., 11.]])
#数组正弦
In [35]: np.sin(grid)
Out[35]: 
array([[-0.99999021, -0.99999021, -0.99999021, ..., -0.99999021,
        -0.99999021, -0.99999021],
       [-0.99999021, -0.99999021, -0.99999021, ..., -0.99999021,
        -0.99999021, -0.99999021],
       [-0.99999021, -0.99999021, -0.99999021, ..., -0.99999021,
        -0.99999021, -0.99999021],
       ...,
       [-0.99999021, -0.99999021, -0.99999021, ..., -0.99999021,
        -0.99999021, -0.99999021],
       [-0.99999021, -0.99999021, -0.99999021, ..., -0.99999021,
        -0.99999021, -0.99999021],
       [-0.99999021, -0.99999021, -0.99999021, ..., -0.99999021,
        -0.99999021, -0.99999021]])
#numpy扩展了python列表的索引功能
In [36]: a = np.array([[1,2,3],[4,5,6],[7,8,9]])

In [37]: a
Out[37]: 
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])
#索引第一层
In [38]: a[1]
Out[38]: array([4, 5, 6])

In [39]: a[0]
Out[39]: array([1, 2, 3])

In [40]: a[:]
Out[40]: 
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])
# 索引第二层
In [41]: a[:,1]
Out[41]: array([2, 5, 8])

In [42]: a[1:3,1:3]
Out[42]: 
array([[5, 6],
       [8, 9]])

In [43]: a[0:3,0:3]
Out[43]: 
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

In [44]: a[0:3,1:3]
Out[44]: 
array([[2, 3],
       [5, 6],
       [8, 9]])
#对索引进行运算操作
In [45]: a[0:3,1:3] += 10

In [46]: a
Out[46]: 
array([[ 1, 12, 13],
       [ 4, 15, 16],
       [ 7, 18, 19]])

In [47]: a + 10
Out[47]: 
array([[11, 22, 23],
       [14, 25, 26],
       [17, 28, 29]])

In [48]: a
Out[48]: 
array([[ 1, 12, 13],
       [ 4, 15, 16],
       [ 7, 18, 19]])
#对数组中小于10以外的值运算加10
In [49]: np.where(a < 10 ,a ,a+10)
Out[49]: 
array([[ 1, 22, 23],
       [ 4, 25, 26],
       [ 7, 28, 29]])
#numpy是使用最为庞大和复杂的模块之一，官方站点：http://www.numpy.org

10、矩阵和线性代数的计算

#numpy库中的matrix对象可以用来处理线性代数
In [50]: import numpy as np

In [51]: m = np.matrix([[1,2,3],[4,5,6],[7,8,9]])

In [52]: m
Out[52]: 
matrix([[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]])

In [53]: m.T
Out[53]: 
matrix([[1, 4, 7],
        [2, 5, 8],
        [3, 6, 9]])
In [55]: v = np.matrix([[22],[33],[44]])

In [56]: v
Out[56]: 
matrix([[22],
        [33],
        [44]])

In [57]: m * v
Out[57]: 
matrix([[220],
        [517],
        [814]])
In [58]: from numpy import linalg

In [59]: linalg.det(m)
Out[59]: 0.0

In [60]: linalg.eigvals(m)
Out[60]: array([ 1.61168440e+01, -1.11684397e+00, -1.30367773e-15])

11、随机选择

#random模块中的choice()提供随机选择元素
In [62]: import random

In [63]: values = [1,2,3,4,5,6,7,8,9]

In [64]: random.choice(values)
Out[64]: 4

In [65]: random.choice(values)
Out[65]: 2

In [66]: random.choice(values)
Out[66]: 6
#随机选出多个元素可以使用random.samle()
In [68]: random.sample(values,2)
Out[68]: [4, 8]

In [69]: random.sample(values,3)
Out[69]: [3, 8, 9]

In [70]: random.sample(values,5)
Out[70]: [1, 7, 4, 3, 5]
#原地打乱元素顺序可以使用random.shuffle()
In [71]: values
Out[71]: [1, 2, 3, 4, 5, 6, 7, 8, 9]

In [72]: random.shuffle(values)

In [73]: values
Out[73]: [8, 7, 2, 5, 9, 3, 1, 6, 4]

#生成随机数可以使用random.randint()
In [74]: random.randint(1,1000)
Out[74]: 534

In [75]: random.randint(1,1000)
Out[75]: 675

In [76]: random.randint(1,1000)
Out[76]: 969
#产生0到1之间的浮点随机数可以使用random.random()
In [77]: random.random()
Out[77]: 0.4467371549631729

In [78]: random.random()
Out[78]: 0.870836619476411

In [79]: random.random()
Out[79]: 0.7285090986539235
#如果要得到由N个随机比特位表示的整数，可以使用random.getrandbits()
In [81]: random.getrandbits(50)
Out[81]: 898644577661596

In [82]: random.getrandbits(50)
Out[82]: 825711475826498

In [83]: random.getrandbits(50)
Out[83]: 877330983329038

12、时间换算

#利用datetime模块来完成不同时间单位间的换算，timedelta实例完成时间间隔换算
In [103]: from datetime import datetime,timedelta
#当前时间加2天后的时间
In [104]: datetime.now() + timedelta(days=2)
Out[104]: datetime.datetime(2018, 11, 17, 14, 0, 16, 257925)
#当前时间加5小时后的时间
In [105]: datetime.now() + timedelta(hours=5)
Out[105]: datetime.datetime(2018, 11, 15, 19, 0, 49, 178027)
#当前时间加30秒后的时间
In [106]: datetime.now() + timedelta(seconds=30)
Out[106]: datetime.datetime(2018, 11, 15, 14, 2, 26, 290114)
#创建一个小时实例
In [109]: x = timedelta(hours=2)
#创建一个60秒的时间实例
In [110]: y = timedelta(seconds=60)

In [111]: c = x + y

In [112]: c.days
Out[112]: 0
#换算成秒
In [113]: c
Out[113]: datetime.timedelta(seconds=7260)
#换算成时间
In [114]: c.seconds / 3600
Out[114]: 2.0166666666666666
In [117]: c.total_seconds()
Out[117]: 7260.0

In [118]: c.total_seconds() / 3600
Out[118]: 2.0166666666666666

#创建时间实例
In [119]: a = datetime(2018,10,10)
#输出10天后的时间
In [120]: print(a + timedelta(days=10))
2018-10-20 00:00:00
#创建时间实例
In [121]: b = datetime(2017,5,8)

In [122]: d = a - b
#两个时间的时间差
In [123]: d
Out[123]: datetime.timedelta(days=520)

In [124]: d.days
Out[124]: 520
#时间差的秒数
In [125]: d.total_seconds()
Out[125]: 44928000.0

13、计算上周5的日期

from datetime import datetime,timedelta

weekdays = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']

def get_previous_byday(dayname,start_date=None):
    if start_date is None:
        start_date = datetime.today()  #获取当前时间

    day_num = start_date.weekday() #获取时间的星期
    day_num_target = weekdays.index(dayname) #获取查询星期
    days_ago = (7 + day_num - day_num_target) % 7  #获取日期差的天数
    if days_ago == 0:
        days_ago = 7
    target_date = start_date - timedelta(days=days_ago) #计算时间差
    return target_date


print('现在时间：',datetime.today())
print(get_previous_byday('Monday'))
print(get_previous_byday('Tuesday',datetime(2018,10,23)))
print(get_previous_byday('Saturday',datetime(2018,8,8)))
print(get_previous_byday('Friday'))

#
现在时间： 2018-11-15 15:41:03.775963
2018-11-12 15:41:03.775963
2018-10-16 00:00:00
2018-08-04 00:00:00
2018-11-09 15:41:03.775963

14、找出当月的日期范围

from datetime import datetime,date,timedelta
import calendar

def get_month_range(start_date=None):
    if start_date is None:
        start_date = date.today().replace(day=1)
    else:
        start_date = start_date.replace(day=1) #替换输入时间的日期为1得到开始时间
    _,days_in_month = calendar.monthrange(start_date.year,start_date.month) #calendar.monthrange()函数返回当月的第一个工作日和当月的天数
    end_date = start_date + timedelta(days=days_in_month) #起始时间加当月天数获得截至时间
    a_day = timedelta(days=1)  #定义一天时间对象
    while start_date < end_date:
        print(start_date)
        start_date += a_day


get_month_range()
get_month_range(date(2018,10,23))

#
2018-10-01
2018-10-02
2018-10-03
2018-10-04
2018-10-05
2018-10-06
2018-10-07
2018-10-08
......

from datetime import datetime,timedelta

def date_range(start,stop,step):
    while start < stop:
        yield start
        start += step

for i in date_range(datetime(2018,10,15),datetime(2018,11,10),timedelta(hours=24)):
    print(i)

#
2018-10-15 00:00:00
2018-10-16 00:00:00
2018-10-17 00:00:00
2018-10-18 00:00:00
2018-10-19 00:00:00
2018-10-20 00:00:00
2018-10-21 00:00:00
2018-10-22 00:00:00
......

15、将字符串转换为日期

In [10]: from datetime import datetime

In [11]: date = '2018-11-16'
#将字符串转换为日期
In [12]: datetime.strptime(date,'%Y-%m-%d')
Out[12]: datetime.datetime(2018, 11, 16, 0, 0)
#获取当前日期
In [13]: datetime.now()
Out[13]: datetime.datetime(2018, 11, 16, 10, 56, 7, 487189)

In [14]: z = datetime.now()
#将日期格式化为阅读的日期形式
In [15]: datetime.strftime(z,'%A %B %d, %Y')
Out[15]: 'Friday November 16, 2018'
#使用自编写函数来处理字符串转日期要比datetime.strptime()快很多
In [16]: def parse_ymd(s):
    ...:     year_s,mon_s,day_s = s.split('-')
    ...:     return datetime(int(year_s),int(mon_s),int(day_s))
    ...: 
    ...: 

In [17]: parse_ymd('2018-11-16')
Out[17]: datetime.datetime(2018, 11, 16, 0, 0)

16、处理涉及到时区的日期问题

In [24]: from datetime import datetime,time,date

In [25]: import pytz
#查看中国时区
In [26]: pytz.country_timezones('cn')
Out[26]: ['Asia/Shanghai', 'Asia/Urumqi']
#创建中国时区对象
In [28]: tz = pytz.timezone('Asia/Shanghai')
#创建时间对象时指定时区
In [29]: datetime.now(tz)
Out[29]: datetime.datetime(2018, 11, 16, 13, 32, 59, 744669, tzinfo=<DstTzInfo 'Asia/Shanghai' CST+8:00:00 STD>)
#指定时区创建日期对象
In [30]: datetime(2018,11,16,tzinfo=tz)
Out[30]: datetime.datetime(2018, 11, 16, 0, 0, tzinfo=<DstTzInfo 'Asia/Shanghai' LMT+8:06:00 STD>)
#指定时区创建时间对象
In [31]: time(13,33,00,tzinfo=tz)
Out[31]: datetime.time(13, 33, tzinfo=<DstTzInfo 'Asia/Shanghai' LMT+8:06:00 STD>)
#本地化时间对象
In [33]: tz.localize(datetime.now())
Out[33]: datetime.datetime(2018, 11, 16, 13, 41, 28, 395602, tzinfo=<DstTzInfo 'Asia/Shanghai' CST+8:00:00 STD>)
#创建本地化时间对象
In [34]: loc_d = tz.localize(datetime.now())
#通过本地化时间对象转化为其他时区时间
In [35]: loc_d.astimezone(pytz.timezone('America/New_York'))
Out[35]: datetime.datetime(2018, 11, 16, 0, 42, 43, 666067, tzinfo=<DstTzInfo 'America/New_York' EST-1 day, 19:00:00 STD>)
#转换为UTC时间对象
In [36]: loc_d.astimezone(pytz.utc)
Out[36]: datetime.datetime(2018, 11, 16, 5, 42, 43, 666067, tzinfo=<UTC>)

In [37]: loc_d
Out[37]: datetime.datetime(2018, 11, 16, 13, 42, 43, 666067, tzinfo=<DstTzInfo 'Asia/Shanghai' CST+8:00:00 STD>)

In [38]: utc_d = loc_d.astimezone(pytz.utc)

In [39]: print(utc_d)
2018-11-16 05:42:43.666067+00:00
#将UTC时间转换为合适的时区
In [40]: later_utc = utc_d + timedelta(minutes=30)

In [41]: print(later_utc.astimezone(tz))
2018-11-16 14:12:43.666067+08:00