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python-for-data-时区处理

本文中主要讲解的是pandas对于时区是如何处理的

时区处理

很多时间用户选择世界协调时间或者UTC,它是格林治时间的后继者,目前的国家标准。时区通常表示为UTC的偏置。

Python语言中,时区信息通常是来自于第三库pytzpandas中封装了pytz的功能。

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
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# 获取时区名称
import pytz
pytz.common_timezones[-5:]
['US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']
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# 获取pytz对象,使用pytz-timezone
tz = pytz.timezone('America/New_York')
tz
<DstTzInfo 'America/New_York' LMT-1 day, 19:04:00 STD>

时区集合生成

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rng = pd.date_range('5/10/2020 11:30'
,periods=6
,freq='D')
ts = pd.Series(np.random.randn(len(rng))
,index=rng)
ts
2020-05-10 11:30:00    1.072220
2020-05-11 11:30:00    2.088327
2020-05-12 11:30:00   -0.795575
2020-05-13 11:30:00    1.230427
2020-05-14 11:30:00   -0.012184
2020-05-15 11:30:00   -0.786641
Freq: D, dtype: float64
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ts.index
DatetimeIndex(['2020-05-10 11:30:00', '2020-05-11 11:30:00',
               '2020-05-12 11:30:00', '2020-05-13 11:30:00',
               '2020-05-14 11:30:00', '2020-05-15 11:30:00'],
              dtype='datetime64[ns]', freq='D')
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print(ts.index.tz)  # tz属性为None
None
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# 时区集合生成
pd.date_range('5/10/2020',periods=10,freq='D',tz='UTC')
DatetimeIndex(['2020-05-10 00:00:00+00:00', '2020-05-11 00:00:00+00:00',
               '2020-05-12 00:00:00+00:00', '2020-05-13 00:00:00+00:00',
               '2020-05-14 00:00:00+00:00', '2020-05-15 00:00:00+00:00',
               '2020-05-16 00:00:00+00:00', '2020-05-17 00:00:00+00:00',
               '2020-05-18 00:00:00+00:00', '2020-05-19 00:00:00+00:00'],
              dtype='datetime64[ns, UTC]', freq='D')

简单时区转换到本地化:tz_localize

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ts
2020-05-10 11:30:00    1.072220
2020-05-11 11:30:00    2.088327
2020-05-12 11:30:00   -0.795575
2020-05-13 11:30:00    1.230427
2020-05-14 11:30:00   -0.012184
2020-05-15 11:30:00   -0.786641
Freq: D, dtype: float64
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ts_utc=ts.tz_localize('UTC')
ts_utc
2020-05-10 11:30:00+00:00    1.072220
2020-05-11 11:30:00+00:00    2.088327
2020-05-12 11:30:00+00:00   -0.795575
2020-05-13 11:30:00+00:00    1.230427
2020-05-14 11:30:00+00:00   -0.012184
2020-05-15 11:30:00+00:00   -0.786641
Freq: D, dtype: float64
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ts_utc.index
DatetimeIndex(['2020-05-10 11:30:00+00:00', '2020-05-11 11:30:00+00:00',
               '2020-05-12 11:30:00+00:00', '2020-05-13 11:30:00+00:00',
               '2020-05-14 11:30:00+00:00', '2020-05-15 11:30:00+00:00'],
              dtype='datetime64[ns, UTC]', freq='D')

转换到其他时区:tz_convert

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ts_utc.tz_convert("America/New_York")   # 转到纽约时区
2020-05-10 07:30:00-04:00    1.072220
2020-05-11 07:30:00-04:00    2.088327
2020-05-12 07:30:00-04:00   -0.795575
2020-05-13 07:30:00-04:00    1.230427
2020-05-14 07:30:00-04:00   -0.012184
2020-05-15 07:30:00-04:00   -0.786641
Freq: D, dtype: float64
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ts_utc.tz_convert("Asia/Shanghai")     # 转到上海时区
2020-05-10 19:30:00+08:00    1.072220
2020-05-11 19:30:00+08:00    2.088327
2020-05-12 19:30:00+08:00   -0.795575
2020-05-13 19:30:00+08:00    1.230427
2020-05-14 19:30:00+08:00   -0.012184
2020-05-15 19:30:00+08:00   -0.786641
Freq: D, dtype: float64

实例化方法

tz_localzie、tz_convert是DatetimeIndex的实例化方法

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ts.index.tz_localize('Asia/Shanghai')
DatetimeIndex(['2020-05-10 11:30:00+08:00', '2020-05-11 11:30:00+08:00',
               '2020-05-12 11:30:00+08:00', '2020-05-13 11:30:00+08:00',
               '2020-05-14 11:30:00+08:00', '2020-05-15 11:30:00+08:00'],
              dtype='datetime64[ns, Asia/Shanghai]', freq='D')

时区感知时间戳对象的操作

单独的Timestamp对象也可以从简单时间戳本地为时区感知时间戳

Timestamp对象的转化

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stamp = pd.Timestamp('2020-05-10 23:49')
stamp
Timestamp('2020-05-10 23:49:00')
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stamp_utc = stamp.tz_localize('utc')  # 本地化
stamp_utc
Timestamp('2020-05-10 23:49:00+0000', tz='UTC')
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stamp_utc.tz_convert("Asia/Shanghai")  # 时区转化
Timestamp('2020-05-11 07:49:00+0800', tz='Asia/Shanghai')

创建的时候直接传递时区

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stamp_shanghai = pd.Timestamp("2020-05-10 23:58"
,tz="Asia/Shanghai") # 直接传递时区
stamp_shanghai
Timestamp('2020-05-10 23:58:00+0800', tz='Asia/Shanghai')

时间戳数值不变性

时区感知的Timestamp对象内部存储的一个UNix到现在的时间戳数值,保持不变

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stamp_shanghai.value
1589126280000000000
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# 结果同上
stamp_shanghai.tz_convert("America/New_York").value
1589126280000000000

dateOffset

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from pandas.tseries.offsets import Hour
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data = pd.Timestamp("2020-05-10 01:30"   # 创建一个Timestamp对象
,tz="Asia/Shanghai")
data
Timestamp('2020-05-10 01:30:00+0800', tz='Asia/Shanghai')
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data + Hour(2)  # 加上2个小时
# data +2 * Hour()
Timestamp('2020-05-10 03:30:00+0800', tz='Asia/Shanghai')

不同时区的操作

如果两个不同时区的时间序列需要联合,结果将是UTC时间的。时间戳按照UTC格式存储

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rng = pd.date_range("2020-05-10 23:43"
,periods=10
,freq="B")
rng
DatetimeIndex(['2020-05-11 23:43:00', '2020-05-12 23:43:00',
               '2020-05-13 23:43:00', '2020-05-14 23:43:00',
               '2020-05-15 23:43:00', '2020-05-18 23:43:00',
               '2020-05-19 23:43:00', '2020-05-20 23:43:00',
               '2020-05-21 23:43:00', '2020-05-22 23:43:00'],
              dtype='datetime64[ns]', freq='B')
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ts = pd.Series(np.random.randn(len(rng))   # 随机取值
,index=rng) # 行index
ts
2020-05-11 23:43:00    0.258933
2020-05-12 23:43:00    0.416673
2020-05-13 23:43:00    0.089695
2020-05-14 23:43:00   -0.347376
2020-05-15 23:43:00   -0.304925
2020-05-18 23:43:00   -0.891367
2020-05-19 23:43:00   -0.960866
2020-05-20 23:43:00   -0.420829
2020-05-21 23:43:00    0.591673
2020-05-22 23:43:00    1.431417
Freq: B, dtype: float64
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ts1 = ts[:7].tz_localize('Asia/Shanghai')
ts2 = ts1[2:].tz_convert('Europe/Moscow')
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ts1
2020-05-11 23:43:00+08:00    0.258933
2020-05-12 23:43:00+08:00    0.416673
2020-05-13 23:43:00+08:00    0.089695
2020-05-14 23:43:00+08:00   -0.347376
2020-05-15 23:43:00+08:00   -0.304925
2020-05-18 23:43:00+08:00   -0.891367
2020-05-19 23:43:00+08:00   -0.960866
Freq: B, dtype: float64
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ts2
2020-05-13 18:43:00+03:00    0.089695
2020-05-14 18:43:00+03:00   -0.347376
2020-05-15 18:43:00+03:00   -0.304925
2020-05-18 18:43:00+03:00   -0.891367
2020-05-19 18:43:00+03:00   -0.960866
Freq: B, dtype: float64
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result = ts1 + ts2
result
2020-05-11 15:43:00+00:00         NaN
2020-05-12 15:43:00+00:00         NaN
2020-05-13 15:43:00+00:00    0.179391
2020-05-14 15:43:00+00:00   -0.694751
2020-05-15 15:43:00+00:00   -0.609850
2020-05-18 15:43:00+00:00   -1.782735
2020-05-19 15:43:00+00:00   -1.921732
Freq: B, dtype: float64
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result.index
DatetimeIndex(['2020-05-11 15:43:00+00:00', '2020-05-12 15:43:00+00:00',
               '2020-05-13 15:43:00+00:00', '2020-05-14 15:43:00+00:00',
               '2020-05-15 15:43:00+00:00', '2020-05-18 15:43:00+00:00',
               '2020-05-19 15:43:00+00:00'],
              dtype='datetime64[ns, UTC]', freq='B')

本文标题:python-for-data-时区处理

发布时间:2020年05月11日 - 00:05

原始链接:http://www.renpeter.cn/2020/05/11/python-for-data-%E6%97%B6%E5%8C%BA%E5%A4%84%E7%90%86.html

许可协议: 署名-非商业性使用-禁止演绎 4.0 国际 转载请保留原文链接及作者。

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