1、生成一个时间段
In [1]:import pandas as pd
In [2]:import numpy as np
#1、生成一个时间区间段，间隔为小时
In [3]:rng = pd.date_range('1/1/2011', periods=72, freq='H')
#2、生成一个Series，并制定索引为时间段
In [4]:ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [5]:ts
Out[5]:
2011-01-01 00:00:00 -0.204085
2011-01-01 01:00:00 1.101711
2011-01-01 02:00:00 1.840500
2011-01-01 03:00:00 0.112426
2011-01-01 04:00:00 -0.310413
2011-01-01 05:00:00 1.180762
2011-01-01 06:00:00 0.087775
2011-01-01 07:00:00 1.087877
2011-01-01 08:00:00 -0.950237
2011-01-01 09:00:00 -0.468453
Freq: H, dtype: float64
#3、改变时间间隔
In [6]:converted = ts.asfreq('45Min', method='pad')
In [7]:converted
Out[7]:
2011-01-01 00:00:00 -0.204085
2011-01-01 00:45:00 -0.204085
2011-01-01 01:30:00 1.101711
2011-01-01 02:15:00 1.840500
2011-01-01 03:00:00 0.112426
2011-01-01 03:45:00 0.112426
2011-01-01 04:30:00 -0.310413
2011-01-01 05:15:00 1.180762
2011-01-01 06:00:00 0.087775
2011-01-01 06:45:00 0.087775
2011-01-01 07:30:00 1.087877
2011-01-01 08:15:00 -0.950237
2011-01-01 09:00:00 -0.468453
Freq: 45T, dtype: float64
2、转华为日期格式
2.1 数字生成日期格式
In [8]: pd.Timestamp(datetime(2012, 5, 1))
Out[8]: Timestamp('2012-05-01 00:00:00')
2.2 字符生成日期格式
In [9]: pd.Timestamp('2012-05-01')
Out[9]: Timestamp('2012-05-01 00:00:00')
2.3 只有年月
In [10]: pd.Period('2011-01')
Out[10]: Period('2011-01', 'M')
In [11]: pd.Period('2012-05', freq='D')
Out[11]: Period('2012-05-01', 'D')
2.4 转化为日期格式
In [22]: pd.to_datetime(pd.Series(['Jul 31, 2009', '2010-01-10', None]))
Out[22]:
0 2009-07-31
1 2010-01-10
2 NaT
dtype: datetime64[ns]
In [23]: pd.to_datetime(['2005/11/23', '2010.12.31'])
Out[23]: DatetimeIndex(['2005-11-23', '2010-12-31'], dtype='datetime64[ns]', freq=None)
3、生成一个时间段
3.1 生成索引的方法
In [35]: dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)]
# Note the frequency information
In [36]: index = pd.DatetimeIndex(dates)
In [37]: index
Out[37]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)
# Automatically converted to DatetimeIndex
In [38]: index = pd.Index(dates)
In [39]: index
Out[39]: DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)
# date_range日历，bdate_range工作日
In [40]: index = pd.date_range('2000-1-1', periods=1000, freq='M')
In [41]: index
Out[41]:
DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31', '2000-04-30',
'2000-05-31', '2000-06-30', '2000-07-31', '2000-08-31',
'2000-09-30', '2000-10-31',
...
'2082-07-31', '2082-08-31', '2082-09-30', '2082-10-31',
'2082-11-30', '2082-12-31', '2083-01-31', '2083-02-28',
'2083-03-31', '2083-04-30'],
dtype='datetime64[ns]', length=1000, freq='M')
In [42]: index = pd.bdate_range('2012-1-1', periods=250)
In [43]: index
Out[43]:
DatetimeIndex(['2012-01-02', '2012-01-03', '2012-01-04', '2012-01-05',
'2012-01-06', '2012-01-09', '2012-01-10', '2012-01-11',
'2012-01-12', '2012-01-13',
...
'2012-12-03', '2012-12-04', '2012-12-05', '2012-12-06',
'2012-12-07', '2012-12-10', '2012-12-11', '2012-12-12',
'2012-12-13', '2012-12-14'],
dtype='datetime64[ns]', length=250, freq='B')
In [44]: start = datetime(2011, 1, 1)
In [45]: end = datetime(2012, 1, 1)
In [46]: rng = pd.date_range(start, end)
In [47]: rng
Out[47]:
DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04',
'2011-01-05', '2011-01-06', '2011-01-07', '2011-01-08',
'2011-01-09', '2011-01-10',
...
'2011-12-23', '2011-12-24', '2011-12-25', '2011-12-26',
'2011-12-27', '2011-12-28', '2011-12-29', '2011-12-30',
'2011-12-31', '2012-01-01'],
dtype='datetime64[ns]', length=366, freq='D')
In [48]: rng = pd.bdate_range(start, end)
In [49]: rng
Out[49]:
DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06',
'2011-01-07', '2011-01-10', '2011-01-11', '2011-01-12',
'2011-01-13', '2011-01-14',
...
'2011-12-19', '2011-12-20', '2011-12-21', '2011-12-22',
'2011-12-23', '2011-12-26', '2011-12-27', '2011-12-28',
'2011-12-29', '2011-12-30'],
dtype='datetime64[ns]', length=260, freq='B')
3.2 每个月末，每隔一周
In [50]: pd.date_range(start, end, freq='BM')
Out[50]:
DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29',
'2011-05-31', '2011-06-30', '2011-07-29', '2011-08-31',
'2011-09-30', '2011-10-31', '2011-11-30', '2011-12-30'],
dtype='datetime64[ns]', freq='BM')
In [51]: pd.date_range(start, end, freq='W')
Out[51]:
DatetimeIndex(['2011-01-02', '2011-01-09', '2011-01-16', '2011-01-23',
'2011-01-30', '2011-02-06', '2011-02-13', '2011-02-20',
'2011-02-27', '2011-03-06', '2011-03-13', '2011-03-20',
'2011-03-27', '2011-04-03', '2011-04-10', '2011-04-17',
'2011-04-24', '2011-05-01', '2011-05-08', '2011-05-15',
'2011-05-22', '2011-05-29', '2011-06-05', '2011-06-12',
'2011-06-19', '2011-06-26', '2011-07-03', '2011-07-10',
'2011-07-17', '2011-07-24', '2011-07-31', '2011-08-07',
'2011-08-14', '2011-08-21', '2011-08-28', '2011-09-04',
'2011-09-11', '2011-09-18', '2011-09-25', '2011-10-02',
'2011-10-09', '2011-10-16', '2011-10-23', '2011-10-30',
'2011-11-06', '2011-11-13', '2011-11-20', '2011-11-27',
'2011-12-04', '2011-12-11', '2011-12-18', '2011-12-25',
'2012-01-01'],
dtype='datetime64[ns]', freq='W-SUN')
3.3 从End往前数20个工作日，从start往后数20个工作日
In [52]: pd.bdate_range(end=end, periods=20)
Out[52]:
DatetimeIndex(['2011-12-05', '2011-12-06', '2011-12-07', '2011-12-08',
'2011-12-09', '2011-12-12', '2011-12-13', '2011-12-14',
'2011-12-15', '2011-12-16', '2011-12-19', '2011-12-20',
'2011-12-21', '2011-12-22', '2011-12-23', '2011-12-26',
'2011-12-27', '2011-12-28', '2011-12-29', '2011-12-30'],
dtype='datetime64[ns]', freq='B')
In [53]: pd.bdate_range(start=start, periods=20)
Out[53]:
DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06',
'2011-01-07', '2011-01-10', '2011-01-11', '2011-01-12',
'2011-01-13', '2011-01-14', '2011-01-17', '2011-01-18',
'2011-01-19', '2011-01-20', '2011-01-21', '2011-01-24',
'2011-01-25', '2011-01-26', '2011-01-27', '2011-01-28'],
dtype='datetime64[ns]', freq='B')
4、根据部分索引选择，切片
In [56]: rng = pd.date_range(start, end, freq='BM')
In [57]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [58]: ts.index
Out[58]:
DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29',
'2011-05-31', '2011-06-30', '2011-07-29', '2011-08-31',
'2011-09-30', '2011-10-31', '2011-11-30', '2011-12-30'],
dtype='datetime64[ns]', freq='BM')
In [59]: ts[:5].index
Out[59]:
DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-29',
'2011-05-31'],
dtype='datetime64[ns]', freq='BM')
In [60]: ts[::2].index
Out[60]:
DatetimeIndex(['2011-01-31', '2011-03-31', '2011-05-31', '2011-07-29',
'2011-09-30', '2011-11-30'],
dtype='datetime64[ns]', freq='2BM')
In [61]: ts['1/31/2011']
Out[61]: -1.2812473076599531
In [62]: ts[pd.datetime(2011, 12, 25):]
Out[62]:
2011-12-30 0.687738
Freq: BM, dtype: float64
In [63]: ts['10/31/2011':'12/31/2011']
Out[63]:
2011-10-31 0.149748
2011-11-30 -0.732339
2011-12-30 0.687738
Freq: BM, dtype: float64
In [64]: ts['2011']
Out[64]:
2011-01-31 -1.281247
2011-02-28 -0.727707
2011-03-31 -0.121306
2011-04-29 -0.097883
2011-05-31 0.695775
2011-06-30 0.341734
2011-07-29 0.959726
2011-08-31 -1.110336
2011-09-30 -0.619976
2011-10-31 0.149748
2011-11-30 -0.732339
2011-12-30 0.687738
Freq: BM, dtype: float64
In [65]: ts['2011-6']
Out[65]:
2011-06-30 0.341734
Freq: BM, dtype: float64
# DataFrame中指定了时间索引，可以根据时间索引提取子集
In [66]: dft = pd.DataFrame(np.random.randn(100000,1),columns=['A'],index=pd.date_range('20130101',periods=100000,freq='T'))
In [67]: dft
Out[67]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-03-11 10:33:00 -0.293083
2013-03-11 10:34:00 -0.059881
2013-03-11 10:35:00 1.252450
2013-03-11 10:36:00 0.046611
2013-03-11 10:37:00 0.059478
2013-03-11 10:38:00 -0.286539
2013-03-11 10:39:00 0.841669
[100000 rows x 1 columns]
In [68]: dft['2013']
Out[68]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-03-11 10:33:00 -0.293083
2013-03-11 10:34:00 -0.059881
2013-03-11 10:35:00 1.252450
2013-03-11 10:36:00 0.046611
2013-03-11 10:37:00 0.059478
2013-03-11 10:38:00 -0.286539
2013-03-11 10:39:00 0.841669
[100000 rows x 1 columns]
In [69]: dft['2013-1':'2013-2']
Out[69]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-02-28 23:53:00 0.103114
2013-02-28 23:54:00 -1.303422
2013-02-28 23:55:00 0.451943
2013-02-28 23:56:00 0.220534
2013-02-28 23:57:00 -1.624220
2013-02-28 23:58:00 0.093915
2013-02-28 23:59:00 -1.087454
[84960 rows x 1 columns]
In [70]: dft['2013-1':'2013-2-28']
Out[70]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-02-28 23:53:00 0.103114
2013-02-28 23:54:00 -1.303422
2013-02-28 23:55:00 0.451943
2013-02-28 23:56:00 0.220534
2013-02-28 23:57:00 -1.624220
2013-02-28 23:58:00 0.093915
2013-02-28 23:59:00 -1.087454
[84960 rows x 1 columns]
In [71]: dft['2013-1':'2013-2-28 00:00:00']
Out[71]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-02-27 23:54:00 0.897051
2013-02-27 23:55:00 -0.309230
2013-02-27 23:56:00 1.944713
2013-02-27 23:57:00 0.369265
2013-02-27 23:58:00 0.053071
2013-02-27 23:59:00 -0.019734
2013-02-28 00:00:00 1.388189
[83521 rows x 1 columns]
In [72]: dft['2013-1-15':'2013-1-15 12:30:00']
Out[72]:
A
2013-01-15 00:00:00 0.501288
2013-01-15 00:01:00 -0.605198
2013-01-15 00:02:00 0.215146
2013-01-15 00:03:00 0.924732
2013-01-15 00:04:00 -2.228519
2013-01-15 00:05:00 1.517331
2013-01-15 00:06:00 -1.188774
... ...
2013-01-15 12:24:00 1.358314
2013-01-15 12:25:00 -0.737727
2013-01-15 12:26:00 1.838323
2013-01-15 12:27:00 -0.774090
2013-01-15 12:28:00 0.622261
2013-01-15 12:29:00 -0.631649
2013-01-15 12:30:00 0.193284
[751 rows x 1 columns]
In [73]: dft.loc['2013-1-15 12:30:00']
Out[73]:
A 0.193284
Name: 2013-01-15 12:30:00, dtype: float64
6、常用时间
| 类别 |
解释 |
| year |
年 |
| month |
月 |
| day |
日 |
| hour |
时 |
| minute |
分钟 |
| second |
秒 |
| microsecond |
微秒 |
| nanosecond |
纳秒 |
| date |
返回日期 |
| time |
返回时间 |
| dayofyear |
年序日 |
| weekofyear |
年序周 |
| week |
周 |
| dayofweek |
周中的第几天，Monday=0, Sunday=6 |
| weekday |
周中的第几天，Monday=0, Sunday=6 |
| weekday_name |
周中的星期几，ex: Friday |
| quarter |
季度 |
| days_in_month |
一个月中有多少天 |
| is_month_start |
是否月初第一天 |
| is_month_end |
是否月末最后一天 |
| is_quarter_start |
是否季度的最开始 |
| is_quarter_end |
是否季度的最后一个 |
| is_year_start |
是否年初第一天 |
| is_year_end |
是否年末第一天 |
7、某一时间点，往前往后加一段时间
| 类别 |
解释 |
| BDay |
工作日 |
| CDay |
自定义日期 |
| Week |
周 |
| WeekOfMonth |
月中的第几周 |
| LastWeekOfMonth |
月中的最后一周 |
| MonthEnd |
日历上月末 |
| MonthBegin |
日历上月初 |
| BMonthEnd |
工作月初 |
| BMonthBegin |
月开始营业 |
| CBMonthEnd |
自定义月末 |
| CBMonthBegin |
自定义月初 |
| QuarterEnd |
日历季末 |
| QuarterBegin |
日历季初 |
| BQuarterEnd |
工作季末 |
| BQuarterBegin |
工作季初 |
| FY5253Quarter |
retail (aka 52-53 week) quarter |
| YearEnd |
日历年末 |
| YearBegin |
日历年初 |
| BYearEnd |
工作年末 |
| BYearBegin |
工作年初 |
| FY5253 |
retail (aka 52-53 week) year |
| BusinessHour |
工作小时 |
| CustomBusinessHour |
自定义小时 |
| Hour |
小时 |
| Minute |
分钟 |
| Second |
秒 |
In [84]: d = pd.datetime(2008, 8, 18, 9, 0)
In [86]: from pandas.tseries.offsets import *
In [87]: d + DateOffset(months=4, days=5)
Out[87]: Timestamp('2008-12-23 09:00:00')
In [88]: d - 5 * BDay()
Out[88]: Timestamp('2008-08-11 09:00:00')
# 月末
In [89]: d + BMonthEnd()
Out[89]: Timestamp('2008-08-29 09:00:00')
In [90]: d
Out[90]: datetime.datetime(2008, 8, 18, 9, 0)
# 往前数月末
In [91]: offset = BMonthEnd()
In [92]: offset.rollforward(d)
Out[92]: Timestamp('2008-08-29 09:00:00')
# 往后数月末
In [93]: offset.rollback(d)
Out[93]: Timestamp('2008-07-31 09:00:00')
# 时间方面的
In [94]: day = Day()
In [95]: day.apply(pd.Timestamp('2014-01-01 09:00'))
Out[95]: Timestamp('2014-01-02 09:00:00')
In [96]: day = Day(normalize=True)
In [97]: day.apply(pd.Timestamp('2014-01-01 09:00'))
Out[97]: Timestamp('2014-01-02 00:00:00')
In [98]: hour = Hour()
In [99]: hour.apply(pd.Timestamp('2014-01-01 22:00'))
Out[99]: Timestamp('2014-01-01 23:00:00')
In [100]: hour = Hour(normalize=True)
In [101]: hour.apply(pd.Timestamp('2014-01-01 22:00'))
Out[101]: Timestamp('2014-01-01 00:00:00')
In [102]: hour.apply(pd.Timestamp('2014-01-01 23:00'))
Out[102]: Timestamp('2014-01-02 00:00:00')
# 周相关的
In [103]: d
Out[103]: datetime.datetime(2008, 8, 18, 9, 0)
In [104]: d + Week()
Out[104]: Timestamp('2008-08-25 09:00:00')
In [105]: d + Week(weekday=4)
Out[105]: Timestamp('2008-08-22 09:00:00')
In [106]: (d + Week(weekday=4)).weekday()
Out[106]: 4
In [107]: d - Week()
Out[107]: Timestamp('2008-08-11 09:00:00')
8、时间序列相关的时间处理
In [213]: ts = ts[:5]
In [214]: ts.shift(1)
Out[214]:
2011-01-31 NaN
2011-02-28 -1.281247
2011-03-31 -0.727707
2011-04-29 -0.121306
2011-05-31 -0.097883
Freq: BM, dtype: float64
In [215]: ts.shift(5, freq=datetools.bday)
Out[215]:
2011-02-07 -1.281247
2011-03-07 -0.727707
2011-04-07 -0.121306
2011-05-06 -0.097883
2011-06-07 0.695775
dtype: float64
In [216]: ts.shift(5, freq='BM')
Out[216]:
2011-06-30 -1.281247
2011-07-29 -0.727707
2011-08-31 -0.121306
2011-09-30 -0.097883
2011-10-31 0.695775
Freq: BM, dtype: float64
In [217]: ts.tshift(5, freq='D')
Out[217]:
2011-02-05 -1.281247
2011-03-05 -0.727707
2011-04-05 -0.121306
2011-05-04 -0.097883
2011-06-05 0.695775
dtype: float64
作者：一刀Q
链接：https://www.jianshu.com/p/96ea42c58abe
來源：简书
简书著作权归作者所有，任何形式的转载都请联系作者获得授权并注明出处。