pandas最大的时间间隔_Pandas中时间和日期处理

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#pandas最大的时间间隔

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]:

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]:

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]:

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]:

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]:

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]:

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