核心：pd.date_range()
# pd.DatetimeIndex()与TimeSeries时间序列
rng = pd.DatetimeIndex(['12/1/2017','12/2/2017','12/3/2017','12/4/2017','12/5/2017'])
print("1".center(40,'*'))
print(rng,type(rng))
print("2".center(40,'*'))
print(rng[0],type(rng[0]))
# 直接生成时间戳索引，支持str、datetime.datetime
# 单个时间戳为Timestamp，多个时间戳为DatetimeIndex
st = pd.Series(np.random.rand(len(rng)), index = rng)
print("3".center(40,'*'))
print(st,type(st))
print("4".center(40,'*'))
print(st.index)
# 以DatetimeIndex为index的Series，为TimeSries，时间序列
#执行结果
*******************1********************
DatetimeIndex(['2017-12-01', '2017-12-02', '2017-12-03', '2017-12-04',
'2017-12-05'],
dtype='datetime64[ns]', freq=None) <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
*******************2********************
2017-12-01 00:00:00 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
*******************3********************
2017-12-01 0.700239
2017-12-02 0.579206
2017-12-03 0.360233
2017-12-04 0.704604
2017-12-05 0.505148
dtype: float64 <class 'pandas.core.series.Series'>
*******************4********************
DatetimeIndex(['2017-12-01', '2017-12-02', '2017-12-03', '2017-12-04',
'2017-12-05'],
dtype='datetime64[ns]', freq=None)
# pd.date_range()-日期范围：生成日期范围
# 2种生成方式：①start + end； ②start/end + periods
# 默认频率：day
rng1 = pd.date_range('1/1/2017','1/10/2017', normalize=True)
rng2 = pd.date_range(start = '1/1/2017', periods = 10)
rng3 = pd.date_range(end = '1/30/2017 15:00:00', periods = 10) # 增加了时、分、秒
print(rng1,type(rng1))
print(rng2)
print(rng3)
# 直接生成DatetimeIndex
# pd.date_range(start=None, end=None, periods=None, freq='D', tz=None, normalize=False, name=None, closed=None, **kwargs)
# start：开始时间
# end：结束时间
# periods：偏移量
# freq：频率，默认天，pd.date_range()默认频率为日历日，pd.bdate_range()默认频率为工作日
# tz：时区
# 执行结果
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
'2017-01-05', '2017-01-06', '2017-01-07', '2017-01-08',
'2017-01-09', '2017-01-10'],
dtype='datetime64[ns]', freq='D') <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
'2017-01-05', '2017-01-06', '2017-01-07', '2017-01-08',
'2017-01-09', '2017-01-10'],
dtype='datetime64[ns]', freq='D')
DatetimeIndex(['2017-01-21 15:00:00', '2017-01-22 15:00:00',
'2017-01-23 15:00:00', '2017-01-24 15:00:00',
'2017-01-25 15:00:00', '2017-01-26 15:00:00',
'2017-01-27 15:00:00', '2017-01-28 15:00:00',
'2017-01-29 15:00:00', '2017-01-30 15:00:00'],
dtype='datetime64[ns]', freq='D')
rng4 = pd.date_range(start = '1/1/2017 15:30', periods = 10, name = 'hello world!', normalize = True)
print("1".center(40,'*'))
print(rng4)
# normalize：时间参数值正则化到午夜时间戳（这里最后就直接变成0:00:00，并不是15:30:00）
# name：索引对象名称
print("2".center(40,'*'))
print(pd.date_range('20170101','20170104')) # 20170101也可读取
print(pd.date_range('20170101','20170104',closed = 'right'))
print(pd.date_range('20170101','20170104',closed = 'left'))
# closed：默认为None的情况下，左闭右闭，left则左闭右开，right则左开右闭
print("3".center(40,'*'))
print(pd.bdate_range('20170101','20170107'))
# pd.bdate_range()默认频率为工作日
print("4".center(40,'*'))
print(list(pd.date_range(start = '1/1/2017', periods = 10)))
# 直接转化为list，元素为Timestamp
#执行结果
*******************1********************
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
'2017-01-05', '2017-01-06', '2017-01-07', '2017-01-08',
'2017-01-09', '2017-01-10'],
dtype='datetime64[ns]', name='hello world!', freq='D')
*******************2********************
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'], dtype='datetime64[ns]', freq='D')
*******************3********************
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04', '2017-01-05',
'2017-01-06'],
dtype='datetime64[ns]', freq='B')
*******************4********************
[Timestamp('2017-01-01 00:00:00', freq='D'), Timestamp('2017-01-02 00:00:00', freq='D'), Timestamp('2017-01-03 00:00:00', freq='D'), Timestamp('2017-01-04 00:00:00', freq='D'), Timestamp('2017-01-05 00:00:00', freq='D'), Timestamp('2017-01-06 00:00:00', freq='D'), Timestamp('2017-01-07 00:00:00', freq='D'), Timestamp('2017-01-08 00:00:00', freq='D'), Timestamp('2017-01-09 00:00:00', freq='D'), Timestamp('2017-01-10 00:00:00', freq='D')]
# pd.date_range()-日期范围：频率(1)
print(pd.date_range('2017/1/1','2017/1/4')) # 默认freq = 'D'：每日历日
print(pd.date_range('2017/1/1','2017/1/4', freq = 'B')) # B：每工作日
print(pd.date_range('2017/1/1','2017/1/2', freq = 'H')) # H：每小时
print(pd.date_range('2017/1/1 12:00','2017/1/1 12:10', freq = 'T')) # T/MIN：每分
print(pd.date_range('2017/1/1 12:00:00','2017/1/1 12:00:10', freq = 'S')) # S：每秒
print(pd.date_range('2017/1/1 12:00:00','2017/1/1 12:00:10', freq = 'L')) # L：每毫秒（千分之一秒）
print(pd.date_range('2017/1/1 12:00:00','2017/1/1 12:00:10', freq = 'U')) # U：每微秒（百万分之一秒）
print(pd.date_range('2017/1/1','2017/2/1', freq = 'W-MON'))
# W-MON：从指定星期几开始算起，每周
# 星期几缩写：MON/TUE/WED/THU/FRI/SAT/SUN
print(pd.date_range('2017/1/1','2017/5/1', freq = 'WOM-2MON'))
# WOM-2MON：每月的第几个星期几开始算，这里是每月第二个星期一
#执行结果
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='B')
DatetimeIndex(['2017-01-01 00:00:00', '2017-01-01 01:00:00',
'2017-01-01 02:00:00', '2017-01-01 03:00:00',
'2017-01-01 04:00:00', '2017-01-01 05:00:00',
'2017-01-01 06:00:00', '2017-01-01 07:00:00',
'2017-01-01 08:00:00', '2017-01-01 09:00:00',
'2017-01-01 10:00:00', '2017-01-01 11:00:00',
'2017-01-01 12:00:00', '2017-01-01 13:00:00',
'2017-01-01 14:00:00', '2017-01-01 15:00:00',
'2017-01-01 16:00:00', '2017-01-01 17:00:00',
'2017-01-01 18:00:00', '2017-01-01 19:00:00',
'2017-01-01 20:00:00', '2017-01-01 21:00:00',
'2017-01-01 22:00:00', '2017-01-01 23:00:00',
'2017-01-02 00:00:00'],
dtype='datetime64[ns]', freq='H')
DatetimeIndex(['2017-01-01 12:00:00', '2017-01-01 12:01:00',
'2017-01-01 12:02:00', '2017-01-01 12:03:00',
'2017-01-01 12:04:00', '2017-01-01 12:05:00',
'2017-01-01 12:06:00', '2017-01-01 12:07:00',
'2017-01-01 12:08:00', '2017-01-01 12:09:00',
'2017-01-01 12:10:00'],
dtype='datetime64[ns]', freq='T')
DatetimeIndex(['2017-01-01 12:00:00', '2017-01-01 12:00:01',
'2017-01-01 12:00:02', '2017-01-01 12:00:03',
'2017-01-01 12:00:04', '2017-01-01 12:00:05',
'2017-01-01 12:00:06', '2017-01-01 12:00:07',
'2017-01-01 12:00:08', '2017-01-01 12:00:09',
'2017-01-01 12:00:10'],
dtype='datetime64[ns]', freq='S')
DatetimeIndex([ '2017-01-01 12:00:00', '2017-01-01 12:00:00.001000',
'2017-01-01 12:00:00.002000', '2017-01-01 12:00:00.003000',
'2017-01-01 12:00:00.004000', '2017-01-01 12:00:00.005000',
'2017-01-01 12:00:00.006000', '2017-01-01 12:00:00.007000',
'2017-01-01 12:00:00.008000', '2017-01-01 12:00:00.009000',
...
'2017-01-01 12:00:09.991000', '2017-01-01 12:00:09.992000',
'2017-01-01 12:00:09.993000', '2017-01-01 12:00:09.994000',
'2017-01-01 12:00:09.995000', '2017-01-01 12:00:09.996000',
'2017-01-01 12:00:09.997000', '2017-01-01 12:00:09.998000',
'2017-01-01 12:00:09.999000', '2017-01-01 12:00:10'],
dtype='datetime64[ns]', length=10001, freq='L')
DatetimeIndex([ '2017-01-01 12:00:00', '2017-01-01 12:00:00.000001',
'2017-01-01 12:00:00.000002', '2017-01-01 12:00:00.000003',
'2017-01-01 12:00:00.000004', '2017-01-01 12:00:00.000005',
'2017-01-01 12:00:00.000006', '2017-01-01 12:00:00.000007',
'2017-01-01 12:00:00.000008', '2017-01-01 12:00:00.000009',
...
'2017-01-01 12:00:09.999991', '2017-01-01 12:00:09.999992',
'2017-01-01 12:00:09.999993', '2017-01-01 12:00:09.999994',
'2017-01-01 12:00:09.999995', '2017-01-01 12:00:09.999996',
'2017-01-01 12:00:09.999997', '2017-01-01 12:00:09.999998',
'2017-01-01 12:00:09.999999', '2017-01-01 12:00:10'],
dtype='datetime64[ns]', length=10000001, freq='U')
DatetimeIndex(['2017-01-02', '2017-01-09', '2017-01-16', '2017-01-23',
'2017-01-30'],
dtype='datetime64[ns]', freq='W-MON')
DatetimeIndex(['2017-01-09', '2017-02-13', '2017-03-13', '2017-04-10'], dtype='datetime64[ns]', freq='WOM-2MON')
# pd.date_range()-日期范围：频率(2)
print("1".center(40,'*'))
print(pd.date_range('2017','2018', freq = 'M'))
print(pd.date_range('2017','2020', freq = 'Q-DEC'))
print(pd.date_range('2017','2020', freq = 'A-DEC'))
# M：每月最后一个日历日
# Q-月：指定月为季度末，每个季度末最后一月的最后一个日历日
# A-月：每年指定月份的最后一个日历日
# 月缩写：JAN/FEB/MAR/APR/MAY/JUN/JUL/AUG/SEP/OCT/NOV/DEC
# 所以Q-月只有三种情况：1-4-7-10,2-5-8-11,3-6-9-12
print("2".center(40,'*'))
print(pd.date_range('2017','2018', freq = 'BM'))
print(pd.date_range('2017','2020', freq = 'BQ-DEC'))
print(pd.date_range('2017','2020', freq = 'BA-DEC'))
# BM：每月最后一个工作日
# BQ-月：指定月为季度末，每个季度末最后一月的最后一个工作日
# BA-月：每年指定月份的最后一个工作日
print("3".center(40,'*'))
print(pd.date_range('2017','2018', freq = 'MS'))
print(pd.date_range('2017','2020', freq = 'QS-DEC'))
print(pd.date_range('2017','2020', freq = 'AS-DEC'))
# M：每月第一个日历日
# Q-月：指定月为季度末，每个季度末最后一月的第一个日历日
# A-月：每年指定月份的第一个日历日
print("4".center(40,'*'))
print(pd.date_range('2017','2018', freq = 'BMS'))
print(pd.date_range('2017','2020', freq = 'BQS-DEC'))
print(pd.date_range('2017','2020', freq = 'BAS-DEC'))
# BM：每月第一个工作日
# BQ-月：指定月为季度末，每个季度末最后一月的第一个工作日
# BA-月：每年指定月份的第一个工作日
# 执行结果
*******************1********************
DatetimeIndex(['2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30',
'2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31',
'2017-09-30', '2017-10-31', '2017-11-30', '2017-12-31'],
dtype='datetime64[ns]', freq='M')
DatetimeIndex(['2017-03-31', '2017-06-30', '2017-09-30', '2017-12-31',
'2018-03-31', '2018-06-30', '2018-09-30', '2018-12-31',
'2019-03-31', '2019-06-30', '2019-09-30', '2019-12-31'],
dtype='datetime64[ns]', freq='Q-DEC')
DatetimeIndex(['2017-12-31', '2018-12-31', '2019-12-31'], dtype='datetime64[ns]', freq='A-DEC')
*******************2********************
DatetimeIndex(['2017-01-31', '2017-02-28', '2017-03-31', '2017-04-28',
'2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31',
'2017-09-29', '2017-10-31', '2017-11-30', '2017-12-29'],
dtype='datetime64[ns]', freq='BM')
DatetimeIndex(['2017-03-31', '2017-06-30', '2017-09-29', '2017-12-29',
'2018-03-30', '2018-06-29', '2018-09-28', '2018-12-31',
'2019-03-29', '2019-06-28', '2019-09-30', '2019-12-31'],
dtype='datetime64[ns]', freq='BQ-DEC')
DatetimeIndex(['2017-12-29', '2018-12-31', '2019-12-31'], dtype='datetime64[ns]', freq='BA-DEC')
*******************3********************
DatetimeIndex(['2017-01-01', '2017-02-01', '2017-03-01', '2017-04-01',
'2017-05-01', '2017-06-01', '2017-07-01', '2017-08-01',
'2017-09-01', '2017-10-01', '2017-11-01', '2017-12-01',
'2018-01-01'],
dtype='datetime64[ns]', freq='MS')
DatetimeIndex(['2017-03-01', '2017-06-01', '2017-09-01', '2017-12-01',
'2018-03-01', '2018-06-01', '2018-09-01', '2018-12-01',
'2019-03-01', '2019-06-01', '2019-09-01', '2019-12-01'],
dtype='datetime64[ns]', freq='QS-DEC')
DatetimeIndex(['2017-12-01', '2018-12-01', '2019-12-01'], dtype='datetime64[ns]', freq='AS-DEC')
*******************4********************
DatetimeIndex(['2017-01-02', '2017-02-01', '2017-03-01', '2017-04-03',
'2017-05-01', '2017-06-01', '2017-07-03', '2017-08-01',
'2017-09-01', '2017-10-02', '2017-11-01', '2017-12-01',
'2018-01-01'],
dtype='datetime64[ns]', freq='BMS')
DatetimeIndex(['2017-03-01', '2017-06-01', '2017-09-01', '2017-12-01',
'2018-03-01', '2018-06-01', '2018-09-03', '2018-12-03',
'2019-03-01', '2019-06-03', '2019-09-02', '2019-12-02'],
dtype='datetime64[ns]', freq='BQS-DEC')
DatetimeIndex(['2017-12-01', '2018-12-03', '2019-12-02'], dtype='datetime64[ns]', freq='BAS-DEC')
# pd.date_range()-日期范围：复合频率
print(pd.date_range('2017/1/1','2017/2/1', freq = '7D')) # 7天
print(pd.date_range('2017/1/1','2017/1/2', freq = '2h30min')) # 2小时30分钟
print(pd.date_range('2017','2018', freq = '2M')) # 2月，每月最后一个日历日
#执行结果
DatetimeIndex(['2017-01-01', '2017-01-08', '2017-01-15', '2017-01-22',
'2017-01-29'],
dtype='datetime64[ns]', freq='7D')
DatetimeIndex(['2017-01-01 00:00:00', '2017-01-01 02:30:00',
'2017-01-01 05:00:00', '2017-01-01 07:30:00',
'2017-01-01 10:00:00', '2017-01-01 12:30:00',
'2017-01-01 15:00:00', '2017-01-01 17:30:00',
'2017-01-01 20:00:00', '2017-01-01 22:30:00'],
dtype='datetime64[ns]', freq='150T')
DatetimeIndex(['2017-01-31', '2017-03-31', '2017-05-31', '2017-07-31',
'2017-09-30', '2017-11-30'],
dtype='datetime64[ns]', freq='2M')
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