Pandas之DataFrame——Part 2
''' 【课程2.8】 时间模块：datetime datetime模块，主要掌握：datetime.date(), datetime.datetime(), datetime.timedelta() 日期解析方法：parser.parse '''
# datetime.date：date对象 import datetime # 也可以写 from datetime import date today = datetime.date.today() print(today,type(today)) print(str(today),type(str(today))) # datetime.date.today 返回今日 # 输出格式为 date类 t = datetime.date(2016,6,1) print(t) # (年，月，日) → 直接得到当时日期
输出：
2017-12-26 <class 'datetime.date'> 2017-12-26 <class 'str'> 2016-06-01
# datetime.datetime：datetime对象 now = datetime.datetime.now() print(now,type(now)) print(str(now),type(str(now))) # .now()方法，输出当前时间 # 输出格式为 datetime类 # 可通过str()转化为字符串 t1 = datetime.datetime(2016,6,1) t2 = datetime.datetime(2014,1,1,12,44,33) print(t1,t2) # (年，月，日，时，分，秒)，至少输入年月日 t2-t1 # 相减得到时间差 —— timedelta
输出：
2017-12-26 11:50:04.769222 <class 'datetime.datetime'> 2017-12-26 11:50:04.769222 <class 'str'> 2016-06-01 00:00:00 2014-01-01 12:44:33
# datetime.timedelta：时间差 today = datetime.datetime.today() # datetime.datetime也有today()方法 yestoday = today - datetime.timedelta(1) # print(today) print(yestoday) print(today - datetime.timedelta(7)) # 时间差主要用作时间的加减法，相当于可被识别的时间“差值”
输出：
2017-12-26 11:50:06.484283 2017-12-25 11:50:06.484283 2017-12-19 11:50:06.484283
# parser.parse：日期字符串转换 from dateutil.parser import parse date = '12-21-2017' t = parse(date) print(t,type(t)) # 直接将str转化成datetime.datetime print(parse('2000-1-1'),'\n', parse('5/1/2014'),'\n', parse('5/1/2014', dayfirst = True),'\n', # 国际通用格式中，日在月之前，可以通过dayfirst来设置 parse('22/1/2014'),'\n', parse('Jan 31, 1997 10:45 PM')) # 各种格式可以解析，但无法支持中文
输出：
2017-12-21 00:00:00 <class 'datetime.datetime'> 2000-01-01 00:00:00 2014-05-01 00:00:00 2014-01-05 00:00:00 2014-01-22 00:00:00 1997-01-31 22:45:00
# pd.Timestamp() import numpy as np import pandas as pd date1 = datetime.datetime(2016,12,1,12,45,30) # 创建一个datetime.datetime date2 = '2017-12-21' # 创建一个字符串 t1 = pd.Timestamp(date1) t2 = pd.Timestamp(date2) print(t1,type(t1)) print(t2) print(pd.Timestamp('2017-12-21 15:00:22')) # 直接生成pandas的时刻数据 → 时间戳 # 数据类型为 pandas的Timestamp
输出：
2016-12-01 12:45:30 <class 'pandas.tslib.Timestamp'> 2017-12-21 00:00:00 2017-12-21 15:00:22
# pd.to_datetime from datetime import datetime date1 = datetime(2016,12,1,12,45,30) date2 = '2017-12-21' t1 = pd.to_datetime(date1) t2 = pd.to_datetime(date2) print(t1,type(t1)) print(t2,type(t2)) # pd.to_datetime()：如果是单个时间数据，转换成pandas的时刻数据，数据类型为Timestamp lst_date = [ '2017-12-21', '2017-12-22', '2017-12-23'] t3 = pd.to_datetime(lst_date) print(t3,type(t3)) # 多个时间数据，将会转换为pandas的DatetimeIndex
输出：
2016-12-01 12:45:30 <class 'pandas.tslib.Timestamp'> 2017-12-21 00:00:00 <class 'pandas.tslib.Timestamp'> DatetimeIndex(['2017-12-21', '2017-12-22', '2017-12-23'], dtype='datetime64[ns]', freq=None) <class 'pandas.tseries.index.DatetimeIndex'>
# pd.to_datetime → 多个时间数据转换时间戳索引 date1 = [datetime(2015,6,1),datetime(2015,7,1),datetime(2015,8,1),datetime(2015,9,1),datetime(2015,10,1)] date2 = ['2017-2-1','2017-2-2','2017-2-3','2017-2-4','2017-2-5','2017-2-6'] print(date1) print(date2) t1 = pd.to_datetime(date2) t2 = pd.to_datetime(date2) print(t1) print(t2) # 多个时间数据转换为 DatetimeIndex date3 = ['2017-2-1','2017-2-2','2017-2-3','hello world!','2017-2-5','2017-2-6'] t3 = pd.to_datetime(date3, errors = 'ignore') print(t3,type(t3)) # 当一组时间序列中夹杂其他格式数据，可用errors参数返回 # errors = 'ignore':不可解析时返回原始输入，这里就是直接生成一般数组 t4 = pd.to_datetime(date3, errors = 'coerce') print(t4,type(t4)) # errors = 'coerce':不可扩展，缺失值返回NaT（Not a Time），结果认为DatetimeIndex
输出：
[datetime.datetime(2015, 6, 1, 0, 0), datetime.datetime(2015, 7, 1, 0, 0), datetime.datetime(2015, 8, 1, 0, 0), datetime.datetime(2015, 9, 1, 0, 0), datetime.datetime(2015, 10, 1, 0, 0)] ['2017-2-1', '2017-2-2', '2017-2-3', '2017-2-4', '2017-2-5', '2017-2-6'] DatetimeIndex(['2017-02-01', '2017-02-02', '2017-02-03', '2017-02-04', '2017-02-05', '2017-02-06'], dtype='datetime64[ns]', freq=None) DatetimeIndex(['2017-02-01', '2017-02-02', '2017-02-03', '2017-02-04', '2017-02-05', '2017-02-06'], dtype='datetime64[ns]', freq=None) ['2017-2-1' '2017-2-2' '2017-2-3' 'hello world!' '2017-2-5' '2017-2-6'] <class 'numpy.ndarray'> DatetimeIndex(['2017-02-01', '2017-02-02', '2017-02-03', 'NaT', '2017-02-05', '2017-02-06'], dtype='datetime64[ns]', freq=None) <class 'pandas.tseries.index.DatetimeIndex'>
''' 【课程2.10】 Pandas时间戳索引：DatetimeIndex 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(rng,type(rng)) 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(st,type(st)) print(st.index) # 以DatetimeIndex为index的Series，为TimeSries，时间序列
输出：
DatetimeIndex(['2017-12-01', '2017-12-02', '2017-12-03', '2017-12-04', '2017-12-05'], dtype='datetime64[ns]', freq=None) <class 'pandas.tseries.index.DatetimeIndex'> 2017-12-01 00:00:00 <class 'pandas.tslib.Timestamp'> 2017-12-01 0.837612 2017-12-02 0.539392 2017-12-03 0.100238 2017-12-04 0.285519 2017-12-05 0.939607 dtype: float64 <class 'pandas.core.series.Series'> 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) print('-------') # 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 rng4 = pd.date_range(start = '1/1/2017 15:30', periods = 10, name = 'hello world!', normalize = True) print(rng4) print('-------') # 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(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')) print('-------') # 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(pd.bdate_range('20170101','20170107')) # pd.bdate_range()默认频率为工作日 print(list(pd.date_range(start = '1/1/2017', periods = 10))) # 直接转化为list，元素为Timestamp
输出：
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.tseries.index.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') ------- 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') ------- 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') ------- DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04', '2017-01-05', '2017-01-06'], dtype='datetime64[ns]', freq='B') [Timestamp('2017-01-01 00:00:00', offset='D'), Timestamp('2017-01-02 00:00:00', offset='D'), Timestamp('2017-01-03 00:00:00', offset='D'), Timestamp('2017-01-04 00:00:00', offset='D'), Timestamp('2017-01-05 00:00:00', offset='D'), Timestamp('2017-01-06 00:00:00', offset='D'), Timestamp('2017-01-07 00:00:00', offset='D'), Timestamp('2017-01-08 00:00:00', offset='D'), Timestamp('2017-01-09 00:00:00', offset='D'), Timestamp('2017-01-10 00:00:00', offset='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
输出：
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(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')) print('------') # 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(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')) print('------') # 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(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')) print('------') # 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(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')) print('------') # 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
输出：
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') ------ 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') ------ 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') ------ 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
输出：
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')
# asfreq：时期频率转换 ts = pd.Series(np.random.rand(4), index = pd.date_range('20170101','20170104')) print(ts) print(ts.asfreq('4H',method = 'ffill')) # 改变频率，这里是D改为4H # method：插值模式，None不插值，ffill用之前值填充，bfill用之后值填充
输出：
2017-01-01 0.945391 2017-01-02 0.656020 2017-01-03 0.295795 2017-01-04 0.318078 Freq: D, dtype: float64 2017-01-01 00:00:00 0.945391 2017-01-01 04:00:00 0.945391 2017-01-01 08:00:00 0.945391 2017-01-01 12:00:00 0.945391 2017-01-01 16:00:00 0.945391 2017-01-01 20:00:00 0.945391 2017-01-02 00:00:00 0.656020 2017-01-02 04:00:00 0.656020 2017-01-02 08:00:00 0.656020 2017-01-02 12:00:00 0.656020 2017-01-02 16:00:00 0.656020 2017-01-02 20:00:00 0.656020 2017-01-03 00:00:00 0.295795 2017-01-03 04:00:00 0.295795 2017-01-03 08:00:00 0.295795 2017-01-03 12:00:00 0.295795 2017-01-03 16:00:00 0.295795 2017-01-03 20:00:00 0.295795 2017-01-04 00:00:00 0.318078 Freq: 4H, dtype: float64
# pd.date_range()-日期范围：超前/滞后数据 ts = pd.Series(np.random.rand(4), index = pd.date_range('20170101','20170104')) print(ts) print(ts.shift(2)) print(ts.shift(-2)) print('------') # 正数：数值后移（滞后）；负数：数值前移（超前） per = ts/ts.shift(1) - 1 print(per) print('------') # 计算变化百分比，这里计算：该时间戳与上一个时间戳相比，变化百分比 print(ts.shift(2, freq = 'D')) print(ts.shift(2, freq = 'T')) # 加上freq参数：对时间戳进行位移，而不是对数值进行位移
输出：
2017-01-01 0.967312 2017-01-02 0.945871 2017-01-03 0.555347 2017-01-04 0.872889 Freq: D, dtype: float64 2017-01-01 NaN 2017-01-02 NaN 2017-01-03 0.967312 2017-01-04 0.945871 Freq: D, dtype: float64 2017-01-01 0.555347 2017-01-02 0.872889 2017-01-03 NaN 2017-01-04 NaN Freq: D, dtype: float64 ------ 2017-01-01 NaN 2017-01-02 -0.022166 2017-01-03 -0.412872 2017-01-04 0.571790 Freq: D, dtype: float64 ------ 2017-01-03 0.967312 2017-01-04 0.945871 2017-01-05 0.555347 2017-01-06 0.872889 Freq: D, dtype: float64 2017-01-01 00:02:00 0.967312 2017-01-02 00:02:00 0.945871 2017-01-03 00:02:00 0.555347 2017-01-04 00:02:00 0.872889 Freq: D, dtype: float64
''' 【课程2.11】 Pandas时期：Period 核心：pd.Period() '''
# pd.Period()创建时期 p = pd.Period('2017', freq = 'M') print(p, type(p)) # 生成一个以2017-01开始，月为频率的时间构造器 # pd.Period()参数：一个时间戳 + freq 参数 → freq 用于指明该 period 的长度，时间戳则说明该 period 在时间轴上的位置 print(p + 1) print(p - 2) print(pd.Period('2012', freq = 'A-DEC') - 1) # 通过加减整数，将周期整体移动 # 这里是按照 月、年 移动
输出：
2017-01 <class 'pandas._period.Period'> 2017-02 2016-11 2011
# pd.period_range()创建时期范围 prng = pd.period_range('1/1/2011', '1/1/2012', freq='M') print(prng,type(prng)) print(prng[0],type(prng[0])) # 数据格式为PeriodIndex，单个数值为Period ts = pd.Series(np.random.rand(len(prng)), index = prng) print(ts,type(ts)) print(ts.index) # 时间序列 # Period('2011', freq = 'A-DEC')可以看成多个时间期的时间段中的游标 # Timestamp表示一个时间戳，是一个时间截面；Period是一个时期，是一个时间段！！但两者作为index时区别不大
输出：
PeriodIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05', '2011-06', '2011-07', '2011-08', '2011-09', '2011-10', '2011-11', '2011-12', '2012-01'], dtype='int64', freq='M') <class 'pandas.tseries.period.PeriodIndex'> 2011-01 <class 'pandas._period.Period'> 2011-01 0.342571 2011-02 0.826151 2011-03 0.370505 2011-04 0.137151 2011-05 0.679976 2011-06 0.265928 2011-07 0.416502 2011-08 0.874078 2011-09 0.112801 2011-10 0.112504 2011-11 0.448408 2011-12 0.851046 2012-01 0.370605 Freq: M, dtype: float64 <class 'pandas.core.series.Series'> PeriodIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05', '2011-06', '2011-07', '2011-08', '2011-09', '2011-10', '2011-11', '2011-12', '2012-01'], dtype='int64', freq='M')
# asfreq：频率转换 p = pd.Period('2017','A-DEC') print(p) print(p.asfreq('M', how = 'start')) # 也可写 how = 's' print(p.asfreq('D', how = 'end')) # 也可写 how = 'e' # 通过.asfreq(freq, method=None, how=None)方法转换成别的频率 prng = pd.period_range('2017','2018',freq = 'M') ts1 = pd.Series(np.random.rand(len(prng)), index = prng) ts2 = pd.Series(np.random.rand(len(prng)), index = prng.asfreq('D', how = 'start')) print(ts1.head(),len(ts1)) print(ts2.head(),len(ts2)) # asfreq也可以转换TIMESeries的index
输出：
2017 2017-01 2017-12-31 2017-01 0.060797 2017-02 0.441994 2017-03 0.971933 2017-04 0.000334 2017-05 0.545191 Freq: M, dtype: float64 13 2017-01-01 0.447614 2017-02-01 0.679438 2017-03-01 0.891729 2017-04-01 0.949993 2017-05-01 0.942548 Freq: D, dtype: float64 13
# 时间戳与时期之间的转换：pd.to_period()、pd.to_timestamp() rng = pd.date_range('2017/1/1', periods = 10, freq = 'M') prng = pd.period_range('2017','2018', freq = 'M') ts1 = pd.Series(np.random.rand(len(rng)), index = rng) print(ts1.head()) print(ts1.to_period().head()) # 每月最后一日，转化为每月 ts2 = pd.Series(np.random.rand(len(prng)), index = prng) print(ts2.head()) print(ts2.to_timestamp().head()) # 每月，转化为每月第一天
输出：
2017-01-31 0.125288 2017-02-28 0.497174 2017-03-31 0.573114 2017-04-30 0.665665 2017-05-31 0.263561 Freq: M, dtype: float64 2017-01 0.125288 2017-02 0.497174 2017-03 0.573114 2017-04 0.665665 2017-05 0.263561 Freq: M, dtype: float64 2017-01 0.748661 2017-02 0.095891 2017-03 0.280341 2017-04 0.569813 2017-05 0.067677 Freq: M, dtype: float64 2017-01-01 0.748661 2017-02-01 0.095891 2017-03-01 0.280341 2017-04-01 0.569813 2017-05-01 0.067677 Freq: MS, dtype: float64
''' 【课程2.12】 时间序列 - 索引及切片 TimeSeries是Series的一个子类，所以Series索引及数据选取方面的方法基本一样 同时TimeSeries通过时间序列有更便捷的方法做索引和切片 '''
# 索引 from datetime import datetime rng = pd.date_range('2017/1','2017/3') ts = pd.Series(np.random.rand(len(rng)), index = rng) print(ts.head()) print(ts[0]) print(ts[:2]) print('-----') # 基本下标位置索引 print(ts['2017/1/2']) print(ts['20170103']) print(ts['1/10/2017']) print(ts[datetime(2017,1,20)]) print('-----') # 时间序列标签索引，支持各种时间字符串，以及datetime.datetime # 时间序列由于按照时间先后排序，故不用考虑顺序问题 # 索引方法同样适用于Dataframe
输出：
2017-01-01 0.107736 2017-01-02 0.887981 2017-01-03 0.712862 2017-01-04 0.920021 2017-01-05 0.317863 Freq: D, dtype: float64 0.107735945027 2017-01-01 0.107736 2017-01-02 0.887981 Freq: D, dtype: float64 ----- 0.887980757812 0.712861778966 0.788336674948 0.93070380011 -----
# 切片 rng = pd.date_range('2017/1','2017/3',freq = '12H') ts = pd.Series(np.random.rand(len(rng)), index = rng) print(ts['2017/1/5':'2017/1/10']) print('-----') # 和Series按照index索引原理一样，也是末端包含 print(ts['2017/2'].head()) # 传入月，直接得到一个切片
输出：
2017-01-05 00:00:00 0.462085 2017-01-05 12:00:00 0.778637 2017-01-06 00:00:00 0.356306 2017-01-06 12:00:00 0.667964 2017-01-07 00:00:00 0.246857 2017-01-07 12:00:00 0.386956 2017-01-08 00:00:00 0.328203 2017-01-08 12:00:00 0.260853 2017-01-09 00:00:00 0.224920 2017-01-09 12:00:00 0.397457 2017-01-10 00:00:00 0.158729 2017-01-10 12:00:00 0.501266 Freq: 12H, dtype: float64 ----- 2017-02-01 00:00:00 0.243932 2017-02-01 12:00:00 0.220830 2017-02-02 00:00:00 0.896107 2017-02-02 12:00:00 0.476584 2017-02-03 00:00:00 0.515817 Freq: 12H, dtype: float64
# 重复索引的时间序列 dates = pd.DatetimeIndex(['1/1/2015','1/2/2015','1/3/2015','1/4/2015','1/1/2015','1/2/2015']) ts = pd.Series(np.random.rand(6), index = dates) print(ts) print(ts.is_unique,ts.index.is_unique) print('-----') # index有重复，is_unique检查 → values唯一，index不唯一 print(ts['20150101'],type(ts['20150101'])) print(ts['20150104'],type(ts['20150104'])) print('-----') # index有重复的将返回多个值 print(ts.groupby(level = 0).mean()) # 通过groupby做分组，重复的值这里用平均值处理
输出：
2015-01-01 0.300286 2015-01-02 0.603865 2015-01-03 0.017949 2015-01-04 0.026621 2015-01-01 0.791441 2015-01-02 0.526622 dtype: float64 True False ----- 2015-01-01 0.300286 2015-01-01 0.791441 dtype: float64 <class 'pandas.core.series.Series'> 2015-01-04 0.026621 dtype: float64 <class 'pandas.core.series.Series'> ----- 2015-01-01 0.545863 2015-01-02 0.565244 2015-01-03 0.017949 2015-01-04 0.026621 dtype: float64
''' 【课程2.13】 时间序列 - 重采样 将时间序列从一个频率转换为另一个频率的过程，且会有数据的结合 降采样：高频数据 → 低频数据，eg.以天为频率的数据转为以月为频率的数据 升采样：低频数据 → 高频数据，eg.以年为频率的数据转为以月为频率的数据 '''
# 重采样：.resample()
# 创建一个以天为频率的TimeSeries，重采样为按2天为频率
rng = pd.date_range('20170101', periods = 12)
ts = pd.Series(np.arange(12), index = rng)
print(ts)
ts_re = ts.resample('5D')
ts_re2 = ts.resample('5D').sum()
print(ts_re, type(ts_re))
print(ts_re2, type(ts_re2))
print('-----')
# ts.resample('5D')：得到一个重采样构建器，频率改为5天
# ts.resample('5D').sum():得到一个新的聚合后的Series，聚合方式为求和
# freq：重采样频率 → ts.resample('5D')
# .sum()：聚合方法
print(ts.resample('5D').mean(),'→ 求平均值\n')
print(ts.resample('5D').max(),'→ 求最大值\n')
print(ts.resample('5D').min(),'→ 求最小值\n')
print(ts.resample('5D').median(),'→ 求中值\n')
print(ts.resample('5D').first(),'→ 返回第一个值\n')
print(ts.resample('5D').last(),'→ 返回最后一个值\n')
print(ts.resample('5D').ohlc(),'→ OHLC重采样\n')
# OHLC:金融领域的时间序列聚合方式 → open开盘、high最大值、low最小值、close收盘
输出：
2017-01-01 0 2017-01-02 1 2017-01-03 2 2017-01-04 3 2017-01-05 4 2017-01-06 5 2017-01-07 6 2017-01-08 7 2017-01-09 8 2017-01-10 9 2017-01-11 10 2017-01-12 11 Freq: D, dtype: int32 DatetimeIndexResampler [freq=<5 * Days>, axis=0, closed=left, label=left, convention=start, base=0] <class 'pandas.tseries.resample.DatetimeIndexResampler'> 2017-01-01 10 2017-01-06 35 2017-01-11 21 Freq: 5D, dtype: int32 <class 'pandas.core.series.Series'> ----- 2017-01-01 2.0 2017-01-06 7.0 2017-01-11 10.5 Freq: 5D, dtype: float64 → 求平均值 2017-01-01 4 2017-01-06 9 2017-01-11 11 Freq: 5D, dtype: int32 → 求最大值 2017-01-01 0 2017-01-06 5 2017-01-11 10 Freq: 5D, dtype: int32 → 求最小值 2017-01-01 2.0 2017-01-06 7.0 2017-01-11 10.5 Freq: 5D, dtype: float64 → 求中值 2017-01-01 0 2017-01-06 5 2017-01-11 10 Freq: 5D, dtype: int32 → 返回第一个值 2017-01-01 4 2017-01-06 9 2017-01-11 11 Freq: 5D, dtype: int32 → 返回最后一个值 open high low close 2017-01-01 0 4 0 4 2017-01-06 5 9 5 9 2017-01-11 10 11 10 11 → OHLC重采样
# 降采样 rng = pd.date_range('20170101', periods = 12) ts = pd.Series(np.arange(1,13), index = rng) print(ts) print(ts.resample('5D').sum(),'→ 默认\n') print(ts.resample('5D', closed = 'left').sum(),'→ left\n') print(ts.resample('5D', closed = 'right').sum(),'→ right\n') print('-----') # closed：各时间段哪一端是闭合（即包含）的，默认 左闭右闭 # 详解：这里values为0-11，按照5D重采样 → [1,2,3,4,5],[6,7,8,9,10],[11,12] # left指定间隔左边为结束 → [1,2,3,4,5],[6,7,8,9,10],[11,12] # right指定间隔右边为结束 → [1],[2,3,4,5,6],[7,8,9,10,11],[12] print(ts.resample('5D', label = 'left').sum(),'→ leftlabel\n') print(ts.resample('5D', label = 'right').sum(),'→ rightlabel\n') # label：聚合值的index，默认为取左 # 值采样认为默认（这里closed默认）
输出：
2017-01-01 1 2017-01-02 2 2017-01-03 3 2017-01-04 4 2017-01-05 5 2017-01-06 6 2017-01-07 7 2017-01-08 8 2017-01-09 9 2017-01-10 10 2017-01-11 11 2017-01-12 12 Freq: D, dtype: int32 2017-01-01 15 2017-01-06 40 2017-01-11 23 Freq: 5D, dtype: int32 → 默认 2017-01-01 15 2017-01-06 40 2017-01-11 23 Freq: 5D, dtype: int32 → left 2016-12-27 1 2017-01-01 20 2017-01-06 45 2017-01-11 12 Freq: 5D, dtype: int32 → right ----- 2017-01-01 15 2017-01-06 40 2017-01-11 23 Freq: 5D, dtype: int32 → leftlabel 2017-01-06 15 2017-01-11 40 2017-01-16 23 Freq: 5D, dtype: int32 → rightlabel
# 升采样及插值 rng = pd.date_range('2017/1/1 0:0:0', periods = 5, freq = 'H') ts = pd.DataFrame(np.arange(15).reshape(5,3), index = rng, columns = ['a','b','c']) print(ts) print(ts.resample('15T').asfreq()) print(ts.resample('15T').ffill()) print(ts.resample('15T').bfill()) # 低频转高频，主要是如何插值 # .asfreq()：不做填充，返回Nan # .ffill()：向上填充 # .bfill()：向下填充
输出：
a b c 2017-01-01 00:00:00 0 1 2 2017-01-01 01:00:00 3 4 5 2017-01-01 02:00:00 6 7 8 2017-01-01 03:00:00 9 10 11 2017-01-01 04:00:00 12 13 14 a b c 2017-01-01 00:00:00 0.0 1.0 2.0 2017-01-01 00:15:00 NaN NaN NaN 2017-01-01 00:30:00 NaN NaN NaN 2017-01-01 00:45:00 NaN NaN NaN 2017-01-01 01:00:00 3.0 4.0 5.0 2017-01-01 01:15:00 NaN NaN NaN 2017-01-01 01:30:00 NaN NaN NaN 2017-01-01 01:45:00 NaN NaN NaN 2017-01-01 02:00:00 6.0 7.0 8.0 2017-01-01 02:15:00 NaN NaN NaN 2017-01-01 02:30:00 NaN NaN NaN 2017-01-01 02:45:00 NaN NaN NaN 2017-01-01 03:00:00 9.0 10.0 11.0 2017-01-01 03:15:00 NaN NaN NaN 2017-01-01 03:30:00 NaN NaN NaN 2017-01-01 03:45:00 NaN NaN NaN 2017-01-01 04:00:00 12.0 13.0 14.0 a b c 2017-01-01 00:00:00 0 1 2 2017-01-01 00:15:00 0 1 2 2017-01-01 00:30:00 0 1 2 2017-01-01 00:45:00 0 1 2 2017-01-01 01:00:00 3 4 5 2017-01-01 01:15:00 3 4 5 2017-01-01 01:30:00 3 4 5 2017-01-01 01:45:00 3 4 5 2017-01-01 02:00:00 6 7 8 2017-01-01 02:15:00 6 7 8 2017-01-01 02:30:00 6 7 8 2017-01-01 02:45:00 6 7 8 2017-01-01 03:00:00 9 10 11 2017-01-01 03:15:00 9 10 11 2017-01-01 03:30:00 9 10 11 2017-01-01 03:45:00 9 10 11 2017-01-01 04:00:00 12 13 14 a b c 2017-01-01 00:00:00 0 1 2 2017-01-01 00:15:00 3 4 5 2017-01-01 00:30:00 3 4 5 2017-01-01 00:45:00 3 4 5 2017-01-01 01:00:00 3 4 5 2017-01-01 01:15:00 6 7 8 2017-01-01 01:30:00 6 7 8 2017-01-01 01:45:00 6 7 8 2017-01-01 02:00:00 6 7 8 2017-01-01 02:15:00 9 10 11 2017-01-01 02:30:00 9 10 11 2017-01-01 02:45:00 9 10 11 2017-01-01 03:00:00 9 10 11 2017-01-01 03:15:00 12 13 14 2017-01-01 03:30:00 12 13 14 2017-01-01 03:45:00 12 13 14 2017-01-01 04:00:00 12 13 14
# 时期重采样 - Period prng = pd.period_range('2016','2017',freq = 'M') ts = pd.Series(np.arange(len(prng)), index = prng) print(ts) print(ts.resample('3M').sum()) # 降采样 print(ts.resample('15D').ffill()) # 升采样
输出：
2016-01 0 2016-02 1 2016-03 2 2016-04 3 2016-05 4 2016-06 5 2016-07 6 2016-08 7 2016-09 8 2016-10 9 2016-11 10 2016-12 11 2017-01 12 Freq: M, dtype: int32 2016-01-31 0 2016-04-30 6 2016-07-31 15 2016-10-31 24 2017-01-31 33 Freq: 3M, dtype: int32 2016-01-01 0 2016-01-16 0 2016-01-31 0 2016-02-15 1 2016-03-01 2 2016-03-16 2 2016-03-31 2 2016-04-15 3 2016-04-30 3 2016-05-15 4 2016-05-30 4 2016-06-14 5 2016-06-29 5 2016-07-14 6 2016-07-29 6 2016-08-13 7 2016-08-28 7 2016-09-12 8 2016-09-27 8 2016-10-12 9 2016-10-27 9 2016-11-11 10 2016-11-26 10 2016-12-11 11 2016-12-26 11 Freq: 15D, dtype: int32
