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("1".center(40,'*'))
print(ts)
ts_re = ts.resample('5D')
ts_re2 = ts.resample('5D').sum()
print("2".center(40,'*'))
print(ts_re, type(ts_re))
print(ts_re2, type(ts_re2))
# 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("3".center(40,'*'))
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
#执行结果
*******************1********************
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
*******************2********************
DatetimeIndexResampler [freq=<5 * Days>, axis=0, closed=left, label=left, convention=start, base=0] <class 'pandas.core.resample.DatetimeIndexResampler'>
2017-01-01 10
2017-01-06 35
2017-01-11 21
dtype: int32 <class 'pandas.core.series.Series'>
*******************3********************
2017-01-01 2.0
2017-01-06 7.0
2017-01-11 10.5
dtype: float64 → 求平均值
2017-01-01 4
2017-01-06 9
2017-01-11 11
dtype: int32 → 求最大值
2017-01-01 0
2017-01-06 5
2017-01-11 10
dtype: int32 → 求最小值
2017-01-01 2.0
2017-01-06 7.0
2017-01-11 10.5
dtype: float64 → 求中值
2017-01-01 0
2017-01-06 5
2017-01-11 10
dtype: int32 → 返回第一个值
2017-01-01 4
2017-01-06 9
2017-01-11 11
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("1".center(40,'*'))
print(ts)
print("2".center(40,'*'))
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')
# 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("3".center(40,'*'))
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）
# 执行结果
*******************1********************
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
*******************2********************
2017-01-01 15
2017-01-06 40
2017-01-11 23
dtype: int32 → 默认
2017-01-01 15
2017-01-06 40
2017-01-11 23
dtype: int32 → left
2016-12-27 1
2017-01-01 20
2017-01-06 45
2017-01-11 12
dtype: int32 → right
*******************3********************
2017-01-01 15
2017-01-06 40
2017-01-11 23
dtype: int32 → leftlabel
2017-01-06 15
2017-01-11 40
2017-01-16 23
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('30T').asfreq())
print(ts.resample('30T').ffill())
print(ts.resample('30T').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
#执行结果
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 00:30:00 NaN NaN NaN
2017-01-01 01:00:00 3.0 4.0 5.0
2017-01-01 01:30:00 NaN NaN NaN
2017-01-01 02:00:00 6.0 7.0 8.0
2017-01-01 02:30:00 NaN NaN NaN
2017-01-01 03:00:00 9.0 10.0 11.0
2017-01-01 03:30:00 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 00:30:00 0 1 2
2017-01-01 01:00:00 3 4 5
2017-01-01 01:30:00 3 4 5
2017-01-01 02:00:00 6 7 8
2017-01-01 02:30:00 6 7 8
2017-01-01 03:00:00 9 10 11
2017-01-01 03:30:00 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 00:30:00 3 4 5
2017-01-01 01:00:00 3 4 5
2017-01-01 01:30:00 6 7 8
2017-01-01 02:00:00 6 7 8
2017-01-01 02:30:00 9 10 11
2017-01-01 03:00:00 9 10 11
2017-01-01 03:30:00 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('90D').sum()) # 降采样
print(ts.resample('20D').ffill()) # 升采样
#执行结果
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
2016-01-01 0.0
2016-03-31 NaN
2016-06-29 NaN
2016-09-27 NaN
2016-12-26 NaN
Freq: 90D, dtype: float64
2016-01-01 0
2016-01-21 0
2016-02-10 1
2016-03-01 2
2016-03-21 2
2016-04-10 3
2016-04-30 3
2016-05-20 4
2016-06-09 5
2016-06-29 5
2016-07-19 6
2016-08-08 7
2016-08-28 7
2016-09-17 8
2016-10-07 9
2016-10-27 9
2016-11-16 10
2016-12-06 11
2016-12-26 11
2017-01-15 12
Freq: 20D, dtype: int32
