Pandas —— resample()重采样和asfreq()频度转换

resample()

• resample()进行重采样。

• 重采样（Resampling）指的是把时间序列的频度变为另一个频度的过程。把高频度的数据变为低频度叫做降采样（downsampling），把低频度变为高频度叫做增采样（upsampling）。

降采样

考虑因素：

• 各区间哪边是闭合的（参数：closed）

• 如何标记各聚合面元，用区间的开头还是末尾（参数：label）

In [232]: ts_index = pd.date_range('2018-08-03',periods =12,freq = 'T')

In [233]: ts = pd.Series(np.arange(12),index = ts_index)

In [234]: ts
Out[234]:
2018-08-03 00:00:00     0
2018-08-03 00:01:00     1
2018-08-03 00:02:00     2
2018-08-03 00:03:00     3
2018-08-03 00:04:00     4
2018-08-03 00:05:00     5
2018-08-03 00:06:00     6
2018-08-03 00:07:00     7
2018-08-03 00:08:00     8
2018-08-03 00:09:00     9
2018-08-03 00:10:00    10
2018-08-03 00:11:00    11
Freq: T, dtype: int32

默认使用左标签（label=’left’），左闭合（closed=’left’）

此时第一个区间为：2018-08-03 00:00:00~2018-08-03 00:04:59，故sum为10，label为：2018-08-03 00:00:00

In [235]: ts.resample('5min').sum()
Out[235]:
2018-08-03 00:00:00    10
2018-08-03 00:05:00    35
2018-08-03 00:10:00    21
Freq: 5T, dtype: int32

可以指定为右闭合（closed=’right’），默认使用左标签（label=’left’）

此时第一个区间为：2018-08-02 23:55:01~2018-08-03 00:00:00，故sum为0，label为：2018-08-02 23:55:00

In [236]: ts.resample('5min',closed='right').sum()
Out[236]:
2018-08-02 23:55:00     0
2018-08-03 00:00:00    15
2018-08-03 00:05:00    40
2018-08-03 00:10:00    11
Freq: 5T, dtype: int32

可以指定为右闭合（closed=’right’），右标签（label=’right’）

此时第一个区间为：2018-08-02 23:55:01~2018-08-03 00:00:00，故sum为0，label为：2018-08-03 00:00:00

In [237]: ts.resample('5min',closed='right',label='right').sum()
Out[237]:
2018-08-03 00:00:00     0
2018-08-03 00:05:00    15
2018-08-03 00:10:00    40
2018-08-03 00:15:00    11
Freq: 5T, dtype: int32

升采样

考虑因素：

• 没有聚合，但是需要填充

In [244]: frame = pd.DataFrame(np.random.randn(2, 4),
     ...:                      index=pd.date_range('1/1/2000', periods=2,
     ...:                                          freq='W-WED'),  # freq='W-WED'表示按周
     ...:                      columns=['Colorado', 'Texas', 'New York', 'Ohio'])

In [245]: frame
Out[245]:
            Colorado     Texas  New York      Ohio
2000-01-05  1.201713  0.029819 -1.366082 -1.325252
2000-01-12 -0.711291 -1.070133  1.469272  0.809806

当我们对这个数据进行聚合的的时候，每个组只有一个值，以及gap（间隔）之间的缺失值。在不使用任何聚合函数的情况下，我们使用asfreq方法将其转换为高频度：

In [246]: df_daily = frame.resample('D').asfreq()

In [247]: df_daily
Out[247]:
            Colorado     Texas  New York      Ohio
2000-01-05  1.201713  0.029819 -1.366082 -1.325252
2000-01-06       NaN       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN       NaN
2000-01-08       NaN       NaN       NaN       NaN
2000-01-09       NaN       NaN       NaN       NaN
2000-01-10       NaN       NaN       NaN       NaN
2000-01-11       NaN       NaN       NaN       NaN
2000-01-12 -0.711291 -1.070133  1.469272  0.809806

使用ffill()进行填充

In [248]: frame.resample('D').ffill()
Out[248]:
            Colorado     Texas  New York      Ohio
2000-01-05  1.201713  0.029819 -1.366082 -1.325252
2000-01-06  1.201713  0.029819 -1.366082 -1.325252
2000-01-07  1.201713  0.029819 -1.366082 -1.325252
2000-01-08  1.201713  0.029819 -1.366082 -1.325252
2000-01-09  1.201713  0.029819 -1.366082 -1.325252
2000-01-10  1.201713  0.029819 -1.366082 -1.325252
2000-01-11  1.201713  0.029819 -1.366082 -1.325252
2000-01-12 -0.711291 -1.070133  1.469272  0.809806

In [249]: frame.resample('D').ffill(limit=2)
Out[249]:
            Colorado     Texas  New York      Ohio
2000-01-05  1.201713  0.029819 -1.366082 -1.325252
2000-01-06  1.201713  0.029819 -1.366082 -1.325252
2000-01-07  1.201713  0.029819 -1.366082 -1.325252
2000-01-08       NaN       NaN       NaN       NaN
2000-01-09       NaN       NaN       NaN       NaN
2000-01-10       NaN       NaN       NaN       NaN
2000-01-11       NaN       NaN       NaN       NaN
2000-01-12 -0.711291 -1.070133  1.469272  0.809806

新的日期索引没必要跟旧的重叠

In [250]: frame.resample('W-THU').ffill()
Out[250]:
            Colorado     Texas  New York      Ohio
2000-01-06  1.201713  0.029819 -1.366082 -1.325252
2000-01-13 -0.711291 -1.070133  1.469272  0.809806

分组重采样

In [279]: times = pd.date_range('2018-08-3 00:00', freq='1min', periods=10)

In [280]: df2 = pd.DataFrame({'time': times.repeat(3),
     ...:                     'key': np.tile(['a', 'b', 'c'], 10),
     ...:                     'value': np.arange(30)})

In [281]: df2[:5]
Out[281]:
   key                time  value
0    a 2018-08-03 00:00:00      0
1    b 2018-08-03 00:00:00      1
2    c 2018-08-03 00:00:00      2
3    a 2018-08-03 00:01:00      3
4    b 2018-08-03 00:01:00      4

In [282]: df2.groupby(['key',pd.Grouper(key='time',freq='5min')]).sum()
Out[282]:
                         value
key time
a   2018-08-03 00:00:00     30
    2018-08-03 00:05:00    105
b   2018-08-03 00:00:00     35
    2018-08-03 00:05:00    110
c   2018-08-03 00:00:00     40
    2018-08-03 00:05:00    115

---

asfreq()

• asfreq()进行频度转换。

>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s':series})
>>> df
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:01:00    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:03:00    3.0

将频度转换为30s

>>> df.asfreq(freq='30S')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    NaN
2000-01-01 00:03:00    3.0

将频度转换为2min，不会进行重采样（与resample的不同之处）

>>> df.asfreq(freq='2min')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:02:00    2.0

使用bfill()进行填充

>>> df.asfreq(freq='30S').bfill()
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    2.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    3.0
2000-01-01 00:03:00    3.0