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

以上这篇Pandas —— resample()重采样和asfreq()频度转换方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持python博客。