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Pandas —— resample()重采樣和asfreq()頻度轉(zhuǎn)換方式

 更新時(shí)間：2020年02月26日 09:48:06   作者：starter_zheng  
今天小編就為大家分享一篇Pandas —— resample()重采樣和asfreq()頻度轉(zhuǎn)換方式，具有很好的參考價(jià)值，希望對(duì)大家有所幫助。一起跟隨小編過(guò)來(lái)看看吧

resample()

resample()進(jìn)行重采樣。

重采樣（Resampling）指的是把時(shí)間序列的頻度變?yōu)榱硪粋€(gè)頻度的過(guò)程。把高頻度的數(shù)據(jù)變?yōu)榈皖l度叫做降采樣（downsampling），把低頻度變?yōu)楦哳l度叫做增采樣（upsampling）。

降采樣

考慮因素：

各區(qū)間哪邊是閉合的（參數(shù)：closed）

如何標(biāo)記各聚合面元，用區(qū)間的開(kāi)頭還是末尾（參數(shù)：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

默認(rèn)使用左標(biāo)簽（label=‘left'），左閉合（closed='left'）

此時(shí)第一個(gè)區(qū)間為：2018-08-03 00:00:00~2018-08-03 00:04:59，故sum為10，label為：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

可以指定為右閉合（closed='right'），默認(rèn)使用左標(biāo)簽（label=‘left'）

此時(shí)第一個(gè)區(qū)間為：2018-08-02 23:55:01~2018-08-03 00:00:00，故sum為0，label為：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

可以指定為右閉合（closed='right'），右標(biāo)簽（label=‘right'）

此時(shí)第一個(gè)區(qū)間為：2018-08-02 23:55:01~2018-08-03 00:00:00，故sum為0，label為：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

升采樣

考慮因素：

沒(méi)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

當(dāng)我們對(duì)這個(gè)數(shù)據(jù)進(jìn)行聚合的的時(shí)候，每個(gè)組只有一個(gè)值，以及gap（間隔）之間的缺失值。在不使用任何聚合函數(shù)的情況下，

我們使用asfreq方法將其轉(zhuǎn)換為高頻度：

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()進(jìn)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

新的日期索引沒(méi)必要跟舊的重疊

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

分組重采樣

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()進(jìn)行頻度轉(zhuǎn)換。

>>> 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

將頻度轉(zhuǎn)換為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

將頻度轉(zhuǎn)換為2min，不會(huì)進(jìn)行重采樣（與resample的不同之處）

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

使用bfill()進(jìn)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

以上這篇Pandas —— resample()重采樣和asfreq()頻度轉(zhuǎn)換方式就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持腳本之家。

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