如何获取DataFrame.pct_change来计算日价格数据的月变化？

Question

我知道可以用periods参数来抵消，但是如何返回一个月内（例如，交易日）的每日价格数据呢？

示例数据为：

In [1]: df.AAPL
2009-01-02 16:00:00    90.36
2009-01-05 16:00:00    94.18
2009-01-06 16:00:00    92.62
2009-01-07 16:00:00    90.62
2009-01-08 16:00:00    92.30
2009-01-09 16:00:00    90.19
2009-01-12 16:00:00    88.28
2009-01-13 16:00:00    87.34
2009-01-14 16:00:00    84.97
2009-01-15 16:00:00    83.02
2009-01-16 16:00:00    81.98
2009-01-20 16:00:00    77.87
2009-01-21 16:00:00    82.48
2009-01-22 16:00:00    87.98
2009-01-23 16:00:00    87.98
...
2009-12-10 16:00:00    195.59
2009-12-11 16:00:00    193.84
2009-12-14 16:00:00    196.14
2009-12-15 16:00:00    193.34
2009-12-16 16:00:00    194.20
2009-12-17 16:00:00    191.04
2009-12-18 16:00:00    194.59
2009-12-21 16:00:00    197.38
2009-12-22 16:00:00    199.50
2009-12-23 16:00:00    201.24
2009-12-24 16:00:00    208.15
2009-12-28 16:00:00    210.71
2009-12-29 16:00:00    208.21
2009-12-30 16:00:00    210.74
2009-12-31 16:00:00    209.83
Name: AAPL, Length: 252

如您所见，简单地用30来抵消并不能产生正确的结果，因为时间戳数据中存在缺口，不是每个月都是30天，等等。我知道一定有一个简单的方法来使用pandas。

Answer 1

您可以将数据重新采样到业务月。如果不需要平均价格（这是resample中的默认值），可以使用关键字参数how使用自定义重采样方法：

In [31]: from pandas.io import data as web

# read some example data, note that this is not exactly your data!
In [32]: s = web.get_data_yahoo('AAPL', start='2009-01-02',
...                             end='2009-12-31')['Adj Close']

# resample to business month and return the last value in the period
In [34]: monthly = s.resample('BM', how=lambda x: x[-1])

In [35]: monthly
Out[35]: 
Date
2009-01-30     89.34
2009-02-27     88.52
2009-03-31    104.19
...
2009-10-30    186.84
2009-11-30    198.15
2009-12-31    208.88
Freq: BM

In [36]: monthly.pct_change()
Out[36]: 
Date
2009-01-30         NaN
2009-02-27   -0.009178
2009-03-31    0.177022
...
2009-10-30    0.016982
2009-11-30    0.060533
2009-12-31    0.054151
Freq: BM