How to get DataFrame.pct_change to calculate monthly change on daily price data?


Question

I know that it is possible to offset with the periods argument, but how would one go about return-izing daily price data that is spread throughout a month (trading days, for example)?

Example data is:

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

As you can see, simply offsetting by 30 would not produce correct results, as there are gaps in the timestamp data, not every month is 30 days, etc. I know there must be an easy way to do this using pandas.

1
5
1/6/2013 5:35:24 AM

Accepted Answer

You can resample the data to business month. If you don't want the mean price (which is the default in resample) you can use a custom resample method using the keyword argument how:

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
10
12/26/2012 10:54:56 AM

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