Pandas and Rolling_Mean offset (average daily volume calculation)

Question

When I output the stock data to the framework from Yahoo, I want to be able to calculate the average value for 5 days, excluding the current date.

Is there a way to use a moving average with an offset? For example, a 5-day average that excludes the current day and is based on the previous 5 days.

When I run the following code

r = DataReader("BBRY", "yahoo", '2015-01-01','2015-01-31')

r['ADV']=pd.rolling_mean(r['Volume'], window=5)

It returns a 5-day volume, including the current date, so when you look below, 1/8 has an average volume of 1 / 2.1 / 5.1 / 6.1 / 7 and 1/8. I would like 1/9 to be the first date that returns the average volume, and it contains data from 1 / 2,1 / 5,1 / 6,1 / 7 and 1/8.

Date    Open    High    Low Close   Volume  Adj Close   Symbol   ADV 

1/2/2015    11.01   11.11   10.79   10.82   9733200 10.82   BBRY    
1/5/2015    10.6    10.77   10.37   10.76   12318100    10.76   BBRY    
1/6/2015    10.8    10.85   10.44   10.62   10176400    10.62   BBRY    
1/7/2015    10.65   10.8    10.48   10.67   10277400    10.67   BBRY    
1/8/2015    10.75   10.78   10.57   10.63   6868300 10.63   BBRY     9,874,680.00 

1/9/2015    10.59   10.65   10.28   10.38   7745600 10.38   BBRY     9,477,160.00 

thanks for the help

Answer 1

shift :

In [44]:
r['ADV'] = pd.rolling_mean(r['Volume'].shift(), window=5)
r

Out[44]:
             Open   High    Low  Close    Volume  Adj Close       ADV
Date                                                                 
2015-01-02  11.01  11.11  10.79  10.82   9733200      10.82       NaN
2015-01-05  10.60  10.77  10.37  10.76  12318100      10.76       NaN
2015-01-06  10.80  10.85  10.44  10.62  10176400      10.62       NaN
2015-01-07  10.65  10.80  10.48  10.67  10277400      10.67       NaN
2015-01-08  10.75  10.78  10.57  10.63   6868300      10.63       NaN
2015-01-09  10.59  10.65  10.28  10.38   7745600      10.38   9874680
2015-01-12  10.36  10.37  10.02  10.12   7739600      10.12   9477160
2015-01-13  10.05  10.23   9.68   9.71  15292900       9.71   8561460
2015-01-14   9.61  12.63   9.32  12.60  83543900      12.60   9584760
2015-01-15  10.36  10.71  10.01  10.11  52574600      10.11  24238060
2015-01-16  10.12  10.39  10.11  10.24  16068900      10.24  33379320
2015-01-20  10.28  10.37   9.82  10.03  15185900      10.03  35043980
2015-01-21  10.03  10.38   9.81   9.93  19614500       9.93  36533240
2015-01-22  10.44  11.11  10.24  10.51  44594300      10.51  37397560
2015-01-23  10.78  11.03  10.61  10.71  21079800      10.71  29607640
2015-01-26  10.67  10.71  10.40  10.52   6982000      10.52  23308680
2015-01-27  10.38  10.63  10.32  10.56   7057200      10.56  21491300
2015-01-28  10.65  10.67  10.10  10.12   9705000      10.12  19865560
2015-01-29  10.05  10.27   9.85  10.25  12304700      10.25  17883660
2015-01-30  10.15  10.26  10.00  10.15   9203400      10.15  11425740

Answer 2

pandas ( > 0.18.0) :

df['Volume'].rolling(window=5).mean().shift(1)