我目前有一些半小时的时间序列数据。它采用csv格式，如下所示：

SETTLEMENTDATE NSW DEMAND ... VIC DEMAND VIC RRP

0 2006/01/01 00:30:00 8013.27833 ... 5657.67500 20.03

1 2006/01/01 01:00:00 7726.89167 ... 5460.39500 18.66

2 2006/01/01 01:30:00 7372.85833 ... 5766.02500 20.38

3 2006/01/01 02:00:00 7071.83333 ... 5503.25167 18.59

4 2006/01/01 02:30:00 6865.44000 ... 5214.01500 17.53

我想要做的是计算此数据集中每列的52周移动平均值（不包括结算日期）。所以52weeks = 2*24*365 = 17520半个小时，我希望移动平均每次移动一周，所以2*24*7 = 336。

到目前为止我的代码看起来像这样：

import pandas as pd

data = pd.read_csv("master_file.csv")

data['NSW DEMAND'] = data['NSW DEMAND'].rolling(17520).mean()

data['QLD DEMAND'] = data['QLD DEMAND'].rolling(17520).mean()

data['SA DEMAND'] = data['SA DEMAND'].rolling(17520).mean()

data['TAS DEMAND'] = data['TAS DEMAND'].rolling(17520).mean()

data['VIC DEMAND'] = data['VIC DEMAND'].rolling(17520).mean()

data['NSW RRP'] = data['NSW RRP'].rolling(17520).mean()

data['QLD RRP'] = data['QLD RRP'].rolling(17520).mean()

data['SA RRP'] = data['SA RRP'].rolling(17520).mean()

data['TAS RRP'] = data['TAS RRP'].rolling(17520).mean()

data['VIC RRP'] = data['VIC RRP'].rolling(17520).mean()