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()








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