在Tableau中运行python（预测）时出错

我对这个系统非常陌生，对python也相当陌生。因此，代码中可能很少有冗余行

我试图用x（混合函数）预测y（CARA_流）。虽然相同的代码在Python中工作得非常好，但我在tableau中遇到了错误。错误窗口本身向我显示了正确的预测（以及对未来12个月的预测）

而且，集成也没有问题。 有人能帮我理解这里的问题吗

SCRIPT_REAL(
"
import pandas as pd
import numpy as np

dateparse = lambda dates: pd.datetime.strptime(dates, '%Y%m')
data = pd.read_excel('S:\AIM India\Anup\Requests_2018\CTI_Forecasting_Tableau\Forecast_CTI_2.xlsx',parse_dates=['YYYYMM'], index_col='YYYYMM',date_parser=dateparse)

ts_exogenMF = data['Hybrid_MF'] 

from statsmodels.tsa.arima_model import ARIMA
model = ARIMA(ts_exogenMF,order=(2, 0, 2))  
results_ARIMA1 = model.fit(disp=-1)  
forecast1,std,conf=results_ARIMA1.forecast(steps=12,alpha=0.5)
forecastMF=forecast1
MF_Arr=[]
MF_Arr=forecastMF

ts = data['CARA_Flows'] 
from statsmodels.tsa.stattools import adfuller
ts_log = np.log(ts)
ts_log_diff = ts_log - ts_log.shift()

model = ARIMA(ts_log,exog=ts_exogenMF,order=(2, 0, 2))  
results_ARIMA2 = model.fit(disp=1)  
Final_Untransformed_Forecast=results_ARIMA2.predict(start=1, end=46, exog=MF_Arr,  dynamic=False)
predictions_ARIMA_log = pd.Series(ts_log.ix[0], index=ts_log.index)
predictions_ARIMA_cumsum = predictions_ARIMA_log.add(Final_Untransformed_Forecast,fill_value=0)
predictions_12M = np.exp(Final_Untransformed_Forecast)

return predictions_12M

",SUM([Hybrid MF]), SUM([CARA Flows]))

---

错误是因为日期格式与代码和输出不匹配。因此，您应该将

pd.datetime.strtime（x，'%Y-%m-%d'）

替换为

pd.datetime.strtime（dates，'%Y%m'）

当我将输出转换为列表时，这一问题得到了解决。下面是整个代码：

SCRIPT_REAL(
"
import pandas as pd
import numpy as np

dateparse = lambda dates: pd.datetime.strptime(dates, '%Y%m')
data = pd.read_excel('S:\AIM.....\...\ =dateparse)

ts_exogenMF = data['Hybrid_MF'] 

from statsmodels.tsa.arima_model import ARIMA
model = ARIMA(ts_exogenMF,order=(2, 0, 2))  
results_ARIMA1 = model.fit(disp=-1)  
forecast1,std,conf=results_ARIMA1.forecast(steps=12,alpha=0.5)
forecastMF=forecast1
MF_Arr=[]
MF_Arr=forecastMF

ts = data['CARA_Flows'] 
from statsmodels.tsa.stattools import adfuller
ts_log = np.log(ts)
ts_log_diff = ts_log - ts_log.shift()

model = ARIMA(ts_log,exog=ts_exogenMF,order=(2, 0, 2))  
results_ARIMA2 = model.fit(disp=1)  
Final_Untransformed_Forecast=results_ARIMA2.predict(start=0, end=46, exog=MF_Arr,  dynamic=False)
predictions_ARIMA_log = pd.Series(ts_log.ix[0], index=ts_log.index)
predictions_ARIMA_cumsum = predictions_ARIMA_log.add(Final_Untransformed_Forecast,fill_value=0)
predictions_12M = np.exp(Final_Untransformed_Forecast)

predList=pd.Series.tolist(predictions_12M)

return predList

",SUM([Hybrid MF]), SUM([CARA Flows]))