changed final to csv

data/final_afgh_wheat.csv

@@ -0,0 +1,65 @@
+Date,actual_prices,historical_forecasts,Lower_Limit,Wheat_Forecast,Upper_Limit
+2018-06-30,22.0,22.60625987521026,,,
+2018-07-31,23.0,22.184862976296447,,,
+2018-08-31,23.25,22.657340316868947,,,
+2018-09-30,24.25,25.04052203731671,,,
+2018-10-31,25.000000000000004,24.92335593148524,,,
+2018-11-30,25.75,25.47190149016384,,,
+2018-12-31,26.0,26.393780879099893,,,
+2019-01-31,26.0,25.79043445550817,,,
+2019-02-28,26.0,26.176970775919564,,,
+2019-03-31,26.0,26.26955182653693,,,
+2019-04-30,26.0,26.37715028335086,,,
+2019-05-31,26.250000000000004,27.00501879506457,,,
+2019-06-30,27.0,27.14056186241368,,,
+2019-07-31,27.0,27.05608360323741,,,
+2019-08-31,27.0,25.988674454351717,,,
+2019-09-30,27.0,26.06487841848165,,,
+2019-10-31,27.0,26.10466545084964,,,
+2019-11-30,27.0,26.45266704841746,,,
+2019-12-31,27.5,26.96833224136951,,,
+2020-01-31,27.75,27.625717660048444,,,
+2020-02-29,27.75,27.74511886772119,,,
+2020-03-31,31.08333333,31.18134829250906,,,
+2020-04-30,34.66666667,34.41884040203179,,,
+2020-05-31,35.5625,36.6873394602967,,,
+2020-06-30,34.99999999999999,34.78332274073414,,,
+2020-07-31,33.29999999999999,32.86432211955058,,,
+2020-08-31,32.0,31.7056441513348,,,
+2020-09-30,31.25,30.20069399245794,,,
+2020-10-31,32.0,31.00861924775572,,,
+2020-11-30,32.0,30.95455160461017,,,
+2020-12-31,32.0,31.423915615754836,,,
+2021-01-31,32.0,32.15428688782523,,,
+2021-02-28,33.5,31.54836876118987,,,
+2021-03-31,33.0,33.85976408267113,,,
+2021-04-30,31.5,32.74250006768389,,,
+2021-05-31,30.0,29.961083382353245,,,
+2021-06-30,30.75,29.94322115942682,,,
+2021-07-31,34.0,33.75168791179691,,,
+2021-08-31,36.99999999999999,37.32569324683625,,,
+2021-09-30,37.66666666666666,37.68780024252922,,,
+2021-10-31,41.5,40.28816013143026,,,
+2021-11-30,41.99999999999999,44.56037708615648,,,
+2021-12-31,47.50000000000001,46.93843054203655,,,
+2022-01-31,46.8,45.495863141045696,,,
+2022-02-28,44.0,44.09305478863504,,,
+2022-03-31,43.75,43.28855588541151,,,
+2022-04-30,47.15,46.56883214455284,,,
+2022-05-31,48.25,48.29339516810606,,,
+2022-06-30,51.95000000000001,49.72866853341333,,,
+2022-07-31,49.35,51.72574364554157,,,
+2022-08-31,51.5,50.89776333419384,,,
+2022-09-30,48.625,49.117512595673006,,,
+2022-10-31,45.5,47.208256286227225,,,
+2022-11-30,45.72249999999999,45.622123661339785,,,
+2022-12-31,43.125,44.47175690778241,,,
+2023-01-31,42.15,39.63090324223333,,,
+2023-02-28,41.75,39.98321072828004,,,
+2023-03-31,36.7,39.55643342817749,,,
+2023-04-30,,,30.09366567791765,33.437406308797385,36.78114693967713
+2023-05-31,,,30.662993164982836,34.06999240553648,37.47699164609013
+2023-06-30,,,30.158518816712277,33.50946535190253,36.86041188709278
+2023-07-31,,,33.03351535159148,36.70390594621276,40.37429654083404
+2023-08-31,,,33.681952906309085,37.4243921181212,41.16683132993333
+2023-09-30,,,34.34849708318217,38.164996759091295,41.98149643500043

visual_pbi.py

@@ -3,5 +3,5 @@ import pandas as pd
 merged_df = pd.read_csv('data/afghan_wheatfcasts.csv', parse_dates = ['Date'])
 df_backtest_ = pd.read_csv('data/aghanwheat_allhistorical.csv', parse_dates=['Date'])
 # df_backtest_.rename(columns={'date':"Date"},inplace=True)
-final_maize = pd.merge(df_backtest_,merged_df,on=['Date'],how='outer')
-final_maize.to_excel('data/final_afgh_wheat.xlsx',index=False)
+final_wheat = pd.merge(df_backtest_,merged_df,on=['Date'],how='outer')
+final_wheat.to_csv('data/final_afgh_wheat.csv',index=False)