Ortsvergleich eingefügt

Data_Analysis.py

@@ -4,6 +4,17 @@ from datetime import datetime, timedelta
 import numpy as np
 
 
+def getPropertyDataFromDB():
+    db = MySQLdb.connect(host="localhost",user="root",passwd="admin",db="consultancy")
+    cur = db.cursor()
+
+    cur.execute("SELECT id, seed_id, check_data "
+                "FROM properties ")
+    propData = cur.fetchall()
+    db.close()
+
+    return propData
+
 def getDataFromDB(propId):
     '''
     Function to get data from MySQL database filter with the given propId
@@ -147,6 +158,7 @@ def getAccuracy(df, baseLine, compLine):
 
 def getMeanAccuracy(accList):
     '''
+    Get the mean Accuracy of the entire timedelay of one property
     :param accList: List of accuracy Values of a comparison
     :return: Average of the accuracy values while ignoring the '-1' values
     '''
@@ -155,3 +167,6 @@ def getMeanAccuracy(accList):
         row = [x for x in row if x != -1]
         out.append(np.average(row))
     return out
+
+
+

accuracyPerLocation.py

@@ -0,0 +1,58 @@
+import Data_Analysis as DA
+import pandas as pd
+
+accuracy = pd.read_csv(f'results/accMeanDf.csv')
+
+propData = DA.getPropertyDataFromDB()
+propData = pd.DataFrame(propData, columns =['property_id', 'region', 'geoLocation'])
+propData = propData.drop(columns=['geoLocation'])
+#print(propData)
+
+merge = pd.merge(propData, accuracy, on="property_id")
+#print(merge)
+
+#1 = Heidiland, 2 = Davos, 3 = Engadin 4 = St.Moritz
+
+heidiAcc = merge[merge['region'] == 1]
+davosAcc = merge[merge['region'] == 2]
+EngadAcc = merge[merge['region'] == 3]
+StMorAcc = merge[merge['region'] == 4]
+
+
+heidiMean = heidiAcc.mean(axis=0)
+davosMean = davosAcc.mean(axis=0)
+EngadMean = EngadAcc.mean(axis=0)
+StMorMean = StMorAcc.mean(axis=0)
+
+heidiSDev = heidiAcc.std(axis=0)
+davosSDev = davosAcc.std(axis=0)
+EngadSDev = EngadAcc.std(axis=0)
+StMorSDev = StMorAcc.std(axis=0)
+
+
+accuracyOverview = pd.DataFrame()
+
+accuracyOverview.insert(0, "St. Moritz StdDev", StMorSDev, True)
+accuracyOverview.insert(0, "St. Moritz Mean", StMorMean, True)
+accuracyOverview.insert(0, "Engadin StdDev", EngadSDev, True)
+accuracyOverview.insert(0, "Engadin Mean", EngadMean, True)
+accuracyOverview.insert(0, "Davos StdDev", davosSDev, True)
+accuracyOverview.insert(0, "Davos Mean", davosMean, True)
+accuracyOverview.insert(0, "Heidi StdDev", heidiSDev, True)
+accuracyOverview.insert(0, "Heidi Mean", heidiMean, True)
+
+
+accuracyOverview.drop(index=accuracyOverview.index[0], axis=0, inplace=True)
+accuracyOverview.drop(index=accuracyOverview.index[0], axis=0, inplace=True)
+accuracyOverview.to_csv('results/accuracyOverview.csv', index=False)
+
+#delete unused DF's
+del merge, accuracy, propData
+del heidiAcc, davosAcc, EngadAcc, StMorAcc
+del heidiMean, davosMean, EngadMean, StMorMean
+del heidiSDev, davosSDev, EngadSDev, StMorSDev
+
+
+print(accuracyOverview)
+
+

results/accuracyOverview.csv

@@ -0,0 +1,5 @@
+Heidi Mean,Heidi StdDev,Davos Mean,Davos StdDev,Engadin Mean,Engadin StdDev,St. Moritz Mean,St. Moritz StdDev
+0.8205301612054612,0.03521328245140846,0.8399836284786809,0.048358617863451414,0.8584327389672194,0.05319145459441233,0.8405512800767019,0.05180554811101561
+0.8066005018861457,0.06818803676300687,0.830601813557425,0.04949425409715446,0.8484564978404832,0.05396669349535696,0.8289395302705753,0.05637417919934374
+0.7368379473832369,0.06546064555588836,0.7598050837068276,0.06886580034893092,0.7667137312752639,0.06523018886732877,0.7565382226489596,0.06984023355676583
+0.6590943554763651,0.09741268862524224,0.6767196066764449,0.09656146924686429,0.670509578923442,0.07935806376665934,0.6633952429541463,0.08233444282881987