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zscore-generator.py
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#-----------------------------------HEADERS-----------------------------------#
import pandas as pd
#-----------------------------------HEADERS-----------------------------------#
#---------------IMPORT FILES and PREPROCESSING--------------------------------#
id_week_Dict = dict()
data1 = pd.read_csv('cases-week.csv')
for i,row in data1.iterrows():
id_week_Dict[str(row['districtid'])+'+'+str(row['weekid'])] = row['cases']
id_month_Dict = dict()
data2 = pd.read_csv('cases-month.csv')
for i,row in data2.iterrows():
id_month_Dict[str(row['districtid'])+'+'+str(row['monthid'])] = row['cases']
id_overall_Dict = dict()
data3 = pd.read_csv('cases-overall.csv')
for i,row in data3.iterrows():
id_overall_Dict[str(row['districtid'])+'+'+str(row['overallid'])] = row['cases']
neighbor_week_Mean = dict()
neighbor_week_SD = dict()
neighbor_week = pd.read_csv('neighbor-week.csv')
for i,row in neighbor_week.iterrows():
neighbor_week_Mean[str(int(row['districtid']))+'+'+str(int(row['weekid']))] = row['neighbormean']
neighbor_week_SD[str(int(row['districtid']))+'+'+str(int(row['weekid']))] = row['neighborstdev']
neighbor_month_Mean = dict()
neighbor_month_SD = dict()
neighbor_month = pd.read_csv('neighbor-month.csv')
for i,row in neighbor_month.iterrows():
neighbor_month_Mean[str(int(row['districtid']))+'+'+str(int(row['monthid']))] = row['neighbormean']
neighbor_month_SD[str(int(row['districtid']))+'+'+str(int(row['monthid']))] = row['neighborstdev']
neighbor_overall_Mean = dict()
neighbor_overall_SD = dict()
neighbor_overall = pd.read_csv('neighbor-overall.csv')
for i,row in neighbor_overall.iterrows():
neighbor_overall_Mean[str(int(row['districtid']))+'+'+str(int(row['overallid']))] = row['neighbormean']
neighbor_overall_SD[str(int(row['districtid']))+'+'+str(int(row['overallid']))] = row['neighborstdev']
state_week_Mean = dict()
state_week_SD = dict()
state_week = pd.read_csv('state-week.csv')
for i,row in state_week.iterrows():
state_week_Mean[str(int(row['districtid']))+'+'+str(int(row['weekid']))] = row['statemean']
state_week_SD[str(int(row['districtid']))+'+'+str(int(row['weekid']))] = row['statestdev']
state_month_Mean = dict()
state_month_SD = dict()
state_month = pd.read_csv('state-month.csv')
for i,row in state_month.iterrows():
state_month_Mean[str(int(row['districtid']))+'+'+str(int(row['monthid']))] = row['statemean']
state_month_SD[str(int(row['districtid']))+'+'+str(int(row['monthid']))] = row['statestdev']
state_overall_Mean = dict()
state_overall_SD = dict()
state_overall = pd.read_csv('state-overall.csv')
for i,row in state_overall.iterrows():
state_overall_Mean[str(int(row['districtid']))+'+'+str(int(row['overallid']))] = row['statemean']
state_overall_SD[str(int(row['districtid']))+'+'+str(int(row['overallid']))] = row['statestdev']
#---------------IMPORT FILES and PREPROCESSING--------------------------------#
#-------------------------PROCESSING WEEK DATA--------------------------------#
dictNeighWeekZS = dict()
dictStateWeekZS = dict()
for j in id_week_Dict.items():
if neighbor_week_SD[j[0]]!= 0:
dictNeighWeekZS[j[0]] = '{0:.2f}'.format((j[1] - neighbor_week_Mean[j[0]])/neighbor_week_SD[j[0]])
else:
dictNeighWeekZS[j[0]] = '{0:.2f}'.format(float(0))
if state_week_SD[j[0]]!=0:
dictStateWeekZS[j[0]] = '{0:.2f}'.format((j[1] - state_week_Mean[j[0]])/state_week_SD[j[0]])
else:
dictStateWeekZS[j[0]] = '{0:.2f}'.format(float(0))
weekZS = []
weekZS.append(['districtid','weekid','neighborhoodzscore','statezscore'])
for i in dictNeighWeekZS.items():
tmpList = []
split = i[0].split('+')
tmpList.append(split[0])
tmpList.append(split[1])
tmpList.append(dictNeighWeekZS[i[0]])
tmpList.append(dictStateWeekZS[i[0]])
weekZS.append(tmpList)
out = pd.DataFrame(weekZS)
out.to_csv('zscore-week.csv',index=False,header=False)
#-------------------------PROCESSING WEEK DATA--------------------------------#
#-------------------------PROCESSING MONTH DATA-------------------------------#
dictNeighMonthZS = dict()
dictStateMonthZS = dict()
for j in id_month_Dict.items():
if neighbor_month_SD[j[0]]!= 0:
dictNeighMonthZS[j[0]] = '{0:.2f}'.format((j[1] - neighbor_month_Mean[j[0]])/neighbor_month_SD[j[0]])
else:
dictNeighMonthZS[j[0]] = '{0:.2f}'.format(float(0))
if state_month_SD[j[0]]!=0:
dictStateMonthZS[j[0]] = '{0:.2f}'.format((j[1] - state_month_Mean[j[0]])/state_month_SD[j[0]])
else:
dictStateMonthZS[j[0]] = '{0:.2f}'.format(float(0))
monthZS = []
monthZS.append(['districtid','monthid','neighborhoodzscore','statezscore'])
for i in dictNeighMonthZS.items():
tmpList = []
split = i[0].split('+')
tmpList.append(split[0])
tmpList.append(split[1])
tmpList.append(dictNeighMonthZS[i[0]])
tmpList.append(dictStateMonthZS[i[0]])
monthZS.append(tmpList)
out = pd.DataFrame(monthZS)
out.to_csv('zscore-month.csv',index=False,header=False)
#-------------------------PROCESSING MONTH DATA-------------------------------#
#-------------------------PROCESSING OVERALL DATA-----------------------------#
dictNeighOverallZS = dict()
dictStateOverallZS = dict()
for j in id_overall_Dict.items():
if neighbor_overall_SD[j[0]]!= 0:
dictNeighOverallZS[j[0]] = '{0:.2f}'.format((j[1] - neighbor_overall_Mean[j[0]])/neighbor_overall_SD[j[0]])
else:
dictNeighOverallZS[j[0]] = '{0:.2f}'.format(float(0))
if state_overall_SD[j[0]]!=0:
dictStateOverallZS[j[0]] = '{0:.2f}'.format((j[1] - state_overall_Mean[j[0]])/state_overall_SD[j[0]])
else:
dictStateOverallZS[j[0]] = '{0:.2f}'.format(float(0))
overallZS = []
overallZS.append(['districtid','overallid','neighborhoodzscore','statezscore'])
for i in dictNeighOverallZS.items():
tmpList = []
split = i[0].split('+')
tmpList.append(split[0])
tmpList.append(split[1])
tmpList.append(dictNeighOverallZS[i[0]])
tmpList.append(dictStateOverallZS[i[0]])
overallZS.append(tmpList)
out = pd.DataFrame(overallZS)
out.to_csv('zscore-overall.csv',index=False,header=False)
#-------------------------PROCESSING OVERALL DATA-----------------------------#