communities = com_shi[:, 0]
# Transfer to set to remove duplications
dis_set = set(districts)
com_set = set(communities)
# Form a new list without duplications
dis_new = list(dis_set)
# Combine shi data according to districts
dis_shi = []
m = len(dis_set)
for i in range(m):
dis_shi.append([])
dis_shi[i].append(0)
dis_shi[i].append(0)
for i in range(len(dis_com)):
index1 = communities.tolist().index(dis_com[i][1])
index0 = dis_new.index(dis_com[i][0])
dis_shi[index0][0] = dis_shi[index0][0] + com_shi[index1][1]
dis_shi[index0][1] = dis_shi[index0][1] + com_shi[index1][3]
for i in range(m):
dis_shi[i].append(round(dis_shi[i][1] / dis_shi[i][0] * 100, 2))
shi_district = []
for i in range(m):
shi_district.append([])
shi_district[i].append(dis_new[i])
shi_district[i].append(dis_shi[i][0])
shi_district[i].append(dis_shi[i][1])
shi_district[i].append(dis_shi[i][2])
processData.write_csv('district_shi.csv', shi_district,
['District', 'total_units', 'shi_units', '%'])
import numpy as np
# read data
district_transportation = processData.import_csv(
'data\\district_transportation.csv')
ct_transportation = processData.import_csv('data\\ct_transportation.csv')
district_shi = processData.import_csv('data\\district_shi.csv')
ct_shi = processData.import_csv('data\\ct_shi.csv')
# add a column to ct_transportation and district_transportation of percentage of shi units
ct_value = ct_shi.values
district_value = district_shi.values
ct_list = ct_value[:, 0].tolist()
district_list = district_value[:, 0].tolist()
ct_transportation_list = ct_transportation.values.tolist()
district_transportation_list = district_transportation.values.tolist()
for i in district_transportation_list:
index = district_list.index(i[0])
i.append(district_value[index][3])
for i in ct_transportation_list:
index = ct_list.index(i[0])
i.append(ct_value[index][4])
# write back to file
X_district = np.array(district_transportation_list)
header_district = district_transportation.columns.values.tolist()
header_district.append('percentage_of_shi_units')
X_ct = np.array(ct_transportation_list)
header_ct = ct_transportation.columns.values.tolist()
header_ct.append('percentage_of_shi_units')
processData.write_csv('data\\district_transportation.csv', X_district,
header_district)
processData.write_csv('data\\ct_transportation.csv', X_ct, header_ct)
import numpy as np
# read data
district_vacant_housing = processData.import_csv(
'data\\district_housing_vacant.csv')
ct_vacant_housing = processData.import_csv('data\\ct_housing_vacant.csv')
district_shi = processData.import_csv('data\\district_shi.csv')
ct_shi = processData.import_csv('data\\ct_shi.csv')
# add a column to ct_vacant_housing and district_vacant_housing of percentage of shi units
ct_value = ct_shi.values
district_value = district_shi.values
ct_list = ct_value[:, 0].tolist()
district_list = district_value[:, 0].tolist()
ct_vacant_housing_list = ct_vacant_housing.values.tolist()
district_vacant_housing_list = district_vacant_housing.values.tolist()
for i in district_vacant_housing_list:
index = district_list.index(i[0])
i.append(district_value[index][3])
for i in ct_vacant_housing_list:
index = ct_list.index(i[0])
i.append(ct_value[index][4])
# write back to file
X_district = np.array(district_vacant_housing_list)
header_district = district_vacant_housing.columns.values.tolist()
header_district.append('percentage_of_shi_units')
X_ct = np.array(ct_vacant_housing_list)
header_ct = ct_vacant_housing.columns.values.tolist()
header_ct.append('percentage_of_shi_units')
processData.write_csv('data\\district_housing_vacant.csv', X_district,
header_district)
processData.write_csv('data\\ct_housing_vacant.csv', X_ct, header_ct)