def generalData(Bibliography):
df1 = nfv.dfFix(Bibliography, "Mujeres menores de 5 años (%)",
"Total population")
df2 = nfv.dfFix(Bibliography, "Growth rate of populatoin (%)", "Culture")
GD_Demography = nt.concatDF(df1, df2)
nt.mkCSV(GD_Demography, "GD_Demography.csv")
GD_Ethnicgroup = nfv.dfFix(Bibliography, "Ethnich group 1", "Religion").T
nt.mkCSV(GD_Ethnicgroup, "GD_Ethnicgroup.csv")
df1 = nfv.dfFix(Bibliography, "Parliamentary republic",
"Territorial and Urbanistic")
GD_Government = df1
GD_Government = GD_Government.isin(["Si"])
GD_Government = GD_Government.any(
) #Lista con indice de columna y True si un contiene un True o False en caso contrario
GD_Government = list(
GD_Government[GD_Government == True].index) #lista de indices con true
GD_Government = pd.DataFrame(GD_Government)
nt.mkCSV(GD_Government, "GD_Government.csv")
GD_Economy = nfv.dfFix(Bibliography, "Agriculture (%)", "Government")
nt.mkCSV(GD_Economy, "GD_Economy.csv")
df1 = nfv.dfFix(Bibliography, "Urban population (%)", "Population density")
df2 = nfv.dfFix(Bibliography, "Urban (inhabitants/hectares)",
"Infrastructures")
GD_Urbanism = nt.concatDF(df1, df2)
nt.mkCSV(GD_Urbanism, "GD_Urbanism.csv")
df1 = nfv.dfFix(Bibliography, "Rural agua (%)",
"Access to improved sanitation")
df2 = nfv.dfFix(Bibliography, "Rural saneamiento(%)",
"Access to electricity")
df3 = nfv.dfFix(Bibliography, "Rural electricidad (%)",
"Matrix of electricity generation")
GD_Infrastructure = nt.concatDF(nt.concatDF(df1, df2), df3)
nt.mkCSV(GD_Infrastructure, "GD_Infrastructure.csv")
GD_ElectricGenerationMix = nfv.dfFix(Bibliography, "Hydropower (%)",
"High voltage (kV)")
nt.mkCSV(GD_ElectricGenerationMix, "GD_ElectricGenerationMix.csv")
GD_ServiceAccess = nfv.dfFix(Bibliography, "Illiteracy rate (%)",
"Shelter")
nt.mkCSV(GD_ServiceAccess, "GD_ServiceAccess.csv")
GD_Shelter = nfv.dfFix(Bibliography, "Slum population rate (%)",
"SPECIFIC INFORMATION - SETTLEMENTS LEVEL")
nt.mkCSV(GD_Shelter, "GD_Shelter.csv")
Comun = pd.read_excel(nfv.getPath(nt.mainpath, "Bibliography_120220.xlsx"))
Comun = nfv.fixBibliography(Comun)
GD_Religion = nfv.dfFix(Comun, "Religion 1", "Language")
df1 = nfv.dropRow(GD_Religion, 1)
np_array1 = np.array(df1)
df2 = nfv.dropRow(GD_Religion, 0)
np_array2 = np.array(df2)
np_array3 = np.concatenate((np_array1, np_array2), axis=1)
GD_Religion = pd.DataFrame(np_array3)
GD_Religion = GD_Religion.transpose()
GD_Religion = GD_Religion[0].unique()
GD_Religion = pd.DataFrame(GD_Religion)
GD_Religion = GD_Religion.dropna()
nt.mkCSV(GD_Religion, "GD_Religion.csv")
GD_Language = nfv.dfFix(Comun, "Language 1", "Economy and well-being")
df1 = nfv.dropRow(GD_Language, 1)
np_array1 = np.array(df1)
df2 = nfv.dropRow(GD_Language, 0)
np_array2 = np.array(df2)
np_array3 = np.concatenate((np_array1, np_array2), axis=1)
GD_Language = pd.DataFrame(np_array3)
GD_Language = GD_Language.transpose()
GD_Language = GD_Language[0].unique()
GD_Language = pd.DataFrame(GD_Language)
GD_Language = GD_Language.dropna()
nt.mkCSV(GD_Language, "GD_Language.csv")
def __init__(self, communityType):
Bibliography = pd.read_excel(
nfv.getPath(nfv.mainpath, "Bibliography_120220.xlsx"))
Bibliography = nfv.fixBibliography(Bibliography)
self.Bibliography = nfv.setDataByIndex(Bibliography, communityType)
self.Entities = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Entities_Interview_results.csv"),
float_precision="high"), communityType)
self.LocalLeaders = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(nfv.mainpath,
"NAUTIA_1_0_Local_leaders_v3_results.csv"),
float_precision="high"), communityType)
self.HouseHold = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Survey_household_v6_results.csv"),
float_precision="high"), communityType)
self.WomenGroup = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Women_Focus_Group2_results.csv"),
float_precision="high"), communityType)
self.SanitationInfra = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath,
"NAUTIA_V1_0_Sanitation_Infrastructre_results.csv"),
float_precision="high"), communityType)
self.Priorities = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(nfv.mainpath,
"NAUTIA_1_0_Priorities_v3_results.csv"),
float_precision="high"), communityType)
self.GeneralForm = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(nfv.mainpath,
"NAUTIA_1_0_General_form_v3_results.csv"),
float_precision="high"), communityType)
self.PublicSpace = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(nfv.mainpath,
"NAUTIA_1_0_Public_Space_results.csv"),
float_precision="high"), communityType)
self.WaterInf = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Water_Infrastructure_results.csv"),
float_precision="high"), communityType)
self.SanitationInf = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath,
"NAUTIA_V1_0_Sanitation_Infrastructre_results.csv"),
float_precision="high"), communityType)
self.WasteManagementInf = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath,
"NAUTIA_1_0_Waste_Management_Infrastructure_results.csv"),
float_precision="high"), communityType)
self.EnergyINF = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Energy_Infrastructure_results.csv"),
float_precision="high"), communityType)
self.Business = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA1_0_Business_surveys_v3_results.csv"),
float_precision="high"), communityType)
self.MobilityINF = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath,
"NAUTIA_1_0__Transport_servicesaccess_points_results.csv"),
float_precision="high"), communityType)
self.ComunalServices = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Communal_Services_results.csv"),
float_precision="high"), communityType)
self.GeneralCitizen = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath,
"NAUTIA_1_0_General_Citizen_Focus_Group_results.csv"),
float_precision="high"), communityType)
self.Shelter = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(nfv.mainpath,
"NAUTIA_1_0_Shelter_results.csv"),
float_precision="high"), communityType)
self.FarmyardCrop = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Farmyard_and_Crops_results.csv"),
float_precision="high"), communityType)
nt.mkCSV(GD_Infrastructure, "GD_Infrastructure.csv")
GD_ElectricGenerationMix = nfv.dfFix(Bibliography, "Hydropower (%)",
"High voltage (kV)")
nt.mkCSV(GD_ElectricGenerationMix, "GD_ElectricGenerationMix.csv")
GD_ServiceAccess = nfv.dfFix(Bibliography, "Illiteracy rate (%)", "Shelter")
nt.mkCSV(GD_ServiceAccess, "GD_ServiceAccess.csv")
GD_Shelter = nfv.dfFix(Bibliography, "Slum population rate (%)",
"SPECIFIC INFORMATION - SETTLEMENTS LEVEL")
nt.mkCSV(GD_Shelter, "GD_Shelter.csv")
#%%COMMUN DATA
Comun = pd.read_excel(nfv.getPath(nt.mainpath, "Bibliography_120220.xlsx"))
Comun = nfv.fixBibliography(Comun)
GD_Religion = nfv.dfFix(Comun, "Religion 1", "Language")
df1 = nfv.dfFix(GD_Religion, 1)
np_array1 = np.array(df1)
df2 = nfv.dfFix(GD_Religion, 0)
np_array2 = np.array(df2)
np_array3 = np.concatenate((np_array1, np_array2), axis=1)
GD_Religion = pd.DataFrame(np_array3)
GD_Religion = GD_Religion.transpose()
GD_Religion = GD_Religion[0].unique()
GD_Religion = pd.DataFrame(GD_Religion)
GD_Religion = GD_Religion.dropna()
nt.mkCSV(GD_Religion, "GD_Religion.csv")
GD_Language = nfv.dfFix(Comun, "Language 1", "Economy and well-being")