def get_valueBySector(df1, df2):
df2 = df2.reset_index()
array1 = np.array(df1)
i = 0
for row in array1:
for elem in row:
if (elem == False):
df2 = nfv.dropRow(df2, i)
i += 1
df2 = df2.set_index('index')
return df2
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")