def header_file(self):
# for this case we use a template file and update the data
df_base = pd.read_csv(self.template_name)
df_base['Codigo region'] = df_base['Codigo region'].fillna(0)
df_base['Codigo comuna'] = df_base['Codigo comuna'].fillna(0)
df_base['Comuna'] = df_base['Comuna'].fillna(0)
todrop = df_base.loc[df_base['Comuna'] == 0]
df_base.drop(todrop.index, inplace=True)
df_base['Codigo region'] = df_base['Codigo region'].astype(int)
df_base['Codigo comuna'] = df_base['Codigo comuna'].astype(int)
desconocido = df_base['Codigo comuna'] != 0
df_base['Codigo comuna'].where(desconocido, '', inplace=True)
self.Comp = df_base.loc[df_base['Comuna'] != 'Total']
self.Comp.reset_index(inplace=True)
utils.desconocidoName(self.Comp)
for k in range(len(self.Comp)):
if self.Comp.loc[k, 'Codigo region'] < 10:
self.Comp.loc[k, 'Codigo region'] = '0' + str(
self.Comp.loc[k, 'Codigo region'])
else:
self.Comp.loc[k, 'Codigo region'] = str(
self.Comp.loc[k, 'Codigo region'])
if self.Comp.loc[k, 'Codigo comuna'] != '':
if self.Comp.loc[k, 'Codigo comuna'] < 10000:
self.Comp.loc[k, 'Codigo comuna'] = '0' + str(
self.Comp.loc[k, 'Codigo comuna'])
else:
self.Comp.loc[k, 'Codigo comuna'] = str(
self.Comp.loc[k, 'Codigo comuna'])
self.comuna = self.Comp['Comuna']
def last_to_csv(self):
df_base = pd.read_csv(
'../input/DistribucionDEIS/baseFiles/DEIS_template.csv')
df_base['Codigo region'] = df_base['Codigo region'].fillna(0)
df_base['Codigo comuna'] = df_base['Codigo comuna'].fillna(0)
todrop = df_base.loc[df_base['Comuna'] == 0]
df_base['Comuna'] = df_base['Comuna'].fillna(0)
df_base.drop(todrop.index, inplace=True)
df_base['Codigo region'] = df_base['Codigo region'].astype(int)
df_base['Codigo comuna'] = df_base['Codigo comuna'].astype(int)
desconocido = df_base['Codigo comuna'] != 0
df_base['Codigo comuna'].where(desconocido, '', inplace=True)
Comp = df_base.loc[df_base['Comuna'] != 'Total']
Comp.reset_index(inplace=True)
utils.desconocidoName(Comp)
for k in range(len(Comp)):
if Comp.loc[k, 'Codigo region'] < 10:
Comp.loc[k, 'Codigo region'] = '0' + str(
Comp.loc[k, 'Codigo region'])
else:
Comp.loc[k, 'Codigo region'] = str(Comp.loc[k,
'Codigo region'])
if Comp.loc[k, 'Codigo comuna'] != '':
if Comp.loc[k, 'Codigo comuna'] < 10000:
Comp.loc[k, 'Codigo comuna'] = '0' + str(
Comp.loc[k, 'Codigo comuna'])
else:
Comp.loc[k,
'Codigo comuna'] = str(Comp.loc[k,
'Codigo comuna'])
comuna = Comp['Comuna']
self.last_added = self.last_added.dropna(subset=['Fecha defunciones'])
self.last_added.sort_values(
by=['region_residencia', 'comuna_residencia', 'edad'],
inplace=True)
self.last_added.rename(columns={'comuna_residencia': 'comuna'},
inplace=True)
self.last_added.rename(columns={'region_residencia': 'Region'},
inplace=True)
self.last_added = utils.normalizaNombreCodigoRegionYComuna(
self.last_added)
df_sup = Comp[['Codigo comuna', 'Comuna']]
df_sup['Codigo comuna'] = df_sup['Codigo comuna'].replace('', 0)
self.last_added.drop(columns={'Comuna'}, inplace=True)
for k in range(len(self.last_added)):
if self.last_added.loc[k, 'Codigo comuna'] != '':
if self.last_added.loc[k, 'Codigo comuna'] < 10000:
self.last_added.loc[k, 'Codigo comuna'] = '0' + str(
self.last_added.loc[k, 'Codigo comuna'])
else:
self.last_added.loc[k, 'Codigo comuna'] = str(
self.last_added.loc[k, 'Codigo comuna'])
self.last_added = self.last_added.merge(df_sup,
on="Codigo comuna",
how="left")
self.last_added.set_index('Comuna', inplace=True)
columns_name = self.last_added.columns.values
maxSE = self.last_added[columns_name[4]].max()
minSE = self.last_added[columns_name[4]].min()
print(minSE, maxSE)
lenSE = (pd.to_datetime(maxSE) - pd.to_datetime(minSE)).days + 1
startdate = pd.to_datetime(minSE)
date_list = pd.date_range(startdate, periods=lenSE).tolist()
date_list = [dt.datetime.strftime(x, "%Y-%m-%d") for x in date_list]
print(date_list)
#vector con las variables
SE_comuna = self.last_added[columns_name[4]]
for edad in [
'-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90-'
]:
if edad == '-39':
df_edad = self.last_added[self.last_added['edad'] <= 39]
if edad == '40-49':
df_edad = self.last_added[self.last_added['edad'] <= 49]
df_edad = df_edad[df_edad['edad'] >= 40]
if edad == '50-59':
df_edad = self.last_added[self.last_added['edad'] <= 59]
df_edad = df_edad[df_edad['edad'] >= 50]
if edad == '60-69':
df_edad = self.last_added[self.last_added['edad'] <= 69]
df_edad = df_edad[df_edad['edad'] >= 60]
if edad == '70-79':
df_edad = self.last_added[self.last_added['edad'] <= 79]
df_edad = df_edad[df_edad['edad'] >= 70]
if edad == '80-89':
df_edad = self.last_added[self.last_added['edad'] <= 89]
df_edad = df_edad[df_edad['edad'] >= 80]
if edad == '90-':
df_edad = self.last_added[self.last_added['edad'] >= 90]
for k in [5, 6, 7]:
df = pd.DataFrame(np.zeros((len(comuna), lenSE)))
dicts = {}
keys = range(lenSE)
# values = [i for i in range(lenSE)]
for i in keys:
dicts[i] = date_list[i]
df.rename(columns=dicts, inplace=True)
value_comuna = df_edad[columns_name[k]]
value_comuna.fillna(0, inplace=True)
i = 0
for row in df_edad.index:
idx = comuna.loc[comuna == row].index.values
if idx.size > 0:
col = SE_comuna[i]
df[col][idx] = value_comuna[i].astype(int)
i += 1
df_output = pd.concat([Comp, df], axis=1)
df_output.drop(columns=['index'], axis=1, inplace=True)
nComunas = [
len(list(group))
for key, group in groupby(df_output['Codigo region'])
]
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
variables = [
x for x in df_output.columns if x not in identifiers
]
begRow = 0
for i in range(len(nComunas)):
endRow = begRow + nComunas[i]
firstList = df_output[identifiers].iloc[endRow -
1].values.tolist()
firstList[2] = 'Total'
firstList[3] = ''
valuesTotal = df_output[variables][begRow:endRow].sum(
axis=0).tolist()
regionTotal = pd.DataFrame(
(firstList + valuesTotal),
index=df_output.columns.values).transpose()
if i < len(nComunas) - 1:
blank_line = pd.Series(
np.empty((len(regionTotal), 0)).tolist())
regionTotal = pd.concat([regionTotal, blank_line],
axis=0)
regionTotal.drop(columns=0, axis=1, inplace=True)
temp = pd.concat(
[df_output.iloc[begRow:endRow], regionTotal], axis=0)
if i == 0:
outputDF2 = temp
else:
outputDF2 = pd.concat([outputDF2, temp], axis=0)
if i < len(nComunas) - 1:
begRow = endRow
outputDF2.reset_index(inplace=True)
outputDF2.drop(columns=['index'], axis=1, inplace=True)
outputDF2[variables] = outputDF2[variables].dropna(
) # .astype(int)
print(outputDF2.head(20))
outputDF2.dropna(how='all', inplace=True)
todrop = outputDF2.loc[outputDF2['Comuna'] == 'Total']
outputDF2.drop(todrop.index, inplace=True)
if k == 5:
name = self.output + '_' + str(edad) + '_confirmadas.csv'
outputDF2.to_csv(name, index=False)
outputDF2_T = outputDF2.T
outputDF2_T.to_csv(name.replace('.csv', '_T.csv'),
header=False)
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
variables = [
x for x in outputDF2.columns if x not in identifiers
]
outputDF2_std = pd.melt(outputDF2,
id_vars=identifiers,
value_vars=variables,
var_name='Fecha',
value_name='Confirmada')
outputDF2_std.to_csv(name.replace('.csv', '_std.csv'),
index=False)
elif k == 6:
name = self.output + '_' + str(edad) + '_sospechosas.csv'
outputDF2.to_csv(name, index=False)
outputDF2_T = outputDF2.T
outputDF2_T.to_csv(name.replace('.csv', '_T.csv'),
header=False)
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
variables = [
x for x in outputDF2.columns if x not in identifiers
]
outputDF2_std = pd.melt(outputDF2,
id_vars=identifiers,
value_vars=variables,
var_name='Fecha',
value_name='Sospechosa')
outputDF2_std.to_csv(name.replace('.csv', '_std.csv'),
index=False)
elif k == 7:
name = self.output + '_' + str(edad) + '_totales.csv'
outputDF2.to_csv(name, index=False)
outputDF2_T = outputDF2.T
outputDF2_T.to_csv(name.replace('.csv', '_T.csv'),
header=False)
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
variables = [
x for x in outputDF2.columns if x not in identifiers
]
outputDF2_std = pd.melt(outputDF2,
id_vars=identifiers,
value_vars=variables,
var_name='Fecha',
value_name='Total')
outputDF2_std.to_csv(name.replace('.csv', '_std.csv'),
index=False)
def last_to_csv(self):
df_base = pd.read_csv('../input/DistribucionDEIS/baseFiles/DEIS_template.csv')
df_base['Codigo region'] = df_base['Codigo region'].fillna(0)
df_base['Codigo comuna'] = df_base['Codigo comuna'].fillna(0)
todrop = df_base.loc[df_base['Comuna'] == 0]
df_base['Comuna'] = df_base['Comuna'].fillna(0)
df_base.drop(todrop.index, inplace=True)
df_base['Codigo region'] = df_base['Codigo region'].astype(int)
df_base['Codigo comuna'] = df_base['Codigo comuna'].astype(int)
desconocido = df_base['Codigo comuna'] != 0
df_base['Codigo comuna'].where(desconocido, '', inplace=True)
Comp = df_base.loc[df_base['Comuna'] != 'Total']
Comp.reset_index(inplace=True)
utils.desconocidoName(Comp)
for k in range(len(Comp)):
if Comp.loc[k, 'Codigo region'] < 10:
Comp.loc[k, 'Codigo region'] = '0' + str(Comp.loc[k, 'Codigo region'])
else:
Comp.loc[k, 'Codigo region'] = str(Comp.loc[k, 'Codigo region'])
if Comp.loc[k, 'Codigo comuna'] != '':
if Comp.loc[k, 'Codigo comuna'] < 10000:
Comp.loc[k, 'Codigo comuna'] = '0' + str(Comp.loc[k, 'Codigo comuna'])
else:
Comp.loc[k, 'Codigo comuna'] = str(Comp.loc[k, 'Codigo comuna'])
comuna = Comp['Comuna']
self.last_added = self.last_added.dropna(subset=['Fecha defunciones'])
self.last_added.sort_values(by=['region_residencia','comuna_residencia','edad'], inplace=True)
self.last_added.rename(columns={'comuna_residencia': 'comuna'}, inplace=True)
self.last_added.rename(columns={'region_residencia': 'Region'}, inplace=True)
self.last_added = utils.normalizaNombreCodigoRegionYComuna(self.last_added)
df_sup = Comp[['Codigo comuna', 'Comuna']]
df_sup['Codigo comuna'] = df_sup['Codigo comuna'].replace('', 0)
self.last_added.drop(columns={'Comuna'}, inplace=True)
for k in range(len(self.last_added)):
if self.last_added.loc[k, 'Codigo comuna'] != '':
if self.last_added.loc[k, 'Codigo comuna'] < 10000:
self.last_added.loc[k, 'Codigo comuna'] = '0' + str(self.last_added.loc[k, 'Codigo comuna'])
else:
self.last_added.loc[k, 'Codigo comuna'] = str(self.last_added.loc[k, 'Codigo comuna'])
self.last_added = self.last_added.merge(df_sup, on="Codigo comuna", how="left")
self.last_added.set_index('Comuna', inplace=True)
columns_name = self.last_added.columns.values
maxSE = self.last_added[columns_name[4]].max()
minSE = self.last_added[columns_name[4]].min()
#print(minSE, maxSE)
lenSE = (pd.to_datetime(maxSE) - pd.to_datetime(minSE)).days + 1
startdate = pd.to_datetime(minSE)
date_list = pd.date_range(startdate, periods=lenSE).tolist()
date_list = [dt.datetime.strftime(x, "%Y-%m-%d") for x in date_list]
def edad2rango(df,comuna):
cols = df.columns.tolist()
df2 = pd.DataFrame(columns = cols)
p = 0
for row in comuna:
aux = df.loc[df.index == row]
aux2 = aux.groupby(['Fecha defunciones']).sum()
aux2['Comuna'] = row
aux2.set_index(['Comuna'], inplace=True)
identifiers = ['Region', 'Codigo region', 'Codigo comuna', 'Fecha defunciones']
temp = aux[identifiers].copy()
temp.drop_duplicates(keep='first', inplace=True)
temp2 = pd.concat([temp,aux2], axis=1)
if p == 0:
df2 = temp2
p +=1
else:
df2 = pd.concat([df2,temp2], axis=0)
return df2
kl = 0
for edad in ['<=39','40-49','50-59','60-69','70-79','80-89','>=90']:
if edad == '<=39':
df_edad = self.last_added[self.last_added['edad'] <= 39].copy()
if edad == '40-49':
df_edad = self.last_added[self.last_added['edad'] <= 49].copy()
df_edad = df_edad[df_edad['edad'] >= 40]
if edad == '50-59':
df_edad = self.last_added[self.last_added['edad'] <= 59].copy()
df_edad = df_edad[df_edad['edad'] >= 50]
if edad == '60-69':
df_edad = self.last_added[self.last_added['edad'] <= 69].copy()
df_edad = df_edad[df_edad['edad'] >= 60]
if edad == '70-79':
df_edad = self.last_added[self.last_added['edad'] <= 79].copy()
df_edad = df_edad[df_edad['edad'] >= 70]
if edad == '80-89':
df_edad = self.last_added[self.last_added['edad'] <= 89].copy()
df_edad = df_edad[df_edad['edad'] >= 80]
if edad == '>=90':
df_edad = self.last_added[self.last_added['edad'] >= 90].copy()
df_edad.drop(columns=['edad'], inplace=True)
df_edad.sort_values(by=['Fecha defunciones'], inplace=True)
df_edad2 = edad2rango(df_edad,comuna)
for k in [5,6,7]:
df = pd.DataFrame(np.zeros((len(comuna), lenSE)))
dicts = {}
keys = range(lenSE)
for i in keys:
dicts[i] = date_list[i]
df.rename(columns=dicts, inplace=True)
value_comuna = df_edad2[columns_name[k]].copy()
value_comuna.fillna(0,inplace=True)
SE_comuna = df_edad2['Fecha defunciones'].copy()
j=0
for row in df_edad2.index:
idx = comuna.loc[comuna == row].index.values
if idx.size > 0:
col = SE_comuna[j]
df[col][idx] = value_comuna[j].astype(int)
j += 1
df_output = pd.concat([Comp, df], axis=1)
df_output.drop(columns=['index'], axis=1, inplace=True)
df_output['Edad'] = str(edad)
identifiers = ['Region', 'Codigo region', 'Comuna', 'Codigo comuna','Poblacion', 'Edad']
variables = [x for x in df_output.columns if x not in identifiers]
cols = identifiers + variables
df_output = df_output[cols].copy()
i = 0
if i == 0:
outputDF2 = df_output
i += 1
else:
outputDF2 = pd.concat([outputDF2, df_output], axis=0)
outputDF2.reset_index(drop=True, inplace=True)
outputDF2[variables] = outputDF2[variables].dropna() # .astype(int)
outputDF2.dropna(how='all', inplace=True)
if k == 5:
if kl == 0:
outputDF3_c = outputDF2
else:
outputDF3_c = pd.concat([outputDF3_c,outputDF2], axis=0)
elif k == 6:
if kl == 0:
outputDF3_s = outputDF2
else:
outputDF3_s = pd.concat([outputDF3_s,outputDF2], axis=0)
elif k == 7:
if kl == 0:
outputDF3_t = outputDF2
else:
outputDF3_t = pd.concat([outputDF3_t,outputDF2], axis=0)
kl += 1
outputDF3_c.sort_values(by=['Region', 'Codigo region', 'Comuna', 'Codigo comuna'], inplace=True)
outputDF3_s.sort_values(by=['Region', 'Codigo region', 'Comuna', 'Codigo comuna'], inplace=True)
outputDF3_t.sort_values(by=['Region', 'Codigo region', 'Comuna', 'Codigo comuna'], inplace=True)
outputDF3_c.dropna(inplace=True)
outputDF3_s.dropna(inplace=True)
outputDF3_t.dropna(inplace=True)
outputDF3_c.drop(['Poblacion'], axis=1, inplace=True)
outputDF3_s.drop(['Poblacion'], axis=1, inplace=True)
outputDF3_t.drop(['Poblacion'], axis=1, inplace=True)
name = self.output + '_confirmadas.csv'
outputDF3_c.to_csv(name, index=False)
outputDF3_c_T = outputDF3_c.T
outputDF3_c_T.to_csv(name.replace('.csv', '_T.csv'), header=False)
identifiers = ['Region', 'Codigo region', 'Comuna', 'Codigo comuna','Edad']
variables = [x for x in outputDF3_c.columns if x not in identifiers]
outputDF3_std = pd.melt(outputDF3_c, id_vars=identifiers, value_vars=variables, var_name='Fecha', value_name='Total')
outputDF3_std.to_csv(name.replace('.csv', '_std.csv'), index=False)
name = self.output + '_sospechosas.csv'
outputDF3_s.to_csv(name, index=False)
outputDF3_s_T = outputDF3_s.T
outputDF3_s_T.to_csv(name.replace('.csv', '_T.csv'), header=False)
identifiers = ['Region', 'Codigo region', 'Comuna', 'Codigo comuna','Edad']
variables = [x for x in outputDF3_s.columns if x not in identifiers]
outputDF3_std = pd.melt(outputDF3_s, id_vars=identifiers, value_vars=variables, var_name='Fecha',
value_name='Total')
outputDF3_std.to_csv(name.replace('.csv', '_std.csv'), index=False)
name = self.output + '_totales.csv'
outputDF3_t.to_csv(name, index=False)
outputDF3_t_T = outputDF3_t.T
outputDF3_t_T.to_csv(name.replace('.csv', '_T.csv'), header=False)
identifiers = ['Region', 'Codigo region', 'Comuna', 'Codigo comuna','Edad']
variables = [x for x in outputDF3_t.columns if x not in identifiers]
outputDF3_std = pd.melt(outputDF3_t, id_vars=identifiers, value_vars=variables, var_name='Fecha', value_name='Total')
outputDF3_std.to_csv(name.replace('.csv', '_std.csv'), index=False)
def last_to_csv(self):
if self.indicador == 'fabricante':
## campana por fabricante
self.last_added.rename(columns={'Dose': 'Dosis'}, inplace=True)
self.last_added.rename(columns={'Type': 'Fabricante'},
inplace=True)
self.last_added["Dosis"] = self.last_added["Dosis"].replace({
"First":
"Primera",
"Second":
"Segunda"
})
identifiers = ['Fabricante', 'Dosis']
variables = [
x for x in self.last_added.columns if x not in identifiers
]
self.last_added = self.last_added[identifiers + variables]
self.last_added.to_csv(self.output + '.csv', index=False)
df_t = self.last_added.T
df_t.to_csv(self.output + '_t.csv', header=False)
df_std = pd.melt(self.last_added,
id_vars=identifiers,
value_vars=variables,
var_name=['Fecha'],
value_name='Cantidad')
df_std.to_csv(self.output + '_std.csv', index=False)
elif self.indicador == 'campana':
## campana por region
self.last_added.rename(columns={'Dose': 'Dosis'}, inplace=True)
utils.regionName(self.last_added)
self.last_added["Dosis"] = self.last_added["Dosis"].replace({
"First":
"Primera",
"Second":
"Segunda"
})
identifiers = ['Region', 'Dosis']
variables = [
x for x in self.last_added.columns if x not in identifiers
]
self.last_added = self.last_added[identifiers + variables]
self.last_added.to_csv(self.output + '.csv', index=False)
df_t = self.last_added.T
df_t.to_csv(self.output + '_t.csv', header=False)
df_std = pd.melt(self.last_added,
id_vars=identifiers,
value_vars=variables,
var_name=['Fecha'],
value_name='Cantidad')
df_std.to_csv(self.output + '_std.csv', index=False)
elif self.indicador == 'edad':
## campana por edad
self.last_added.rename(columns={
'Dose': 'Dosis',
'Age': 'Rango_etario'
},
inplace=True)
self.last_added["Dosis"] = self.last_added["Dosis"].replace({
"First":
"Primera",
"Second":
"Segunda"
})
identifiers = ['Rango_etario', 'Dosis']
variables = [
x for x in self.last_added.columns if x not in identifiers
]
self.last_added = self.last_added[identifiers + variables]
self.last_added.to_csv(self.output + '.csv', index=False)
df_t = self.last_added.T
df_t.to_csv(self.output + '_t.csv', header=False)
df_std = pd.melt(self.last_added,
id_vars=identifiers,
value_vars=variables,
var_name=['Fecha'],
value_name='Cantidad')
df_std.to_csv(self.output + '_std.csv', index=False)
elif self.indicador == 'caracteristicas_del_vacunado':
## campana por caracter del vacunado
self.last_added.rename(columns={
'Dose': 'Dosis',
'Group': 'Grupo'
},
inplace=True)
self.last_added["Dosis"] = self.last_added["Dosis"].replace({
"First":
"Primera",
"Second":
"Segunda"
})
identifiers = ['Grupo', 'Dosis']
variables = [
x for x in self.last_added.columns if x not in identifiers
]
self.last_added = self.last_added[identifiers + variables]
self.last_added.to_csv(self.output + '.csv', index=False)
df_t = self.last_added.T
df_t.to_csv(self.output + '_t.csv', header=False)
df_std = pd.melt(self.last_added,
id_vars=identifiers,
value_vars=variables,
var_name=['Fecha'],
value_name='Cantidad')
df_std.to_csv(self.output + '_std.csv', index=False)
elif self.indicador == 'vacunas_region':
self.last_added.rename(columns={
'REGION_CORTO': 'Region',
'COD_COMUNA_FINAL': 'Comuna',
'FECHA_INMUNIZACION': 'Fecha',
'SUM_of_SUM_of_2aDOSIS': 'Segunda_comuna',
'SUM_of_SUM_of_1aDOSIS': 'Primera_comuna'
},
inplace=True)
self.last_added = self.last_added.dropna(subset=['Fecha'])
self.last_added['Fecha'] = pd.to_datetime(
self.last_added['Fecha'],
format='%d/%m/%Y').dt.strftime("%Y-%m-%d")
self.last_added.sort_values(by=['Region', 'Fecha'], inplace=True)
utils.regionName(self.last_added)
regiones = pd.DataFrame(self.last_added['Region'].unique())
#transformar
## agrupar por comuna
self.last_added['Primera'] = self.last_added.groupby(
['Region', 'Fecha'])['Primera_comuna'].transform('sum')
self.last_added['Segunda'] = self.last_added.groupby(
['Region', 'Fecha'])['Segunda_comuna'].transform('sum')
self.last_added = self.last_added[[
'Region', 'Fecha', 'Primera', 'Segunda'
]]
self.last_added.drop_duplicates(inplace=True)
##llenar fechas para cada region y crear total
idx = pd.date_range(self.last_added['Fecha'].min(),
self.last_added['Fecha'].max())
df = pd.DataFrame()
total = pd.DataFrame(
columns=['Region', 'Fecha', 'Primera', 'Segunda'])
total = utils.fill_in_missing_dates(total, 'Fecha', 0, idx)
total["Region"] = total["Region"].replace({0: 'Total'})
for region in regiones[0]:
df_region = self.last_added.loc[self.last_added['Region'] ==
region]
df_region = utils.fill_in_missing_dates(
df_region, 'Fecha', 0, idx)
df_region["Region"] = df_region["Region"].replace({0: region})
total['Primera'] = df_region['Primera'] + total['Primera']
total['Segunda'] = df_region['Segunda'] + total['Segunda']
df = df.append(df_region, ignore_index=True)
total = total.append(df, ignore_index=True)
total['Fecha'] = total['Fecha'].dt.strftime("%Y-%m-%d")
self.last_added = total
##sumar totales
self.last_added['Primera'] = pd.to_numeric(
self.last_added['Primera'])
self.last_added['Segunda'] = pd.to_numeric(
self.last_added['Segunda'])
self.last_added['Primera'] = self.last_added.groupby(
['Region'])['Primera'].transform('cumsum')
self.last_added['Segunda'] = self.last_added.groupby(
['Region'])['Segunda'].transform('cumsum')
#self.last_added['Total'] = self.last_added.sum(numeric_only=True, axis=1)
##transformar en input
df = pd.DataFrame()
regiones = pd.DataFrame(self.last_added['Region'].unique())
for region in regiones[0]:
df_region = self.last_added.loc[self.last_added['Region'] ==
region]
df_region.set_index('Fecha', inplace=True)
df_region = df_region[['Primera', 'Segunda']].T
df_region.reset_index(drop=True, inplace=True)
df = df.append(df_region, ignore_index=True)
new_col = [
'Primera', 'Segunda', 'Primera', 'Segunda', 'Primera',
'Segunda', 'Primera', 'Segunda', 'Primera', 'Segunda',
'Primera', 'Segunda', 'Primera', 'Segunda', 'Primera',
'Segunda', 'Primera', 'Segunda', 'Primera', 'Segunda',
'Primera', 'Segunda', 'Primera', 'Segunda', 'Primera',
'Segunda', 'Primera', 'Segunda', 'Primera', 'Segunda',
'Primera', 'Segunda', 'Primera', 'Segunda'
]
df.insert(0, column='Dosis', value=new_col)
new_col = pd.DataFrame()
for region in regiones[0]:
col = [region, region]
new_col = new_col.append(col, ignore_index=True)
df.insert(0, column='Region', value=new_col)
self.last_added = df
identifiers = ['Region', 'Dosis']
variables = [
x for x in self.last_added.columns if x not in identifiers
]
self.last_added = self.last_added[identifiers + variables]
self.last_added.to_csv(self.output + '.csv', index=False)
df_t = self.last_added.T
df_t.to_csv(self.output + '_t.csv', header=False)
df_std = pd.melt(self.last_added,
id_vars=identifiers,
value_vars=variables,
var_name=['Fecha'],
value_name='Cantidad')
df_std.to_csv(self.output + '_std.csv', index=False)
df_std.to_json(self.output + '.json',
orient='values',
force_ascii=False)
elif self.indicador == 'vacunas_edad_region':
self.last_added.rename(columns={
'NOMBRE_REGION': 'Region',
'COD_COMUNA': 'Comuna',
'EDAD_ANOS': 'Edad',
'POBLACION': 'Poblacion',
'2aDOSIS_RES': 'Segunda_comuna',
'1aDOSIS_RES': 'Primera_comuna'
},
inplace=True)
self.last_added.sort_values(by=['Region', 'Edad'], inplace=True)
utils.regionName(self.last_added)
regiones = pd.DataFrame(self.last_added['Region'].unique())
# transformar
## agrupar por comuna
self.last_added['Primera'] = self.last_added.groupby(
['Region', 'Edad'])['Primera_comuna'].transform('sum')
self.last_added['Segunda'] = self.last_added.groupby(
['Region', 'Edad'])['Segunda_comuna'].transform('sum')
self.last_added['Poblacion'] = self.last_added.groupby(
['Region', 'Edad'])['Poblacion'].transform('sum')
self.last_added = self.last_added[[
'Region', 'Edad', 'Poblacion', 'Primera', 'Segunda'
]]
self.last_added.drop_duplicates(inplace=True)
##crear total
df = pd.DataFrame()
total = pd.DataFrame(
columns=['Region', 'Edad', 'Poblacion', 'Primera', 'Segunda'])
total['Edad'] = list(range(15, 81))
total["Region"] = total["Region"].fillna('Total')
for region in regiones[0]:
df_region = self.last_added.loc[self.last_added['Region'] ==
region]
df_region.reset_index(drop=True, inplace=True)
total['Primera'] = total.Primera.fillna(
0) + df_region.Primera.fillna(0)
total['Segunda'] = total.Segunda.fillna(
0) + df_region.Segunda.fillna(0)
total['Poblacion'] = total.Poblacion.fillna(
0) + df_region.Poblacion.fillna(0)
df = df.append(df_region, ignore_index=True)
edad = total
total = total.append(df, ignore_index=True)
self.last_added = total
##transformar en input
df = pd.DataFrame()
regiones = pd.DataFrame(self.last_added['Region'].unique())
for region in regiones[0]:
df_region = self.last_added.loc[self.last_added['Region'] ==
region]
df_region.set_index('Edad', inplace=True)
df_region = df_region[['Primera', 'Segunda']].T
df_region.reset_index(drop=True, inplace=True)
df = df.append(df_region, ignore_index=True)
new_col = [
'Primera', 'Segunda', 'Primera', 'Segunda', 'Primera',
'Segunda', 'Primera', 'Segunda', 'Primera', 'Segunda',
'Primera', 'Segunda', 'Primera', 'Segunda', 'Primera',
'Segunda', 'Primera', 'Segunda', 'Primera', 'Segunda',
'Primera', 'Segunda', 'Primera', 'Segunda', 'Primera',
'Segunda', 'Primera', 'Segunda', 'Primera', 'Segunda',
'Primera', 'Segunda', 'Primera', 'Segunda', 'Primera',
'Segunda'
]
df.insert(0, column='Dosis', value=new_col)
new_col = pd.DataFrame()
for region in regiones[0]:
col = [region, region]
new_col = new_col.append(col, ignore_index=True)
df.insert(0, column='Region', value=new_col)
self.last_added = df
identifiers = ['Region', 'Dosis']
variables = [
x for x in self.last_added.columns if x not in identifiers
]
self.last_added = self.last_added[identifiers + variables]
self.last_added.to_csv(self.output + '.csv', index=False)
df_t = self.last_added.T
df_t.to_csv(self.output + '_t.csv', header=False)
df_std = pd.melt(self.last_added,
id_vars=identifiers,
value_vars=variables,
var_name=['Edad'],
value_name='Cantidad')
df_std.to_csv(self.output + '_std.csv', index=False)
df_std.to_json(self.output + '.json',
orient='values',
force_ascii=False)
elif self.indicador == 'vacunas_edad_sexo':
self.last_added.rename(columns={
'NOMBRE_REGION': 'Region',
'SEXO': 'Sexo',
'EDAD_ANOS': 'Edad',
'POBLACION': 'Poblacion',
'SUM_of_1aDOSIS': 'Primera',
'SUM_of_2aDOSIS': 'Segunda'
},
inplace=True)
self.last_added.sort_values(by=['Sexo', 'Edad'], inplace=True)
self.last_added = self.last_added[[
'Sexo', 'Edad', 'Primera', 'Segunda'
]]
sexo = pd.DataFrame(self.last_added['Sexo'].unique())
##crear total
df = pd.DataFrame()
for sex in sexo[0]:
total = pd.DataFrame(
columns=['Sexo', 'Edad', 'Primera', 'Segunda'])
total['Edad'] = list(
range(self.last_added.Edad.min(),
self.last_added.Edad.max() + 1))
df_sex = self.last_added.loc[self.last_added['Sexo'] == sex]
df_sex.reset_index(drop=True, inplace=True)
df_sex.index = df_sex['Edad']
total.index = total['Edad']
total['Sexo'] = total.Sexo.fillna(sex)
total['Primera'] = total.Primera.fillna(
0) + df_sex.Primera.fillna(0)
total['Segunda'] = total.Segunda.fillna(
0) + df_sex.Segunda.fillna(0)
df = df.append(total, ignore_index=True)
self.last_added = df
##transformar en input
df = pd.DataFrame()
sexo = pd.DataFrame(self.last_added['Sexo'].unique())
for sex in sexo[0]:
df_sex = self.last_added.loc[self.last_added['Sexo'] == sex]
df_sex.set_index('Edad', inplace=True)
df_sex = df_sex[['Primera', 'Segunda']].T
df_sex.reset_index(drop=True, inplace=True)
df = df.append(df_sex, ignore_index=True)
new_col = [
'Primera', 'Segunda', 'Primera', 'Segunda', 'Primera',
'Segunda', 'Primera', 'Segunda'
]
df.insert(0, column='Dosis', value=new_col)
new_col = pd.DataFrame()
for sex in sexo[0]:
col = [sex, sex]
new_col = new_col.append(col, ignore_index=True)
df.insert(0, column='Sexo', value=new_col)
self.last_added = df
identifiers = ['Sexo', 'Dosis']
variables = [
x for x in self.last_added.columns if x not in identifiers
]
self.last_added = self.last_added[identifiers + variables]
self.last_added.to_csv(self.output + '.csv', index=False)
df_t = self.last_added.T
df_t.to_csv(self.output + '_t.csv', header=False)
df_std = pd.melt(self.last_added,
id_vars=identifiers,
value_vars=variables,
var_name=['Edad'],
value_name='Cantidad')
df_std.to_csv(self.output + '_std.csv', index=False)
df_std.to_json(self.output + '.json',
orient='values',
force_ascii=False)
elif self.indicador == 'vacunas_prioridad':
self.last_added.rename(columns={
'Criterio': 'Grupo',
'Subcriterio': 'Subgrupo',
'1aDOSIS': 'Primera',
'2aDOSIS': 'Segunda'
},
inplace=True)
self.last_added.sort_values(by=['Grupo', 'Subgrupo'], inplace=True)
self.last_added = self.last_added[[
'Grupo', 'Subgrupo', 'Primera', 'Segunda'
]]
##transformar en input
df = pd.DataFrame()
grupos = pd.DataFrame(self.last_added['Grupo'].unique())
for grupo in grupos[0]:
df_grupo = self.last_added.loc[self.last_added['Grupo'] ==
grupo]
df_grupo.set_index('Subgrupo', inplace=True)
df_grupo = df_grupo[['Primera', 'Segunda']].T
df_grupo.reset_index(drop=True, inplace=True)
df = df.append(df_grupo, ignore_index=True)
new_col = [
'Primera', 'Segunda', 'Primera', 'Segunda', 'Primera',
'Segunda', 'Primera', 'Segunda', 'Primera', 'Segunda',
'Primera', 'Segunda', 'Primera', 'Segunda'
]
df.insert(0, column='Dosis', value=new_col)
new_col = pd.DataFrame()
for grupo in grupos[0]:
col = [grupo, grupo]
new_col = new_col.append(col, ignore_index=True)
df.insert(0, column='Grupo', value=new_col)
self.last_added = df
identifiers = ['Grupo', 'Dosis']
variables = [
x for x in self.last_added.columns if x not in identifiers
]
self.last_added = self.last_added[identifiers + variables]
self.last_added.to_csv(self.output + '.csv', index=False)
df_t = self.last_added.T
df_t.to_csv(self.output + '_t.csv', header=False)
df_std = pd.melt(self.last_added,
id_vars=identifiers,
value_vars=variables,
var_name=['Subgrupo'],
value_name='Cantidad')
df_std.to_csv(self.output + '_std.csv', index=False)
df_std.to_json(self.output + '.json',
orient='values',
force_ascii=False)
elif self.indicador == 'vacunas_comuna':
##template por comuna
df_base = pd.read_csv(
'../input/DistribucionDEIS/baseFiles/DEIS_template.csv')
df_base['Codigo region'] = df_base['Codigo region'].fillna(0)
df_base['Codigo comuna'] = df_base['Codigo comuna'].fillna(0)
df_base['Comuna'] = df_base['Comuna'].fillna(0)
todrop = df_base.loc[df_base['Comuna'] == 0]
df_base.drop(todrop.index, inplace=True)
df_base['Codigo region'] = df_base['Codigo region'].astype(int)
df_base['Codigo comuna'] = df_base['Codigo comuna'].astype(int)
desconocido = df_base['Codigo comuna'] != 0
df_base['Codigo comuna'].where(desconocido, '', inplace=True)
Comp = df_base.loc[df_base['Comuna'] != 'Total']
Comp.reset_index(inplace=True)
utils.desconocidoName(Comp)
for k in range(len(Comp)):
if Comp.loc[k, 'Codigo region'] < 10:
Comp.loc[k, 'Codigo region'] = '0' + str(
Comp.loc[k, 'Codigo region'])
else:
Comp.loc[k,
'Codigo region'] = str(Comp.loc[k,
'Codigo region'])
if Comp.loc[k, 'Codigo comuna'] != '':
if Comp.loc[k, 'Codigo comuna'] < 10000:
Comp.loc[k, 'Codigo comuna'] = '0' + str(
Comp.loc[k, 'Codigo comuna'])
else:
Comp.loc[k, 'Codigo comuna'] = str(
Comp.loc[k, 'Codigo comuna'])
comuna = Comp['Comuna']
self.last_added.rename(columns={
'REGION_CORTO': 'region_residencia',
'COD_COMUNA_FINAL': 'Codigo comuna',
'FECHA_INMUNIZACION': 'Fecha',
'SUM_of_SUM_of_2aDOSIS': 'Segunda_comuna',
'SUM_of_SUM_of_1aDOSIS': 'Primera_comuna'
},
inplace=True)
self.last_added = self.last_added.dropna(subset=['Fecha'])
self.last_added['Fecha'] = pd.to_datetime(
self.last_added['Fecha'],
format='%d/%m/%Y').dt.strftime("%Y-%m-%d")
self.last_added.sort_values(by=['region_residencia', 'Fecha'],
inplace=True)
utils.regionDEISName(self.last_added)
for k in range(len(self.last_added)):
if self.last_added.loc[k, 'Codigo comuna'] != '':
if self.last_added.loc[k, 'Codigo comuna'] < 10000:
self.last_added.loc[k, 'Codigo comuna'] = '0' + str(
self.last_added.loc[k, 'Codigo comuna'])
else:
self.last_added.loc[k, 'Codigo comuna'] = str(
self.last_added.loc[k, 'Codigo comuna'])
df_sup = Comp[['Codigo comuna', 'Comuna']]
df_sup['Codigo comuna'] = df_sup['Codigo comuna'].replace('', 0)
self.last_added = self.last_added.merge(df_sup,
on="Codigo comuna",
how="left")
self.last_added.set_index('Comuna', inplace=True)
columns_name = self.last_added.columns.values
maxSE = self.last_added[columns_name[2]].max()
minSE = self.last_added[columns_name[2]].min()
print(minSE, maxSE)
lenSE = (pd.to_datetime(maxSE) - pd.to_datetime(minSE)).days + 1
startdate = pd.to_datetime(minSE)
date_list = pd.date_range(startdate, periods=lenSE).tolist()
date_list = [
dt.datetime.strftime(x, "%Y-%m-%d") for x in date_list
]
print(date_list)
SE_comuna = self.last_added[columns_name[2]]
for k in [3, 4]:
df = pd.DataFrame(np.zeros((len(comuna), lenSE)))
dicts = {}
keys = range(lenSE)
# values = [i for i in range(lenSE)]
for i in keys:
dicts[i] = date_list[i]
df.rename(columns=dicts, inplace=True)
value_comuna = self.last_added[columns_name[k]]
value_comuna.fillna(0, inplace=True)
i = 0
for row in self.last_added.index:
idx = comuna.loc[comuna == row].index.values
if idx.size > 0:
col = SE_comuna[i]
df[col][idx] = value_comuna[i].astype(int)
i += 1
df_output = pd.concat([Comp, df], axis=1)
df_output.drop(columns=['index'], axis=1, inplace=True)
nComunas = [
len(list(group))
for key, group in groupby(df_output['Codigo region'])
]
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
variables = [
x for x in df_output.columns if x not in identifiers
]
begRow = 0
for i in range(len(nComunas)):
endRow = begRow + nComunas[i]
firstList = df_output[identifiers].iloc[endRow -
1].values.tolist()
firstList[2] = 'Total'
firstList[3] = ''
valuesTotal = df_output[variables][begRow:endRow].sum(
axis=0).tolist()
regionTotal = pd.DataFrame(
(firstList + valuesTotal),
index=df_output.columns.values).transpose()
if i < len(nComunas) - 1:
blank_line = pd.Series(
np.empty((len(regionTotal), 0)).tolist())
regionTotal = pd.concat([regionTotal, blank_line],
axis=0)
regionTotal.drop(columns=0, axis=1, inplace=True)
temp = pd.concat(
[df_output.iloc[begRow:endRow], regionTotal], axis=0)
if i == 0:
outputDF2 = temp
else:
outputDF2 = pd.concat([outputDF2, temp], axis=0)
if i < len(nComunas) - 1:
begRow = endRow
outputDF2.reset_index(inplace=True)
outputDF2.drop(columns=['index'], axis=1, inplace=True)
outputDF2[variables] = outputDF2[variables].dropna(
) # .astype(int)
print(outputDF2.head(20))
outputDF2.dropna(how='all', inplace=True)
todrop = outputDF2.loc[outputDF2['Comuna'] == 'Total']
outputDF2.drop(todrop.index, inplace=True)
if k == 3:
name = self.output + '_1eraDosis.csv'
outputDF2.to_csv(name, index=False)
outputDF2_T = outputDF2.T
outputDF2_T.to_csv(name.replace('.csv', '_T.csv'),
header=False)
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
outputDF2.drop(columns=['Poblacion'], inplace=True)
variables = [
x for x in outputDF2.columns if x not in identifiers
]
outputDF2_std = pd.melt(outputDF2,
id_vars=identifiers,
value_vars=variables,
var_name='Fecha',
value_name='Primera Dosis')
outputDF2_std.to_csv(name.replace('.csv', '_std.csv'),
index=False)
elif k == 4:
name = self.output + '_2daDosis.csv'
outputDF2.to_csv(name, index=False)
outputDF2_T = outputDF2.T
outputDF2_T.to_csv(name.replace('.csv', '_T.csv'),
header=False)
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
outputDF2.drop(columns=['Poblacion'], inplace=True)
variables = [
x for x in outputDF2.columns if x not in identifiers
]
outputDF2_std = pd.melt(outputDF2,
id_vars=identifiers,
value_vars=variables,
var_name='Fecha',
value_name='Segunda Dosis')
outputDF2_std.to_csv(name.replace('.csv', '_std.csv'),
index=False)
elif self.indicador == 'vacunas_comuna_edad':
##template por comuna
df_base = pd.read_csv(
'../input/DistribucionDEIS/baseFiles/DEIS_template.csv')
df_base['Codigo region'] = df_base['Codigo region'].fillna(0)
df_base['Codigo comuna'] = df_base['Codigo comuna'].fillna(0)
df_base['Comuna'] = df_base['Comuna'].fillna(0)
todrop = df_base.loc[df_base['Comuna'] == 0]
df_base.drop(todrop.index, inplace=True)
df_base['Codigo region'] = df_base['Codigo region'].astype(int)
df_base['Codigo comuna'] = df_base['Codigo comuna'].astype(int)
desconocido = df_base['Codigo comuna'] != 0
df_base['Codigo comuna'].where(desconocido, '', inplace=True)
Comp = df_base.loc[df_base['Comuna'] != 'Total']
Comp.reset_index(inplace=True)
utils.desconocidoName(Comp)
for k in range(len(Comp)):
if Comp.loc[k, 'Codigo region'] < 10:
Comp.loc[k, 'Codigo region'] = '0' + str(
Comp.loc[k, 'Codigo region'])
else:
Comp.loc[k,
'Codigo region'] = str(Comp.loc[k,
'Codigo region'])
if Comp.loc[k, 'Codigo comuna'] != '':
if Comp.loc[k, 'Codigo comuna'] < 10000:
Comp.loc[k, 'Codigo comuna'] = '0' + str(
Comp.loc[k, 'Codigo comuna'])
else:
Comp.loc[k, 'Codigo comuna'] = str(
Comp.loc[k, 'Codigo comuna'])
comuna = Comp['Comuna']
self.last_added.rename(columns={
'NOMBRE_REGION': 'region_residencia',
'COD_COMUNA': 'Codigo comuna',
'EDAD_ANOS': 'Edad',
'SUM_of_SUM_of_2aDOSIS': 'Segunda_comuna',
'SUM_of_SUM_of_1aDOSIS': 'Primera_comuna'
},
inplace=True)
utils.regionDEISName(self.last_added)
for k in range(len(self.last_added)):
if self.last_added.loc[k, 'Codigo comuna'] != '':
if self.last_added.loc[k, 'Codigo comuna'] < 10000:
self.last_added.loc[k, 'Codigo comuna'] = '0' + str(
self.last_added.loc[k, 'Codigo comuna'])
else:
self.last_added.loc[k, 'Codigo comuna'] = str(
self.last_added.loc[k, 'Codigo comuna'])
df_sup = Comp[['Codigo comuna', 'Comuna']]
df_sup['Codigo comuna'] = df_sup['Codigo comuna'].replace('', 0)
self.last_added = self.last_added.merge(df_sup,
on="Codigo comuna",
how="left")
self.last_added.set_index('Comuna', inplace=True)
columns_name = self.last_added.columns.values
maxSE = self.last_added[columns_name[2]].max()
minSE = self.last_added[columns_name[2]].min()
print(minSE, maxSE)
lenSE = maxSE - minSE + 1
date_list = list(range(minSE, maxSE + 1))
print(date_list)
SE_comuna = self.last_added[columns_name[2]]
for k in [4, 5]:
df = pd.DataFrame(np.zeros((len(comuna), lenSE)))
dicts = {}
keys = range(lenSE)
# values = [i for i in range(lenSE)]
for i in keys:
dicts[i] = date_list[i]
df.rename(columns=dicts, inplace=True)
value_comuna = self.last_added[columns_name[k]]
value_comuna.fillna(0, inplace=True)
i = 0
for row in self.last_added.index:
idx = comuna.loc[comuna == row].index.values
if idx.size > 0:
col = SE_comuna[i]
df[col][idx] = value_comuna[i].astype(int)
i += 1
df_output = pd.concat([Comp, df], axis=1)
df_output.drop(columns=['index'], axis=1, inplace=True)
nComunas = [
len(list(group))
for key, group in groupby(df_output['Codigo region'])
]
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
variables = [
x for x in df_output.columns if x not in identifiers
]
begRow = 0
for i in range(len(nComunas)):
endRow = begRow + nComunas[i]
firstList = df_output[identifiers].iloc[endRow -
1].values.tolist()
firstList[2] = 'Total'
firstList[3] = ''
valuesTotal = df_output[variables][begRow:endRow].sum(
axis=0).tolist()
regionTotal = pd.DataFrame(
(firstList + valuesTotal),
index=df_output.columns.values).transpose()
if i < len(nComunas) - 1:
blank_line = pd.Series(
np.empty((len(regionTotal), 0)).tolist())
regionTotal = pd.concat([regionTotal, blank_line],
axis=0)
regionTotal.drop(columns=0, axis=1, inplace=True)
temp = pd.concat(
[df_output.iloc[begRow:endRow], regionTotal], axis=0)
if i == 0:
outputDF2 = temp
else:
outputDF2 = pd.concat([outputDF2, temp], axis=0)
if i < len(nComunas) - 1:
begRow = endRow
outputDF2.reset_index(inplace=True)
outputDF2.drop(columns=['index'], axis=1, inplace=True)
outputDF2[variables] = outputDF2[variables].dropna(
) # .astype(int)
print(outputDF2.head(20))
outputDF2.dropna(how='all', inplace=True)
todrop = outputDF2.loc[outputDF2['Comuna'] == 'Total']
outputDF2.drop(todrop.index, inplace=True)
if k == 4:
name = self.output + '_1eraDosis.csv'
outputDF2.to_csv(name, index=False)
outputDF2_T = outputDF2.T
outputDF2_T.to_csv(name.replace('.csv', '_T.csv'),
header=False)
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
outputDF2.drop(columns=['Poblacion'], inplace=True)
variables = [
x for x in outputDF2.columns if x not in identifiers
]
outputDF2_std = pd.melt(outputDF2,
id_vars=identifiers,
value_vars=variables,
var_name='Edad',
value_name='Primera Dosis')
outputDF2_std.to_csv(name.replace('.csv', '_std.csv'),
index=False)
elif k == 5:
name = self.output + '_2daDosis.csv'
outputDF2.to_csv(name, index=False)
outputDF2_T = outputDF2.T
outputDF2_T.to_csv(name.replace('.csv', '_T.csv'),
header=False)
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
outputDF2.drop(columns=['Poblacion'], inplace=True)
variables = [
x for x in outputDF2.columns if x not in identifiers
]
outputDF2_std = pd.melt(outputDF2,
id_vars=identifiers,
value_vars=variables,
var_name='Edad',
value_name='Segunda Dosis')
outputDF2_std.to_csv(name.replace('.csv', '_std.csv'),
index=False)
def last_to_csv(self):
if self.indicador == 'vacunas_comuna_edad':
##template por comuna
df_base = pd.read_csv(
'../input/DistribucionDEIS/baseFiles/DEIS_template.csv')
df_base['Codigo region'] = df_base['Codigo region'].fillna(0)
df_base['Codigo comuna'] = df_base['Codigo comuna'].fillna(0)
df_base['Comuna'] = df_base['Comuna'].fillna(0)
todrop = df_base.loc[df_base['Comuna'] == 0]
df_base.drop(todrop.index, inplace=True)
df_base['Codigo region'] = df_base['Codigo region'].astype(int)
df_base['Codigo comuna'] = df_base['Codigo comuna'].astype(int)
desconocido = df_base['Codigo comuna'] != 0
df_base['Codigo comuna'].where(desconocido, '', inplace=True)
Comp = df_base.loc[df_base['Comuna'] != 'Total']
Comp.reset_index(inplace=True)
utils.desconocidoName(Comp)
for k in range(len(Comp)):
if Comp.loc[k, 'Codigo region'] < 10:
Comp.loc[k, 'Codigo region'] = '0' + str(
Comp.loc[k, 'Codigo region'])
else:
Comp.loc[k,
'Codigo region'] = str(Comp.loc[k,
'Codigo region'])
if Comp.loc[k, 'Codigo comuna'] != '':
if Comp.loc[k, 'Codigo comuna'] < 10000:
Comp.loc[k, 'Codigo comuna'] = '0' + str(
Comp.loc[k, 'Codigo comuna'])
else:
Comp.loc[k, 'Codigo comuna'] = str(
Comp.loc[k, 'Codigo comuna'])
comuna = Comp['Comuna']
self.last_added.rename(columns={
'NOMBRE_REGION': 'region_residencia',
'COD_COMUNA': 'Codigo comuna',
'POBLACION': 'poblacion',
'EDAD_ANOS': 'Edad',
'SUM_of_SUM_of_2aDOSIS': 'Segunda_comuna',
'SUM_of_SUM_of_1aDOSIS': 'Primera_comuna'
},
inplace=True)
utils.regionDEISName(self.last_added)
for k in range(len(self.last_added)):
if self.last_added.loc[k, 'Codigo comuna'] != '':
if self.last_added.loc[k, 'Codigo comuna'] < 10000:
self.last_added.loc[k, 'Codigo comuna'] = '0' + str(
self.last_added.loc[k, 'Codigo comuna'])
else:
self.last_added.loc[k, 'Codigo comuna'] = str(
self.last_added.loc[k, 'Codigo comuna'])
df_sup = Comp[['Codigo comuna', 'Comuna']]
df_sup['Codigo comuna'] = df_sup['Codigo comuna'].replace('', 0)
self.last_added = self.last_added.merge(df_sup,
on="Codigo comuna",
how="left")
self.last_added.set_index('Comuna', inplace=True)
columns_name = self.last_added.columns.values
maxSE = self.last_added[columns_name[3]].max()
minSE = self.last_added[columns_name[3]].min()
print(minSE, maxSE)
lenSE = maxSE - minSE + 1
date_list = list(range(minSE, maxSE + 1))
print(date_list)
SE_comuna = self.last_added[columns_name[3]]
k = 4
df = pd.DataFrame(np.zeros((len(comuna), lenSE)))
dicts = {}
keys = range(lenSE)
# values = [i for i in range(lenSE)]
for i in keys:
dicts[i] = date_list[i]
df.rename(columns=dicts, inplace=True)
value_comuna = self.last_added[columns_name[k]]
value_comuna.fillna(0, inplace=True)
i = 0
for row in self.last_added.index:
idx = comuna.loc[comuna == row].index.values
if idx.size > 0:
col = SE_comuna[i]
df[col][idx] = value_comuna[i].astype(int)
i += 1
df_output = pd.concat([Comp, df], axis=1)
df_output.drop(columns=['index'], axis=1, inplace=True)
nComunas = [
len(list(group))
for key, group in groupby(df_output['Codigo region'])
]
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
variables = [x for x in df_output.columns if x not in identifiers]
begRow = 0
for i in range(len(nComunas)):
endRow = begRow + nComunas[i]
firstList = df_output[identifiers].iloc[endRow -
1].values.tolist()
firstList[2] = 'Total'
firstList[3] = ''
valuesTotal = df_output[variables][begRow:endRow].sum(
axis=0).tolist()
regionTotal = pd.DataFrame(
(firstList + valuesTotal),
index=df_output.columns.values).transpose()
if i < len(nComunas) - 1:
blank_line = pd.Series(
np.empty((len(regionTotal), 0)).tolist())
regionTotal = pd.concat([regionTotal, blank_line], axis=0)
regionTotal.drop(columns=0, axis=1, inplace=True)
temp = pd.concat([df_output.iloc[begRow:endRow], regionTotal],
axis=0)
if i == 0:
outputDF2 = temp
else:
outputDF2 = pd.concat([outputDF2, temp], axis=0)
if i < len(nComunas) - 1:
begRow = endRow
outputDF2.reset_index(inplace=True)
outputDF2.drop(columns=['index'], axis=1, inplace=True)
outputDF2[variables] = outputDF2[variables].dropna(
) # .astype(int)
print(outputDF2.head(20))
outputDF2.dropna(how='all', inplace=True)
todrop = outputDF2.loc[outputDF2['Comuna'] == 'Total']
outputDF2.drop(todrop.index, inplace=True)
name = self.output + '.csv'
outputDF2.to_csv(name, index=False)
outputDF2_T = outputDF2.T
outputDF2_T.to_csv(name.replace('.csv', '_T.csv'), header=False)
identifiers = [
'Region', 'Codigo region', 'Comuna', 'Codigo comuna'
]
outputDF2.drop(columns=['Poblacion'], inplace=True)
variables = [x for x in outputDF2.columns if x not in identifiers]
outputDF2_std = pd.melt(outputDF2,
id_vars=identifiers,
value_vars=variables,
var_name='Edad',
value_name='Poblacion')
outputDF2_std.to_csv(name.replace('.csv', '_std.csv'), index=False)