old_contents: 
  import pandas as pd
  import numpy as np
  import geocoder as gc
  df = pd.read_csv('hospitalList_processed.csv')
  no_lat_lon = df[(df.lat <= 0.0) & (df.lon <= 0.0)]
  complete = df[~((df.lat <= 0.0) & (df.lon <= 0.0))]
  
  for index, row in no_lat_lon.iterrows():
      try:
          g = gc.google(row['Barangay Name'])
          no_lat_lon.set_value(index, 'lat', g.latlng[0])
          no_lat_lon.set_value(index, 'lon', g.latlng[1])
      except: # evil I know
          continue
  
  complete = complete.append( no_lat_lon[~((no_lat_lon.lat <= 0.0) & (no_lat_lon.lon <= 0.0))])
  missing = no_lat_lon[(no_lat_lon.lat <= 0.0) & (no_lat_lon.lon <= 0.0)]
  missing.to_csv("missing_lat_lon.csv", index=False)
  
  # DO HIV HOSPITALS
  # Service Capabilities
  service = pd.read_csv("serviceCapability.csv")
  hospital_with_service = complete.merge(service, on="Facility Name" , how="left")
  hospital_with_service.drop(["Unnamed: 0", "Province_x", "Province_y", "Region_x", "Region_y"], axis=1, inplace=True)
  
  hospitals = pd.read_csv('hospitals_with_service.csv')
  faci = pd.read_csv('filters.csv')
  hospitals_with_facilities = pd.merge(hospitals, faci, on="level", how="outer")
  
new_contents: 
  import pandas as pd
  import numpy as np
  import geocoder as gc
  df = pd.read_csv('hospitalList_processed.csv')
  no_lat_lon = df[(df.lat <= 0.0) & (df.lon <= 0.0)]
  complete = df[~((df.lat <= 0.0) & (df.lon <= 0.0))]
  
  for index, row in no_lat_lon.iterrows():
      try:
          g = gc.google(row['Barangay Name'])
          no_lat_lon.set_value(index, 'lat', g.latlng[0])
          no_lat_lon.set_value(index, 'lon', g.latlng[1])
      except: # evil I know
          continue
  
  complete = complete.append( no_lat_lon[~((no_lat_lon.lat <= 0.0) & (no_lat_lon.lon <= 0.0))])
  missing = no_lat_lon[(no_lat_lon.lat <= 0.0) & (no_lat_lon.lon <= 0.0)]
  missing.to_csv("missing_lat_lon.csv", index=False)
  
  # Service Capabilities
  service = pd.read_csv("serviceCapability.csv")
  hospital_with_service = complete.merge(service, on="Facility Name" , how="left")
  hospital_with_service.drop(["Unnamed: 0", "Province_x", "Province_y", "Region_x", "Region_y"], axis=1, inplace=True)
  
  hospitals = pd.read_csv('hospitals_with_service.csv')
  faci = pd.read_csv('filters.csv')
  hospitals_with_facilities = pd.merge(hospitals, faci, on="level", how="outer")
  
  # DO HIV HOSPITALS
  treat = pd.read_csv("hiv_treatment_centers.csv")
  treat = treat[treat["region "] == "NCR"]
  treat.drop(["region ", "address", "hact_chair", "lon", "lat", "contact_number", "Unnamed: 0"], axis=1, inplace=True)
  treat["HIVTreat"] = 1
  treat.treatment_hub = treat.treatment_hub.map(lambda x: x.upper())
  hospitals = hospitals.merge(treat, how="outer", left_on="Facility Name", right_on="treatment_hub")
  
  test.drop(['Unnamed: 0', 'address', 'telephone_number', 'lon', 'lat'], axis=1, inplace=True)
  test['reffering_unit_or_laboratories'] = test['reffering_unit_or_laboratories'].map(lambda x: x.upper())
  test['HIVTest'] = 1
  hospitals = hospitals.merge(test, how="outer", left_on="Facility Name", right_on='reffering_unit_or_laboratories')
  
subject: Complete hospital dataset with facilities