text: #!/usr/bin/env python3
  
  # Copyright © 2017 American Red Cross
  # 
  # Permission is hereby granted, free of charge, to any person
  # obtaining a copy of this software and associated documentation
  # files (the “Software”), to deal in the Software without
  # restriction, including without limitation the rights to use,
  # copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the
  # Software is furnished to do so, subject to the following
  # conditions:
  # 
  # The above copyright notice and this permission notice shall be
  # included in all copies or substantial portions of the Software.
  # 
  # THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,
  # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
  # OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
  # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
  # HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
  # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
  # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
  # OTHER DEALINGS IN THE SOFTWARE.
  
  import csv
  import pandas as pd
  import numpy as np
  import requests
  from datetime import datetime
  from dateutil.parser import parse
  from datetime import timedelta
  import json
  import requests
  import re
  
  def combine_data(df, df_unique_geo):
  
      #all addresses with the lat, lng, and census tract for each row in the dataset
      df = df.merge(df_unique_geo, how='left', left_on = df['Block_address_CLEAN'], right_on = df_unique_geo['Address'])
  
      #all fires aggregated per census tract
      fires_per_tract = df['census_tract'].value_counts()
      fires_per_tract_df = pd.DataFrame({'tract':fires_per_tract.index, 'fire_per_tract':fires_per_tract.values})
      df = df.merge(fires_per_tract_df, how='left', left_on = df['census_tract'], right_on = fires_per_tract_df['tract'])
  
      #all unique tracts with the aggregated number of fires in that tract
      df = df.drop_duplicates(subset="census_tract", keep='first')
  
      return df
  