Aggregate

count.py

import csv

lookup = {}

with open('CPCs.csv', 'rb') as input:
  clinics = csv.reader(input)

  for clinic in clinics:
    key = clinic[1].replace(' ', '') + clinic[2]
    if key not in lookup:
      lookup[key] = {"clinics": 0, "city": clinic[1], "state": clinic[2]}

    lookup[key]["clinics"] += 1

  with open('CPCs_grouped.csv', 'w') as output:
    writer = csv.writer(output, delimiter=',')
    writer.writerow(["city", "state", "clinics"])
    for clinic in lookup:
      writer.writerow([lookup[clinic]["city"], lookup[clinic]["state"], lookup[clinic]["clinics"]])
    

group.py

import csv
import json

# Dictionary to keep track of unique city/state combos
lookup = {}

# Open up the CSV
with open('CPCs.csv', 'rb') as input:
  clinics = csv.reader(input)
  
  # For each row in the file...
  for clinic in clinics:
    # Create a unique key (city with spaces removed + state - ex: GreenBayWI)
    key = clinic[1].replace(' ', '') + clinic[2]
    
    # If we haven't seen this city/state combo before, record it
    if key not in lookup:
      lookup[key] = {"clinics": []}
    
    # Add this clinic to the appropriate city/state combo
    lookup[key]["clinics"].append({'name': clinic[0], 'src': clinic[3]})
  
  # Write the output to a JSON file
  with open('CPCs_grouped.json', 'w') as output:
    json.dump(lookup, output)