python - CSV to JSON in python3 with repeted values

Question

I need to convert CSV with hundreds of rows to JSON in such a way that i need to get the key values repeating.

Here is what i have used to convert it to the present state

#Quest 1
import csv 
import json 
def make_json(csvFilePath, jsonFilePath): 
      
    # create a dictionary 
    data = {} 
      
    # Open a csv reader called DictReader 
    with open(csvFilePath, encoding='utf-8') as csvf: 
        csvReader = csv.DictReader(csvf) 
          
        # Convert each row into a dictionary  
        # and add it to data 
        for rows in csvReader: 
              
            # Assuming a column named 'No' to 
            # be the primary key 
            key = rows['MMSA'] 
            data[key] = rows 
  
    # Open a json writer, and use the json.dumps()  
    # function to dump data 
    with open(jsonFilePath, 'w', encoding='utf-8') as jsonf: 
        jsonf.write(json.dumps(data, indent=4)) 
          
csvFilePath = r'/home/user/Downloads/mmsa-icu-beds.csv'
jsonFilePath = r'/home/user/Downloads/mmsa-icu-beds.json'
  

make_json(csvFilePath, jsonFilePath)

The CSV file i have is: Current csv

What i am currently getting is this JSON format

{"MMSA": "Manhattan, KS", "total_percent_at_risk": "47.29%", "high_risk_per_ICU_bed": "4489.84875", "high_risk_per_hospital": "8979.6975", "icu_beds": "8", "hospitals": "4", "total_at_risk": "35918.79"}
{"MMSA": "Hilton Head Island-Bluffton-Beaufort, SC", "total_percent_at_risk": "62.72%", "high_risk_per_ICU_bed": "3904.163571", "high_risk_per_hospital": "36438.86", "icu_beds": "28", "hospitals": "3", "total_at_risk": "109316.58"}
{"MMSA": "Kahului-Wailuku-Lahaina, HI", "total_percent_at_risk": "59.13%", "high_risk_per_ICU_bed": "3860.557", "high_risk_per_hospital": "19302.785", "icu_beds": "20", "hospitals": "4", "total_at_risk": "77211.14"}
{"MMSA": "Spartanburg, SC", "total_percent_at_risk": "66.12%", "high_risk_per_ICU_bed": "3786.115556", "high_risk_per_hospital": "85187.6", "icu_beds": "45", "hospitals": "2", "total_at_risk": "170375.2"}
{"MMSA": "Baton Rouge, LA", "total_percent_at_risk": "66.60%", "high_risk_per_ICU_bed": "3459.7325", "high_risk_per_hospital": "39000.62091", "icu_beds": "124", "hospitals": "11", "total_at_risk": "429006.83"}
{"MMSA": "Rockingham County-Strafford County, NH, Metropolitan Division", "total_percent_at_risk": "57.72%", "high_risk_per_ICU_bed": "3365.052", "high_risk_per_hospital": "40380.624", "icu_beds": "60", "hospitals": "5", "total_at_risk": "201903.12"}
{"MMSA": "Salisbury, MD-DE", "total_percent_at_risk": "68.32%", "high_risk_per_ICU_bed": "3292.271176", "high_risk_per_hospital": "37312.40667", "icu_beds": "68", "hospitals": "6", "total_at_risk": "223874.44"}

Expecting JSON format:

{"MMSA": "Manhattan, KS", "Manhattan, KS total_percent_at_risk": "47.29%", "Manhattan, KS high_risk_per_ICU_bed": "4489.84875", "Manhattan, KS high_risk_per_hospital": "8979.6975", "Manhattan, KS icu_beds": "8", "Manhattan, KS hospitals": "4", "Manhattan, KS total_at_risk": "35918.79"}

question from:https://stackoverflow.com/questions/65644634/csv-to-json-in-python3-with-repeted-values

Answer 1

You need to grab the value of the MMSA key for each item in the list of dicts, then construct a dictionary with that same value at the beginning of each key f'{row["MMSA"]} {k}': v . This should work for you:

import json

# data retuned by your csv
csv_data = [
    {"MMSA": "Manhattan, KS", "total_percent_at_risk": "47.29%", "high_risk_per_ICU_bed": "4489.84875",
        "high_risk_per_hospital": "8979.6975", "icu_beds": "8", "hospitals": "4", "total_at_risk": "35918.79"},
    {"MMSA": "Hilton Head Island-Bluffton-Beaufort, SC", "total_percent_at_risk": "62.72%", "high_risk_per_ICU_bed": "3904.163571",
        "high_risk_per_hospital": "36438.86", "icu_beds": "28", "hospitals": "3", "total_at_risk": "109316.58"},
    {"MMSA": "Kahului-Wailuku-Lahaina, HI", "total_percent_at_risk": "59.13%", "high_risk_per_ICU_bed": "3860.557",
        "high_risk_per_hospital": "19302.785", "icu_beds": "20", "hospitals": "4", "total_at_risk": "77211.14"},
    {"MMSA": "Spartanburg, SC", "total_percent_at_risk": "66.12%", "high_risk_per_ICU_bed": "3786.115556",
        "high_risk_per_hospital": "85187.6", "icu_beds": "45", "hospitals": "2", "total_at_risk": "170375.2"},
    {"MMSA": "Baton Rouge, LA", "total_percent_at_risk": "66.60%", "high_risk_per_ICU_bed": "3459.7325",
        "high_risk_per_hospital": "39000.62091", "icu_beds": "124", "hospitals": "11", "total_at_risk": "429006.83"},
    {"MMSA": "Rockingham County-Strafford County, NH, Metropolitan Division", "total_percent_at_risk": "57.72%",
        "high_risk_per_ICU_bed": "3365.052", "high_risk_per_hospital": "40380.624", "icu_beds": "60", "hospitals": "5", "total_at_risk": "201903.12"},
    {"MMSA": "Salisbury, MD-DE", "total_percent_at_risk": "68.32%", "high_risk_per_ICU_bed": "3292.271176",
        "high_risk_per_hospital": "37312.40667", "icu_beds": "68", "hospitals": "6", "total_at_risk": "223874.44"},
]

# super fancy comprehension with unpacking
json_data = [{'MMSA': row['MMSA'],  # keep the MMSA key
              **{f'{row["MMSA"]} {k}': v  # combine with the other keys and add the MMSA value
                 for k, v in row.items()
                 if k != 'MMSA'}}
             for row in csv_data]

def write_json(path, data, indent=4):
    with open(path, 'w') as file:
        json.dump(data, file, indent=indent)

write_json('./mmsa-icu-beds.json', json_data)

json output:

[
    {
        "MMSA": "Manhattan, KS",
        "Manhattan, KS total_percent_at_risk": "47.29%",
        "Manhattan, KS high_risk_per_ICU_bed": "4489.84875",
        "Manhattan, KS high_risk_per_hospital": "8979.6975",
        "Manhattan, KS icu_beds": "8",
        "Manhattan, KS hospitals": "4",
        "Manhattan, KS total_at_risk": "35918.79"
    },
    {
        "MMSA": "Hilton Head Island-Bluffton-Beaufort, SC",
        "Hilton Head Island-Bluffton-Beaufort, SC total_percent_at_risk": "62.72%",
        "Hilton Head Island-Bluffton-Beaufort, SC high_risk_per_ICU_bed": "3904.163571",
        "Hilton Head Island-Bluffton-Beaufort, SC high_risk_per_hospital": "36438.86",
        "Hilton Head Island-Bluffton-Beaufort, SC icu_beds": "28",
        "Hilton Head Island-Bluffton-Beaufort, SC hospitals": "3",
        "Hilton Head Island-Bluffton-Beaufort, SC total_at_risk": "109316.58"
    },
    {
        "MMSA": "Kahului-Wailuku-Lahaina, HI",
        "Kahului-Wailuku-Lahaina, HI total_percent_at_risk": "59.13%",
        "Kahului-Wailuku-Lahaina, HI high_risk_per_ICU_bed": "3860.557",
        "Kahului-Wailuku-Lahaina, HI high_risk_per_hospital": "19302.785",
        "Kahului-Wailuku-Lahaina, HI icu_beds": "20",
        "Kahului-Wailuku-Lahaina, HI hospitals": "4",
        "Kahului-Wailuku-Lahaina, HI total_at_risk": "77211.14"
    },
    {
        "MMSA": "Spartanburg, SC",
        "Spartanburg, SC total_percent_at_risk": "66.12%",
        "Spartanburg, SC high_risk_per_ICU_bed": "3786.115556",
        "Spartanburg, SC high_risk_per_hospital": "85187.6",
        "Spartanburg, SC icu_beds": "45",
        "Spartanburg, SC hospitals": "2",
        "Spartanburg, SC total_at_risk": "170375.2"
    },
    {
        "MMSA": "Baton Rouge, LA",
        "Baton Rouge, LA total_percent_at_risk": "66.60%",
        "Baton Rouge, LA high_risk_per_ICU_bed": "3459.7325",
        "Baton Rouge, LA high_risk_per_hospital": "39000.62091",
        "Baton Rouge, LA icu_beds": "124",
        "Baton Rouge, LA hospitals": "11",
        "Baton Rouge, LA total_at_risk": "429006.83"
    },
    {
        "MMSA": "Rockingham County-Strafford County, NH, Metropolitan Division",
        "Rockingham County-Strafford County, NH, Metropolitan Division total_percent_at_risk": "57.72%",
        "Rockingham County-Strafford County, NH, Metropolitan Division high_risk_per_ICU_bed": "3365.052",
        "Rockingham County-Strafford County, NH, Metropolitan Division high_risk_per_hospital": "40380.624",
        "Rockingham County-Strafford County, NH, Metropolitan Division icu_beds": "60",
        "Rockingham County-Strafford County, NH, Metropolitan Division hospitals": "5",
        "Rockingham County-Strafford County, NH, Metropolitan Division total_at_risk": "201903.12"
    },
    {
        "MMSA": "Salisbury, MD-DE",
        "Salisbury, MD-DE total_percent_at_risk": "68.32%",
        "Salisbury, MD-DE high_risk_per_ICU_bed": "3292.271176",
        "Salisbury, MD-DE high_risk_per_hospital": "37312.40667",
        "Salisbury, MD-DE icu_beds": "68",
        "Salisbury, MD-DE hospitals": "6",
        "Salisbury, MD-DE total_at_risk": "223874.44"
    }
]