json to array python - Brave Search

How to convert a list of numbers to jsonarray in Python

stackoverflow.com › questions › 19795012 › how-to-convert-a-list-of-numbers-to-jsonarray-in-python

Use the json module to produce JSON output:

import json

with open(outputfilename, 'wb') as outfile:
    json.dump(row, outfile)

This writes the JSON result directly to the file (replacing any previous content if the file already existed).

If you need the JSON result string in Python itself, use json.dumps() (added s, for 'string'):

json_string = json.dumps(row)

The L is just Python syntax for a long integer value; the json library knows how to handle those values, no L will be written.

Demo string output:

>>> import json
>>> row = [1L,[0.1,0.2],[[1234L,1],[134L,2]]]
>>> json.dumps(row)
'[1, [0.1, 0.2], [[1234, 1], [134, 2]]]'

Answer from Martijn Pieters on Stack Overflow

stackoverflow.com › questions › 19795012 › how-to-convert-a-list-of-numbers-to-jsonarray-in-python

json - How to convert a list of numbers to jsonarray in Python - Stack Overflow

Use the json module to produce JSON output:

import json

with open(outputfilename, 'wb') as outfile:
    json.dump(row, outfile)

This writes the JSON result directly to the file (replacing any previous content if the file already existed).

If you need the JSON result string in Python itself, use json.dumps() (added s, for 'string'):

json_string = json.dumps(row)

The L is just Python syntax for a long integer value; the json library knows how to handle those values, no L will be written.

Demo string output:

>>> import json
>>> row = [1L,[0.1,0.2],[[1234L,1],[134L,2]]]
>>> json.dumps(row)
'[1, [0.1, 0.2], [[1234, 1], [134, 2]]]'

import json
row = [1L,[0.1,0.2],[[1234L,1],[134L,2]]]
row_json = json.dumps(row)

JSON to arrays Python

python - Constructing JSON Array

How to convert list of list (of lists) to json array in python?

How to get a python array from JSON

Python: turn JSON object to JSON array

More results from stackoverflow.com

stackoverflow.com › questions › 68459450 › json-to-array-of-values-in-python

JSON to array of values in Python - Stack Overflow

When you load the json you are actually converting it into a python dictionary. You can then simply show all values:

inputJSON = open('input.json')
inputData = json.load(inputJSON)
values = list(inputData.values())

If key:value is in plain text, you can do:

values = [pair.split(":")[1] for pair in inputData]

If by key:value you mean a dict type {key: value}, you can do as follows:

values = [list(pair.values())[0] for pair in inputData]

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How to Return JSON Objects in the Same Array in Python

discuss.python.org › python help

Unable to append data to Json array object with desired output - Python Help - Discussions on Python.org

January 18, 2023 - I’m tried getting help for same issue on stack-overflow but got no help or replies. I’m re-posting here with the hope that someone can please guide me as I’m unable to push the code to repository due to delay. My code import json import re from http.client import responses import vt import requests with open('/home/asad/Downloads/ssh-log-parser/ok.txt', 'r') as file: file = file.read() pattern = re.compile(r'\\d{1,3}.\\d{1,3}.\\d{1,3}.\\d{1,3}') ips = pattern.findall(file) unique_ips = lis...

stackoverflow.com › questions › 10973614 › convert-json-array-to-python-list

Convert JSON array to Python list - Stack Overflow

import json

array = '{"fruits": ["apple", "banana", "orange"]}'
data  = json.loads(array)
print(data['fruits']) 
# the print displays:
# ['apple', 'banana', 'orange']

You had everything you needed. data will be a dict, and data['fruits'] will be a list

Tested on Ideone.

import json
array = '{"fruits": ["apple", "banana", "orange"]}'
data  = json.loads(array)
fruits_list = data['fruits']
print fruits_list

pythonguides.com › json-data-in-python

How to Extract Values from a JSON Array in Python

April 27, 2026 - In this tutorial, I will show you exactly how to navigate these arrays and extract the data you need efficiently. ... In Python, when we talk about a JSON array, we are essentially dealing with a List of Dictionaries.

anvil.works › anvil q&a

Creating JSON Array of data with python - Anvil Q&A - Anvil Community Forum

The anvil server doesn’t know anything about javascript dates Only the client knows about javascript dates. There are two options I can see: pass the data to the client and then do the conversion of python date → js date on the client. use the documented serialization from google How to use Da…

stackoverflow.com › questions › 67091132 › how-to-convert-json-to-array

python - How to convert Json to array? - Stack Overflow

I think you need data into this format

[{'Financial Ratios': 'Ratios',
  'Unnamed: 1': 'Formula ',
  'Unnamed: 2': '2013',
  'Unnamed: 3': 2012.0,
  'Unnamed: 4': 'Point',
  'Unnamed: 5': 'Max Score for the Ratio',
  'Unnamed: 6': 'Weighted Score'},
 {'Financial Ratios': 'Activity Ratios',
  'Unnamed: 1': None,
  'Unnamed: 2': None,
  'Unnamed: 3': None,
  'Unnamed: 4': None,
  'Unnamed: 5': None,
  'Unnamed: 6': None},
 {'Financial Ratios': 'Total Asset Turnover Ratio',
  'Unnamed: 1': ' Sales/Total Assets (S/TA) (X5)',
  'Unnamed: 2': 0.3688805385,
  'Unnamed: 3': 0.46659987,
  'Unnamed: 4': 1,
  'Unnamed: 5': 50,
  'Unnamed: 6': 10},
 {'Financial Ratios': 'Working Capital Turnover',
  'Unnamed: 1': 'Sales/Working Capital',
  'Unnamed: 2': 1.5217292178,
  'Unnamed: 3': 2.3187325549,
  'Unnamed: 4': 0,
  'Unnamed: 5': 15,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'RiskCalc. v4. Activity Ratio 2',
  'Unnamed: 1': 'Account Payable/Sales',
  'Unnamed: 2': 0.3654207703,
  'Unnamed: 3': 0.7809577867,
  'Unnamed: 4': 1,
  'Unnamed: 5': 10,
  'Unnamed: 6': 10},
 {'Financial Ratios': 'Days of Inventory on Hand (DOH)',
  'Unnamed: 1': '365/(Cost of Goods Sold/Inventory)',
  'Unnamed: 2': 126.7687160825,
  'Unnamed: 3': 89.3996349298,
  'Unnamed: 4': 0,
  'Unnamed: 5': 5,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'Days of Sales Outstanding (DSO)',
  'Unnamed: 1': '365/(Sales/Trade Receivables)',
  'Unnamed: 2': 201.1519182976,
  'Unnamed: 3': 136.5175896751,
  'Unnamed: 4': 0,
  'Unnamed: 5': 5,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'Number of Days of Payables',
  'Unnamed: 1': '365/(Cost of Goods Sold/Trade Payables)',
  'Unnamed: 2': 156.6392091208,
  'Unnamed: 3': 343.2088569019,
  'Unnamed: 4': 0,
  'Unnamed: 5': 5,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'Cash Conversion Cycle',
  'Unnamed: 1': 'DOH + DSO – Number of Days of Payables',
  'Unnamed: 2': 171.2814252594,
  'Unnamed: 3': -117.291632297,
  'Unnamed: 4': 0,
  'Unnamed: 5': 10,
  'Unnamed: 6': 0},
 {'Financial Ratios': None,
  'Unnamed: 1': None,
  'Unnamed: 2': None,
  'Unnamed: 3': None,
  'Unnamed: 4': 'Efficiency Score',
  'Unnamed: 5': '100/',
  'Unnamed: 6': 20},
 {'Financial Ratios': 'Liquidity Ratios',
  'Unnamed: 1': None,
  'Unnamed: 2': None,
  'Unnamed: 3': None,
  'Unnamed: 4': None,
  'Unnamed: 5': None,
  'Unnamed: 6': None},
 {'Financial Ratios': 'liquidity',
  'Unnamed: 1': 'X1, Working Capital/Total Assets (WC/TA)',
  'Unnamed: 2': 0.24240879,
  'Unnamed: 3': 0.2012305684,
  'Unnamed: 4': 2,
  'Unnamed: 5': 20,
  'Unnamed: 6': 8},
 {'Financial Ratios': 'Current Ratio',
  'Unnamed: 1': 'Current Assets/Current Liabilities',
  'Unnamed: 2': 1.4011830177,
  'Unnamed: 3': 1.3122086769,
  'Unnamed: 4': 3,
  'Unnamed: 5': 40,
  'Unnamed: 6': 24},
 {'Financial Ratios': 'Quick Ratio',
  'Unnamed: 1': '(Current Assets-Inventory)/Current Liabilities',
  'Unnamed: 2': 1.2206383114,
  'Unnamed: 3': 1.1649433701,
  'Unnamed: 4': 4,
  'Unnamed: 5': 30,
  'Unnamed: 6': 24},
 {'Financial Ratios': 'Cash Ratio',
  'Unnamed: 1': 'Cash and Marketable Securities/Current Liabilities',
  'Unnamed: 2': 0.1198051849,
  'Unnamed: 3': 0.3207784969,
  'Unnamed: 4': 0,
  'Unnamed: 5': 10,
  'Unnamed: 6': 0},
 {'Financial Ratios': None,
  'Unnamed: 1': None,
  'Unnamed: 2': None,
  'Unnamed: 3': None,
  'Unnamed: 4': 'liquidity Score',
  'Unnamed: 5': '100/',
  'Unnamed: 6': 56},
 {'Financial Ratios': 'Solvency Ratios',
  'Unnamed: 1': None,
  'Unnamed: 2': None,
  'Unnamed: 3': None,
  'Unnamed: 4': None,
  'Unnamed: 5': None,
  'Unnamed: 6': None},
 {'Financial Ratios': 'Solvency',
  'Unnamed: 1': 'X4, Equity/ TotalLiabilities (E/TL)',
  'Unnamed: 2': 0.2542669835,
  'Unnamed: 3': 0.3698847524,
  'Unnamed: 4': 0,
  'Unnamed: 5': 10,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'Total Debt Ratio',
  'Unnamed: 1': 'Total Debt/Total Assets',
  'Unnamed: 2': 0.7972784209,
  'Unnamed: 3': 0.7299884156,
  'Unnamed: 4': 2,
  'Unnamed: 5': 25,
  'Unnamed: 6': 10},
 {'Financial Ratios': 'Financial Liabilities Percentage',
  'Unnamed: 1': 'short-term Financial Liabilities/Current Liabilities',
  'Unnamed: 2': 0.1830886471,
  'Unnamed: 3': 0.1413598101,
  'Unnamed: 4': 0,
  'Unnamed: 5': 10,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'Current Liabilities to Net Worth',
  'Unnamed: 1': '(Current Liabilities/Net Worth)*100',
  'Unnamed: 2': 298.0614724129,
  'Unnamed: 3': 238.7077877629,
  'Unnamed: 4': 2,
  'Unnamed: 5': 25,
  'Unnamed: 6': 10},
 {'Financial Ratios': 'Financial Leverage Ratio',
  'Unnamed: 1': 'Total Assets/TotalEquity',
  'Unnamed: 2': 4.9328739664,
  'Unnamed: 3': 3.7035448029,
  'Unnamed: 4': 0,
  'Unnamed: 5': 5,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'Financial Expense Coverage',
  'Unnamed: 1': 'Operating Profit/Total Financial Expense',
  'Unnamed: 2': 0.6270806042,
  'Unnamed: 3': 5.1835849804,
  'Unnamed: 4': 1,
  'Unnamed: 5': 25,
  'Unnamed: 6': 5},
 {'Financial Ratios': None,
  'Unnamed: 1': None,
  'Unnamed: 2': None,
  'Unnamed: 3': None,
  'Unnamed: 4': 'Solvency Score',
  'Unnamed: 5': '100/',
  'Unnamed: 6': 25},
 {'Financial Ratios': 'Profitability Ratios',
  'Unnamed: 1': None,
  'Unnamed: 2': None,
  'Unnamed: 3': None,
  'Unnamed: 4': None,
  'Unnamed: 5': None,
  'Unnamed: 6': None},
 {'Financial Ratios': 'Cumulative Profitability',
  'Unnamed: 1': 'X2, Retained Earnings/Total Assets (RE/TA)',
  'Unnamed: 2': 0.0236571651,
  'Unnamed: 3': 0.1355888945,
  'Unnamed: 4': 0,
  'Unnamed: 5': 10,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'Gross Profit Margin',
  'Unnamed: 1': 'Gross Profit/Sales',
  'Unnamed: 2': 0.1484981192,
  'Unnamed: 3': 0.169457354,
  'Unnamed: 4': 0,
  'Unnamed: 5': 10,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'Operating Profit Margin',
  'Unnamed: 1': 'Operating Profit/Sales',
  'Unnamed: 2': 0.0492337241,
  'Unnamed: 3': 0.0634190537,
  'Unnamed: 4': 0,
  'Unnamed: 5': 15,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'Net Profit Margin',
  'Unnamed: 1': 'Net Profit/Sales',
  'Unnamed: 2': 0.0334269397,
  'Unnamed: 3': 0.1038080661,
  'Unnamed: 4': 0,
  'Unnamed: 5': 15,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'Return on Assets-Productivity',
  'Unnamed: 1': 'X3, Operating Profit/Total Assets',
  'Unnamed: 2': 0.0181613627,
  'Unnamed: 3': 0.0295913222,
  'Unnamed: 4': 1,
  'Unnamed: 5': 25,
  'Unnamed: 6': 5},
 {'Financial Ratios': 'Return on Equity',
  'Unnamed: 1': 'Net Income/Equity',
  'Unnamed: 2': 0.0608250368,
  'Unnamed: 3': 0.1793879706,
  'Unnamed: 4': 2,
  'Unnamed: 5': 25,
  'Unnamed: 6': 10},
 {'Financial Ratios': None,
  'Unnamed: 1': None,
  'Unnamed: 2': None,
  'Unnamed: 3': None,
  'Unnamed: 4': 'Profitability Score',
  'Unnamed: 5': '100/',
  'Unnamed: 6': 15},
 {'Financial Ratios': 'Growth',
  'Unnamed: 1': None,
  'Unnamed: 2': 'Year over year trends 2012 vs 2013',
  'Unnamed: 3': None,
  'Unnamed: 4': None,
  'Unnamed: 5': None,
  'Unnamed: 6': None},
 {'Financial Ratios': 'Sales Growth',
  'Unnamed: 1': 'Annual Sales Growth',
  'Unnamed: 2': 0.121184383,
  'Unnamed: 3': None,
  'Unnamed: 4': 1,
  'Unnamed: 5': 20,
  'Unnamed: 6': 20},
 {'Financial Ratios': 'Operating Profit Growth',
  'Unnamed: 1': 'Annual Operating Profit Growth',
  'Unnamed: 2': 0.0492337241,
  'Unnamed: 3': None,
  'Unnamed: 4': 1,
  'Unnamed: 5': 20,
  'Unnamed: 6': 20},
 {'Financial Ratios': 'NetProfit Growth',
  'Unnamed: 1': 'Annual Net Profit Growth',
  'Unnamed: 2': -0.6389706106,
  'Unnamed: 3': None,
  'Unnamed: 4': 0,
  'Unnamed: 5': 20,
  'Unnamed: 6': 0},
 {'Financial Ratios': 'Tangible Net Worth Growth',
  'Unnamed: 1': 'Annual Net Worth Growth',
  'Unnamed: 2': 0.0647643295,
  'Unnamed: 3': None,
  'Unnamed: 4': 1,
  'Unnamed: 5': 20,
  'Unnamed: 6': 20},
 {'Financial Ratios': 'Cash and cash equivalents Growth',
  'Unnamed: 1': 'Annual Cash and cash equivalents Growth',
  'Unnamed: 2': -0.503449854,
  'Unnamed: 3': None,
  'Unnamed: 4': 0,
  'Unnamed: 5': 20,
  'Unnamed: 6': 0},
 {'Financial Ratios': None,
  'Unnamed: 1': None,
  'Unnamed: 2': None,
  'Unnamed: 3': None,
  'Unnamed: 4': None,
  'Unnamed: 5': None,
  'Unnamed: 6': None},
 {'Financial Ratios': None,
  'Unnamed: 1': None,
  'Unnamed: 2': None,
  'Unnamed: 3': None,
  'Unnamed: 4': 'Growth Score',
  'Unnamed: 5': '100/',
  'Unnamed: 6': 60},
 {'Financial Ratios': 'General Financial Score',
  'Unnamed: 1': 3,
  'Unnamed: 2': 16.8,
  'Unnamed: 3': 5.0,
  'Unnamed: 4': 'General Financial Score',
  'Unnamed: 5': '100/',
  'Unnamed: 6': 36.8},
 {'Financial Ratios': None,
  'Unnamed: 1': 3,
  'Unnamed: 2': 9,
  'Unnamed: 3': None,
  'Unnamed: 4': None,
  'Unnamed: 5': None,
  'Unnamed: 6': None}]

You can use this snippet of code :-

data = json.loads(<stringify_json>)

index_key_mapping = {index: key for index, key in enumerate(data.keys())}
formated_data = [{
    index_key_mapping[index]: value for index, value in enumerate(ord_pair)
} for ord_pair in zip(*[
    dictionary.values() for key, dictionary in data.items()
])]

You can use list and .items():

arr_fr = list(json_fr.items())

print('Child title: ', arr_fr[0][0])
print('Child value: ', arr_fr[0][1])
print('Child of child: ', arr_fr[0][1]['3'])

And I get result:

stackoverflow.com › questions › 45881410 › read-in-json-into-array

python - Read in JSON into array - Stack Overflow

import json


with open('1.json') as data_file:
    data = json.load(data_file)


print([row['close'] for row in data])

Try it like this:

[item['close'] for item in data]

Find elsewhere

Google Bing Mojeek

reqbin.com › json › python › uzykkick › json-array-example

Python | What is JSON Array?

Unlike dictionaries, where you can get the value by its key, in a JSON array, the array elements can only be accessed by their index. The following is an example of a JSON array with numbers. Below, you can find a list of JSON arrays with different data types. The Python code was automatically generated for the JSON Array example.

stackoverflow.com › questions › 49626788 › converting-a-json-strings-list-to-a-json-array-in-python › 49626876

Converting a JSON strings list to a JSON array in Python - Stack Overflow

decode JSON strings into dicts and put them in a list, last, convert the list to JSON

json_list = []
json_list.append(json.loads(JSON_STRING))
json.dumps(json_list)

or more pythonic syntax

output_list = json.dumps([json.loads(JSON_STRING) for JSON_STRING in JSON_STRING_LIST])

Use json.dumps before json.loads to convert your data to dictionary object This also helps prevent valueError: Expecting property name enclosed in double quotes.

Ex:

import json
myJSONStringList = ['{"user": "testuser", "data": {"version": 1, "timestamp": "2018-04-03T09:23:43.388Z"}, "group": "33"}',
'{"user": "otheruser", "data": {"version": 2, "timestamp": "2018-04-03T09:23:43.360Z", }, "group": "44"}']

print([json.loads(json.dumps(i)) for i in myJSONStringList])

Output:

[u'{"user": "testuser", "data": {"version": 1, "timestamp": "2018-04-03T09:23:43.388Z"}, "group": "33"}', u'{"user": "otheruser", "data": {"version": 2, "timestamp": "2018-04-03T09:23:43.360Z", }, "group": "44"}']

geeksforgeeks.org › loop-through-a-json-array-in-python

Loop through a JSON array in Python - GeeksforGeeks

March 28, 2024 - In this example, we will define the JSON data as a string and load it using the and the load() function to convert the JSON data to a Python object. Then using a for loop we will iterate through the array.

w3schools.com › python › gloss_python_convert_into_JSON.asp

Python Convert From Python to JSON

If you have a Python object, you can convert it into a JSON string by using the json.dumps() method.

geeksforgeeks.org › python › python-json-to-list

Python Json To List - GeeksforGeeks

April 18, 2026 - JSON (JavaScript Object Notation) is a lightweight data-interchange format widely used in web development and data exchange. In Python, converting JSON to a list is a common task.

docs.python.org › 3 › library › json.html

JSON encoder and decoder — Python 3.14.8 documentation

The old version of JSON specified by the obsolete RFC 4627 required that the top-level value of a JSON text must be either a JSON object or array (Python dict or list), and could not be a JSON null, boolean, number, or string value.

stackoverflow.com › questions › 47060035 › python-parse-json-array

Python Parse JSON array - Stack Overflow

In your for loop statement, Each item in json_array is a dictionary and the dictionary does not have a key store_details. So I modified the program a little bit

import json

input_file = open ('stores-small.json')
json_array = json.load(input_file)
store_list = []

for item in json_array:
    store_details = {"name":None, "city":None}
    store_details['name'] = item['name']
    store_details['city'] = item['city']
    store_list.append(store_details)

print(store_list)

If you arrived at this question simply looking for a way to read a json file into memory, then use the built-in json module.

with open(file_path, 'r') as f:
    data = json.load(f)

If you have a json string in memory that needs to be parsed, use json.loads() instead:

data = json.loads(my_json_string)

Either way, now data is converted into a Python data structure (list/dictionary) that may be (deeply) nested and you'll need Python methods to manipulate it.

If you arrived here looking for ways to get values under several keys as in the OP, then the question is about looping over a Python data structure. For a not-so-deeply-nested data structure, the most readable (and possibly the fastest) way is a list / dict comprehension. For example, for the requirement in the OP, a list comprehension does the job.

store_list = [{'name': item['name'], 'city': item['city']} for item in json_array]
# [{'name': 'Mall of America', 'city': 'Bloomington'}, {'name': 'Tempe Marketplace', 'city': 'Tempe'}]

Other types of common data manipulation:

For a nested list where each sub-list is a list of items in the json_array.

store_list = [[item['name'], item['city']] for item in json_array]
# [['Mall of America', 'Bloomington'], ['Tempe Marketplace', 'Tempe']]

For a dictionary of lists where each key-value pair is a category-values in the json_array.

store_data = {'name': [], 'city': []}
for item in json_array:
    store_data['name'].append(item['name'])
    store_data['city'].append(item['city'])
# {'name': ['Mall of America', 'Tempe Marketplace'], 'city': ['Bloomington', 'Tempe']}

For a "transposed" nested list where each sub-list is a "category" in json_array.

store_list = list(store_data.values())
# [['Mall of America', 'Tempe Marketplace'], ['Bloomington', 'Tempe']]

Towards Data Science

towardsdatascience.com › home › latest › how to perform json conversion, serialization, and comparison in python

How to Perform JSON Conversion, Serialization, and Comparison in Python | Towards Data Science

March 5, 2025 - In Python, the json library can be used for this type of conversion. We use the loads function to convert a JSON string into an object or an array, and use the dumps function to perform the opposite conversion.

stackoverflow.com › questions › 39373836 › python-json-to-array

Python Json to array - Stack Overflow

September 7, 2016 - Do I have to initialize a 3d array? Later it would be fine if I can work on the result like that: ... no - you have to use a list instead: it would work with data=[0,0] instead but see my answer for better solutions ... import json import urllib2 urls=["http://example.com/json","https://example.com/json2"] # your urls here data=[] for u in urls: response = urllib2.urlopen(u) data.append(json.loads(response.read())) # while this normally works with Python 2, it is better to use data.append(json.loads(response.read().decode("utf8"))

quora.com › How-will-you-pass-values-to-the-JSON-array-using-Python

How will you pass values to the JSON array using Python? - Quora

Answer (1 of 4): Your question is so wrong on so many things. I won’t really tell you to read the manual. You don’t do this in one pass you see. JSON stands for JavaScript Object Notation, which means, it is Javascript, it is not Python, C or anything else.

w3resource.com › python-exercises › numpy › read-numeric-data-from-a-json-file-into-a-numpy-array.php

Read numeric data from a JSON file into a NumPy array

September 1, 2025 - Import NumPy and JSON Libraries: Import the NumPy and JSON libraries to handle arrays and JSON data. Define JSON File Path: Specify the path to the JSON file containing the numeric data.

TestMu AI Community

community.testmuai.com › ask a question

How can I convert a JSON array to a Python list? - Ask a Question - TestMu AI (formerly LambdaTest) Community

January 9, 2025 - I have the following JSON array: import json array = '{"fruits": ["apple", "banana", "orange"]}' data = json.loads(array) In this case, I want to convert all the values in the ‘fruits’ string to a Python list. What is…