Data Type Conversions

JSON Conversion

This note covers JSON conversion in Python.

Key modules: json

Encoding (Python objects to JSON):

import json

data = {
    'name': 'John Doe',
    'age': 30,
    'city': '[New York](./../new-york/)'
}

json_data = json.dumps(data, indent=4) # indent for pretty printing
print(json_data)

#writing to a file
with open('data.json', 'w') as f:
    json.dump(data, f, indent=4)

Decoding (JSON to Python objects):

import json

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

print(loaded_data)
print(type(loaded_data)) # confirms it's a dictionary


json_string = '{"name": "Jane Doe", "age": 25, "city": "London"}'
loaded_data_string = json.loads(json_string)
print(loaded_data_string)

Handling Errors: Use try...except blocks to handle json.JSONDecodeError in case of invalid JSON.

import json

try:
    invalid_json = '{"name": "Alice", "age": 28, "city": "Paris"}'  #missing quote
    data = json.loads(invalid_json)
except json.JSONDecodeError as e:
    print(f"Error decoding JSON: {e}")

Error Handling in Python (JSON Data Structures)

Special Considerations:

Data types: Python’s dictionaries and lists map naturally to JSON objects and arrays. However, be mindful of unsupported data types (e.g., sets). You might need custom encoding/decoding logic for these.
Large files: For extremely large JSON files, consider using iterative parsing to avoid loading the entire file into memory at once. Libraries like ijson can be helpful here. Iterative JSON Parsing

JSON Conversion

Carson West

Data Type Conversions

JSON Conversion