Json encoder and decoder – Python

JSON (JavaScript Object Notation) – Python Document is a lightweight data interchange format that provides a structured representation of data. In Python, you can easily manipulate JSON data using the built-in json module. In this article, I’ll explain how to encode (serialize) and decode (deserialize) JSON in Python.

JSON is a lightweight data interchange format defined in RFC 7159 (replacing the previous RFC 4627) and ECMA-404 that is inspired by the JavaScript object syntax, but is not a subset of JavaScript.

Features of JSON

• Lightweight and simple: More compact than XML, reducing data transfer size.
• Highly readable: Easy for humans to read and understand.
• Language-independent: Works with many languages, including Python, Java, C++, and Go, not just JavaScript.
• Key-value pair structure: Makes data representation intuitive.
• Supports arrays and nested structures: Can handle complex data structures efficiently.

json.dumps() Parameters

• indent : 들여쓰기 적용 (json.dumps(data, indent=4))
• sort_keys : 키 정렬 (json.dumps(data, sort_keys=True))
• ensure_ascii : ASCII 문자 강제 (json.dumps(data, ensure_ascii=False))

json.loads() Features

• Converting JSON strings to Python’s native data types (dictionaries, lists, etc.)
• Possible TypeError, ValueError exceptions (when entering an invalid JSON format)

Json Python Code

import json

#References : https://www.alphavantage.co/
source_string = 
    '{"Meta Data": {"economy": "Intraday (5min) open, high, low, ' \
    'close prices and volume", "Symbol": "IBM", "Last Refreshed": ' \
    '"2024-07-12 19:50:00", "Interval": "5min", "Output Size": ' \
    '"Compact", "6. Time Zone": "US/Eastern"}, "Time Series (5min)": ' \
    '{"2024-07-12 19:50:00": {"open": "182.7850", "high": ' \
    '"182.7850", "low": "182.7000", "close": "182.7050", "volume": ' \
    '"40"}, "2024-07-12 19:40:00": {"open": "182.7700", "high": ' \
    '"182.8300", "low": "182.7700", "close": "182.8300", "volume": ' \
    '"527"}, "2024-07-12 19:35:00": {"open": "182.7000", "high": ' \
    '"182.7000", "low": "182.7000", "close": "182.7000", "' \
    'volume": "111"}}}'

# decoder
json_data = json.loads(source_string)

print(json_data["Meta Data"]["economy"])

for data in json_data["Time Series (5min)"]:
    _data = json_data["Time Series (5min)"][data]
    avg = (float(tmp_data["high"]) + float(tmp_data["low"]))/2
    print(data, "open:", _data["open"], "AVG:", avg, "volume:", _data["volume"] )

# encoder
print(type(json_data)) # <class 'dict'>
print(type(json.dumps(json_data))) # <class 'str'>

Output

Intraday (5min) open, high, low, close prices and volume
2024-07-12 19:50:00 open: 182.7850 AVG: 182.7425 volume: 40
2024-07-12 19:40:00 open: 182.7700 AVG: 182.8 volume: 527
2024-07-12 19:35:00 open: 182.7000 AVG: 182.7 volume: 111
<class 'dict'>
<class 'str'>

Troubleshooting possible issues with JSON conversion

1. Unicode character issues: Use ensure_ascii=False to preserve UTF-8 encoding when working with non-ASCII characters.
2. Datetime serialization issues: Use default=str or isoformat() to properly format datetime objects.
3. Object conversion issues: Use the default parameter to define custom conversion functions for non-serializable objects.

JSON vs. other data formats

Python’s json module simplifies encoding and decoding JSON data.

Beyond the basic dumps() and loads() functions, you can:

• Save and load JSON files using dump() and load().
• Convert custom objects using the default parameter in dumps().

By leveraging these features, you can efficiently process and manage JSON data in your applications.