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File Handling

JSON Files

Comprehensive JSON handling in Python — serialization, deserialization, custom encoders/decoders, working with JSON Lines, and streaming large JSON data.

Interview: JSON is the universal data interchange format — interviewers test your ability to parse, generate, and manipulate JSON data efficiently.

Last Updated: June 12, 2026 • 10 min read

JSON (JavaScript Object Notation) is the most common data format for APIs, configuration files, and data storage. Python's json module provides four core functions for working with JSON data, plus support for custom serialization of complex objects.

Core JSON Functions

  • json.dump(obj, file) — serialize Python object to JSON file
  • json.dumps(obj) — serialize Python object to JSON string
  • json.load(file) — deserialize JSON file to Python object
  • json.loads(string) — deserialize JSON string to Python object
  • Key parameters: indent (pretty-print), sort_keys, ensure_ascii, default (custom encoder)

Type Mapping

  • JSON object → Python dict, JSON array → Python list
  • JSON string → Python str, JSON number → int or float
  • JSON true/false → Python True/False, JSON null → None
  • Not supported: sets, tuples, datetime, custom classes — need custom encoder

Custom Encoders and Decoders

For complex objects, you can subclass json.JSONEncoder or use the default parameter:

  • default function: receives objects that can't be serialized, returns serializable version
  • object_hook function: called for every decoded JSON object — useful for custom deserialization
  • JSON Lines (.jsonl): one JSON object per line — ideal for streaming large datasets

Interview Insight

Know how to handle non-serializable types (datetime, custom classes) with custom encoders. Be prepared to explain the difference between json.load/loads and json.dump/dumps, and when to use JSON Lines for large datasets.

Use Cases

REST API communication — sending and receiving JSON data

Configuration files — app settings in human-readable format

Data storage — JSON Lines for large-scale log files and datasets

Inter-process communication — serializing data between services

Web scraping — parsing JSON responses from web APIs

Common Mistakes

Forgetting json.dump writes to file, json.dumps returns string — different functions

Not handling non-serializable types — datetime, set, and custom classes need encoders

Loading untrusted JSON without validation — can cause unexpected data types

Reading entire large JSON file into memory — use JSON Lines for streaming instead

Not using indent parameter during debugging — makes JSON unreadable in output