Python JSON Module - Simple Guide with Examples

What You'll Learn Here

What is JSON — the most common data format for APIs
loads() — parse JSON strings to Python objects
dumps() — convert Python objects to JSON strings
load() — read JSON from files
dump() — write JSON to files
Advanced options — pretty printing, custom serialization

What is JSON?

JSON (JavaScript Object Notation) is a lightweight data format that is easy for humans to read and write, and easy for machines to parse and generate. It's the most common format for sending data between servers and web applications.

Think of JSON like a universal translator for data. Python, JavaScript, Java, C#, and almost every programming language can understand JSON. It's the common language that allows different systems to talk to each other.

JSON looks a lot like Python dictionaries and lists, but with a few differences. It's perfect for storing and exchanging data.

💡 Key concept: JSON is the most common data format for APIs. Python's JSON module helps you convert between JSON and Python objects.

Parsing JSON - loads()

Convert JSON Strings to Python Objects

loads() (load string) converts a JSON string into a Python object.

# Parsing JSON - loads()

import json

print("=" * 50)
print("PARSING JSON - LOADS()")
print("=" * 50)

# ============================================================
# PARSE JSON STRING
# ============================================================

print("\n1. PARSE JSON STRING")

# JSON string
json_str = '{"name": "Alice", "age": 30, "city": "NYC"}'
print(f"   JSON string: {json_str}")

# Parse to Python dict
data = json.loads(json_str)
print(f"   Python dict: {data}")
print(f"   Type: {type(data)}")
print(f"   Name: {data['name']}")
print(f"   Age: {data['age']}")
print(f"   City: {data['city']}")


# ============================================================
# PARSE JSON ARRAY
# ============================================================

print("\n2. PARSE JSON ARRAY")

json_array = '[{"name": "Alice", "age": 30}, {"name": "Bob", "age": 25}]'
print(f"   JSON array: {json_array}")

data = json.loads(json_array)
print(f"   Python list: {data}")
print(f"   Type: {type(data)}")
print(f"   First person: {data[0]}")
print(f"   Second person: {data[1]}")


# ============================================================
# JSON TO PYTHON MAPPING
# ============================================================

print("\n3. JSON TO PYTHON MAPPING")

print("""
┌──────────────┬──────────────────────┐
│ JSON Type    │ Python Type          │
├──────────────┼──────────────────────┤
│ object       │ dict                 │
│ array        │ list                 │
│ string       │ str                  │
│ number       │ int or float         │
│ true         │ True                 │
│ false        │ False                │
│ null         │ None                 │
└──────────────┴──────────────────────┘
""")


# ============================================================
# COMPLEX JSON
# ============================================================

print("\n4. COMPLEX JSON")

complex_json = '''
{
    "name": "Alice",
    "age": 30,
    "is_active": true,
    "score": null,
    "skills": ["Python", "JavaScript", "SQL"],
    "address": {
        "street": "123 Main St",
        "city": "NYC",
        "zip": "10001"
    },
    "projects": [
        {"name": "Project A", "status": "active"},
        {"name": "Project B", "status": "completed"}
    ]
}
'''

data = json.loads(complex_json)
print(f"   Name: {data['name']}")
print(f"   Is active: {data['is_active']}")
print(f"   Skills: {data['skills']}")
print(f"   City: {data['address']['city']}")
print(f"   First project: {data['projects'][0]['name']}")

loads() key points:

loads() — parse JSON string to Python
JSON object — becomes Python dict
JSON array — becomes Python list
Types map — JSON types map to Python types

Quick Check: What function converts a JSON string to a Python object? (Answer: json.loads())

Creating JSON - dumps()

Convert Python Objects to JSON Strings

dumps() (dump string) converts a Python object into a JSON string.

# Creating JSON - dumps()

import json

print("=" * 50)
print("CREATING JSON - DUMPS()")
print("=" * 50)

# ============================================================
# BASIC CONVERSION
# ============================================================

print("\n1. BASIC CONVERSION")

# Python dict
data = {
    "name": "Alice",
    "age": 30,
    "city": "NYC",
    "is_active": True,
    "score": None
}

print(f"   Python dict: {data}")

# Convert to JSON
json_str = json.dumps(data)
print(f"   JSON string: {json_str}")
print(f"   Type: {type(json_str)}")


# ============================================================
# LIST TO JSON
# ============================================================

print("\n2. LIST TO JSON")

people = [
    {"name": "Alice", "age": 30},
    {"name": "Bob", "age": 25}
]

json_str = json.dumps(people)
print(f"   Python list: {people}")
print(f"   JSON string: {json_str}")


# ============================================================
# PRETTY PRINTING (indent)
# ============================================================

print("\n3. PRETTY PRINTING (indent)")

data = {
    "name": "Alice",
    "age": 30,
    "skills": ["Python", "JavaScript", "SQL"],
    "address": {
        "street": "123 Main St",
        "city": "NYC"
    }
}

# Without pretty printing
json_str = json.dumps(data)
print(f"   Without indent: {json_str}")

# With pretty printing
json_pretty = json.dumps(data, indent=2)
print(f"   With indent=2:")
print(json_pretty)

# With different indent size
json_pretty4 = json.dumps(data, indent=4)
print(f"\n   With indent=4:")
print(json_pretty4)


# ============================================================
# SORTING KEYS
# ============================================================

print("\n4. SORTING KEYS")

data = {"z": 1, "a": 2, "m": 3, "b": 4}

# Without sorting
print(f"   Without sort: {json.dumps(data)}")

# With sorting
print(f"   With sort: {json.dumps(data, sort_keys=True)}")


# ============================================================
# SEPARATORS - Compact JSON
# ============================================================

print("\n5. SEPARATORS")

data = {"name": "Alice", "age": 30}

# Default separators
print(f"   Default: {json.dumps(data)}")

# Compact separators (no spaces)
print(f"   Compact: {json.dumps(data, separators=(',', ':'))}")

dumps() key points:

dumps() — convert Python to JSON string
indent — pretty print with indentation
sort_keys — sort dictionary keys
separators — control spacing

Quick Check: What parameter makes JSON output readable? (Answer: indent)

Reading Files - load()

Read JSON from Files

load() reads JSON from a file and converts it to Python objects.

# Reading Files - load()

import json

print("=" * 50)
print("READING FILES - LOAD()")
print("=" * 50)

# ============================================================
# CREATE SAMPLE JSON FILE
# ============================================================

print("\n1. CREATE SAMPLE JSON FILE")

sample_data = {
    "users": [
        {"id": 1, "name": "Alice", "email": "alice@example.com"},
        {"id": 2, "name": "Bob", "email": "bob@example.com"}
    ],
    "total": 2
}

# Write sample data to file
with open("users.json", "w") as f:
    json.dump(sample_data, f, indent=2)

print("   Created users.json")


# ============================================================
# READ JSON FILE
# ============================================================

print("\n2. READ JSON FILE")

# Read the JSON file
with open("users.json", "r") as f:
    data = json.load(f)

print(f"   Data type: {type(data)}")
print(f"   Total users: {data['total']}")
print("   Users:")
for user in data['users']:
    print(f"      {user['id']}: {user['name']} ({user['email']})")


# ============================================================
# READ FROM API RESPONSE
# ============================================================

print("\n3. READ FROM API RESPONSE")

# Simulating an API response
api_response = '''
{
    "status": "success",
    "data": {
        "user": {
            "id": 123,
            "name": "Charlie",
            "email": "charlie@example.com"
        }
    }
}
'''

# Parse the response
response = json.loads(api_response)

if response['status'] == 'success':
    user = response['data']['user']
    print(f"   User: {user['name']} ({user['email']})")
else:
    print("   API call failed")

load() key points:

load() — reads JSON from file
Works with file objects — use with open()
Returns Python objects — dict, list, etc.

Quick Check: What function reads JSON from a file? (Answer: json.load())

Writing Files - dump()

Write JSON to Files

dump() writes Python objects to a file in JSON format.

# Writing Files - dump()

import json

print("=" * 50)
print("WRITING FILES - DUMP()")
print("=" * 50)

# ============================================================
# BASIC WRITE
# ============================================================

print("\n1. BASIC WRITE")

data = {
    "name": "Alice",
    "age": 30,
    "city": "NYC",
    "is_active": True
}

# Write to file
with open("user.json", "w") as f:
    json.dump(data, f)

print("   Written to user.json")

# Check the content
with open("user.json", "r") as f:
    content = f.read()
    print(f"   File content: {content}")


# ============================================================
# PRETTY PRINT WRITE
# ============================================================

print("\n2. PRETTY PRINT WRITE")

data = {
    "users": [
        {"id": 1, "name": "Alice"},
        {"id": 2, "name": "Bob"}
    ],
    "total": 2
}

# Write with indentation
with open("pretty_users.json", "w") as f:
    json.dump(data, f, indent=2)

print("   Written to pretty_users.json with indent")

# Show content
with open("pretty_users.json", "r") as f:
    print(f.read())


# ============================================================
# WRITE WITH SORTING
# ============================================================

print("\n3. WRITE WITH SORTING")

data = {"z": 1, "a": 2, "m": 3}

with open("sorted.json", "w") as f:
    json.dump(data, f, sort_keys=True, indent=2)

print("   Written to sorted.json with sorted keys")


# ============================================================
# UPDATE EXISTING FILE
# ============================================================

print("\n4. UPDATE EXISTING FILE")

# Read existing data
with open("users.json", "r") as f:
    data = json.load(f)

print(f"   Original users: {len(data['users'])}")

# Add new user
data['users'].append({"id": 3, "name": "Charlie", "email": "charlie@example.com"})
data['total'] = len(data['users'])

# Write back
with open("users.json", "w") as f:
    json.dump(data, f, indent=2)

print(f"   Updated users: {len(data['users'])}")

dump() key points:

dump() — writes JSON to file
indent — pretty print
sort_keys — sort dictionary keys
Read then write — to update existing files

Quick Check: What function writes JSON to a file? (Answer: json.dump())

Advanced Options

Custom Serialization and More

The JSON module has advanced options for handling special cases.

# Advanced JSON Options

import json
from datetime import datetime

print("=" * 50)
print("ADVANCED OPTIONS")
print("=" * 50)

# ============================================================
# CUSTOM ENCODER FOR DATETIME
# ============================================================

print("\n1. CUSTOM ENCODER FOR DATETIME")

class DateTimeEncoder(json.JSONEncoder):
    """Custom encoder for datetime objects"""
    def default(self, obj):
        if isinstance(obj, datetime):
            return obj.isoformat()
        return super().default(obj)

data = {
    "name": "Alice",
    "created": datetime.now()
}

# Without custom encoder (fails)
try:
    json.dumps(data)
except TypeError as e:
    print(f"   Without custom encoder: {e}")

# With custom encoder
json_str = json.dumps(data, cls=DateTimeEncoder)
print(f"   With custom encoder: {json_str}")


# ============================================================
# CUSTOM DECODER
# ============================================================

print("\n2. CUSTOM DECODER")

def custom_decoder(obj):
    """Custom decoder for datetime strings"""
    if '__datetime__' in obj:
        return datetime.fromisoformat(obj['__datetime__'])
    return obj

# Encode with datetime marker
data = {
    "name": "Alice",
    "created": {
        "__datetime__": datetime.now().isoformat()
    }
}

json_str = json.dumps(data)
print(f"   JSON: {json_str}")

# Decode with custom decoder
decoded = json.loads(json_str, object_hook=custom_decoder)
print(f"   Decoded: {decoded}")


# ============================================================
# HANDLING NON-SERIALIZABLE TYPES
# ============================================================

print("\n3. HANDLING NON-SERIALIZABLE TYPES")

# Using default parameter to handle custom objects
def default_serializer(obj):
    if isinstance(obj, datetime):
        return obj.isoformat()
    if hasattr(obj, '__dict__'):
        return obj.__dict__
    raise TypeError(f"Type {type(obj)} not serializable")

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

person = Person("Alice", 30)
data = {"person": person, "timestamp": datetime.now()}

json_str = json.dumps(data, default=default_serializer)
print(f"   Custom serialization: {json_str}")


# ============================================================
# SKIP NON-SERIALIZABLE VALUES
# ============================================================

print("\n4. SKIP NON-SERIALIZABLE VALUES")

class User:
    def __init__(self, name):
        self.name = name

data = {
    "name": "Alice",
    "user": User("Bob"),  # Non-serializable
    "age": 30
}

# Without skipping (fails)
try:
    json.dumps(data)
except TypeError as e:
    print(f"   Without skip: {e}")

# Not directly supported, use default to skip


# ============================================================
# COMPACT JSON FOR NETWORK TRANSFER
# ============================================================

print("\n5. COMPACT JSON FOR NETWORK TRANSFER")

data = {"name": "Alice", "age": 30, "city": "NYC"}

# Regular
regular = json.dumps(data)
print(f"   Regular: {regular} (length: {len(regular)})")

# Compact (no spaces)
compact = json.dumps(data, separators=(',', ':'))
print(f"   Compact: {compact} (length: {len(compact)})")

# Network transfer uses compact JSON to save bandwidth

Advanced options key points:

Custom encoder — handle datetime and custom objects
Custom decoder — convert JSON back to custom objects
default parameter — handle non-serializable types
Compact JSON — save bandwidth

Quick Check: How do you handle datetime objects in JSON? (Answer: Create a custom encoder that converts datetime to ISO format)

Real-World Example

Building a User API Client

# Real-World Example: User API Client

import json
import requests
from datetime import datetime

print("=" * 60)
print("USER API CLIENT")
print("=" * 60)

# ============================================================
# SAMPLE API RESPONSE (Simulated)
# ============================================================

api_response = '''
{
    "status": "success",
    "code": 200,
    "data": {
        "users": [
            {
                "id": 1,
                "name": "Alice Johnson",
                "email": "alice@example.com",
                "age": 30,
                "is_active": true,
                "joined": "2023-01-15T10:30:00",
                "preferences": {
                    "theme": "dark",
                    "notifications": true
                },
                "skills": ["Python", "JavaScript", "SQL"]
            },
            {
                "id": 2,
                "name": "Bob Smith",
                "email": "bob@example.com",
                "age": 25,
                "is_active": false,
                "joined": "2023-03-20T14:45:00",
                "preferences": {
                    "theme": "light",
                    "notifications": false
                },
                "skills": ["Java", "C++"]
            }
        ],
        "total": 2,
        "page": 1
    }
}
'''

# ============================================================
# PARSE API RESPONSE
# ============================================================

print("\n1. PARSING API RESPONSE")

response = json.loads(api_response)

print(f"   Status: {response['status']}")
print(f"   Code: {response['code']}")
print(f"   Total users: {response['data']['total']}")

users = response['data']['users']

for user in users:
    print(f"\n   User {user['id']}:")
    print(f"      Name: {user['name']}")
    print(f"      Email: {user['email']}")
    print(f"      Age: {user['age']}")
    print(f"      Active: {user['is_active']}")
    print(f"      Joined: {user['joined']}")
    print(f"      Theme: {user['preferences']['theme']}")
    print(f"      Skills: {', '.join(user['skills'])}")

# ============================================================
# PROCESS AND TRANSFORM DATA
# ============================================================

print("\n2. PROCESS AND TRANSFORM DATA")

def transform_user(user):
    """Transform user data for display"""
    return {
        "display_name": user['name'].upper(),
        "age_group": "Adult" if user['age'] >= 18 else "Minor",
        "active_status": "Active" if user['is_active'] else "Inactive",
        "skill_count": len(user['skills']),
        "joined_date": datetime.fromisoformat(user['joined']).strftime("%B %d, %Y")
    }

transformed_users = [transform_user(user) for user in users]

print("   Transformed users:")
for user in transformed_users:
    print(f"      {user}")

# ============================================================
# FILTER AND EXPORT
# ============================================================

print("\n3. FILTER AND EXPORT")

# Filter active users
active_users = [user for user in users if user['is_active']]
print(f"   Active users: {len(active_users)}")

# Create summary
summary = {
    "total": len(users),
    "active": len(active_users),
    "inactive": len(users) - len(active_users),
    "average_age": sum(u['age'] for u in users) / len(users),
    "exported_at": datetime.now().isoformat()
}

print(f"   Summary: {json.dumps(summary, indent=2)}")

# ============================================================
# EXPORT TO JSON FILE
# ============================================================

print("\n4. EXPORT TO JSON FILE")

# Save transformed data
output = {
    "summary": summary,
    "users": transformed_users
}

with open("exported_users.json", "w") as f:
    json.dump(output, f, indent=2)

print("   Exported to exported_users.json")

# ============================================================
# READ BACK
# ============================================================

print("\n5. READ BACK FROM FILE")

with open("exported_users.json", "r") as f:
    data = json.load(f)

print(f"   Read back: {len(data['users'])} users")
print(f"   Summary: {data['summary']}")

print("\n" + "=" * 60)
print("KEY TAKEAWAYS:")
print("=" * 60)
print("""
- JSON is the universal data format
- loads/dumps for string conversion
- load/dump for file operations
- Custom serialization for special types
- Perfect for API data processing
""")

Real-world example key points:

Parse API responses — load JSON from APIs
Transform data — process JSON data
Filter and summarize — extract useful information
Export — save processed data as JSON

Quick Check: What's the most common use of JSON in Python? (Answer: Working with API responses)

Best Practices

Using JSON Effectively

# Best Practices for JSON

import json
from datetime import datetime

print("=" * 60)
print("BEST PRACTICES FOR JSON")
print("=" * 60)

# ============================================================
# 1. USE INDENT FOR READABILITY
# ============================================================

print("\n1. USE INDENT FOR READABILITY")

data = {"name": "Alice", "age": 30, "city": "NYC"}

# Good - readable
pretty = json.dumps(data, indent=2)
print(f"   With indent: {pretty}")

# Bad - hard to read
ugly = json.dumps(data)
print(f"   Without indent: {ugly}")

# ============================================================
# 2. VALIDATE JSON BEFORE PARSING
# ============================================================

print("\n2. VALIDATE JSON BEFORE PARSING")

def safe_json_loads(json_str):
    try:
        return json.loads(json_str)
    except json.JSONDecodeError as e:
        print(f"   Invalid JSON: {e}")
        return None

# Valid JSON
valid = '{"name": "Alice"}'
result = safe_json_loads(valid)
print(f"   Valid: {result}")

# Invalid JSON
invalid = '{"name": "Alice",}'  # Trailing comma
result = safe_json_loads(invalid)
print(f"   Invalid: {result}")

# ============================================================
# 3. USE WITH STATEMENT FOR FILES
# ============================================================

print("\n3. USE WITH STATEMENT FOR FILES")

# Good - automatically closes file
with open("data.json", "w") as f:
    json.dump({"key": "value"}, f)

with open("data.json", "r") as f:
    data = json.load(f)
    print(f"   Read data: {data}")

# Bad - manual close (error-prone)
# f = open("data.json", "w")
# json.dump({"key": "value"}, f)
# f.close()  # Easy to forget!

# ============================================================
# 4. HANDLE ENCODING
# ============================================================

print("\n4. HANDLE ENCODING")

# Use UTF-8 encoding for compatibility
with open("data.json", "w", encoding="utf-8") as f:
    json.dump({"name": "Alice"}, f)

with open("data.json", "r", encoding="utf-8") as f:
    data = json.load(f)
    print(f"   With encoding: {data}")

# ============================================================
# 5. USE CUSTOM ENCODERS FOR NON-STANDARD TYPES
# ============================================================

print("\n5. USE CUSTOM ENCODERS")

class DateTimeEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, datetime):
            return obj.isoformat()
        return super().default(obj)

data = {"created": datetime.now()}
json_str = json.dumps(data, cls=DateTimeEncoder)
print(f"   Custom encoder: {json_str}")

# ============================================================
# 6. SUMMARY
# ============================================================

print("\n" + "=" * 60)
print("BEST PRACTICES SUMMARY")
print("=" * 60)
print("""
- Use indent for readable JSON
- Validate JSON before parsing
- Use with statements for files
- Specify UTF-8 encoding
- Use custom encoders for special types
- Handle JSONDecodeError exceptions
- Use compact JSON for network transfer
""")

Best practices summary:

Use indent — for readability
Validate JSON — handle parsing errors
Use with — for file operations
Encoding — use UTF-8
Custom encoders — for special types

Quick Check: How should you handle JSON parsing errors? (Answer: Use try/except with json.JSONDecodeError)

Try It Yourself

Experiment with JSON in the editor below.

Loading Pyodide... 0%

Python Code Editor

# Python JSON - Practice

import json

print("=" * 50)
print("JSON - PRACTICE")
print("=" * 50)

# ============================================================
# 1. PARSE JSON STRING
# ============================================================

print("\n1. PARSE JSON STRING")

json_str = '{"name": "Alice", "age": 30, "city": "NYC"}'
data = json.loads(json_str)
print(f"   Parsed: {data}")
print(f"   Name: {data['name']}")

# ============================================================
# 2. CONVERT TO JSON
# ============================================================

print("\n2. CONVERT TO JSON")

python_data = {
    "name": "Bob",
    "age": 25,
    "skills": ["Python", "Java"],
    "is_active": True
}

json_str = json.dumps(python_data, indent=2)
print(f"   JSON:\n{json_str}")

# ============================================================
# 3. READ FROM FILE
# ============================================================

print("\n3. READ FROM FILE")

# Create file
sample = {"users": [{"id": 1, "name": "Alice"}]}
with open("sample.json", "w") as f:
    json.dump(sample, f)

# Read file
with open("sample.json", "r") as f:
    data = json.load(f)
    print(f"   Read: {data}")

# ============================================================
# 4. PRETTY PRINT
# ============================================================

print("\n4. PRETTY PRINT")

data = {"name": "Alice", "age": 30, "address": {"city": "NYC", "zip": "10001"}}

# Without pretty
print(f"   Without: {json.dumps(data)}")

# With pretty
print(f"   With indent:\n{json.dumps(data, indent=2)}")

# ============================================================
# 5. SUMMARY
# ============================================================

print("\n" + "=" * 50)
print("SUMMARY")
print("=" * 50)
print("""
JSON — KEY FUNCTIONS

1. loads(): JSON string → Python object
2. dumps(): Python object → JSON string
3. load(): JSON file → Python object
4. dump(): Python object → JSON file
5. indent: Pretty print
6. sort_keys: Sort keys

WHEN TO USE:
• APIs (loads/dumps)
• Configuration files (load/dump)
• Data exchange (JSON format)
• Storing structured data
""")

==================================================
JSON - PRACTICE
==================================================

1. PARSE JSON STRING
Parsed: {'name': 'Alice', 'age': 30, 'city': 'NYC'}
Name: Alice

2. CONVERT TO JSON
JSON:
{
"name": "Bob",
"age": 25,
"skills": [
"Python",
"Java"
],
"is_active": true
}

3. READ FROM FILE
Read: {'users': [{'id': 1, 'name': 'Alice'}]}

4. PRETTY PRINT
Without: {"name": "Alice", "age": 30, "address": {"city": "NYC", "zip": "10001"}}
With indent:
{
"name": "Alice",
"age": 30,
"address": {
"city": "NYC",
"zip": "10001"
}
}

🏆

You've Got It!

You now understand the JSON module in Python. You know how to parse JSON, create JSON, read and write files, and handle advanced cases.

Quick Quiz

Test what you've learned:

1. What function parses a JSON string?

A) json.load() B) json.loads() C) json.dump() D) json.dumps()

2. What function converts Python dict to JSON string?

A) json.load() B) json.loads() C) json.dump() D) json.dumps()

3. What parameter makes JSON output readable?

A) pretty B) indent C) readable D) format

4. What exception is raised for invalid JSON?

A) ValueError B) json.JSONDecodeError C) TypeError D) KeyError

5. Which function reads JSON from a file?

A) json.load() B) json.loads() C) json.dump() D) json.dumps()

Frequently Asked Questions

What is JSON in Python?

JSON (JavaScript Object Notation) is a lightweight data format. Python's json module provides functions to parse JSON strings to Python objects and convert Python objects to JSON strings.

What's the difference between load() and loads()?

load() reads JSON from a file object. loads() reads JSON from a string. Use load() when you have a file, loads() when you have a string (like from an API response).

How do I handle datetime in JSON?

Create a custom JSON encoder that converts datetime to ISO format. Use the cls parameter in json.dumps(). For decoding, use object_hook in json.loads().

Can JSON store Python objects?

JSON only supports basic types: objects (dict), arrays (list), strings, numbers, booleans, and null. For custom objects, you need to convert them to dict first or use a custom encoder.

How do I handle large JSON files?

For large files, use json.load() with a file object instead of loading everything into memory at once. You can also use ijson for streaming parsing of very large JSON files.

What's the most common use of JSON in Python?

The most common use is parsing API responses. Most web APIs return data in JSON format, and Python's json module makes it easy to work with that data.

Where to Go From Here

Now that you understand the JSON module, check out these related topics:

Previous: Datetime Next: OS →

Python

Python: JSON Module

What is JSON?

Parsing JSON - loads()

Convert JSON Strings to Python Objects

Creating JSON - dumps()

Convert Python Objects to JSON Strings

Reading Files - load()

Read JSON from Files

Writing Files - dump()

Write JSON to Files

Advanced Options

Custom Serialization and More

Real-World Example

Building a User API Client

Best Practices

Using JSON Effectively

Try It Yourself

You've Got It!

Quick Quiz

Frequently Asked Questions

What is JSON in Python?

What's the difference between load() and loads()?

How do I handle datetime in JSON?

Can JSON store Python objects?

How do I handle large JSON files?

What's the most common use of JSON in Python?

Where to Go From Here

OS Module

Sys Module

Datetime Module

Python

Python: JSON Module

What is JSON?

Parsing JSON - loads()

Convert JSON Strings to Python Objects

Creating JSON - dumps()

Convert Python Objects to JSON Strings

Reading Files - load()

Read JSON from Files

Writing Files - dump()

Write JSON to Files

Advanced Options

Custom Serialization and More

Real-World Example

Building a User API Client

Best Practices

Using JSON Effectively

Try It Yourself

You've Got It!

Quick Quiz

Frequently Asked Questions

What is JSON in Python? ▼

What's the difference between load() and loads()? ▼

How do I handle datetime in JSON? ▼

Can JSON store Python objects? ▼

How do I handle large JSON files? ▼

What's the most common use of JSON in Python? ▼

Where to Go From Here

OS Module

Sys Module

Datetime Module

What is JSON in Python?

What's the difference between load() and loads()?

How do I handle datetime in JSON?

Can JSON store Python objects?

How do I handle large JSON files?

What's the most common use of JSON in Python?