JSON to Python Converter
Convert JSON data to Python data structures
JSON Input
Python Output
Python code will appear here
Paste JSON in the input area to get started
From JSON Data to Pythonic Dataclasses
Python's dataclasses provide the perfect balance between simplicity and functionality. Converting JSON to Python dataclasses with type hints gives you the benefits of static typing while maintaining Python's elegant syntax and dynamic capabilities.
For telecom data science projects, network automation scripts, or API integrations, properly typed Python classes enable better IDE support, catch errors early, and make your code more maintainable - especially important for complex telecom data processing workflows.
🐍 Pythonic power:
Dataclasses with type hints give you IDE autocomplete, mypy type checking, and cleaner code!
JSON Input
Network analytics data:
{
"analysisId": "ANALYSIS_2024_001",
"networkName": "5G_Metro_Network",
"isCompleted": true,
"totalDataProcessed": 15.7,
"analysisTimestamp": "2024-01-15T16:45:00Z",
"regions": [
{
"regionId": "REGION_001",
"name": "Downtown Core",
"totalCells": 45,
"avgThroughput": 892.5,
"peakHours": [8, 9, 17, 18, 19]
},
{
"regionId": "REGION_002",
"name": "Business District",
"totalCells": 32,
"avgThroughput": 1205.8,
"peakHours": [9, 10, 11, 14, 15]
}
],
"summary": {
"totalCells": 77,
"overallThroughput": 1049.15,
"recommendedUpgrades": 3
}
}Complex analytics data structure
Generated Python Dataclasses
Type-safe Python classes:
from dataclasses import dataclass
from typing import List, Optional
from datetime import datetime
import json
@dataclass
class NetworkAnalysis:
analysis_id: str
network_name: str
is_completed: bool
total_data_processed: float
analysis_timestamp: datetime
regions: List['Region']
summary: 'Summary'
@classmethod
def from_json(cls, json_str: str) -> 'NetworkAnalysis':
data = json.loads(json_str)
return cls(
analysis_id=data['analysisId'],
network_name=data['networkName'],
is_completed=data['isCompleted'],
total_data_processed=data['totalDataProcessed'],
analysis_timestamp=datetime.fromisoformat(
data['analysisTimestamp'].replace('Z', '+00:00')
),
regions=[
Region.from_dict(region)
for region in data['regions']
],
summary=Summary.from_dict(data['summary'])
)
@dataclass
class Region:
region_id: str
name: str
total_cells: int
avg_throughput: float
peak_hours: List[int]
@classmethod
def from_dict(cls, data: dict) -> 'Region':
return cls(
region_id=data['regionId'],
name=data['name'],
total_cells=data['totalCells'],
avg_throughput=data['avgThroughput'],
peak_hours=data['peakHours']
)
@dataclass
class Summary:
total_cells: int
overall_throughput: float
recommended_upgrades: int
@classmethod
def from_dict(cls, data: dict) -> 'Summary':
return cls(
total_cells=data['totalCells'],
overall_throughput=data['overallThroughput'],
recommended_upgrades=data['recommendedUpgrades']
)Clean, type-safe Python code! ✨
When Python Code Generation Accelerates Data Work
Data Science & Analytics
Processing telecom data with pandas, NumPy, or scikit-learn? Typed dataclasses provide structure for complex datasets and enable better data validation.
API Development
Building FastAPI or Django REST APIs? Dataclasses work seamlessly with Pydantic for automatic request/response validation and OpenAPI documentation generation.
Network Automation
Automating network configuration or monitoring with Python scripts? Structured classes make it easier to handle complex network device responses and configurations.
Machine Learning
Training ML models on telecom data? Dataclasses help structure training data, model parameters, and prediction results with proper type safety.
🔮 Python magic:
Combine dataclasses with Pydantic for automatic validation, serialization, and JSON Schema generation!