json

name	json
description	Use this skill any time a JSON (JavaScript Object Notation) file is the primary input or output. This includes tasks to: read, parse, validate, analyze, or modify JSON files; create new JSON files; merge or transform JSON structures; extract data from JSON; convert data to JSON format. If the user mentions a JSON file by name, extension, or path (.json), use this skill.

JSON File Handling in EverFern

Overview

Users may ask you to read, analyze, create, or modify JSON files. Use Python with built-in json module or pandas for structured data. Use absolute Windows paths (e.g., C:\Users\Username\Downloads\data.json).

Reading JSON Files

Read and Parse JSON

import json

# Read JSON file
with open(r'C:\path\to\file.json', 'r') as f:
    data = json.load(f)

# Print formatted JSON
print(json.dumps(data, indent=2))

# Get specific values
value = data['key']
nested_value = data['parent']['child']

Read with pandas (for tabular JSON)

import pandas as pd

# Read JSON file as DataFrame
df = pd.read_json(r'C:\path\to\file.json')

# Preview data
print(df.head())
print(df.info())

Writing JSON Files

Write Python Objects as JSON

import json

data = {
    'name': 'John',
    'age': 30,
    'email': '[e-mail]',
    'hobbies': ['reading', 'coding', 'gaming']
}

# Write to file (formatted)
with open(r'C:\path\to\output.json', 'w') as f:
    json.dump(data, f, indent=2)

# Write minified (compact)
with open(r'C:\path\to\output_minified.json', 'w') as f:
    json.dump(data, f)

Parsing and Transforming JSON

Parse JSON String

import json

json_string = '{"name": "Alice", "age": 25}'
data = json.loads(json_string)  # Parse string
print(data['name'])

Convert JSON to String

json_string = json.dumps(data, indent=2)
print(json_string)

Validate JSON

import json

def validate_json(json_file):
    try:
        with open(json_file, 'r') as f:
            json.load(f)
        print("✓ Valid JSON")
        return True
    except json.JSONDecodeError as e:
        print(f"✗ Invalid JSON: {e}")
        return False

validate_json(r'C:\path\to\file.json')

Data Transformation

Extract and Transform Data

import json

# Read JSON
with open(r'C:\path\to\input.json', 'r') as f:
    data = json.load(f)

# Transform (example: extract specific fields)
transformed = [
    {'id': item['id'], 'name': item['name']}
    for item in data
]

# Write transformed data
with open(r'C:\path\to\output.json', 'w') as f:
    json.dump(transformed, f, indent=2)

Flatten Nested JSON

import json
import pandas as pd

with open(r'C:\path\to\nested.json', 'r') as f:
    data = json.load(f)

# Flatten to DataFrame then back to JSON
df = pd.json_normalize(data)
flattened = json.loads(df.to_json(orient='records'))

with open(r'C:\path\to\flattened.json', 'w') as f:
    json.dump(flattened, f, indent=2)

Merge Multiple JSON Files

import json

files = [
    r'C:\path\to\file1.json',
    r'C:\path\to\file2.json',
    r'C:\path\to\file3.json'
]

merged = []
for file in files:
    with open(file, 'r') as f:
        merged.extend(json.load(f))

with open(r'C:\path\to\merged.json', 'w') as f:
    json.dump(merged, f, indent=2)

Query and Filter JSON

Filter Arrays in JSON

import json

with open(r'C:\path\to\file.json', 'r') as f:
    data = json.load(f)

# Filter: get items where age > 25
filtered = [item for item in data if item['age'] > 25]

with open(r'C:\path\to\filtered.json', 'w') as f:
    json.dump(filtered, f, indent=2)

Group JSON Data

import json
from itertools import groupby

with open(r'C:\path\to\file.json', 'r') as f:
    data = json.load(f)

# Group by category
grouped = {}
for item in data:
    category = item['category']
    if category not in grouped:
        grouped[category] = []
    grouped[category].append(item)

with open(r'C:\path\to\grouped.json', 'w') as f:
    json.dump(grouped, f, indent=2)

Special Cases

Handle Large JSON Files

import json

# Process in chunks
def process_large_json(file_path, chunk_size=1000):
    with open(file_path, 'r') as f:
        data = json.load(f)

    for i in range(0, len(data), chunk_size):
        chunk = data[i:i + chunk_size]
        # Process chunk
        print(f"Processing {len(chunk)} items...")

        # Save chunk results
        with open(f'output_{i}.json', 'w') as out:
            json.dump(chunk, out)

process_large_json(r'C:\path\to\large_file.json')

Add UTF-8 BOM (for Excel compatibility)

import json

with open(r'C:\path\to\file.json', 'w', encoding='utf-8-sig') as f:
    json.dump(data, f, indent=2, ensure_ascii=False)

Tips and Best Practices

• Always validate: Use try/except with json.JSONDecodeError when reading untrusted JSON
• Pretty print: Use indent=2 or indent=4 for human-readable output
• Encoding: Always specify encoding='utf-8' for Windows compatibility
• Escape characters: Use ensure_ascii=False to preserve non-ASCII characters
• Large files: For very large JSON, consider streaming or chunking
• Comments: JSON doesn't support comments; use separate .jsonc files if comments are needed