everfern-ai/everfern/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']

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())

import json
data = {
    'name': 'John',
    'age': 30,
    'email': 'john@example.com',
    '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)

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

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

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')

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)

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)

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)

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)

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)

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')

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)