# Efficient data processing pattern
import pandas as pd
import numpy as np

# Read data in chunks for large files
chunk_size = 10000
processed_data = []

for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size):
    # Process each chunk
    chunk_processed = chunk.groupby('category').agg({
        'value': 'sum',
        'count': 'size'
    })
    processed_data.append(chunk_processed)

# Combine results
final_result = pd.concat(processed_data).groupby(level=0).sum()
final_result.to_csv('output.csv')

# Install required packages at runtime
import subprocess
import sys

def install_package(package):
    subprocess.check_call([sys.executable, '-m', 'pip', 'install', package])

# Install packages as needed
required_packages = ['scikit-learn', 'seaborn']
for package in required_packages:
    install_package(package)

# Now use the packages
import sklearn
import seaborn as sns

import logging
import traceback

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

try:
    # Your code here
    result = process_data()
    logger.info(f"Processing completed successfully: {len(result)} records")
    
except MemoryError:
    logger.error("Memory limit exceeded. Try processing in smaller chunks.")
    raise
except TimeoutError:
    logger.error("Execution timeout. Optimize code or request more time.")
    raise
except Exception as e:
    logger.error(f"Unexpected error: {str(e)}")
    logger.error(traceback.format_exc())
    raise

# Workflow: Upload → Process → Download
# 1. File uploaded via file system tools
input_file = 'uploaded_data.csv'

# 2. Process with code execution
data = pd.read_csv(input_file)
processed = perform_analysis(data)
processed.to_csv('results.csv')

# 3. Download results via file system tools
output_file = 'results.csv'

# Workflow: Web Search → Process → Generate Report
# 1. Data collected via web tools
search_results = 'search_data.json'

# 2. Process with code execution
with open(search_results, 'r') as f:
    data = json.load(f)

analysis = analyze_web_data(data)
generate_insights(analysis)

# 3. Results ready for document generation