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of 30What are the key differences between `os.path` and `pathlib` modules for file operations?
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The os.path module uses string-based operations while pathlib provides object-oriented path handling. pathlib is more modern, readable, and cross-platform compatible.
Key differences:
- os.path: String-based, requires multiple function calls
- pathlib: Object-oriented, chainable methods, more intuitive
# os.path approach
import os
path = os.path.join('home', 'user', 'documents', 'file.txt')
if os.path.exists(path):
size = os.path.getsize(path)
# pathlib approach
from pathlib import Path
path = Path('home') / 'user' / 'documents' / 'file.txt'
if path.exists():
size = path.stat().st_size
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How would you recursively find all files with a specific extension in a directory tree?
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You can use os.walk(), glob.glob(), or pathlib.Path.rglob(). Each has different advantages for automation scripts.
# Using pathlib (recommended)
from pathlib import Path
def find_files_by_extension(directory, extension):
return list(Path(directory).rglob(f'*.{extension}'))
# Using glob
import glob
files = glob.glob('/path/**/*.py', recursive=True)
# Using os.walk
import os
def find_files_walk(directory, extension):
files = []
for root, dirs, filenames in os.walk(directory):
for filename in filenames:
if filename.endswith(f'.{extension}'):
files.append(os.path.join(root, filename))
return files
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Explain the difference between text and binary file modes. When would you use each?
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Text mode handles encoding/decoding and line endings automatically, while binary mode works with raw bytes. Choice depends on file content and automation requirements.
- Text mode ('r', 'w'): For human-readable files (logs, configs, CSVs)
- Binary mode ('rb', 'wb'): For executables, images, encrypted files, or when exact byte control is needed
# Text mode - automatic encoding handling
with open('config.txt', 'r', encoding='utf-8') as f:
content = f.read() # Returns string
# Binary mode - raw bytes
with open('backup.tar.gz', 'rb') as f:
content = f.read() # Returns bytes
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How do you monitor system resources (CPU, memory, disk) using Python?
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Use psutil for comprehensive system monitoring. It provides cross-platform system information essential for automation monitoring.
import psutil
def get_system_info():
return {
'cpu_percent': psutil.cpu_percent(interval=1),
'memory': psutil.virtual_memory()._asdict(),
'disk': psutil.disk_usage('/')._asdict(),
'network': psutil.net_io_counters()._asdict()
}
# Monitor with thresholds
def check_system_health():
cpu = psutil.cpu_percent(interval=1)
memory = psutil.virtual_memory().percent
alerts = []
if cpu > 80:
alerts.append(f"High CPU usage: {cpu}%")
if memory > 85:
alerts.append(f"High memory usage: {memory}%")
return alerts
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How do you parse and analyze log files efficiently in Python?
How do you make HTTP requests reliably in automation scripts?
How would you check network connectivity and service availability in automation scripts?
How do you handle configuration files in Python automation scripts?
How do you implement scheduled tasks in Python?
How do you safely handle file operations in automation scripts to prevent resource leaks?
What's the difference between `os.system()`, `subprocess.run()`, and `subprocess.Popen()`?
How do you monitor and manage running processes programmatically?
How would you implement a process timeout in a Python automation script?
How would you create a system health monitoring script that sends alerts?
How do you monitor log files in real-time for automation purposes?
How would you implement log rotation in a Python application?
How do you handle API rate limiting in automation scripts?
How do you securely handle sensitive configuration data?
How do you implement comprehensive error handling in automation scripts?
How do you handle timeouts and retries in automation scripts?
How do you implement caching in automation scripts?
How do you implement secure practices in Python automation scripts?
How do you validate and sanitize user inputs in automation scripts?
How do you build and consume REST APIs for automation purposes?
How do you handle database operations in Python automation scripts?
How would you implement a robust task queue system?
How do you optimize Python scripts for large-scale automation tasks?
How do you automate cloud infrastructure using Python?
How do you implement infrastructure monitoring and alerting?
How do you implement automated deployment and rollback strategies?
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Python for Automation cheatsheet
Python System Administration Automation Cheat Sheet
- Summary01
- Essential Libraries02
- File System Operations03
- Process Management04
- System Information05
- Network Operations06
- Configuration Management07
- Log Processing08
- Task Scheduling09
- SSH and Remote Operations10
- Database Operations11
- Best Practices12
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