Python Basics for DevOps Engineers with Examples

🚀 Python for DevOps – Triggering Jenkins Jobs via API Explored how to automate CI/CD by triggering Jenkins jobs using Python and APIs — a key real-world DevOps capability. 📂 Use Case: Instead of manually clicking “Build Now” in Jenkins, we can trigger jobs programmatically using APIs. 💻 Python Script: import requests jenkins_url = "http://your-jenkins-url/job/your-job-name/build" username = "your-username" api_token = "your-api-token" response = requests.post(jenkins_url, auth=(username, api_token)) if response.status_code == 201: print("✅ Jenkins job triggered successfully") else: print("❌ Failed to trigger job:", response.status_code) 🔐 Handling CSRF (Crumb Token): crumb_url = f"{jenkins_url}/crumbIssuer/api/json" crumb_data = requests.get(crumb_url, auth=(username, api_token)).json() headers = { crumb_data['crumbRequestField']: crumb_data['crumb'] } ⚙️ Trigger Job with Parameters: params = { "ENV": "prod", "VERSION": "1.0" } response = requests.post( "http://your-jenkins-url/job/your-job-name/buildWithParameters", params=params, auth=("user", "token") ) 🔍 What this enables: Automate CI/CD pipelines Trigger builds from scripts or monitoring tools Integrate Jenkins with other systems Reduce manual intervention 🔥 Why this matters in DevOps: Automation is the backbone of DevOps. Using APIs, we can connect tools and build end-to-end automated workflows. 💡 Key Learning: Jenkins + APIs + Python = powerful combination for pipeline automation and integration. 📈 Next Steps: Trigger Jenkins from log monitoring script Send build status to Slack Integrate with cloud deployments (AWS) #DevOps #Jenkins #Python #Automation #CICD #API #Cloud #Scripting #Learning #100DaysOfCode

“How much Python should a DevOps Engineer know?” 🤔 After working through different DevOps tasks, one thing became clear: It’s not about how much Python you know… It’s about how effectively you can use it in real scenarios. Here’s how I see it 👇 💡 Core Skills (Non-negotiable) ✔ Writing clean scripts ✔ Working with files & logs ✔ Handling errors properly 👉 This is the foundation for automation 💡 Practical DevOps Usage ✔ Calling APIs (cloud / tools) ✔ Parsing JSON & YAML ✔ Automating workflows 👉 This is where Python becomes powerful 💡 Advanced Usage (Context-driven) ✔ Building CLI tools ✔ Writing reusable modules ✔ Optimizing scripts for scale 👉 Needed when you're solving larger problems ⚡ Key Insight: In DevOps, Python is not a goal… 👉 It’s a tool to automate, integrate, and scale systems 🚀 For hands-on practice, I found this repo really useful: Check out Abhishek Veeramalla's work 🫡 : 👉 https://lnkd.in/dTqaK8fQ 🧠 Final Thought: Strong DevOps engineers don’t just “know Python”… They use it to eliminate manual work and improve systems How are you using Python in your DevOps workflow? #DevOps #Python #Automation #Cloud #Learning #Engineering

🚀 Python for DevOps – Automating Disk Monitoring with subprocess As part of my DevOps learning, I worked on automating system monitoring using Python’s subprocess module. Instead of manually checking disk usage, I built a simple script to monitor it and trigger alerts. 🔧 Here’s the code: import subprocess Run disk usage command result = subprocess.run(["df", "-h", "/"], capture_output=True, text=True) Print full output print(result.stdout) Parse and check usage lines = result.stdout.split("\n") for line in lines[1:]: if line: usage = line.split()[4] # Extract usage % print(f"Disk Usage: {usage}") if int(usage.replace("%","")) > 80: print("⚠️ ALERT: Disk usage is above 80%!") Output: ubuntu@satheesha:~/python$ python3 import-subprocess.py Disk Usage: 3% 💡 Key Learnings: ✔️ subprocess helps automate Linux commands using Python ✔️ Useful for real-time monitoring and automation ✔️ Can be extended to trigger alerts, emails, or restart services 🚀 Real DevOps Use Cases: System health monitoring Auto-alerting when resources are high Integrating with cron jobs Automating routine checks Small automation like this can save a lot of manual effort in production environments. #Python #DevOps #Automation #Linux #Scripting #Cloud #Learning #Monitoring

🚀 Python for DevOps – API Monitoring with requests Practiced using Python’s requests library to check API health, a common real-world DevOps task. 📂 Use Case: In production, services depend on APIs. We need to continuously verify if APIs are reachable and healthy. 💻 Python Script: import requests url = "https://api.github.com" try: res = requests.get(url, timeout=5) if res.status_code == 200: print("✅ GitHub API is UP") else: print("⚠️ GitHub API issue:", res.status_code) except requests.exceptions.RequestException as e: print("🚨 API call failed:", e) Output: Status_code: 200 Response: {'current_user_url': 'https://lnkd.in/guvkNT7k', 'current_user_authorizations_html_url': 'https://lnkd.in/gx-65ERd', 'authorizations_url': 'https://lnkd.in/gzcehbTu', 'code_search_url': 'https://lnkd.in/gQU8cghE', 'commit_search_url': 'https://lnkd.in/g62A-__n', 'emails_url': 'https://lnkd.in/gXaZyEkK', 'emojis_url': 'https://lnkd.in/gp3Scn2Y', 'events_url': 'https://lnkd.in/grbt4NNg', 'feeds_url': 'https://lnkd.in/gCBk-eSN', 'followers_url': 'https://lnkd.in/gQvSEXqB', 'following_url': 'https://lnkd.in/grh4YDpJ', 🔍 What this does: Sends HTTP request to API Uses timeout to avoid hanging Checks response status Handles failures gracefully 🔥 Why this matters in DevOps: Monitor service availability Validate endpoints in CI/CD pipelines Detect outages early Automate health checks 💡 Key Learning: APIs are everywhere in DevOps, and Python makes it easy to integrate, monitor, and automate systems. 📈 Next Steps: Send alerts (Slack/Email) if API fails Combine with log monitoring scripts Build a full monitoring + alerting system #Python #DevOps #API #Automation #Monitoring #Scripting #Cloud #Learning #100DaysOfCode

🚀 Python for DevOps – Stop Learning, Start Automating Most people learn Python… But very few use it the right way in DevOps. Here’s the truth 👇 👉 You don’t need deep theory. 👉 You need practical automation skills. 🔹 What to Focus On (DevOps Style) ✔ Variables, loops, conditions ✔ Functions ✔ File handling (logs, configs) ✔ Error handling (try/except) ✔ Key modules: os → system operations subprocess → run shell commands json / yaml → config management 🔧 Real Example: Run Linux Command Using Python import subprocess result = subprocess.run(["df", "-h", "/"], capture_output=True, text=True) lines = result.stdout.strip().split("\n") if len(lines) > 1:     parts = lines[1].split()     usage = int(parts[4].replace("%", ""))     if usage > 80:         print(f"🚨 ALERT: Disk usage is {usage}%")     else:         print(f"✅ OK: Disk usage is {usage}%") else:     print("❌ Unexpected output:", result.stdout) Output: ubuntu@satheesha:~/python$ python3 Disk-Usage.py ✅ OK: Disk usage is 3% 💡 What This Shows ✔ You can interact with the OS ✔ You can parse real-time system data ✔ You can build automation scripts ✔ You think like a DevOps Engineer 🎯 How I Explain This in Interviews “I use Python’s subprocess module to execute system commands, parse outputs, and automate monitoring tasks like disk usage alerts.” 🔥 Pro Tip Take it one step further: Send alerts to Slack/Email 📩 Schedule with cron ⏰ Integrate with Jenkins 🔁 💬 If you're learning DevOps, stop just writing scripts… Start solving real problems. #DevOps #Python #Automation #Linux #SRE #Cloud #Jenkins #Learning

🚀 Python for DevOps – Log Level Automation Project Today I built a practical DevOps script using Python to analyze logs and separate them based on log levels. 📂 Problem: Manually checking logs is time-consuming. Needed a way to automatically filter and organize logs. 💻 Solution (Python Script): with open("app.log") as f, \ open("error.log", "w") as err, \ open("warning.log", "w") as warn, \ open("info.log", "w") as info: for line in f: if "ERROR" in line: err.write(line) elif "WARNING" in line: warn.write(line) elif "INFO" in line: info.write(line) ####################################### Output: ubuntu@satheesha:~/python$ python3 multiple-log_level.py ubuntu@satheesha:~/python$ ls -ltr error.log warning.log info.log -rw-r--r-- 1 ubuntu ubuntu 18 Apr 21 07:45 warning.log -rw-r--r-- 1 ubuntu ubuntu 44 Apr 21 07:45 info.log -rw-r--r-- 1 ubuntu ubuntu 17 Apr 21 07:45 error.log ubuntu@satheesha:~/python$ cat app.log INFO: Service startes WARNING: High CPU INFO: Service startes ERROR: Disk full ubuntu@satheesha:~/python$ cat error.log ERROR: Disk full ubuntu@satheesha:~/python$ cat warning.log WARNING: High CPU ubuntu@satheesha:~/python$ cat info.log INFO: Service startes INFO: Service startes ######################################### does: Reads app.log Filters logs into: error.log warning.log info.log 📊 Outcome: Faster troubleshooting Organized logs for better monitoring Reduced manual effort 🔥 Real DevOps Use Cases: Production log monitoring CI/CD pipeline validation Incident detection and alerting 💡 Key Learning: Python is a powerful tool for automation in DevOps, especially for handling logs and system data. 📈 Next Step: Enhancing this script to: Count log levels Trigger alerts (email/Slack) Monitor logs in real-time (tail -f style) #Python #DevOps #Automation #Scripting #Cloud #Learning #100DaysOfCode

🚀 Python Basics for DevOps Engineers (Practical Examples) Python is a powerful tool for automation in DevOps. Here’s a quick guide to essential data types with real-world use cases 👇 🔹 1. String (str) Used for text (server names, logs, messages) server = "web-server-1" print(server) 💡 DevOps Example: log = "ERROR: Disk full" if "ERROR" in log: print("Issue found") 🔹 2. Integer (int) Used for numbers (CPU, memory, ports) cpu = 75 print(cpu) 💡 DevOps Example: cpu = 85 if cpu > 80: print("Alert: High CPU") 🔹 3. Boolean (True / False) Used for status (running/stopped, success/failure) is_running = True if is_running: print("Service is running") 💡 DevOps Example: deployment_success = False if not deployment_success: print("Rollback required") 🔹 4. List (list) Used to store multiple values (servers, services) servers = ["web1", "web2", "web3"] print(servers[0]) 💡 DevOps Example: services = ["nginx", "docker", "jenkins"] for service in services: print(service) 🔹 5. Combine All (Real Example) servers = ["web1", "web2"] cpu_usage = 85 status = True if cpu_usage > 80: print("Alert: scale up needed") if status: for s in servers: print(f"{s} CPU: {cpu_usage}") 🔹 6. Quick Practice services = ["web1", "web2"] status = True cpu_usage = 85 # fixed variable name if status: for s in services: print(f"Server {s} CPU {cpu_usage}") if cpu_usage > 80: print(f"Alert: CPU {cpu_usage}") Out put: >>> services = ["web1", "web2"] >>> status = True >>> cup_usage = 85 >>>  >>> if status: ...  for s in services: ...    print(f"server {s} CPU {cpu_usage}") ...  server web1 CPU 85 server web2 CPU 85 >>> if cpu_usage > 80: ...  print(f"Alert: CPU {cpu_usage}") ...  Alert: CPU 85 💡 Key Takeaway: Mastering these basics helps automate monitoring, alerts, and system management in real DevOps environments. #DevOps #Python #Automation #Scripting #Learning #AWS #Kubernetes

🚨 I used to overcomplicate Python in DevOps… until real CI/CD pipelines taught me something simple. When I started working with automation, I thought I needed heavy frameworks and advanced Python structures to build “real DevOps scripts”. But in production environments, I realized something very different: 👉 DevOps automation is not about complexity 👉 It’s about using the right simple tools reliably In most CI/CD and cloud automation work, I ended up using only a small set of Python standard library modules: os → environment variables, system interaction subprocess → running real commands (docker, kubectl, terraform) json → APIs, Kubernetes configs, pipeline responses logging → production-grade observability pathlib → clean file and artifact handling datetime → deployment tracking & audit logs sys → CLI control and pipeline exit handling shutil → backups and artifact management Real example from DevOps work: Instead of building complex tools, I often use Python scripts to: automate deployment steps execute validation commands capture logs from CI/CD pipelines interact with cloud APIs The biggest lesson I learned: 👉 In DevOps, simplicity always wins over complexity. Because in production, reliability matters more than clever code. What Python modules do you find yourself using the most in DevOps automation? #DevOps #Python #CloudComputing #CI/CD #Automation #SRE

🚀 Python for DevOps – Log Analysis with Metrics Today I enhanced my log automation script to not only separate logs by level but also count them for quick insights. 📂 Problem: Manually analyzing logs is slow and inefficient. 💻 Solution (Python Script): error = warning = info_count = 0 with open("app.log") as f, \ open("error.log", "w") as err, \ open("warning.log", "w") as warn, \ open("info.log", "w") as info: for line in f: if "ERROR" in line: err.write(line) error += 1 elif "WARNING" in line: warn.write(line) warning += 1 elif "INFO" in line: info.write(line) info_count += 1 print("ERROR:", error) print("WARNING:", warning) print("INFO:", info_count) 🔍 What this does: Reads app.log Splits logs into separate files Counts each log level 📊 Sample Output: ERROR: 1 WARNING: 1 INFO: 2 Output: ubuntu@satheesha:~/python$ python3 multiple-log_level.py ERROR: 1 WARNING: 1 INFO_COUNT: 2 ubuntu@satheesha:~/python$ ls -ltr error.log warning.log info.log -rw-r--r-- 1 ubuntu ubuntu 18 Apr 21 08:20 warning.log -rw-r--r-- 1 ubuntu ubuntu 44 Apr 21 08:20 info.log -rw-r--r-- 1 ubuntu ubuntu 17 Apr 21 08:20 error.log ubuntu@satheesha:~/python$ cat error.log ERROR: Disk full ubuntu@satheesha:~/python$ cat warning.log WARNING: High CPU ubuntu@satheesha:~/python$ cat info.log INFO: Service startes INFO: Service startes 🔥 Why this matters: Quick visibility into system health Helps prioritize issues (ERROR > WARNING > INFO) Reduces manual troubleshooting time 💡 Key Learning: Python can be used not just for automation, but also for real-time insights and monitoring in DevOps. 📈 Next Step: Add alerting when ERROR count exceeds threshold Integrate with monitoring tools Build real-time log monitoring (tail -f in Python) #Python #DevOps #Automation #Scripting #Monitoring #Cloud #Learning

🚀 Simple Python Script for DevOps Practice In DevOps, even small scripts can make a big difference in automation and monitoring. Here’s a simple Python script I practiced to simulate basic server details and loop operations 👇 name = "server1" cpu_usage = 75 is_running = True print(f"Name: {name}") print(f"CPU Usage: {cpu_usage}%") print(f"Is Running: {is_running}") print("-" * 30) for i in range(6): print(i) print("-" * 30) for i in range(1, 12, 2): print(i) print("-" * 30) for i in range(10, -1, -1): print(i) print("-" * 30) Output: ubuntu@satheesha:~/python$ python3 variable-for_loop.py Name: server1 CPU Usage: 75% Is Running: True ------------------------------  0 ------------------------------ 1 ------------------------------ 2 ------------------------------ 3 ------------------------------ 4 ------------------------------ 5 ------------------------------ 1 ------------------------------ 3 ------------------------------ 5 ------------------------------ 7 ------------------------------ 9 ------------------------------ 11 ------------------------------ 10 ------------------------------ 9 ------------------------------ 8 ------------------------------ 7 ------------------------------ 6 ------------------------------ 5 ------------------------------ 4 ------------------------------ 3 ------------------------------ 2 ------------------------------ 1 ------------------------------ 0 ------------------------------ 🔹 Key concepts used: ✔ Variables & data types ✔ f-strings for clean output ✔ Loops with range() ✔ Reverse iteration 💡 These basics are very useful for: Automation scripts Monitoring tasks Log analysis Learning step by step and practicing regularly helps build strong DevOps scripting skills. #DevOps #Python #Automation #Scripting #Learning #AWS #Kubernetes #Jenkins