Python for Network Engineers

Manual, device-by-device CLI work does not scale. When a team touches four routers, it is tedious. When it touches hundreds or thousands, it is slow, error-prone, and hard to audit. This course gives network teams a shared Python foundation so they can stop treating automation as a specialist skill held by one or two people.

Role fit: This course suits Network Administrators and IT Practitioners with 1–3 years of networking experience and no prior programming background. The instructor starts from zero and builds up to applied work:

• Scripting SSH sessions with Scrapli against Cisco devices
• Calling REST APIs
• Parsing CLI output with tools like TextFSM and Genie parsers
• Writing tests with unittest and pytest, including basic checks against networking devices
• Running concurrent connections across many devices

Time investment: Plan for about 22 hours across 23 skills, roughly one hour each. That pace fits a weekly cadence over a quarter. Team Leads should expect object-oriented programming to take longer. The instructor openly recommends rewatching those skills and practicing until the concepts click.

Change-management notes: The course teaches Git branching, commits, and history, so changes can be tracked and reviewed without disrupting stable code. It also covers PEP 8, code comments, debugging, and testing. These practices help IT Directors bring automation under the same review discipline as other production changes.

Lab access: Demos use the publicly available Cisco DevNet sandbox, so learners can follow along without building their own lab.

Platform support: Preview Nuggets help Training Managers evaluate fit. Team Reporting lets Training Managers track progress across the team.

IT teams use this training to replace repetitive, box-by-box CLI work with scripts that scale across the network. The course grounds concepts in networking scenarios: SSH sessions to Cisco devices in the DevNet sandbox, a multi-router EVE-NG lab, and REST API workflows. Network engineers can see where Python fits in day-to-day operations from the start.

• Faster changes across many devices: Your team learns to pull network state from routers and turn raw CLI output into structured data. That matters when sequential, one-device-at-a-time work won't scale to larger environments.
• Fewer surprises in production: Engineers write Python-based tests against network conditions, such as whether devices respond correctly, BGP prefix counts match expectations, and uptime stays above a defined threshold. These checks help catch drift before it causes an outage.
• Shared, auditable automation code: Git branching lets team members build new features without disturbing the stable code base and track who changed what. PEP 8, linting, and clear code comments keep scripts readable for the next engineer.
• Quicker troubleshooting: Practitioners use try/except handling, logging, type hints, and the command-line Python debugger (PDB) to inspect live device data. PDB works over a simple SSH connection, even on hosts without a GUI.

For IT Directors, the result is a network team that can automate safely and work from shared code, with less reliance on outside help for routine automation.

Knowledge

• Python fundamentals for network work: variables, mutable vs. immutable data types, strings, Boolean logic, and control flow
• How lists, tuples, dictionaries, and sets turn raw CLI output into structured data that scripts can work with
• Functions, comprehensions, functional programming, decorators, generators, and object-oriented concepts such as classes, private members, inheritance, and composition
• How REST APIs and RESTCONF fit into network programmability, and how Jinja2 templates generate consistent configurations
• Git fundamentals for team collaboration: commits, the git log, and branches that keep changes away from the stable code base
• When to use multiprocessing, threading, or asyncio, based on whether a task is CPU-bound or waiting on network I/O

Ability

• Set up isolated virtual environments and connect to Cisco DevNet Sandbox or lab devices over SSH using Python network automation libraries
• Run commands against multiple routers and switches, then store or report results using text files and spreadsheets
• Troubleshoot scripts with exception handling, logging, and type hints, including over a plain SSH session
• Write Pytest tests with fixtures and parametrization to check network conditions such as BGP prefix counts, uptime, and software versions
• Apply best practices such as PEP 8, code comments, and linting so teammates can read and maintain shared automation code
• Run device tasks concurrently instead of one at a time, cutting run times as automation grows from a few devices to large environments