Rick Donato

pyATS Tip! By default, pyATS runs several exec/config commands and logs to stdout when connecting. You can disable these using the parameters in the example below: device.connect( init_exec_commands=[], # Disable default exec commands init_config_commands=[], # Disable default config commands log_stdout=False # Disable logging to stdout

Within this post, we are going to explore the network automation platform Netpicker. Netpicker is a Python-powered network automation platform that provides various features to allow you to configure, back up, and perform compliance testing across 150+ vendors. Because it’s built using open-source libraries such as Netmiko, Pytest, TextFSM,

Jinja Tip: Validate and check your Jinja templates for syntax errors with j2lint. Ideal for integrating into a CI pipeline. ➜ https://github.com/aristanetworks/j2lint Here is an example below 👇 $ uv tool install j2lint $ cat config.j2 hostname {%for int in interfaces%} interface ip address {{int.ip}} {{int.NetMask}} {%endfor

Introduction When you're working with structured data in Python, there are two common options: TypedDict (from the typing module) and Pydantic (a popular validation library). Both are useful, but they serve different needs depending on whether you prioritize static type checking, runtime validation, or performance. In this post,

PySentry - Scan your Python dependencies for known vulnerabilities. Built in Rust for performance, PySentry is fast, easy to use, and supports flexible output formats (Markdown, JSON, etc.). Here are some examples 👇 # Install pysentry-rs using uv (or use pip install pysentry-rs) $ uv add pysentry-rs # Run a security scan on the

If I’m honest, I feel like Jinja gets a bad rap. Its role is often thought to be only for generating network configurations. And with model-driven automation being on the rise it can feel like Jinja is a legacy, tainted tool that we should be moving away from. I’

📅 Events * 5-Day Bootcamp (14th–18th July) * An Intro to GenAI and LLMs for Network Automation (members only) – September 24 * Building a ChatBot with OpenAI, MCP, and Netmiko (members only) – October 16 * Front-End Development with React (members only) – November 27 * How to Build a GraphQL API (members only) – November 27 Note:

A quick Panda tip for you... Import nested data into a DataFrame using json_normalize with record_path and meta. Here’s an example 👇 import pandas as pd data = [ { "network_device": { "hostname": "spine1-nxos", "os_version": "9.3.7", "interface&

When writing code, clarity and maintainability are everything. One of the most powerful design principles you can apply to achieve this is CQS - Command Query Separation. What is CQS? CQS (Command Query Separation) means: A function should either do something (a Command) or answer something (a Query) - never