Rick Donato

Tip! Validate your env vars from your .env files before they hit your application by using Pydantic. This also eliminates the need to call python-dotenv manually. Here's an example: # uv add pydantic pydantic-settings python-dotenv from pydantic import Field, AnyHttpUrl, ValidationError from pydantic_settings import BaseSettings, SettingsConfigDict # Define settings

One of the big announcements from AutoCon4 last week came from the team behind Netpicker and Slurpit. They have now released major upgrades, and have aligned the product catalog under the umbrella of the Flock9 platform, bringing Netpicker and Slurpit even closer together. The TL;DR * Netpicker AI Assistant Netpicker

For those still working with Excel spreadsheets, here's a quick tip ... To easily work with data from an Excel spreadsheet in Python, use the Pandas library. First, import the spreadsheet, then iterate over the data, using itertuples, like so: # Install - pip install pandas import pandas as pd

🐍 Python Tip: Improve code readability by enforcing named arguments. To enforce all arguments to be named, start your parameter list with *. Here’s an example: def add_vlan(*, vlan_id, name): print(f"VLAN ID: {vlan_id}") print(f"Name: {name}") add_vlan(vlan_id=10,

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