Category: Python

Jason Yousef has a script:

Below is a production-friendly pattern that:

• Uses a requests.Session with retries, backoff, and a real User-Agent
• Sets sane timeouts and handles common HTTP errors
• Respects robots.txt (and tells you if scraping is disallowed)
• Parses only mailto: links by default to avoid scraping personal data you shouldn’t
• Handles pagination with a “Next” link when present
• Exports to CSV
• Can be run from the command line with arguments

Click through for the code, some explanation of how it works, and a few tips.

Hristo Hristov tries out a driver:

Programmatic interaction with SQL Server or Azure SQL from a Python script is possible using a driver. A popular driver has been pyodbc that can be used standalone or with a SQLAlchemy wrapper. SQLAlchemy on its own is the Python SQL toolkit and Object Relational Mapper for developers. In the end of 2025 Microsoft released v1 of their own Python SQL driver called mssql-python. How do you get started using mssql-python for programmatic access to your SQL Server?

Click through to see how it works. Hristo points out a couple of benefits to this driver over the classic pyodbc driver, though I’m curious if there are any performance differences between the two.

Andy Brown writes a companion piece:

Four years ago I wrote a blog on this site explaining why Python is better than C# and, arguably, most other programming languages. To redress the balance, here are 10 reasons why you might want to avoid getting caught up in Python’s oh-so-tempting coils – particularly when building large, long-lived systems.

If this sounds like an attempt to have my cake and eat it, my defense is that I follow in my work what I preach here: I use Python for ad-hoc jobs, at which it is unsurpassed. For larger systems – such as our MV website – I use C#, due to its strengths in maintainability, tooling as well as the practical consideration that my personal preference for Visual Basic is not shared by the wider team.

Some of it is opinion, some of it is annoying. I’ve grown to appreciate the spacing, though it can be really painful when copying code from somewhere and the spacing gets all messed up. My short version of Python is that it requires you to have more discipline as a developer to prevent messes from occurring, and I think that’s a negative on net. But that same aspect simultaneously makes it so much easier to prototype and rapidly solve problems, so there’s a natural trade-off here.

Kanwal Mehreen hosts a model:

In this article, you will learn how to package a trained machine learning model behind a clean, well-validated HTTP API using FastAPI, from training to local testing and basic production hardening.

Topics we will cover include:

• Training, saving, and loading a scikit-learn pipeline for inference
• Building a FastAPI app with strict input validation via Pydantic
• Exposing, testing, and hardening a prediction endpoint with health checks

Let’s explore these techniques. 

I definitely enjoy how simple it is to use FastAPI.

Brendan Tierney has a multi-part key:

It’s a little annoying when an API changes the structure of the data it returns and you end up with your code breaking. In my case, I experienced it when a dataframe having a single column index went to having a multi-column index. This was a new experience for me, at this time, as I hadn’t really come across it before. The following illustrates one particular case similar (not the same) that you might encounter. In this test/demo scenario I’ll be using the yfinance API to illustrate how you can remove the multi-column index and go back to having a single column index.

In this case, Brendan essentially builds a hierarchy and filters down to make a single key column (in this case, a date) relevant.