Autoptic launches AI-powered change resilience agents that analyze DevOps telemetry to detect root causes, prevent incidents, and boost confidence before production changes.
Author: drweb
Python is probably a programming language that any discussion on data analysis would inevitably include. Besides, it is not only a data analysis tool that is gradually making its way into gaming environments. Python, whether in video games or gambling, opens the door for developers, analysts, and even enthusiasts to collect, organize, and analyze large amounts of performance data. Game performance and stats tracking used to be a domain of professionals only; however, with Python, even beginners can perform very advanced analyses. In this write up, we look at how Python can help track game performance, understand player behavior, and…
Press enter or click to view image in full sizeNOTE: I’m going to publish more and more YouTube videos this year. If interested, you can subscribe to my YouTube page.Angular 21.1 is available and offers many interesting features, but keep in mind that almost all of them are experimental at this point, meaning they’re not ready for production use.Signal Forms UpdatesSignal Forms are an experimental feature, and it shows in version 21.1, as there are two breaking API changes:The field directive was renamed to formFieldThe field property was renamed to fieldTreeThis means we have to use the form field directive…
I was listening to the radio the other day and the hosts were discussing the NFL playoffs in 2026. Someone mentioned the winningest coach was Nick Sirianni of the Philadelphia Eagles and no one wanted to face the Eagles. I was wondering if the first part of that was true. I used Claude and SQL Prompt AI to help.Note, I enjoyed watching the Eagles lose in the first round to the 48ers. As a lifelong Cowboys fan, that was great. However, I am impressed with Jalen Hurts and was glad to see him win last year.This is part of a…
# Create a set numbers = {1, 2, 3, 4, 5} empty_set = set() # Not {}, that’s a dictionary unique_items = set([1, 2, 2, 3, 3, 4]) # {1, 2, 3, 4} Sets are Python’s built-in data structure for storing unique, unordered collections. They automatically eliminate duplicates and provide fast membership testing. If you’ve ever needed to remove duplicate entries from a list or check whether something exists in a collection without iterating through every item, sets solve that problem. Creating and initializing Python sets You can create Python sets in three ways. The curly brace syntax works for…
Charity Majors delivers a wake-up jab to DevOps, sparking debate on its evolution and success. Is it a failure or just getting interesting? Alan dives into the discussion.
Syntax: numpy.where(condition, x, y) Quick example: import numpy as np arr = np.array([1, 2, 3, 4, 5]) result = np.where(arr > 3, ‘big’, ‘small’) # Output: [‘small’ ‘small’ ‘small’ ‘big’ ‘big’] That’s Python np where in action. You give it a condition, tell it what to return when True, what to return when False, and it runs that logic across your entire array without loops.What Python np where actually doesThe numpy.where() method acts as a vectorized if-else statement for arrays. You’re essentially broadcasting a conditional operation across every element in your array simultaneously. This matters because it’s fast. Really fast.…
Atlanta, GA, United States, 20th January 2026, CyberNewsWire
range(start, stop, step) range(5) # 0, 1, 2, 3, 4 The range() function generates a sequence of numbers that you can iterate over. That’s it. Three parameters, one simple job. You give it boundaries, and it gives you integers within those boundaries.Most Python developers use range() every single day without thinking about what it actually does under the hood. They slap it into a for loop and move on. But understanding how this function works will save you from some genuinely weird bugs and help you write faster code.How the Python range function actually worksThe range() function doesn’t create a…
How Python for loops actually work under the hoodWhen you write a for loop, Python calls the __iter__() method on your sequence. This returns an iterator object. Then Python repeatedly calls __next__() on that iterator until it raises a StopIteration exception. numbers = [1, 2, 3] iterator = iter(numbers) print(next(iterator)) # 1 print(next(iterator)) # 2 print(next(iterator)) # 3 print(next(iterator)) # StopIteration exception This matters because it means anything implementing the iterator protocol works in a for loop. Lists, tuples, strings, dictionaries, files, generators, custom objects. If it’s iterable, you can loop it.Running Python for loops through different data structuresStrings iterate…