Python 3.14 Code Safety Features Explained

Want to take a #Python #Pandas series of strings, and get datetime values? Use pd.to_datetime: pd.to_datetime(df['x']) Notice: It's not a method! It's a top-level pd function. Specify a non-standard "format" with a strftime string: pd.to_datetime(df['x'], format='%d/%m/%Y')

Generate a #Python #Pandas DatetimeIndex of datetime values, specifying the frequency, by passing freq and a code: pd.date_range(start='2026-03-01', end='2026-05-01', freq='8h') This returns a 184-element DatetimeIndex with evenly-spaced datetime values in that period.

Free Threading — Python's way to "goroutines", sort of. I’ve been experimenting with the new python3.14t builds. By combining Free-threading with AsyncIO, you can now: 1. Spawn worker threads (Parallelism). 2. Run an AsyncIO loop inside each (Concurrency). 3. Use queue.Queue as your "Channels" for thread-safe communication. The Result? True parallelism without the memory overhead of multiple processes. I’ve broken down the benchmarks and the "serialization tax" of subinterpreters in my latest write-up. If you're building high-scale backends, this is required reading. Read more: https://lnkd.in/gp3KBR_G #Python #DistributedSystems #Concurrency #Scalability #Performance

Python 🐍 3.14.4 Just Released This release includes several break fixes, including a few related to multiprocessing and asyncio subprocesses. For the full list, see as follows. #python #programming #python3144 #asyncio #multiprocessing https://lnkd.in/g-8qUX_Q

Explore five beginner-friendly platforms that let you host Python apps for free, compare their limits, and pick the right one. https://lnkd.in/eiNhtfje

#Python threads do not always make code faster. This article explains where threading helps, where it slows things down, and why understanding workload type matters for better performance decisions. #Threading #Blog #Famro #Informational Read more: https://lnkd.in/dKX63JNb

🚀 Day 11 of My Python + DSA Journey Today’s problem was all about greedy approach👇 ✅ Jump Game (#55) 💡 Jump Game Check if you can reach the last index 🔍 Approach: Tracked maximum reachable index → if current index exceeds it, return False ⏱ O(n) time | O(1) space 🔥 What I learned today: • Greedy approach can simplify complex problems • Tracking max reach avoids unnecessary checks • Early exit conditions improve efficiency Turning logic into faster decisions ⚡ #Day11 #LeetCode #Python #DSA #CodingJourney #100DaysOfCode

💡 Tiny Python tip that improves code clarity With a normal tuple, you have to remember what each index stands for. That knowledge lives in your head, not in the code. `namedtuple` fixes this by giving semantic meaning to each position. You still get immutability and performance, but with clear, self-documenting access. #Python #PythonProgramming #CleanCode #CodingBestPractices #CodeReadability

How rustystats makes use of Python & Rust to fit GLMs. Faster, less memory required, and more accurate*. I've had this conversation a few times over the last couple of weeks - it is quite common to use other languages for heavy computation within Python libraries. This is how I used Rust to make fitting GLMs as fast and memory efficient as I could. You can find the code here: https://lnkd.in/e4cR7gwc *based on the single conversion GLM I migrated from statsmodel that made use of ordered target encoding for categorical factors and a monotonic spline for one of the continuous factors.

The best debugging tool isn't a profiler. It's a walk. 🚶♂️ Staring at the same block of Python code for 3 hours will give you tunnel vision. Stepping away from the keyboard lets your subconscious process the logic flow. I've solved more bugs making a cup of coffee than I have staring at VS Code. #DeveloperLife #Coding #MentalHealth #Python