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Introducing 𝐀𝐬𝐲𝐧𝐜-𝐆𝐞𝐨𝐓𝐈𝐅𝐅, a new high-level Python library for reading GeoTIFFs and Cloud-Optimized GeoTIFFs. Release post: https://lnkd.in/edD2qJJ6 - High-level, easy-to-use, and familiar to rasterio - Load from full-resolution or reduced-resolution overviews - Fast, with a Rust core. - Automatically puts image decoding onto a thread pool to avoid blocking async tasks. - Integration with NumPy, PyProj, Affine, & Morecantile - Obstore integration for use with S3, GCS & Azure - Lightweight with no GDAL dependency - Full type hinting - Broad decompression support A Development Seed project.

Another subtle Python quirk that silently breaks production… The logs showed empty lists where data should be. No errors. No crashes. Just… silence from a core function. After tracing through permissions, database calls, and network timeouts, I found this: (See screenshot for code snippet) The villain? Iterator exhaustion. zip returns an iterator. The first list comprehension consumed it entirely. The second got nothing. This pattern is everywhere: map, filter, csv.reader, generator expressions — they’re all one-time use. Key Insight for System Design: Treat iterators as streams, not containers. A stream flows once. If you need a container, materialize it: data = list(iterator) It’s a simple rule that prevents insidious bugs. What looks like harmless variable reuse can become a source of Heisenbugs — bugs that vanish when you add debug prints (which also consume the iterator!). Python backend issues that don’t throw errors, but break systems quietly. What’s your most memorable “silent failure” bug? #python #debugging #bestpractices #backend #engineering

𝙔𝙤𝙪𝙧 𝙋𝙮𝙩𝙝𝙤𝙣 𝘾𝙤𝙙𝙚 𝙄𝙨 𝙒𝙖𝙨𝙩𝙞𝙣𝙜 𝙏𝙞𝙢𝙚, 𝙃𝙚𝙧𝙚’𝙨 𝙃𝙤𝙬 𝙩𝙤 𝙁𝙞𝙭 𝙄𝙩 Most Python scripts work fine… But fine isn’t fast. And slow code costs you time, memory, and sometimes even money. The good news? Just a few smart tweaks can make your scripts run fast. Here are 8 easy ways to speed up your Python code: ☉ 𝗨𝘀𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗱𝗮𝘁𝗮 𝘁𝘆𝗽𝗲 → set() is way faster than list() for lookups. ☉ 𝗨𝘀𝗲 𝘃𝗲𝗰𝘁𝗼𝗿𝗶𝘇𝗲𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 → NumPy & Pandas process data in bulk, avoiding slow Python loops. ☉ 𝗨𝘀𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿𝘀 → Process big data without eating up memory. ☉ 𝗥𝘂𝗻 𝘁𝗮𝘀𝗸𝘀 𝗶𝗻 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹 → Threads for I/O, processes for heavy CPU work. ☉ 𝗙𝗶𝗻𝗱 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 𝗳𝗶𝗿𝘀𝘁 → Use cProfile before guessing what’s slow. ☉ 𝗖𝘂𝘁 𝘂𝗻𝗻𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝘆 𝗹𝗼𝗼𝗽𝘀 → List comprehensions are faster and cleaner. ☉ 𝗨𝘀𝗲 𝗯𝘂𝗶𝗹𝘁-𝗶𝗻 𝘁𝗼𝗼𝗹𝘀 → Python’s standard library is already optimized. ☉ 𝗖𝗮𝗰𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 → Don’t repeat expensive work, store it once. Doc Credits - Abhishek Agrawal ♻️ Repost if you found this useful 🤝 Follow me for more  👨💻 For 1:1 guidance → https://topmate.io/sateesh #python #pyspark #pysparklearning #dataengineering #azuredataengineer #bigdata #spark #datalearning #datacareer #azuredataengineering #dataengineeringjobs #linkedinlearning

Why Python Handles Data Faster Than You Think 🚀 “Python is slow.” That’s the common assumption. But in real-world data engineering and ML workloads, Python often performs far better than expected. Here’s why 👇 1️⃣ Python Doesn’t Work Alone When you use: -NumPy -Pandas -PyArrow You’re executing highly optimized C/C++ and Fortran code under the hood. Python acts as the orchestrator — not the heavy lifter. 2️⃣ Vectorization > Loops Operations like: df["price"] * 2 can be 10–100x faster than manual iteration. Why? Because they run at the native level — avoiding Python loop overhead entirely. 3️⃣ The Modern Python Data Stack Is Built for Scale Tools that dramatically improve performance: • Polars – Rust-powered, extremely fast • Dask – Parallel & distributed computing • Modin – Scales Pandas automatically • Numba – JIT compilation for speed • Vaex – Efficient large dataset processing • Cython – Compile Python to C Python isn’t winning because of raw interpreter speed. It wins because of its ecosystem. 4️⃣ Speed = Time to Solution In production systems, performance matters. But so does: -Development speed -Debugging speed -Deployment speed -Hiring availability In real-world engineering, time to solution often matters more than microsecond benchmarks. The biggest mistake? Benchmarking Python loops instead of benchmarking Python libraries. Huge difference. 💬 What’s the largest dataset you’ve handled in Python? #Python #DataEngineering #MachineLearning #BackendDevelopment #Performance #AI

Just came across this insightful piece from KDnuggets on integrating Rust and Python for data science—it's a timely look at boosting your workflows beyond Python's usual limits. Instead of sticking solely to Python's convenience, it shows how Rust can inject serious performance gains, especially in areas demanding tight memory management and predictability. This resource is free and available here: https://lnkd.in/ef4ErP7V Here's the summarised version, with 6 key insights you can apply now: #1 Why Rust? → It offers low-level control to optimize bottlenecks in data pipelines where Python falls short. #2 Integration Tools → Use libraries like PyO3 or rust-cpython to seamlessly bind Rust code into Python scripts. #3 Performance Boosts → Rust excels in compute-heavy tasks, reducing execution time in ML model training or data processing. #4 Memory Management → Gain fine-grained control to avoid Python's garbage collection overhead in large datasets. #5 Use Cases → Ideal for high-throughput ETL jobs, real-time analytics, or embedded systems in enterprise AI. #6 Getting Started → Start with simple extensions, test interoperability, and scale to production for reliable gains. Bottom line → Pairing Rust with Python isn't hype—it's a pragmatic way to make data science tools enterprise-ready without overhauling your stack. ♻️ If this was useful, repost it so others can benefit too. Follow me here or on X → @ernesttheaiguy for daily insights on data engineering and AI implementation.

Most Python tutorials stop at lists and loops. Real-world data work starts with files and control flow. As part of rebuilding my Python foundations for Data, ML, and AI, I’m now revising two topics that show up everywhere in production systems: 📁 File Handling 🔀 Control Structures Here are short, practical notes that make these concepts easy to grasp 👇 (Save this if you work with data) 🧠 Python Essentials — Short Notes 🔹 1. File Handling (Reading & Writing Files) File handling allows Python to interact with external data. Common modes: • 'r' → read • 'w' → write (overwrite) • 'a' → append with open("data.txt", "r") as f: data = f.read() Why with? ✔ Automatically closes the file ✔ Safer & cleaner code Used heavily in ETL, logging, configs, batch jobs 🔹 2. Reading Files Line by Line Efficient for large files. with open("data.txt") as f: for line in f: print(line) Prevents memory overload in data pipelines. 🔹 3. Control Structures – if / elif / else Control structures let your program make decisions. if score > 90: grade = "A" elif score > 75: grade = "B" else: grade = "C" Core to validation, branching logic, error handling 🔹 4. break, continue, pass • break → exit loop • continue → skip current iteration • pass → placeholder (do nothing) for x in range(5): if x == 3: continue print(x) 🔹 5. try / except (Bonus – Production Essential) Handle runtime errors gracefully. try: result = 10 / 0 except ZeroDivisionError: print("Error handled") Critical for robust, fault-tolerant systems. Python isn’t just about syntax. It’s about controlling flow and handling data safely. #Python #DataEngineering #LearningInPublic #Analytics #ETL #Programming #AIJourney

🔥 Python is not just a language. It’s a universe. Everyone talks about Pandas, NumPy, FastAPI… But real Python power? It lives in the modules most people IGNORE. 👀 Today I went deep into Python internals and explored: abc | aifc | argparse And honestly? 🤯 Mind = Blown 🧠 1️⃣ abc – Abstract Base Classes Define rules before implementation. Think like an architect, not a coder from abc import ABC, abstractmethod class DataPipeline(ABC): @abstractmethod def process(self): pass class ETL(DataPipeline): def process(self): return "Processing Data..." 👉 Forces structure. Clean design. Enterprise mindset. 🎧 2️⃣ aifc – Audio File Handling Yes, Python can read AIFF audio files. import aifc with aifc.open('sample.aiff', 'r') as f: print(f.getnchannels()) Not common. But powerful in media processing. 🛠 3️⃣ argparse – Build CLI Tools Like a Pro import argparse parser = argparse.ArgumentParser() parser.add_argument("--name") args = parser.parse_args() python app.py --name Kartik Boom 💥 Instant CLI tool. #Python #AsyncIO #BackendEngineering #CleanCode #100DaysOfCode #DataEngineering #TechLeadership

Vectorization vs Loops: How it affects performance. People often say “Python is slow”. when I take a closer look I find out it has nothing to do with Python. It is how the code is written. I’ve seen data analysis scripts that loop through rows like this: - for each row - do a calculation - append results Let’s quickly look at a practical example. - We have a dataset with 1,000,000 rows and you want to apply a simple rule: If sales > 1000, mark it as high, else low. 1. Loop Approach labels = [] for value in df["sales"]: if value > 1000: labels.append("high") else: labels.append("low") df["category"] = labels What does this do? - Loops through every row in Python - Scales poorly as data grows - It’s hard to optimize further While looping works, it doesn’t scale and performance is at the lowest optimal level. Let’s try another approach for the same example. 2. Vectorized Approach df["category"] = np.where(df["sales"] > 1000, "high", "low") What does this do? - Operates on the entire column at once - Makes code easier and cleaner to reason about - Stays fast even as rows increase This gives exactly the same result and even a faster performance. Half the time optimal performance is not dependent on the bulk or beauty in pattern of code. A simple switch from row to row thinking to column level thinking can help achieve the best performance as data grows in your dataframe and model. #Python #Dataanalytics #Numpy #Optimization #Datascience