Python Regex Engine with Recursive Backtracking

✍️Compress - Compressor - Compression - Python : '..The trick is to rebuild the compressor every time a new labelled document is received. Thankfully, instantiating a ZstdCompressor with a ZstdDict is very fast – tens of microseconds in my experiments. This makes it affordable to rebuild the compressor very frequently. Here are the steps to take to turn this into a learning algorithm: For each class, maintain a buffer of text that belongs to that class. When a new labelled document is received, append it to the buffer of its class. Rebuild the compressor for that class with the updated buffer. To classify a new document, find the compressor that produces the smallest compressed output for that document. There are several parameters that can be tuned to balance between throughput and correctness: Window size: the maximum number of bytes to keep in the buffer for each class. A smaller window means less data to compress, which means faster compressor rebuilding and compression. But it also means less data to learn from, which ...' - Extract #skdscans #python #compressor #ZstdCompressor #github #maxhalford #classification https://lnkd.in/d-sDt-PK

Python Deep Dive | Understanding *args Like a Pro One small symbol… big impact. When we use *args in a Python function, where are the values actually stored? Many beginners guess list. Some even think it’s a generic collection. But the correct answer is: tuple ✅ 💡 Why tuple? When you define a function like this: def my_function(*args): print(type(args)) And call it like this: my_function(1, 2, 3) The output will be: <class 'tuple'> Python automatically collects all positional arguments into a tuple. ⸻ 🔍 Why did Python choose tuple instead of list? Because tuples are: • Immutable (cannot be modified) • Memory efficient • Faster than lists • Safer for internal function handling Since *args is meant to collect values, not modify them, immutability makes perfect design sense. ⸻ 🚀 Bonus Insight While: • *args → stores data in a tuple • **kwargs → stores data in a dictionary Understanding this difference is essential for writing clean, scalable, and flexible functions — especially in larger AI or backend systems. Small details like this separate someone who “writes Python” from someone who truly understands Python. #Python #Programming #AI #DataScience #CodingJourney #SoftwareEngineering #30DayChallenge

💬 Discussion Question In Python, if we have a list called "lst1", there are multiple ways to sort the data. Two of the most common approaches are: 1️⃣ "lst1.sort()" 2️⃣ "sorted(lst1)" But what is the difference between them, and when should we use each one? 🔹 "lst1.sort()" - It is a list method. - It sorts the elements in-place, meaning it modifies the original list itself. - It does not return a new list (it returns "None"). Example: lst1 = [4, 2, 7, 1] lst1.sort() print(lst1) Output: [1, 2, 4, 7] 🔹 "sorted(lst1)" - It is a built-in Python function. - It returns a new sorted list without modifying the original one. Example: lst1 = [4, 2, 7, 1] new_list = sorted(lst1) print(lst1) print(new_list) Output: [4, 2, 7, 1] [1, 2, 4, 7] 📌 When to use each one? ✔ Use "sort()" when you want to sort the original list and don’t need the previous order. ✔ Use "sorted()" when you want to keep the original data unchanged and create a new sorted version. 💡 Another advantage of "sorted()" is that it works with many iterable types such as lists, tuples, sets, and dictionaries. #Python #Programming #DataAnalytics #MachineLearning #Coding #30DayChallenge #AI #Instant

Unlock a new way to write AI-powered code: define inputs, return type, and post-conditions; let AI implement and self-correct. Focus on validation and correctness to cut boilerplate. Try AI Functions for receipts parsing. 🔎💡 #Python #AIFunctions #DevTools #CodeQuality #AI

I recently conducted a benchmark comparing Python 3.13 and 3.14 on the same CPU-heavy task, initially out of curiosity. The results were surprising; the performance difference was significant and has changed my perspective on parallelism in Python. While optimizing a CPU-bound data pipeline, my usual approach was to use ProcessPoolExecutor. Although it effectively handles tasks, the OS-level process spawn cost can accumulate quickly. Python 3.14 introduced a new option: InterpreterPoolExecutor. This allows for multiple isolated Python interpreters within the same process, eliminating GIL conflicts. I benchmarked the performance of Python 3.13 versus 3.14 as follows: ───────────────────────────────────────── 📊 1. HEAVY CPU TASKS (8 tasks, 4 workers) 🔴 Threads: 2.519s (GIL serializes everything) 🟠 Processes: 1.222s (parallel, but costly to spawn) 🟢 Subinterpreters: 1.130s (parallel and lighter) ───────────────────────────────────────── ⚡ 2. STARTUP COST (50 tiny tasks, where it really shows) 🟠 Processes: 0.271s 🟢 Subinterpreters: 0.128s (about 2x faster to start) 📈 3. SCALING (1 → 8 workers) 🔴 Threads: flatlined at ~1.9s (no real scaling benefit) 🟢 Subinterpreters: 2.16s → 0.91s (close to linear scaling) ───────────────────────────────────────── The key takeaway is that we can achieve process-level parallelism with thread-like startup speed, without GIL contention or extra process memory overhead, all within the standard library. Are you still using ProcessPoolExecutor for CPU-bound work? I am genuinely interested in whether subinterpreters could be a practical improvement in your stack. #Python #Python314 #SoftwareEngineering #Performance #Concurrency #BackendDevelopment #DataEngineering

Multipart (Python library), ReDoS, CVE-2026-28356 (High) The `parse_options_header()` function in `multipart.py` versions up to `1.3.0` contains a regular expression with an ambiguous alternation . When parsing a maliciously crafted `Content-Type` header (specifically the options section), the regex engine attempts to validate the string against multiple paths . Due to the nested repetition and alternation in the pattern, providing a carefully crafted input with many backslashes and quotes causes exponential backtracking ....

Multipart (Python library), ReDoS, CVE-2026-28356 (High) The `parse_options_header()` function in `multipart.py` versions up to `1.3.0` contains a regular expression with an ambiguous alternation . When parsing a maliciously crafted `Content-Type` header (specifically the options section), the regex engine attempts to validate the string against multiple paths . Due to the nested repetition and alternation in the pattern, providing a carefully crafted input with many backslashes and quotes causes exponential backtracking ....

Shallow Copy vs Deep Copy in Python 🐍 Understanding copying in Python is very important when working with lists and objects. ✅ Shallow Copy - Creates a new object - But nested objects are still shared - Changes in nested elements will affect both copies Example: import copy a = [[1, 2], [3, 4]] b = copy.copy(a) b[0][0] = 100 print(a) # Changed because nested list is shared ✅ Deep Copy - Creates a completely independent copy - Nested objects are also copied - Changes will NOT affect original object Example: import copy a = [[1, 2], [3, 4]] b = copy.deepcopy(a) b[0][0] = 100 print(a) # Original list unchanged 📌 Key Difference: - "copy.copy()" → Shallow Copy - "copy.deepcopy()" → Deep Copy Understanding this concept helps avoid unexpected bugs in real-world projects. #Python #Programming #Coding #Learning #SoftwareDevelopmentIf

Stop Using sort() Just to Check Order in Python I still see this pattern in production code: def is_sorted_bad(lst): return lst == sorted(lst) It works… but it’s inefficient. Python’s built-in sort (Timsort) runs in O(n log n) and creates a new list — just to answer a yes/no question. If your list has 1 million elements, that’s ~20 million comparisons and a full memory copy. All that… when you only need to check adjacent pairs. The Right Way (O(n) + Early Exit) def is_sorted(lst): return all(x <= y for x, y in zip(lst, lst[1:])) ✔ O(n) time ✔ Stops at first violation ✔ No unnecessary sorting ✔ Clean & Pythonic And if you're on Python 3.10+, itertools.pairwise() is even cleaner. What This Article Covers In my latest blog post, I break down: • 5 efficient O(n) methods • Non-decreasing vs strictly increasing • Descending checks • Edge cases (None, mixed types, duplicates) • When sort() is actually the right choice • NumPy & Pandas native solutions • Performance comparison table Plus real benchmarks and interview-ready explanations. If you're writing performance-sensitive Python — especially in data pipelines — this is worth knowing. Full article here: https://lnkd.in/giChyRRc Curious — how many times have you seen lst == sorted(lst) in real projects?

Here’s a LinkedIn-ready post on Expressions in Python 👇 --- 🧮 Expressions in Python In Python, an expression is a combination of values, variables, operators, and function calls that evaluates to a result. Simply put 👉 If Python can calculate it and return a value, it’s an expression. --- 🔹 Types of Expressions in Python 1️⃣ Arithmetic Expressions Used to perform mathematical operations. a = 10 b = 5 result = a + b # 15 Operators: + - * / % ** // --- 2️⃣ Relational (Comparison) Expressions Compare two values and return True or False. x = 10 y = 20 print(x < y) # True Operators: == != > < >= <= --- 3️⃣ Logical Expressions Combine multiple conditions. age = 25 print(age > 18 and age < 60) # True Operators: and or not --- 4️⃣ Bitwise Expressions Perform operations at the binary level. a = 5 # 0101 b = 3 # 0011 print(a & b) # 1 Operators: & | ^ ~ << >> --- 5️⃣ Assignment Expressions Assign values using = (and shortcut operators like +=, -=, etc.) x = 5 x += 3 # 8 --- 6️⃣ Conditional (Ternary) Expression Short-hand for if-else. num = 10 result = "Even" if num % 2 == 0 else "Odd" --- ✅ Advantages of Using Expressions ✔ Makes code concise and readable ✔ Helps perform computations efficiently ✔ Improves logical decision-making ✔ Reduces unnecessary lines of code --- 🚀 Final Thought Mastering expressions is the foundation of writing efficient Python programs. The better you understand expressions, the stronger your coding logic becomes! #Python #Programming #Coding #LearnPython #Tech #Developers #LinkedInLearning