TA-Lib for Python: Technical Analysis Library

Day 460: 7/1/2026 Why Python Execution Is Slow? Python is expressive, flexible, and easy to use — but when performance matters, it often struggles. This is not because Python is “badly written,” but because of how Python executes code and accesses memory. Let’s break it down. ⚙️ 1. Python Works With Objects, Not Raw Data In Python, data is not stored contiguously like in C or C++. Instead: --> Each value is a Python object --> Objects live at arbitrary locations in memory --> Variables hold pointers to those objects When Python accesses a value: --> The pointer is loaded --> The CPU jumps to that memory location --> The Python object is loaded --> Metadata is inspected --> The actual value is read This pointer-chasing happens for every operation. 🔁 2. Python Is Interpreted, Not Compiled to Machine Code Python source code is not executed directly. Execution flow: --> Python source is compiled into bytecode --> Bytecode consists of many small opcodes --> The Python interpreter: fetches an opcode, decodes it, dispatches it to the corresponding C implementation --> This repeats for every operation --> Each step adds overhead. Even a simple arithmetic operation involves: --> multiple bytecode instructions --> multiple function calls in C --> dynamic type checks at runtime ⚠️ 3. Dynamic Typing Adds Runtime Checks Because Python is dynamically typed: --> Types are not known at compile time --> Every operation checks type compatibility --> Method lookups happen at runtime This flexibility makes Python powerful — but it prevents many low-level optimizations. Stay tuned for more AI insights! 😊 #Python #Performance #SystemsProgramming

Day 459: 6/1/2026 Why Python Objects Are Heavy? Python is loved for its simplicity and flexibility. But that flexibility comes with a cost — Python objects are memory-heavy by design. ⚙️ What Happens When You Create a Python Object? Let’s take a simple example: a string. When you create a string in Python, you are not just storing characters. Python allocates a full object structure around that value. Every Python object carries additional metadata. 🧱 1. Object Header (Core Overhead) Every Python object has an object header that stores: --> Type Pointer Points to the object’s type (e.g., str, int, list) Required because Python is dynamically typed Enables runtime checks like: which methods are valid whether operations are allowed This is why “a” + 1 raises a TypeError Unlike C/C++, Python must always know the object’s type at runtime. --> Reference Count Tracks how many variables reference the object Used for Python’s memory management When the count drops to zero, the object is immediately deallocated This bookkeeping happens for every object, all the time. 🔐 2. Hash Cache (For Immutable Objects) Immutable objects like strings store their hash value inside the object. Why? Hashing strings is expensive Dictionaries need fast lookups So Python caches the hash: Hash computed once Reused for dictionary and set operations Enables average O(1) lookups This improves speed — but adds more memory per object. 📏 3. Length Metadata Strings also store their length internally. This allows: len(s) to run in O(1) slicing and iteration without recomputing length efficient bounds checking Again: faster execution, but extra memory. Stay tuned for more AI insights! 😊 #Python #MemoryManagement #PerformanceOptimization

https://lnkd.in/e73QC5-9 FST fst Version 0.2.5 Overview This module exists in order to facilitate quick and easy high level editing of Python source in the form of an AST tree while preserving formatting. It is meant to allow you to change Python code functionality while not having to deal with the details of: Operator precedence and parentheses Indentation and line continuations Commas, semicolons, and tuple edge cases Comments and docstrings Various Python version-specific syntax quirks Lots more...

🔐 Understanding Python’s GIL (in simple terms) If you’ve ever heard “Python doesn’t scale with threads”, the reason is usually the GIL. GIL (Global Interpreter Lock) is a rule in CPython that says: 👉 Only one thread can execute Python code at a time. Think of it like this 👇 You have one whiteboard (Python interpreter) and multiple people (threads). Even if everyone is ready to write, only one person can use the marker at any moment. 🧠 Why does Python do this? Python uses a simple memory management system. The GIL keeps things safe and fast for single-threaded code, but limits parallel execution. 📌 What does this mean in practice? ❌ CPU-heavy tasks (calculations, loops) don’t get faster with threads ✅ I/O-heavy tasks (API calls, DB queries, file reads) do benefit from threads Why? Because during I/O, Python waits, and the GIL is released for other threads. 🚀 How developers work around GIL Use multiprocessing (multiple processes, multiple GILs) Use NumPy / Pandas (they release GIL internally) Use async for I/O-heavy workloads 🎯 Key takeaway GIL doesn’t make Python slow — it makes it predictable. The trick is choosing the right concurrency model.

🐍📰 Learn how to build a Model Context Protocol (MCP) server in Python. Connect tools, prompts, and data to AI agents like Cursor for smarter assistants #python

🚀 Full Stack Journey Day 46: Advanced Python - Mastering Object Serialization & Deserialization! 💾🐍 Day 46 of my #FullStackDevelopment learning series took a deep dive into an absolutely fundamental concept for data persistence and communication: Object Serialization and Deserialization! 💡 This process allows us to convert complex Python objects into a format that can be easily stored or transmitted, and then reconstructed back into their original form. Today's crucial advanced Python topics covered: Object Serialization: Explored the process of converting a Python object (which can be a simple data type, a list, a dictionary, or even an instance of a custom class) into a stream of bytes or a string format. Understood its importance for saving application state, persisting data to disk, sending data across network connections, or caching. Object Deserialization: Mastered the reverse process – converting that stream of bytes or string format back into a fully functional Python object. Learned how deserialization allows an application to reload previously saved data or receive structured data from another source and immediately use it as a native Python object. We touched upon key Python tools for this, such as the pickle module for Python-specific object serialization and the json module for human-readable, language-agnostic data serialization (especially for web APIs). Serialization and deserialization are indispensable skills for tasks ranging from saving user preferences to transmitting complex data structures between microservices. They are at the heart of robust data management in full-stack applications! 📂 Access my detailed notes here: 👉 GitHub: https://lnkd.in/ggctkyJk #Python #AdvancedPython #Serialization #Deserialization #ObjectPersistence #DataHandling #JSON #Pickle #FullStackDeveloper #LearningToCode #Programming #TechJourney #SoftwareDevelopment #DailyLearning #CodingChallenge #Day46 LinkedIn Samruddhi P.

What happens behind the scenes when you run a Python file? Most developers write Python every day. But very few know what actually happens when you hit python app. py. Here’s what happens behind the scenes -step by step: * Python loads your source code (.py file`) The interpreter reads your raw text -- your Python code. * Lexing Your code is broken into small pieces called tokens (keywords, names, operators). * Parsing Python converts the tokens into a syntax tree that represents the structure of your program. * Bytecode Compilation Python compiles the syntax tree into bytecode --a low-level set of instructions stored as .pyc files inside __pycache__. * Execution by CPython VM The Python Virtual Machine runs each bytecode instruction one by one. This is why Python feels interpreted -because the VM executes the bytecode step-by-step at runtime. * Garbage Collection + Memory Management Python constantly tracks object references and frees unused memory. Takeaway: Running a Python script triggers a whole pipeline: lex → parse → compile → execute. Understanding this is the first step to mastering Python internals. hashtag #Python #Flask #Django #PythonEverywhere

Running LLMs Locally with Docker Model Runner and Python. Learn the process of running large language models (LLMs) locally using Docker, Model Runner, and Python in this comprehensive guide.

🟩🟧🟨 (2/4) The Complete Guide to Python Virtual Environments! BY teclado (https://lnkd.in/g4ChGY85) Q: Why was Python 2.7 being used instead of Python 3.8? A: Because Python 2.7 appeared earlier in the system PATH. Q: What is the PATH environment variable? A: A list of directories the console searches to find executable programs. Q: How does the console decide which Python version to run? A: It runs the first python.exe found in the PATH. Q: Why does activating a virtual environment matter? A: It modifies PATH so the correct Python and pip are used. Q: What happens when a virtual environment is activated? A: Configuration changes are applied to the current console session. Q: Are these changes global across all terminals? A: No, they only apply to the activated console. Q: What visual sign shows a virtual environment is active? A: The environment name appears in brackets in the terminal prompt. Q: Why does activating a virtual environment fix version issues? A: Because it ensures the project uses the intended Python and libraries. Q: How do you know a virtual environment is activated? A: The environment name appears in brackets in the terminal prompt. Q: What happens to PATH when a virtual environment is activated? A: The virtual environment’s Scripts directory is added as the first PATH entry. Q: Why does Python from the virtual environment run first? A: Because its python.exe appears first in the PATH. Q: Why does python -V now show Python 3.8.2? A: Because the virtual environment was created using Python 3.8. Q: What determines the Python version used by a virtual environment? A: The Python executable used at the time the environment was created. Q: Is the virtual environment’s Python executable the same as system Python? A: No, it is a separate copy of the Python binary. Q: Why is the Python binary copied into the virtual environment? A: To isolate the environment from system-level Python installations. Q: Which pip runs inside an activated virtual environment? A: The pip.exe located inside the virtual environment’s Scripts folder. Q: Why do pip, pip3, and pip3.8 point to the same executable? A: They are aliases referring to the same pip installation. Q: Where are libraries installed when using pip inside a virtual environment? A: Inside the virtual environment, not globally. Q: What happens when you install Flask using pip in a virtual environment? A: Flask is installed only inside that environment.

Explore how to generate PDFs from plain text files in Python for various application needs. This tutorial explains the workflow and libraries involved. #python #pdf #backenddev #automation #codewolfy https://lnkd.in/e7qqsFGq