Python 3 Interpreter Internals Guide

7 Python Libraries That Made Me Rethink How I Write Code Small libraries, big shifts in how I design Python systems. Maria Ali Four years ago, my Python code looked exactly like what you’d expect from someone trying to “solve the problem quickly.” Long scripts. Messy utilities. A few copy-pasted Stack Overflow snippets glued together with hope. And to be honest… it worked. Until it didn’t. The turning point came when I started building automation tools seriously. Scripts that processed thousands of files. Tools that summarized research papers. Bots that generated reports overnight. Suddenly, my old approach felt fragile. That’s when a handful of Python libraries quietly changed the way I think about writing code. Not because they were trendy. But because they forced me to rethink how problems should be solved in the first place. Here are seven libraries that did exactly that.

🐍 CPython Internals: Your Guide to the Python 3 Interpreter 📖 ➡ Read and navigate the CPython 3.9 interpreter source code.  ➡ Master CPython’s memory management capabilities. ➡ Debug C and Python code like a true professional. And much more... https://lnkd.in/ejS9mzT

We did not need to replace Python. We needed to move the hot path. On one project, I worked on a scoring platform processing 1,000+ calculations per minute on biological datasets. Python with FastAPI handled the API layer, async jobs, and orchestration very well. The bottleneck showed up in the compute layer: complex mathematical evaluation, 50+ supported functions, and thousands of formula executions per minute. So we moved the compute-intensive formula evaluation into Go modules. The result was a 3x performance improvement over the pure Python implementation. The lesson was not “Go > Python.” It was: strong engineering choices happen per layer, per workload, and per constraint. Python was a great fit for orchestration and product delivery. Go was a better fit for raw compute in the hot path. Both made sense in the same system. I do not really think in terms of favorite languages anymore. I think in terms of what this layer actually needs. #Python #Golang #BackendDevelopment #SoftwareEngineering #SystemDesign #API #GO

I have been seeing a lot of discussion surrounding Python being slow, not good for production, need to use Rust or CPP. Infact it is already CPP or Rust. You are just not seeing it. All the Neural Network packages in python are wrappers on C++ or other high performance language. If you are looking at the Agentic frameworks, which are python heavy abstraction, its natural to think its slow due to python. Its really not Python, its how LLMs work. Python does not actually exist as a standalone machine-level entity. Every single variable, list, or function you create in Python is fundamentally a C structure (a struct) called a PyObject. If you are saying Python is slow, that means you are calling out C Language. Saying in forums that Python is slow, and it needs to be replaced shows your incompetence and lack of attention. Dive deep and understand the architecture, dependencies and how things are wired. These are not taught in Certifications or in your College.

In Python, literals are fixed values written directly in a program. Below is the list of Python literals with one example each. Type of Literal Description Example Numeric Literal Represents numbers (integer, float, complex) x = 10 String Literal Represents text enclosed in quotes name = "Python" Boolean Literal Represents truth values is_active = True Special Literal (None) Represents absence of value value = None List Literal Collection of ordered elements numbers = [1, 2, 3] Tuple Literal Ordered immutable collection t = (10, 20, 30) Set Literal Unordered collection of unique elements s = {1, 2, 3} Dictionary Literal Collection of key–value pairs d = {"a": 1, "b": 2}

🐍 Day 12 of My 30-Day Python Learning Challenge Today I worked on a real-world concept: File Handling in Python. 📌 Problem: Read a file and count how many words it contains. 📌 Code: file = open("sample.txt", "r") content = file.read() words = content.split() print(len(words)) file.close() 📌 Output: Total number of words in the file 💡 Why this matters? File handling is used in: • Data processing • Log analysis • Backend development 📊 Quick Question What will happen if the file does NOT exist? A) Error B) Empty output C) None D) 0 Answer tomorrow 👇 #Python #FileHandling #CodingJourney #LearningInPublic #SoftwareDeveloper

🛩️ Excited to share my new python project--Voice Assistant I have developed a simple Voice Assistant using python that can recognize voice commands and performs tasks like opening websites and telling the current time 🔹 Features . Voice command recognition . open YouTube and google . Tell the current time . Wikipedia search 🛠️ Technologies Used python,SpeechRecognition,pyttsx3,webbrowser This project helped me improve my python programming and understand how voice recognition works. 🔗 GitHub project link: https://lnkd.in/guV8uESc #python #VoiceAssistant #programming #AI #GitHub #Learning

🚀 Day 68 | Python Revision (Up to Recursion) Today I focused on revising all Python concepts up to recursion 📘 🔹 What I Revised: • Basics → variables, data types, input/output • Control statements → if-else, loops • Functions → user-defined functions, arguments • Built-in functions → len(), sum(), min(), max(), etc. • String methods → strip(), split(), replace(), join() • List & Dictionary operations • Lambda functions and functional programming basics • Recursion → factorial, list flattening 💡 Key Learning: • Revision helps in connecting all concepts together • Improved clarity on when to use loops vs recursion • Strengthened understanding of problem-solving approaches 🔥 Takeaway: 👉 Strong fundamentals come from consistent revision Consistency + Revision = Confidence 🚀 #Day68 #Python #Revision #Recursion #ProblemSolving #CodingJourney #10000Coders #PythonDeveloper #SravanKumarSir

Machine Learning Time Series Data using pyflux #machinelearning #datascience #timeseriesdata #pyflux PyFlux is a new library for time series analysis for Python. It brings together a vast array of time series models, including recent models such as score-driven models and variational state space models, as well a flexible choice of inference options, including black box variational inference. https://lnkd.in/gH-ZjwCb