Jupyter Notebook Introduction

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.

🐍📺 In this video course, you'll learn how to sort data in a pandas DataFrame using the pandas sort functions sort_values() and sort_index(). You'll learn how to sort by one or more columns and by index in ascending or descending order. #python

Have you tried using PyO3? It is a library that helps integrate Rust with Python. It allows you to either expose Rust code as a Python extension module or embed the Python interpreter directly into a Rust application. A common usecase is when you have built your application in Python and start running into bottlenecks. Because Python being a dynamically typed interpreted language running on the GIL, has inherent performance limitations. I am sure some of you out there have already ran into it. Rust can give you a significant uplift here. Rather than rewriting the entire program, you profile your Python application, rewrite the hot paths in Rust, compile it as a native extension, and let the Python interpreter consume it seamlessly. Libraries like Polars and Pydantic V2 are great real-world examples of this pattern. In HFT systems built in Rust, the usecase flips. You want the strategies and algorithms written in Python because they are easier to iterate on, backtest, and hand off to quants. But the execution layer needs to be fast and deterministic which is happening in microseconds. Here you embed the Python interpreter into the Rust binary, load your core algorithm logic at runtime, and let the Rust runtime drive execution while calling into Python only when needed. It's fun to do this! PyO3 paired with Maturin (for build tooling) makes both of these workflows surprisingly ergonomic. How effectively have you used PyO3 in your projects or organizations? . . . . #rust #python #pyo3 #HFT #programming

Day 46 : Python Conditional Statements – If/Else Today I understood the conditional statements used in Python. Hands-on : - Today I learned about conditional statements in Python, which are used to control the flow of a program based on conditions. - I started with the basic syntax of the if statement, understanding how Python evaluates conditions and executes code blocks. -I then explored the if/else statement, which allows execution of alternate code when a condition is false. - Moving forward, I practiced if/elif/else statements to handle multiple conditions efficiently. - I also learned how to write if/else in a single line (ternary operator), which makes simple conditions more concise. - Finally, I explored nested if/else statements, where one condition is placed inside another to handle more complex logic. Result : - Successfully understood how to implement conditional logic in Python using different forms of if/else statements. Key Takeaways : - If statement executes code only when a condition is true. - If/Else provides an alternative execution path. - If/Elif/Else helps handle multiple conditions efficiently. - One-line if/else (ternary) makes code concise for simple conditions. - Nested conditions allow handling complex decision-making scenarios. #Python #Programming #DataAnalytics #LearningJourney #ConditionalStatements #CodingBasics #DataScience #BeginnerPython #AnalyticsSkills

🚀 Just put together a handy reference guide on Python’s zipfile module! 📦 Inside this Word doc you’ll find clear, ready‑to‑use code snippets for: ✅ Zipping an entire folder ✅ Extracting ZIP archives ✅ Checking if a file exists ✅ Creating compressed ZIPs (using ZIP_DEFLATED) ✅ Writing text directly into a ZIP file These examples cover the most common file compression tasks – perfect for automating backups, packaging datasets, or simplifying file transfers. Whether you're just starting with Python or you're a seasoned developer, these snippets can save you time and effort. 🐍 Feel free to download, use, and share! Let me know in the comments how you use zipfile in your own projects. 👇 #Python #Automation #Coding #DataEngineering #FileHandling #Zipfile #DeveloperTips

Headline: Making Health Data Actionable with Python! 🐍 I recently worked on a calorie requirement calculator that uses List Comprehension to streamline data processing. By combining conditional logic with Python’s concise syntax, I was able to map out nutritional needs across different age brackets and genders effectively. Key takeaways from this build: Using range() and list comprehension for efficient iterations. Implementing multi-level if-elif statements for precise data output. Practicing clean, readable code in Jupyter Notebook. Always looking for ways to make complex data simpler through code! Muhammad Rafay Shaikh #Python #DataScience #Coding #ListComprehension #JupyterNotebook #WebDevelopment

What if you could forecast any CSV without opening a single Python file? Getting a quick forecast usually means writing a full Python script first. Even for a simple dataset, you often need to load the data, configure a model, and run the code. TimeCopilot removes that setup by allowing you to forecast any public CSV directly from the terminal. Just run timecopilot forecast with a URL to the dataset and it handles the rest. You can also specify the LLM to use or ask a business question in plain English from the same command. After running it, you get: • A forecast generated automatically • The best model selected for your data • A plain-English answer to your question #TimeSeries #Forecasting #Python #CLI

I’ve wanted to write something in Rust for a while, and recently got a bit excited about the idea of a Python library in Rust. So I open-sourced fast-ordset - an ordered set for Python implemented in Rust (PyO3 + indexmap). In benchmarks it’s about 2–10× faster than the pure-Python ordered-set on various operations. The extension is thread-safe and doesn’t hold the GIL, so it works with free-threaded Python. One downside: iteration is noticeably slower - each element crosses the Rust→Python boundary (serialization/copying), so for x in s and list(s) are slower than the Python implementation. For indexing s[i] and bulk operations it’s fine. Install: uv sync --all-extras and uv run maturin develop --release. MIT license. Repo: https://lnkd.in/dXRXdWqY #Python #Rust #OpenSource

When I started writing Python, I used loops for everything. If I had to calculate something, I would go row by row. It worked. Until the dataset became bigger. Suddenly, the code felt slow. And sometimes messy. Then I learned about NumPy. Instead of working with single values, NumPy works with arrays — entire blocks of data at once. No complicated loops. No repeated calculations. Just clean, fast operations. At first, it felt strange. But once I understood it, I noticed something: The code became shorter. The output came faster. The logic became clearer. And that made me realize something important. Efficiency in data work is not just about speed. It’s about writing logic that can scale when data grows. For me, NumPy isn’t just a technical library. It’s what taught me to think bigger than one row at a time. #numpy #python

Day 48 : Python While Loops & Control Statements Today I understood while loop and its usage. Hands-on : - Today I explored while loops in Python, which are used to execute a block of code repeatedly as long as a condition remains true. - I started with the basic syntax of a while loop, understanding how it differs from a for loop by relying on conditions instead of sequences. - I then learned how to use the break statement, which allows exiting the loop immediately when a certain condition is met. - Next, I explored the continue statement, which skips the current iteration and moves to the next loop cycle without stopping the loop entirely. - Additionally, I learned about the else statement in a while loop, which executes only when the loop completes normally (i.e., not terminated by a break statement). Result : - Successfully understood how to use while loops along with break, continue, and else statements to control loop execution effectively. Key Takeaways : - While loops run as long as a condition is true. - Break is used to exit the loop early. - Continue skips the current iteration and moves to the next. - Else block executes when the loop finishes without interruption. - Proper loop control helps avoid infinite loops and improves logic building. #Python #Programming #DataAnalytics #LearningJourney #WhileLoop #CodingBasics #DataScience #BeginnerPython #AnalyticsSkills