New Python Notebooks for Data Analysis on GitHub

If you know data viz theory but are stuck on the coding part, this is for you. Murtaza Ali shares this refresher on Python fundamentals to help you start building visualizations programmatically.

Need a refresher on your #Python fundamentals? Check out my latest article with Towards Data Science below, which covers the foundations of Python necessary to get started with data visualization. #DataScience #Programming #MachineLearning #AI #Education #Technology #coding #DataVisualization #LearnToCode

Feel like you're writing complex loops for simple data cleaning tasks in Python? Ibrahim Salami's debut TDS article shares 7 lesser-known NumPy functions that can help you write cleaner, more efficient code for data analysis.

Python Data Structures Demystified 🧩 | Learn in 5 Minutes Python has several core data structures that every developer should know. They help you store, organize, and manipulate data efficiently, whether you’re building web apps, analyzing data, or automating tasks. Here’s a simple guide to the 5 essential Python data structures, with examples and when to use them: 1️⃣ List [ ] — The Flexible Collection Ordered collection of items. Mutable → you can add, remove, or change items. Example: my_list = [1, 2, 3, "Python"] Use Case: Store sequences that may change, like to-do lists or user inputs. 2️⃣ Tuple ( ) — The Immutable Sequence Ordered collection of items, but cannot be changed → Immutable. Example: my_tuple = (1, 2, 3) Use Case: Store values that should remain constant, like coordinates (x, y) or settings. 3️⃣ Dictionary { } — Key-Value Storage Stores data as key-value pairs. Keys are unique, values can change → Mutable. Example: my_dict = {'name': 'Neeraj', 'age': 21} Use Case: Structured data like user profiles, settings, or mappings. 4️⃣ Set { } — The Unique Collection Unordered collection of unique items → Mutable. Example: my_set = {1, 2, 3} Use Case: Remove duplicates or store unique values like IDs or tags. 5️⃣ String " " — The Text Container Sequence of characters, Immutable. Example: my_str = "Hello Python" Use Case: Store text-based data like messages, names, or labels. ✅ Why This Matters Understanding mutability and data structure behavior helps you: Write cleaner, efficient code Choose the right structure for the right task Avoid bugs and make programs faster and more readable #Python #PythonDeveloper #DataStructures #CodingTips #BackendDevelopment #Django #LearnPython #TechCommunity #100DaysOfCode #Programming

Stop wasting hours cleaning data manually, automate it with Python! Did you know that most data analysts and scientists spend over 60% of their time cleaning messy data instead of analyzing it? Here’s the good news; you can automate almost all of it using Python, with just a few lines of code. In my latest blog post on CodeWithFimi.com, I show you exactly how to: 1. Automate cleaning tasks using Pandas and Dask 2. Handle huge datasets that don’t fit in memory 3. Build fully automated cleaning pipelines with Airflow and Great Expectations Whether you’re a beginner or a pro, this guide will help you save time, reduce errors, and work like a modern data engineer. Read the full guide: https://lnkd.in/dAvrc5Y9 #DataScience #Python #MachineLearning #DataEngineering #Automation #Pandas #Dask #DataAnalytics #CodeWithFimi #Innovation

 From PySpark to Python — rediscovering the fundamentals Over the past few weeks, I’ve been revisiting Python — not just as a language, but as a foundation for strong data engineering. Having worked extensively with PySpark in large-scale data projects, I always thought of Python as “just a helper tool.” But when you go back to the basics — working with APIs, flattening JSONs, cleaning data, and writing small ETL scripts — you realize how powerful and elegant it actually is. Every line of code teaches something new: clarity, structure, and the importance of clean data flow. It’s interesting how rediscovering fundamentals can sharpen your problem-solving skills more than learning something brand new. So here’s a small reminder to anyone preparing for interviews or brushing up on their basics — don’t underestimate the power of revisiting what you already know. #Python #PySpark #DataEngineering #LearningJourney #ETL #CareerGrowth

Learning to Clean Data with Python When I first started working with data, I thought cleaning it would be the easy part. Just fix a few typos and move on. I was wrong. My first experience cleaning data in Python opened my eyes to how messy real-world data can be. I had to deal with: Duplicate entries that distorted results, Missing values that made columns incomplete, and Extra spaces and inconsistent text formats that quietly broke analyses. Using tools like Pandas, I learned to write simple but powerful commands to make the data usable again — drop_duplicates(), fillna(), strip(), and a few others quickly became my best friends. It reminded me so much of my time in data entry, where accuracy was everything. The difference is that, with Python, I wasn’t just typing data, I was transforming it into something clean, structured, and ready for insight. That experience taught me a valuable lesson: Before you can trust your data, you must clean your data. Now, every time I start a new project, I approach raw data with patience and a good cup of coffee. #DataCleaning #Python #DataScience #LearningJourney #Pandas #WednesdayMotivation

Curious about how a modern Python project is structured? We’ve just published a new blog: Hitlog Processing. This guide walks you through building a production-ready pipeline for analysing user journeys and ranking influential content, using the latest tools like UV for fast dependency management, Ruff for lightning-fast linting and formatting, and a robust Makefile-driven workflow for automation. The blog covers: * Clean, modular architecture for scalable Python projects * Two algorithmic approaches for influence ranking * Automated testing and code quality checks with Pytest and Ruff * How UV and Ruff fit into an industry-standard development pipeline 🔗 Read the blog: https://lnkd.in/ga5Muys2 🔗 Explore the repo: https://lnkd.in/g3ePb3aE Whether you’re building attribution systems or want to see how modern Python tooling can elevate your workflow, this is a great place to start! Follow https://lnkd.in/gHSeaqWH for more such posts.. #Python #SoftwareEngineering #UV #Ruff #BestPractices

Data Analysis with Python — Beginner Friendly Guide 📊🐍 Data analysis means turning raw data into clear answers. A single-stop, beginner-friendly guide that covers every essential concept for data analysis in Python from zero knowledge to practical skills. Includes step-by-step explanations, runnable examples, common pitfalls, quick cheats, and exercises you can publish on dev.to. What you need Python 3.8+; install via python.org or use Anaconda for bundled scientific packages. Recommended editors: VS Code or JupyterLab / Jupyter Notebook for interactive work. Install key packages: numpy, pandas, matplotlib, seaborn. Install commands pip install numpy pandas matplotlib seaborn notebook # or with conda conda install numpy pandas matplotlib seaborn notebook Start a notebook jupyter notebook Data types: int, float, bool, str, None. Collections: list, tuple \(immutable\), dict \(key-value\), set \(unique items\). Comprehensions: concise list/dict/set creation. Functions: def, arguments, return. Iteration: for, while; us https://lnkd.in/gf7GXHZN

🚀 Mastering Lists in Python 🧠 Today, I explored one of the most important data structures in Python — Lists! Lists are dynamic, mutable, and versatile — making them a go-to structure for storing and manipulating data efficiently. Here’s what I covered 👇 🔹 Definition & Characteristics – Lists are ordered, mutable, and can store elements of mixed data types. 🔹 Accessing Elements – Using positive and negative indexing. 🔹 Slicing – Extracting sublists efficiently using slice notation. 🔹 Commonly Used Methods – append(), extend(), insert(), remove(), pop(), sort(), reverse(), copy() 🔹 Built-in Functions – len(), sum(), max(), min(), list(), any(), all(), etc. 🔹 Basic Programs – Covered 20+ real examples like inserting, deleting, reversing, merging, finding average, removing duplicates, etc. 🧩 Lists are the foundation for solving many coding problems — from basic logic building to complex data processing! --- 💡 Key Takeaway: Learning Lists helps you understand data manipulation, iteration, and algorithmic thinking — essential for every Python developer and problem solver. --- LogicWhile #Python #LearnCoding #PythonLists #CodingJourney #100DaysOfCode #ProblemSolving #DataStructures #PythonForBeginners #CodeWithMe #DeveloperJourney #ProgrammingBasics #LogicBuilding #TechLearning #CodingCommunity