APIs in Python: Day 38 of Data Engineering Journey

Month 1 Recap: Python & SQL Foundations by Ethel Ayika, MS We just smashed through the first 4 weeks of the Python & SQL Foundations course! From setting up a professional development environment to writing our first logic-driven functions, the progress has been massive. In this video, we’re recapping the core pillars that turn a computer from a simple calculator into a programmable powerhouse. Whether you’re a student at KenteCode AI Academy or a self-taught dev following along, this is the essential review of the building blocks of AI Engineering. 📌 What We Covered: Software Installations: Moving past the basics. Setting up VS Code, terminal mastery, and using high-performance package managers like uv to manage project dependencies. Variables & Data Types: Understanding the "storage units" of code—Strings, Integers, Floats, and Booleans. Arithmetic Operations: How Python handles the math behind dynamic application states. Conditional Logic: Teaching your code to "think" using if, elif, and else statements. Functions: The "Don't Repeat Yourself" (DRY) principle. Learning to wrap logic into reusable, scalable blocks using def and return. 🛠️ The Tech Stack: Language: Python 3.12+ Tools: VS Code, Terminal (zsh/bash), uv package manager Database: SQL Fundamentals 🎓 Join the Community: If you're ready to move from basic scripts to building AI-driven applications, make sure you're subscribed. We’re moving into Data Analysis with NumPy and Pandas next! #PythonProgramming #SQLLearning #AIEngineering #KenteCode #LearnToCode #SoftwareEngineering #PythonBasics https://lnkd.in/gmFgmfns

Practicing Data Engineering with Python Today I worked on a small but very practical data engineering exercise: automating dataset downloads using Python. In this exercise, I built a script that: • Downloads multiple datasets from HTTP URLs • Extracts ZIP files automatically • Stores the CSV data in a structured folder • Handles invalid URLs without crashing I also explored different approaches for improving performance: • Sequential downloads using requests • Parallel downloads using ThreadPoolExecutor • High-performance async downloads using aiohttp This exercise helped me understand how real-world data ingestion pipelines work, where data engineers often need to collect large datasets from APIs or external sources before processing them. Small exercises like this build the foundation for ETL pipelines and data workflows. If you're learning data engineering, try building this yourself — it’s a great way to practice Python for data pipelines. #DataEngineering #Python #ETL #AsyncProgramming #LearningInPublic

🚀 Day 22 of My Data Analytics Journey Today I focused on Data Cleaning with Python (Pandas) — improving data quality before analysis. I realized that most real-world datasets are not clean. They often contain missing values, duplicates, inconsistent formats, or incorrect entries. Cleaning the data is one of the most important steps before performing any analysis. Here’s what I practiced today: • Loading datasets using Pandas • Identifying missing values in datasets • Handling null values (removing or filling them) • Removing duplicate records • Standardizing column names and formats • Checking data types and converting them when needed Key takeaway: Good analysis starts with clean and reliable data. Day 22 completed. The more I learn, the more I understand how important data preparation is in the analytics process. 📊 #Day22 #DataAnalytics #DataCleaning #Python #Pandas #LearningJourney #FutureDataAnalyst #Consistency

𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝗍𝘀 Python is used in software development due to its simplicity and powerful ecosystem. You can build real-world applications quickly and efficiently with Python. You can apply Python in several areas: - Automation: automate tasks like file processing and web scraping - Data Analysis: use libraries like Pandas and NumPy to process large datasets - Web Development: build APIs and backend systems with frameworks like Flask and Django - Machine Learning: use tools like TensorFlow and Scikit-learn for AI systems Source: https://lnkd.in/gxizaR-b Optional learning community: https://lnkd.in/g95CYbP3

Day 46 of my Data Engineering journey 🚀 Today I learned about scheduling and automation with Python an important step toward building real data pipelines. 📘 What I learned today (Automation in Python): • Why automation is essential in data engineering • Running scripts automatically instead of manually • Using Python’s schedule library • Understanding cron jobs for scheduled tasks • Automating repetitive data workflows • Building scripts that run daily or hourly • Thinking about reliability in automated jobs • Moving from scripts → pipelines In real data systems, data pipelines run automatically. No one manually runs scripts every day. Automation is what turns code into a real data pipeline. Why I’m learning in public: • To stay consistent • To build accountability • To improve daily Day 46 done ✅ Next up: connecting Python with databases 💪 #DataEngineering #Python #Automation #LearningInPublic #BigData #CareerGrowth #Consistency

⚡ Dask: Scaling Python Data Processing Beyond Memory 🐍 When working with large datasets in Python, tools like pandas are incredibly powerful, but they can hit limits when data grows beyond memory. That’s where Dask comes in. 🔹 What is Dask? Dask is a parallel computing library that allows you to scale Python workflows from a single machine to a distributed cluster, while keeping a familiar API. ✅ Why Use Dask? → Scales pandas workflows : Dask DataFrame mimics pandas but handles much larger datasets. → Parallel computation : Automatically distributes tasks across CPU cores or clusters. → Out-of-core processing : Work with datasets larger than RAM. → Integration with the Python ecosystem : Works well with NumPy, pandas, scikit-learn, and even machine learning pipelines. → Flexible deployment : Run locally, on Kubernetes, or on distributed clusters. 💡 Typical Use Cases → Large-scale data preprocessing 📊 → ETL pipelines for big datasets 🔄 → Machine learning preprocessing ⚙️ → Data science workflows that exceed memory limits Dask bridges the gap between simple data analysis and large-scale distributed computing, making it possible to scale Python workflows without completely changing your stack. #Python #Dask #DataEngineering #DataScience #ETL

Power up your data capabilities by learning how modern data pipelines are designed, built and managed. Gain practical experience using Python to ingest and connect data through APIs, strengthen your database expertise with advanced SQL and understand the full data lifecycle from source to insight. Walk away with the ability to build end-to-end pipelines that deliver real-world data impact. To find out more, visit Data Engineering Fundamentals: https://lnkd.in/gNtG3S7z Python for Data Engineering: https://lnkd.in/gbXRa9Hs Databases for Data Engineering: https://lnkd.in/gR3qiQHv NUS Computing #dataengineering #python #database

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻? 𝗦𝗮𝘃𝗲 𝘁𝗵𝗲𝘀𝗲 𝗻𝗼𝘁𝗲𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗵𝗲 𝗯𝗮𝘀𝗶𝗰𝘀. 🐍💻. If you're starting your Python journey, having clear and simple notes can make learning much easier. That’s why I created these beginner-friendly Python notes that cover the core fundamentals: • Python Basics • Variables & Data Types • Conditional Statements • Loops • Functions Instead of trying to memorize everything, focus on understanding the concepts and practicing regularly. Strong fundamentals today will make learning frameworks, data science, and backend development much easier later. Keep learning. Keep building. 🚀 📌 𝗦𝗮𝘃𝗲 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘁 𝗳𝗼𝗿 𝗾𝘂𝗶𝗰𝗸 𝗿𝗲𝘃𝗶𝘀𝗶𝗼𝗻 💬 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 “𝗣𝘆𝘁𝗵𝗼𝗻” 𝗶𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝗺𝗼𝗿𝗲 𝗻𝗼𝘁𝗲𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀 🔁 𝗦𝗵𝗮𝗿𝗲 𝘄𝗶𝘁𝗵 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘄𝗵𝗼 𝗶𝘀 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻? 𝗦𝗮𝘃𝗲 𝘁𝗵𝗲𝘀𝗲 𝗻𝗼𝘁𝗲𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗵𝗲 𝗯𝗮𝘀𝗶𝗰𝘀. 🐍💻. If you're starting your Python journey, having clear and simple notes can make learning much easier. That’s why I created these beginner-friendly Python notes that cover the core fundamentals: • Python Basics • Variables & Data Types • Conditional Statements • Loops • Functions Instead of trying to memorize everything, focus on understanding the concepts and practicing regularly. Strong fundamentals today will make learning frameworks, data science, and backend development much easier later. Keep learning. Keep building. 🚀 📌 𝗦𝗮𝘃𝗲 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘁 𝗳𝗼𝗿 𝗾𝘂𝗶𝗰𝗸 𝗿𝗲𝘃𝗶𝘀𝗶𝗼𝗻 💬 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 “𝗣𝘆𝘁𝗵𝗼𝗻” 𝗶𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝗺𝗼𝗿𝗲 𝗻𝗼𝘁𝗲𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀 🔁 𝗦𝗵𝗮𝗿𝗲 𝘄𝗶𝘁𝗵 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘄𝗵𝗼 𝗶𝘀 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻

🚀 Day 11 of #100DaysOfMachineLearning Today’s topic: Working with CSV Files in Python (Pandas) 📂🐼 Reading a CSV is easy. Reading it correctly and efficiently is the real skill. 🔥 Here’s what you should master 👇 📌 Open CSV from URL 📌 Control separators with sep 📌 Set index using index_col 📌 Define headers with header 📌 Load specific columns using usecols 📌 Handle single column with squeeze 📌 Skip or limit rows using skiprows / nrows 📌 Fix encoding issues 📌 Skip bad lines 📌 Control data types with dtype 📌 Parse dates properly 📌 Load huge datasets in chunks to save memory In short: 🔹 Clean loading = Clean analysis 🔹 Right parameters = Better performance 🔹 Memory optimization = Scalable systems Small details make a big difference in real-world data projects 💡 #MachineLearning #DataScience #Pandas #Python #DataAnalysis #LearningInPublic #100DaysOfMachineLearning #campusx