DuckDB: SQL Alternative to Pandas

Day 39 of my Data Engineering journey 🚀 Today I started working with Pandas the most powerful data manipulation library in Python. 📘 What I learned today (Pandas Basics): • What Pandas is and why it’s important • Understanding Series and DataFrame • Reading CSV files using read_csv() • Inspecting data with .head() and .info() • Selecting columns and rows • Basic filtering operations • Understanding data types in DataFrames • Thinking in tabular data structures Pandas turns raw data into something you can explore, clean, and transform. SQL queries databases. Pandas transforms datasets. This is where Python becomes powerful for analytics and pipelines. Why I’m learning in public: • To stay consistent • To build accountability • To improve daily Day 39 done ✅ Next up: filtering, sorting & aggregating data with Pandas 💪 #DataEngineering #Python #Pandas #LearningInPublic #BigData #CareerGrowth #Consistency

7 Python packages for finance, YOU NEED TO KNOW! 🔥 All packages are available for FREE on GitHub. Which would you add? —— Interested in LLMs and AI Agents? Subscribe to our blog for super-detailed articles and get all our FREE data science cheat sheets in a single PDF: https://lnkd.in/dvCwyc4h

SQL Basics: Complete. ✅ Python: Loading... 🐍 I just wrapped up my deep dive into SQL fundamentals! From mastering JOINS and Aggregations to fine-tuning logic with HAVING and GROUP BY, I’m officially comfortable moving and structuring data. My goal is: I’m building a foundation in Analytics Engineering to help international teams turn messy data into clear insights. Next stop: Python for Data. I’m self-studying this path because I’m obsessed with how data moves behind the scenes. If you’re a Data Lead or a fellow learner, I’d love to connect! #SQL #DataAnalytics #AnalyticsEngineering #SelfTaught #DataScience #Python #LearningInPublic #RemoteWork

🎓 Just completed "Hypothesis Testing in Python" on DataCamp! Solid course for anyone who wants to go beyond knowing the theory and actually implement statistical tests in Python. Here's a quick breakdown: 📚 What you'll learn across 4 chapters: 1️⃣ Hypothesis Testing Fundamentals The core workflow — one-sample proportion tests, z-scores, p-values, and false positive/negative errors. The essential foundation. 2️⃣ Two-Sample & ANOVA Tests T-tests for two groups, then ANOVA for 3+ groups — crucial for avoiding Type I error inflation from running too many t-tests. 3️⃣ Proportion Tests & Chi-Square Testing categorical data with chi-square independence and goodness-of-fit tests. Super practical for real-world survey and behavioral data. 4️⃣ Non-Parametric Tests ← my personal highlight 💡 When your data violates normality assumptions — Mann-Whitney U, Wilcoxon, Kruskal-Wallis. Often overlooked, but incredibly useful in practice. 🐍 What makes it stand out: Hands-on Python with real-world datasets. Theory meets code — which is exactly how it should be taught. 📎 I put together a free PDF revision sheet with all key code examples + a cheat sheet for choosing the right test — drop a comment if you like it! 📌 Recommended for: Data Analysts, Economists, Finance professionals, anyone making data-driven decisions. #DataScience #Python #Statistics #HypothesisTesting #DataCamp #ContinuousLearning

What if you could write DataFrame logic once and run it on any SQL database? Many data workflows begin with pandas for quick experimentation, while production pipelines might run on databases like PostgreSQL or BigQuery. Moving from prototype to production usually means rewriting the same transformation logic in SQL. That translation takes time and can easily introduce errors. Ibis solves this by letting you define transformations once in Python and compiling them into native SQL for 25+ backends automatically. --- 🚀 Tools for Portable DataFrames in Python: https://bit.ly/4rB6C7Y #Python #DataScience #SQL #DataEngineer

We are officially done with SQL! Now, we are moving onto Python for data analysis. Welcome to Part 6 of the Data Analyst Roadmap Series (And Part 1 of our Python journey!) If SQL is the tool that answers "What happened?", Python is the analysis engine that answers "Why did it happen, and what should we do next?" It sits perfectly between your database and your final dashboards. But before you can analyze anything, you have to teach Python how to read your reality. That is where the Pandas library comes in. Here is how Python thinks about your data: 1. The Ingestion (read_csv / read_sql) When you load a file, Python doesn't just read it; it transforms static storage into a DataFrame. Think of a DataFrame as a highly optimized, super-powered spreadsheet living directly inside your computer's memory (RAM). 2. The Inspection Phase You can't analyze what you don't understand. Before building complex logic, professionals use these three core tools to audit their new DataFrame: head(): Shows you the first few rows. (This is the Python equivalent of SQL's SELECT * LIMIT 5). It validates that your data loaded correctly. info(): The ultimate x-ray. It instantly tells you your data types, memory usage, and exactly how many missing (null) values are hiding in your dataset. describe(): Instant statistical math. With one line of code, Python runs vectorized calculations to give you the mean, min, max, and standard deviation for every numeric column. Great analytics doesn't start with complex machine learning. It starts with validating and understanding the structure of your raw data. For the Python users out there: what is the very first function you run the second you load a new dataset into your notebook? Let me know in the comments! #DataAnalytics #Python #Pandas #DataScience #DataEngineering #TechCareers #DataAnalyst #LearningPath

Pandas vs. Polars — Is It Time to Switch? If you’ve worked with data in Python, you’ve almost certainly used pandas. It’s been the go-to library for data manipulation for over a decade. But lately, Polars has been gaining serious traction — and for good reason. Polars promises to be: ⚡ Faster (significantly, on most operations) 🧠 More memory-efficient 📖 More intuitive API design But is it actually worth learning? And how different is it really? The short answer: yes, and more than you’d expect. Polars is built in Rust, uses Apache Arrow under the hood, and executes queries lazily — meaning it optimizes your operations before running them. The result? Workflows that used to take minutes can run in seconds. The longer answer: pandas isn’t going anywhere. It has a massive ecosystem, deep integrations, and years of community knowledge. But if you’re regularly working with large datasets or performance-critical pipelines, Polars is worth your time. The best part? Once you understand the mental model shift (from index-based to expression-based), the learning curve is surprisingly manageable. Have you tried Polars yet? #Python #DataScience #Pandas #Polars #DataEngineering #MachineLearning

For more than a decade, Pandas has been the default tool for working with data in Python. But recently I kept hearing about another library that claims to be faster, more memory-efficient, and designed for modern data workloads. That library is Polars. Naturally, I didn’t want to rely on internet benchmarks or hype. So I ran my own experiments comparing Polars vs Pandas using a real dataset and a practical workflow. Here’s what I found: • CSV loading was ~3.6× faster with Polars • GroupBy operations were ~1.7× faster • Memory usage dropped by ~21% But something interesting happened. In one pipeline, Pandas was actually faster. So the real question isn’t “Is Polars better than Pandas?” It’s: When should you use each one? I documented the full comparison — including: ✓ Architecture differences ✓ Lazy query optimization ✓ Benchmark results ✓ Memory usage comparison ✓ Where Polars wins (and where Pandas still shines) All explained with code and experiments. 📄 Full breakdown in the PDF below. Curious to hear from others working with Python data tools: Have you tried Polars in your workflows yet? #Python #DataEngineering #DataScience #Polars #Pandas #dataanalyst #ai

What if you could get LaTeX-quality docs without LaTeX, or query 22 databases without rewriting SQL? This week's best Python finds: • Quarkdown - Build LaTeX-quality documents with clean Markdown syntax • Ibis - Write one query, run it on 22+ database backends • Narwhals - Make any function work with pandas, Polars, and DuckDB using one decorator • Docling - Extract Word document comments as structured data without XML parsing I send 2 tips like these every Tuesday and Thursday in the CodeCut newsletter to help you discover useful Python tools for data and AI. 📬 Subscribe here: https://bit.ly/46fdOPl