Rafal M.’s Post

🐍 Day 2/30 — Python for Data Engineers Lists & Tuples. These two will follow you everywhere. In my 3 years as a Data Engineer, barely a day passed without using either of these. Here's what I wish someone told me on Day 1: Lists = Dynamic. You'll append rows, filter tables, and loop through pipeline stages. Tuples = Fixed. Every DB record you fetch comes back as a tuple. The one mistake beginners always make 👇 one = (42) ❌ # this is just an int one = (42,) ✅ # THIS is a tuple And the thing that makes Python lists actually powerful: List Comprehension — transform data in one line: active = [t for t, ok in all_tables if ok] That single line replaces 5 lines of for-loop code. 📌 Full cheat sheet in the image — save it for your daily reference. Day 3 tomorrow: Dictionaries & Sets 🔑 Follow Jaswanth Thathireddy if you're learning Python for Data Engineering 👇 #Python #DataEngineering #30DaysOfPython #LearnPython #DataEngineer

Python libraries every data analyst needs. The only Python libraries you need to start: 📊 pandas: data manipulation 📈 matplotlib + seaborn: visualization 🔢 numpy: numerical computing 📋 openpyxl: Excel automation 🔌 sqlalchemy: database connections That's it. Master these 5 and you can handle 90% of real-world analytics work. Don't get distracted by ML libraries until the basics are solid. #Python #DataAnalytics #DataTools #Pandas

Knowing Python isn't enough... You need to know how to work with real data. That's where Pandas comes in. Day 5 of my 30-day Data Science challenge Here's what I simplified into this cheat sheet 👇 Data Loading → read_csv, read_excel, read_json Data Inspection → head(), info(), describe() Data Cleaning → dropna(), fillna(), rename() Data Selection → loc, iloc, df['col'] Data Manipulation → groupby(), merge(), sort_values() Filtering → df[df['col'] > value], query() This is something I keep coming back to every single day. Save this — you'll need it Which Pandas function do you use the most? 👇 #Pandas #Python #DataScience #LearningInPublic #DataScienceFresher

Your 2020 Python skills are becoming a 2026 bottleneck. I’ve seen brilliant analysts struggle with memory errors and 10-minute wait times for simple joins. The problem isn't their logic; it’s their toolkit. The "Modern Python Stack" for Analysts has fundamentally shifted. If you are still relying 100% on Pandas and Matplotlib, you are leaving performance and interactivity on the table. I’ve fact-checked the production environments of top data teams this year. Here is the Save-Worthy 2026 Python for Analysts Cheat Sheet. 🚀 Polars: The multi-threaded engine that handles 10GB+ datasets on a laptop. 🦆 DuckDB: Run high-speed SQL directly on your local Parquet files. 📊 Plotly Express: Interactive charts that stakeholders can actually explore. ✅ Pydantic V2: Automated data cleaning that's 20x faster than traditional methods. 👇 The Big Debate: Is it finally time to retire import pandas as pd for good, or is it still the king of small-scale EDA? Let’s settle it in the comments. #Python #DataAnalytics #Polars #DuckDB #DataScience #MicrosoftFabric #2026Trends #Coding

Pandas is essentially Excel in Python — but way more powerful. Here's what you need to know: 📌 Two Core Data Structures: • Series — 1D, single column, homogeneous • DataFrame — 2D, multiple columns, heterogeneous 📌 Essential Operations Covered: • Importing CSV/Excel/SQL datasets • Indexing with .loc (label-based) & .iloc (position-based) • Data Cleaning — handling missing values with dropna() & fillna() • Removing duplicates with drop_duplicates() • Broadcasting — performing operations across entire columns • Joins & Merges — combining multiple datasets • Lambda & Apply — handling invalid values efficiently 📌 Pro Tip: Always use inplace=True if you want changes reflected in your original DataFrame! The best part? All of this with just a few lines of code. 🚀 Starting with a clean dataset is half the battle in Data Science. Master Pandas, and you're already ahead of the curve. #DataScience #Python #Pandas #MachineLearning #DataAnalysis

🚀 Day 1/20 — Python for Data Engineering From SQL to Python: The Next Step After spending time with SQL, I realized something: 👉 SQL helps us query data 👉 But real-world data engineering needs more than that. We need to: process data transform data move data across systems That’s where Python comes in. 🔹 Why Python? Python helps us go beyond querying: ✅ Process data from multiple sources ✅ Build data pipelines ✅ Automate workflows ✅ Handle large datasets efficiently 🔹 Simple Example import pandas as pd df = pd.read_csv("data.csv") print(df.head()) 👉 From raw file → usable data in seconds 🔹 SQL vs Python (Simple View) SQL → Get the data Python → Work with the data Together, they form the foundation of data engineering. 💡 Quick Summary SQL is where data access begins. Python is where data engineering truly starts. 💡 Something to remember SQL gets the data. Python makes the data useful. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

Started learning Pandas — and now data actually makes sense After working with NumPy, I realized something: Handling real-world data (like CSV files) still felt a bit messy. That’s where Pandas comes in. It’s a Python library designed to make working with structured data simple and efficient. 📊 What’s happening here: • read_csv() loads data into a table-like structure • head() shows the first few rows • info() gives a summary of the dataset 💡 What I understood today: – Pandas organizes data in a structured format (DataFrame) – It makes reading and exploring data very easy – This is exactly how real datasets are handled in Data Science This feels like a big step from writing basic programs to actually understanding data. Next: Selecting specific columns and filtering data in Pandas #Python #Pandas #DataAnalysis #MachineLearning #LearningInPublic #DataScience Here is the code:

Most small businesses lose hours every week updating data manually. ⏳ I recently built a reliable Python pipeline that handles the heavy lifting: ✅ Fetches data directly from APIs ✅ Cleans data & removes duplicates ✅ Stores everything in a structured PostgreSQL database ✅ Updates automatically every day No more manual copy-paste. No more messy spreadsheets. 🚫📊 This is a game-changer if you deal with: • Growing Excel files that crash constantly • API data that needs daily manual updates • Repetitive, boring reporting tasks If this sounds familiar, I can help you automate your workflow and reclaim your time. 🚀 Check out the Demo & Code here: 👇 https://lnkd.in/dyXCXSPk #DataAutomation #Python #ETL #SmallBusiness #Automation