Building Python and SQL Fundamentals with Automation

Week 17 — Dates & Time in Python (Data & Libraries) Most bugs in data systems don’t come from logic — they come from time. That’s why mastering Python’s datetime library is a must-have skill. What Python handles effortlessly ✔ timestamps ✔ date arithmetic ✔ formatting & parsing ✔ comparisons & ranges Common real-world uses log timestamps calculate durations filter data by date automate schedules build time-aware analytics 💡 Time management isn’t just for humans ⏰ — Python handles it too. #PythonDatetime #LearnCoding #PythonTips #DataEngineering #Automation

Turning data into insights using Python 🚀 Today I worked on generating human-readable expense summaries by combining SQL analysis with clear explanations. This is how data becomes useful for real users. #Python #SQL #DataAnalysis #BackendDevelopment #AIThinking

DAY 20: Python — Pandas Basics & Data Operations Worked with Pandas Series and DataFrame, practiced indexing and selection (loc, iloc), filtering data, handling missing values, and explored core operations like merge, join, concat, groupby, aggregation, pivot tables, and cross-tabulation. #Python #Pandas #DataAnalysis #DataFrame #LearningInPublic

📅 #Day06 of my 30 Days of Python Practice Refactored data validation and normalization logic into reusable utilities and composed a clean preprocessing pipeline. This reinforced how much real data science depends on maintainable pipelines, not just models. 🔗 GitHub: https://lnkd.in/g-nakbdn #DataScience #Python #DataEngineering #CleanCode #EnergyAnalytics #LearningInPublic #EnergyDataScience #CodeBasics #BuildingInPublic

Analyzing data using Python + SQLite 🚀 Today I worked on identifying the highest spending category using SQL ORDER BY and LIMIT. This is how raw data turns into meaningful insights. #Python #SQL #DataAnalysis #BackendDevelopment

Master web scraping for dynamic sites with 'load more' and infinite scroll. This 2026 guide provides actionable Python techniques to automate data extraction and overcome modern challenges. Unlock the web's full potential. [Read More] #WebScraping #DataExtraction #Python #Selenium https://lnkd.in/dN4TA_UB

🐍 Python Basics – Core Data Types While revisiting Python fundamentals, I focused on data types, which form the backbone of any Python program. 🔹 Numeric Types int → Whole numbers (e.g., 10, 100) float → Decimal values (e.g., 10.5) complex → Real + imaginary numbers 🔹 Sequence Types str → Text data list → Ordered & mutable collection tuple → Ordered & immutable collection 🔹 Set Types set → Unordered, unique elements frozenset → Immutable set 🔹 Mapping Type dict → Key–value pairs for structured data 🔹 Boolean Type bool → True / False 🔹 None Type None → Represents absence of a value 💡 Understanding when and why to use each data type helps write cleaner, more efficient, and bug-free code. #Python #PythonBasics #DataTypes #Programming #Dataengineer #Coding

#Python scripts don’t get smarter because we work harder — they get smarter when we choose better libraries 🧠⚙️ This Medium piece breaks down how a few quietly powerful Python libraries can turn basic scripts into reliable automation helpers ⏱️🤖 No hype. Just tools that save time, reduce repetition, and run without babysitting. If your script still waits for you to press “run”… it’s time for an upgrade. 📖 Worth the read for anyone building automation, data pipelines, or side projects in Python. #Python #Automation #DataEngineering #Programming #DeveloperTools #MediumReads #Productivity #TechLearning

PYTHON PROGRAMMING – DAY 2 Variables & Introduction to Data Types Topics covered: What is a variable in Python How Python stores variables in memory Dynamic typing concept Variable naming rules Valid vs invalid variable names Best practices for writing variables Naming conventions (snake_case vs camelCase) Python data types: Primitive: int, float, bool, str, complex Non-primitive: list, tuple, set Structured learning and hands-on practice with guidance from Ajay Miryala at 10000 Coders #Python #PythonProgramming #Variables #DataTypes #PythonBasics #LearningPython #10000Coders

📊 Pandas Basics: Series vs DataFrame Series = single column (1D) DataFrame = table of columns (2D) Understanding this is the first step in data analysis. #Pandas #Python #DataAnalysis #DataScience #PandasBasics #LearningInPublic #MLPreparation