SQL vs Google Sheets vs Python Logic for Conditional Statements

Today I wrote my first Python script. And it actually worked! The problem: I had 5 CSV files scattered across folders at work. Each file had sales data from different months. Manually combining them in Excel was taking 2+ hours every week, and I kept making copy-paste mistakes. So I decided to learn Python. After a few hours of YouTube tutorials and StackOverflow rabbit holes, I wrote a script that: 1. Reads all CSV files from a folder 2. Merges them into one dataset 3. Cleans column names (removes extra spaces) 4. Converts text numbers to actual numbers 5. Creates a calculated column (package total excluding GST) 6. Exports everything to Excel What used to take 2 hours now takes 3 seconds. Here's what I learned: 1. Python isn't as scary as I thought. pandas library does the heavy lifting. 2. The hardest part wasn't the code — it was figuring out WHAT I needed the code to do. 3. Small automation = big time savings. 2 hours/week = 104 hours/year saved. To anyone hesitating to learn coding: just start. Start with something annoying at work and automate it. Now I'm excited to automate more things. What should I tackle next? Drop suggestions below! 👇 #Python #DataAnalytics #Automation #LearningInPublic #DataScience #Pandas #DataEngineering #Excel #Analytics

The biggest difference between Python and SQL is not syntax... it’s the mindset. One interesting thing about working with data is this.. Python and SQL don’t just solve problems differently. They make you think differently. When you write SQL, your brain starts thinking in sets. You stop thinking row by row. Instead, you ask questions like: How can I filter this entire dataset? How can I group this information? How can I combine multiple tables together? SQL trains your mind to understand relationships, structure, and patterns in data. Python feels different. With Python, the thinking becomes more step-by-step. You start breaking problems into smaller pieces: read the data clean it transform it build logic around it automate or analyze further Python encourages procedural thinking. That’s why people who know both tools often solve problems faster. SQL helps you ask better questions from data. Python helps you build solutions around those answers. Neither replaces the other. They simply train two different parts of your analytical thinking. And when both ways of thinking start working together, data problems suddenly become much easier to solve. #Python #Sql #DataScience #DataAnalyst

Mastering strings in Python is one of those “small” skills that quietly powers almost everything we build—APIs, data pipelines, web apps, you name it. This week I revisited the fundamentals of Python strings: - Creating and slicing strings to access and update specific parts - Formatting with `format()` to build clean, readable outputs - Leveraging powerful built-in methods like `strip()`, `split()`, `join()`, `find()`, `replace()`, and case conversions (`lower()`, `upper()`, `title()`) to clean and transform text efficiently What struck me again is how much you can do *just* with string methods before reaching for heavier tools. If you’re starting with Python (or even revisiting basics), investing time in string operations is one of the highest-ROI steps you can take as a developer or data professional.

#Day 19/ 50daysChallenge #FileHandling 🔥 File Handling: File Handling means reading data from a file and writing data into a file using Python. Example: Saving text, reading notes, storing data, etc. ✅ Open a File: Code file = open("demo.txt", "w") file.close() 👉 "w" = write mode (create file) ✅ Write into a File: Code file = open("demo.txt", "w") file.write("Hello Python") file.close() Output (inside file) Hello Python ✅ Read a File: Code file = open("demo.txt", "r") data = file.read() print(data) file.close() Output Hello Python ✅ Append Data to File: 👉 "a" = append mode Code file = open("demo.txt", "a") file.write("\nWelcome to Python") file.close() Output (inside file) Hello Python Welcome to Python ✅Using with : This automatically closes the file. Code with open("demo.txt", "r") as file:    data = file.read()    print(data) Output Hello Python Welcome to Python 🎯Task: 👉 Ask user to enter name and store it in a file ✅ Code name = input("Enter your name: ") file = open("student.txt", "w") file.write(name) file.close() print("Name saved in file") ✅ Output Input Enter your name: Durga Output Name saved in file File content (student.txt) Durga #50daysChallenge #pythonBasics #coding #BVCEC #ECE

Let us look at the keyword “FROM” in SQL and Python. Sometimes the same word can exist in different environments but serve completely different purposes. Take “FROM” for an example. ● In Python, from is commonly used when importing modules, libraries, or specific functions into your notebook or script. Example: from pandas import DataFrame Here, Python is bringing tools into your workspace so you can perform tasks like data manipulation, analysis, or visualization. ● In SQL, FROM plays a different role. Example: SELECT column_name FROM table_name Here, SQL is specifying where the data is coming from, the table that holds the dataset you want to query. So even though the keyword is the same, the purpose is different. ● In Python, from helps you import tools. ●In SQL, FROM helps you retrieve data from a source. Same keyword. Different environments. Different responsibilities. This reminded me of something beyond tech. Sometimes we compare ourselves with others and wonder why our results look different. Why someone is doing something one way and we are doing it another way. However, like SQL and Python, we may be operating in different environments with different purposes. ● SQL doesn’t compete with Python. ● Python doesn’t compete with SQL. They simply perform different roles in the data ecosystem. So if today you feel like your journey looks different from someone else’s, remember this: You might just be operating in a different environment, and that is perfectly okay. Your uniqueness is part of your design. #DataAnalytics #SQL #Python #TechLearning #WomenInTech #ContinuousLearning #DataJourney #TechMotivation

🐍 Python Data Types – Easy Memory Cheat Sheet When I started learning Python, remembering all the methods for List, Dictionary, Set, and String felt overwhelming. Instead of memorizing everything randomly, I discovered a simple trick: group methods by their functionality. 🔹 List → AROC Add: append(), insert(), extend() Remove: remove(), pop(), clear() Order: sort(), reverse() Check: index(), count() 🔹 Dictionary → AUR Access: keys(), values(), get() Update: update(), setdefault() Remove: pop(), popitem(), clear() 🔹 Set → ARM Add: add(), update() Remove: remove(), discard() Math operations: union(), intersection(), difference() 🔹 String → CCFS Clean: strip(), replace(), split() Check: isalpha(), isdigit() Format: lower(), upper(), title() Search: find(), count(), startswith() 💡 Developer Tip: You don’t need to memorize every method. Use: dir(list), dir(dict), dir(set), dir(str) to explore them interactively. 📌 Sharing this cheat sheet to help beginners learn Python faster. If you're learning Python, this might save you hours of confusion! #Python #PythonProgramming #CodingTips #LearnPython #Programming #SoftwareDevelopment

🔍 Python Regex in Action! Built a zero-setup SQLite demo that creates healthcare patient data and uses PURE Python regex (re.search) to find all @email.com patterns. No SQL LIKE - strictly Python re module! 👨💻 GITHUB LIVE: https://lnkd.in/gR8xTD98 OUTPUT: Table created with 5 patient records Sample data: ID:1 | John Doe | john.doe@email.com | 123-456-7890 [...] Email pattern test (regex '@.*email\.com$'): John Doe: john.doe@email.com Jane Smith: jane.smith@email.com Sarah Wilson: sarah.w@email.com Bob Lee: bob.lee@email.com 💡 Key learning: re.search(r'@.*email\.com$', email) = SQL LIKE '%@email.com' Run it yourself → python test_regex.py (no pip install needed!) #Python #Regex #SQL #DataEngineering #HealthcareData #Portfolio

🚀 Day 12 – Python Sets (Multiple Valued Data Type) Today I explored Sets in Python — one of the powerful multiple valued data types! 🔷 🔹 What is a Set? A Set is a collection of multiple items that is: ✅ Unordered ❌ No indexing ✅ Mutable ❌ No duplicate values 📌 Sets are written using curly brackets {} my_set = {10, 20, 30, 40} print(my_set) 🔷 🔹 Important Characteristics 👉 Duplicates are automatically removed numbers = {1, 2, 2, 3, 4} print(numbers) # Output: {1, 2, 3, 4} 👉 Cannot access using index my_set[0] # ❌ Error 👉 We can add or remove elements fruits = {"apple", "banana"} fruits.add("mango") fruits.remove("banana") 🔷 🔹 Set Operations (Mathematical Power 💪) A = {1, 2, 3} B = {3, 4, 5} ✔️ Union → Combines both sets A.union(B) ✔️ Intersection → Common elements A.intersection(B) ✔️ Difference → Unique elements A.difference(B) 🔷 🔹 When Should We Use Sets? ✨ Removing duplicate data ✨ Performing mathematical operations ✨ Faster searching (membership testing) 🌟 Day 12 Complete! Learning step by step, building strong Python fundamentals. #Python #Day12 #LearningPython #DataTypes #Sets #ProgrammingJourney 💻

📊 Understanding Data Loading in Python: The Foundation Every Analyst Must Know One of the first hurdles in learning data analysis is the misconception that it's about memorizing syntax. Let me clear that up. Here's the code snippet for analysis: [import pandas as pd] We're importing Pandas, the workhorse library for data manipulation in Python. The "as pd" is just a convention — a nickname for tools we use constantly. [sales_file = 'sales data.xlsx'] This variable stores our file path. In practice, this could be a local file, a network path, or even a cloud storage location. [df = pd.read_excel()] This is where the heavy lifting happens. Pandas parses the Excel file, detects data types automatically, and creates a DataFrame object — essentially a spreadsheet on steroids with powerful manipulation capabilities. [df.head()] Always inspect your data after loading. This shows the first 5 rows by default, letting you verify no obvious issues in the first five rows The key insight: We don't need to memorize this like a phonebook. In today's AI-augmented workflow, understanding the logic is what matters — what each component does and why we use it. The syntax is just implementation. When you understand the logic, you can adapt: read_excel() becomes read_csv() for different file types. The file path variable can be replaced with a database connection string .head() can become .sample() or .info() depending on what you need to validate This is the difference between copying code and actually building solutions. #DataAnalytics #Python #Pandas #DataScience #Analytics #CareerGrowth

Practice. Practice. And practice again. I work with SQL and Python every single day — not just to write queries, but to truly understand the logic behind data. For me, it’s not about “getting the result”. It’s about understanding: Why this approach works When to use it And whether there’s a more optimal solution One task — multiple ways to solve it. That’s where real growth happens. I want to move beyond random decisions and reach a level where every solution is conscious, structured, and optimized. Because that’s what makes a strong analyst — not just tools, but thinking. Movement only forward. 🚀 #DataAnalytics #SQL #Python #AnalyticsJourney #ContinuousLearning #PhysicsWallah