Python Data Types: Integers, Floats, Strings, and Booleans

Day 9/120 – Today I learned something most beginners ignore… but pros don’t 😳🔥 Yesterday → Lists Today → CONTROL over data 👇 👉 Tuples & Sets in Python Here’s the problem 🤯 Lists can be changed anytime… But what if your data SHOULD NOT change? ❌ Example: Coordinates 📍 Dates 📅 Configurations ⚙️ That’s where TUPLES come in 👇 data = (10, 20, 30) ✔ Cannot be modified ✔ Safe & reliable Now comes something even more powerful 👇 👉 SETS nums = {1, 2, 2, 3, 3} Output? 😳 {1, 2, 3} ✔ No duplicates ✔ Clean data This is HUGE in Data Analytics 📊 Now I can: ✔ Protect data (Tuples) ✔ Clean data (Sets) This is getting serious now 🔥 Comment “DATA” if you're learning with me 💪 #Day9 #Python #DataAnalytics #LearningInPublic #CodingJourney #Consistency

Ever find yourself writing 10 different Excel formulas just to get basic summary statistics for your data? What if I told you there's a way to replace all those formulas with one simple line of code? 🤯 Save time with Excel's built-in Python: ⏱️ 🔹 One Line = Multiple Functions 🐍: Replace SUM, AVERAGE, COUNT, MIN, MAX with .describe() 🔹 Instant Summary Stats 📊: Get count, mean, std dev, quartiles automatically 🔹 Group Data Effortlessly 🔄: Replace complex SUMIFS with simple .groupby() 🔹 Merge Tables Instantly 🔗: Skip VLOOKUP headaches with .merge() function 🔹 No Coding Experience Needed ⚡: Just type =PY( and Excel guides you through it 🎥 I put together a tutorial showing how Python in Excel can replace your most frustrating formulas. Clear, simple, and will change how you think about data analysis. 👉 Watch here: https://lnkd.in/g7Qsa7CQ Have you tried Python in Excel yet? What Excel functions frustrate you most? Share below! 💬 #Excel #Python #DataAnalysis #ExcelTips #ExcelDictionary

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

4 of my #100DaysOfCode Moving from simple variables into actual Data Structures using Python Lists. As I grow in data analytics, I know organizing and manipulating data is the core of the job, so getting comfortable with lists is a critical foundation. Here is what I tackled in day 4. Randomisation: Using the Mersenne Twister (import random) and randint() to generate unpredictable outcomes. Lists: Creating, altering, and managing data structures using brackets []. List Methods: How to use .append(), .extend(), .insert(), and .pop(). Indexing: Accessing specific data points (and successfully conquering negative indexing!). To put it all together, we built a fully functional Rock, Paper, Scissors game that plays against the user.

I started using Pandas last week. After a month of Python and NumPy, I thought I was ready. First impression: it feels like Excel. But smarter. In code. NumPy gave me arrays—rows of numbers I could analyze mathematically. Pandas gives me DataFrames—full tables with column names, mixed data types, and the ability to ask real questions of real data. The difference hit me immediately: With NumPy I was working with arrays I created myself. With Pandas I loaded an actual CSV file. Real column names. Real messy data. Real supply chain numbers. And in 3 lines of code: pd.read_csv() df.head() df.info() I could already see which suppliers had missing data, what their delivery rates looked like, and which columns needed cleaning. That's not practice anymore. That's actual analysis. This is where Python stops being theoretical and starts being useful. #Python #Pandas #LearningInPublic #SupplyChain #DataAnalytics

🐼 Most people learn Pandas… But forget the syntax when they actually need it. While working on real data, constant searching = wasted time. That’s why this Pandas Cheat Sheet helps 👇 📌 Covers: • Import (CSV, Excel, SQL) • Data inspection • Cleaning & filtering Perfect for: • Interviews • Projects • Quick revision 💡 The right cheat sheet can save hours. #Python #Pandas #DataAnalytics #DataScience #LearnPython

Here's what I learned this week in data analysis: Data theory: Segmentation thinking Spreadsheets: Nested IF SQL: LEFT JOIN Python: Conditionals Visualization: Line charts R: Factors One thing that stood out this week was joins. Learning LEFT JOIN in SQL and then working with similar ideas in R really helped it click for me. At first it feels like you’re just combining tables, but it’s actually more about understanding how data connects and what gets lost or kept depending on your approach. It definitely made me realise how easy it is to get the wrong result if your logic isn’t clear. Slowly starting to see how everything links together.

groupby() in Pandas Most beginners use Pandas but completely ignore groupby(). That’s a mistake. Because groupby() is where real data analysis starts. Think of it like this: You don’t just want data, you want insight by category. Example: - Average salary by department - Total sales by city - Count of customers by country That’s exactly what groupby() does. df.groupby("department")["salary"].mean() If you’re not using groupby properly, you’re not doing analysis you’re just looking at data. Learn how to use groupby effectively : Link in Comment #Python #Pandas #DataAnalysis #MachineLearning

Where do you get data to process and analyse in Excel? Do you rely on manual data entry? But to me, that is tiresome and time-consuming. Here is the secret key. Today I scraped 250 countries plus their details using Python libraries such as BeautifulSoup, Requests, and Pandas. Here is the GitHub link https://lnkd.in/dJh2cDPx And here is the screenshot for an Excel snippet

🚀 From Excel → Python → SQL: The Ultimate Data Transition Cheat Sheet Still jumping between Excel formulas, Pandas code, and SQL queries? 🤯 Feeling like you're learning the same thing again and again… just in different syntax? This visual solves that problem 👇 It shows you how ONE data operation translates across THREE powerful tools: 🟢 Excel 🔵 Python (Pandas) 🟠 SQL 💡 Inside this cheat sheet: ✔️ Load & filter data like a pro ✔️ Select, sort & transform datasets ✔️ Perform aggregations & GroupBy ✔️ Handle missing values & duplicates ✔️ Merge / Join tables effortlessly ✔️ Extract insights from dates ✔️ Work with real interview-level operations 🎯 Why this matters: Once you understand the logic, you don’t need to memorize syntax anymore. You become tool-independent and that’s what top companies look for 💼 🔁 Share it with someone stuck in Excel #data #analytics #excel #sql #python