Introducing ViewTable GUI for Pandas DataFrames

🚀 I just published my first Python library on PyPI! As a self-taught developer learning Data Science, I faced a simple but annoying problem every day: ❌ print(df) → boring console output ❌ Hard to read 3500+ rows in terminal ❌ No visual info about nulls, duplicates So I built the solution myself. 💡 ✅ Introducing ViewTable — A beautiful GUI table viewer for Pandas DataFrames! 📦 pip install viewtb 🔥 What it does: → Opens a beautiful dark-mode GUI table → Shows null cells in Blue → Shows duplicate rows in Red → Sidebar with dataset info — rows, columns, memory → Just ONE line of code! 💻 Usage: ——————————————— from viewtb import ViewTable df = pd.read_csv('data.csv') df.dropna(inplace=True) ViewTable(df, info=True) ✨ ——————————————— Built with: 🐍 Python 🐼 Pandas 🎨 Tkinter Canvas + CustomTkinter This is Day 1 of my Data Science journey. Small library. Big learning. 🙏 👇 Check it out: 🔗 GitHub: [your link] 📦 PyPI: [your link] #Python #DataScience #OpenSource #MachineLearning #100DaysOfCode #Programming #buildinpublic

I Tracked My Expenses Using Python & NumPy — Here's What ₹38,940 Taught Me About My Spending Habits I built a Personal Finance Tracker using just Python and NumPy — no Pandas, no fancy libraries. Here's what I discovered about my own spending 👇 The project started simple: a CSV file with 50 transactions across 3 months. But when I ran the numbers through NumPy, the insights hit different. What the data revealed: • Shopping eats 40% of my budget — with just 6 transactions • My Top 5 purchases alone = 36% of total spending • Average spend (₹779) vs Median (₹465) — proof that a few big buys skew everything • 56% of money goes to just 11 "high-tier" transactions What I actually built: → Read raw CSV data using Python's csv module → Converted everything to NumPy arrays for fast computation → Used np.sum(), np.mean(), np.max(), np.median(), np.std() → Boolean masking to filter by category & month → np.argsort() to rank top expenses → np.percentile() for distribution analysis → A formatted summary report printed right to the console. Key takeaway: You don't need complex tools to get powerful insights. NumPy + a CSV file + curiosity = real, actionable data about your life. Watch the screen recording below to see the full report output! This is Week 1 of my Python data journey. Next stop: Pandas & Matplotlib. #NumPy #DataAnalysis #PersonalFinance #LearningInPublic #PythonProjects #BuildInPublic #Python #DataScience #CodeNewbie #Programming #TechTwitter #DataDriven #100DaysOfCode #FinanceTracker

Did you know that... A 2023 study found that 99.81% of figures generated in a sample of 100,000 Jupyter notebooks did not contain alt text. But take heart! You can use the MatplotAlt Python library to add alt text to your matplotlib figures, making them more accessible to blind and visually impaired (BVI) users. Alt text can be added manually: add_alt_text(‘Your custom description’, methods=[‘html’, ‘markdown’]) Or automatically: show_with_alt(desc_level=3, methods=[‘html’, ’img_file’]) Both functions take a “methods” parameter to control output formats. The automatic version uses a “desc_level” parameter based on a four-level semantic model to control the depth of detail. Learn more about MatplotAlt here: https://lnkd.in/efApReeQ Access the GitHub page here: https://lnkd.in/e2pD2E7m Install via PyPI: pip install matplotalt ...Now you know! And knowledge is power. What else do you use to make your data and visualizations more accessible? I’d love to hear your thoughts in the comments! -Your friendly neighborhood Data Scientist

I used to be really confused about NumPy and Pandas before/while learning them. They both seem similar at first. Here’s a simple way I understood them: 1. Numpy was built first (2005) to solve Python numerical problems. Python lists were slow for numerical work. And numpy made it faster and easier with C-based arrays. And when I learned about substitution, like you don't even have to use loops for those kinda tasks. 2. Pandas came later(2008) because Numpy was great with numbers, but real-world data is messy. So, to work with missing data and to work with other apps like Excel and SQL, it was created. The important part is that in most real projects, you don’t really choose one over the other; you use both together. Use NumPy when: 1. Working with pure numerical computations (linear algebra, mathematical operations) 2. Handling arrays, images, or signal data 3. You need performance and memory efficiency Use Pandas when: 1. Working with tabular or relational data (like Excel or SQL) 2. Dealing with missing or messy real-world data 3. Performing data cleaning, aggregation, or analysis 4. Working with time series data So in practice: NumPy handles the fast numerical backbone, and Pandas builds on top of it to make data handling more practical and readable. #pandas #numpy #NumpyVsPandas

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

The loop that takes 47 seconds becomes 0.3 seconds. Day 11 of 30 -- Advanced Pandas Optimization No new hardware. No rewrite. Just one change. Replace iterrows() with a vectorized expression. Here is what most Pandas developers do not realize: A DataFrame is just a NumPy array -- contiguous C memory. When you write df.iterrows(), Python converts every row to a Python dict. You are running a Python for-loop over a C array. That is where the 47 seconds comes from. Write df['total'] = df['qty'] * df['price'] instead. That is a C loop on the raw array. 157x faster. Today's topic covers: Why Pandas can be slow -- the Python loop trap explained Speed hierarchy -- iterrows 47s vs apply 28s vs itertuples 5s vs vectorized 0.3s dtype optimization -- 6 dtype conversions that cut memory by 70% before writing a single query Auto dtype downcast function that optimizes an entire DataFrame in 10 lines pd.eval and query for complex expressions without intermediate arrays Chunked processing -- 50M rows on a laptop with 6GB RAM Real scenario -- retail analytics, 48GB to 6GB, 4 hours to 8 minutes 8 optimization techniques including the SettingWithCopyWarning trap 5 mistakes including growing DataFrames in loops and loading unused columns Key insight: Pandas is not slow. Writing Python loops over Pandas DataFrames is slow. #Python #Pandas #DataEngineering #Performance #SoftwareEngineering #100DaysOfCode #PythonDeveloper #TechContent #BuildInPublic #TechIndia #DataScience #Analytics #PythonProgramming #LinkedInCreator #LearnPython #PythonTutorial

𝗬𝗼𝘂𝗿 𝗣𝗮𝗻𝗱𝗮𝘀 𝗶𝘀𝗻’𝘁 𝘀𝗹𝗼𝘄. 𝗬𝗼𝘂𝗿 𝗰𝗼𝗱𝗲 𝗶𝘀. If your Python script "hangs" the moment you load a 1GB file, you don't need to go out and buy a 128GB RAM Macbook. You just need to stop treating Pandas like an Excel spreadsheet and start treating it like a Matrix. Here are 4 simple switches that can turn a 10-minute wait into a 10-seconds win: 𝟭. 𝗧𝗵𝗲 𝗟𝗼𝗼𝗽𝘀 Using loops or "iterrows()" is like asking a delivery driver to go back to the warehouse for every single package. It’s exhausting and slow. The Fix: Use NumPy-backed operations (like df['a'] + df['b']) The Magic: This uses something called SIMD, which lets your CPU process a whole "block" of data at once instead of one row at a time. 𝟮. 𝗧𝗵𝗲 "𝗮𝗽𝗽𝗹𝘆()" A lot of people think ".apply()" is fast. It’s not. It’s just a loop wearing a fancy suit. The Hack: Always check for "Accessors" first. Example: Don't use a lambda to capitalize text. Use ".str.upper()". These are built in C and run at lightning speed. 𝟯. 𝗧𝗵𝗲 𝗗𝗼𝘄𝗻𝗰𝗮𝘀𝘁𝗶𝗻𝗴 Pandas is "pessimistic." It defaults to the biggest data sizes (like "int64"), even if your numbers are small. Change "Object" columns (strings) to "category". The Result: You can often shrink your memory usage by 90% just by changing the data type. 𝟰. 𝗨𝘀𝗲 𝗡𝘂𝗺𝗯𝗮 𝗳𝗼𝗿 𝗜𝗺𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝗟𝗼𝗴𝗶𝗰 Sometimes your math is too complex for standard Pandas functions. Instead of going back to slow loops, use the "numba" library. Pro Move: Adding a simple "@jit" decorator compiles your Python code into "machine code" while it runs. It’s basically giving your script a jet engine. #DataScience #Python #Pandas #BigData

🔢 Why NumPy Matters in Data Science (More Than I Thought) Hi everyone! 👋 While learning Python for data work, I came across NumPy — and initially, it just looked like another library. But after spending some time with it, I realized why it’s so widely used. At its core, NumPy is about working efficiently with numbers and arrays. A few things that stood out to me: ✔️ Faster computations compared to regular Python lists ✔️ Ability to perform operations on entire datasets at once (no loops needed) ✔️ Foundation for libraries like Pandas, Scikit-learn For example, instead of looping through values one by one, NumPy lets you do operations in a single line — which is both cleaner and faster. This made me think about real-world scenarios: When dealing with large datasets, performance really matters. Even small optimizations can save a lot of time. Coming from SQL and ETL, this feels similar to optimizing queries — but now at a programming level. Still exploring more, but it’s clear that understanding NumPy well can make a big difference in data processing and model performance. Have you used NumPy in your work? Or do you rely more on Pandas/SQL? #DataScience #Python #NumPy #MachineLearning #LearningInPublic

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

Your All-in-One 𝐏𝐲𝐭𝐡𝐨𝐧 𝐒𝐲𝐧𝐭𝐚𝐱 Cheat Sheet 🐍 When I started with Python, I often found myself googling small syntax details again and again 😅 That’s when having a 𝐰𝐞𝐥𝐥-𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 guide became a game-changer. This 𝐏𝐲𝐭𝐡𝐨𝐧 𝐒𝐲𝐧𝐭𝐚𝐱 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 𝐜𝐨𝐯𝐞𝐫𝐬 everything you need to get started and build a strong foundation: ◼️ Basic Syntax - Print, variables, type casting ◼️ Data Structures - Lists, tuples, sets, dictionaries ◼️ Control Flow - If-else, loops, break & continue ◼️ Functions & Lambdas - Reusable logic made simple ◼️ String & File Handling ◼️ Comprehensions & Error Handling ◼️ NumPy, Pandas & Matplotlib - The data stack essentials 📌 Whether you’re a beginner learning Python or a data professional who wants a quick refresher - this is a must-have reference for your toolkit. Save this post & keep the cheat sheet handy 💾 𝐒𝐭𝐚𝐫𝐭 𝐲𝐨𝐮𝐫 𝐣𝐨𝐮𝐫𝐧𝐞𝐲 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 & 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬👇 🔗 𝐖𝐡𝐚𝐭𝐬𝐚𝐩𝐩 - https://lnkd.in/d_tQPMS7 🔗 𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦- https://t.me/LK_Data_world 💬 If you found this PDF useful, like, save, and repost it to help others in the community! 🔄 📢 Follow Lovee Kumar 🔔 for more content on Data Engineering, Analytics, and Big Data. #Python #DataScience #DataEngineering #CheatSheet #Pandas