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*Free Data Analytics Webinar + Certificate by HCL GUVI: an HCL Group company.* ๐Ÿ’ป๐Ÿš€ Learn Python, SQL, Excel, Power BI, & more! ๐Ÿ“ˆ *Perfect for everyone* โ€“ No experience needed! โœ… Only *500 seats* left! ๐Ÿ˜ณ For *Working Professionals*, Job Aspirants and *Final Year Students* ๐Ÿš€ *Register now (100% Free)*๐Ÿ‘‡ https://link.guvi.in/codehubb02493

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๐Ÿ *How to Learn Python Programming in 2025 โ€“ Step by Step* ๐Ÿ’ปโœจ โœ… *Tip 1: Start with the Basics* Learn Python fundamentals: โ€ข Variables & Data Types (int, float, str, list, dict) โ€ข Loops (`for`, while`) & Conditionals (`if, `else`) โ€ข Functions & Modules โœ… *Tip 2: Practice Small Programs* Build mini-projects to reinforce concepts: โ€ข Calculator โ€ข To-do app โ€ข Dice roller โ€ข Guess-the-number game โœ… *Tip 3: Understand Data Structures* โ€ข Lists, Tuples, Sets, Dictionaries โ€ข How to manipulate, search, and iterate โœ… *Tip 4: Learn File Handling & Libraries* โ€ข Read/write files (`open`, `with`) โ€ข Explore libraries: math, random, datetime, os โœ… *Tip 5: Work with Data* โ€ข Learn pandas for data analysis โ€ข Use matplotlib & seaborn for visualization โœ… *Tip 6: Object-Oriented Programming (OOP)* โ€ข Classes, Objects, Inheritance, Encapsulation โœ… *Tip 7: Practice Coding Challenges* โ€ข Platforms: LeetCode, HackerRank, Codewars โ€ข Focus on loops, strings, arrays, and logic โœ… *Tip 8: Build Real Projects* โ€ข Portfolio website backend โ€ข Chatbot with NLTK or Rasa โ€ข Simple game with pygame โ€ข Data analysis dashboards โœ… *Tip 9: Learn Web & APIs* โ€ข Flask / Django basics โ€ข Requesting & handling APIs (`requests`) โœ… *Tip 10: Consistency is Key* Practice Python daily. Review your old code and improve logic, readability, and efficiency. ๐Ÿ’ฌ *Tap โค๏ธ if this helped you!*

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FREE FREE FREE FREE FREE ๐Ÿš€ Welcome to PythonAdvisor โ€” your ultimate hub for mastering Python programming and AI technologies! ๐Ÿ‘จโ€๐Ÿ’ป Whether youโ€™re a beginner or an advanced developer, join our community for: โ€ข Daily Python tutorials and coding tips โ€ข FREE FREE FREE FREE Hand Written Notes , Ebook. ๐Ÿ”ฅ Start your programming journey with PythonAdvisor today. Subscribe now and unlock the power of coding! ๐Ÿ‘‰ Join us on Telegram: [https://t.me/pythonadvisor] #Python #AI #Programming #LearnPython #PythonAdvisor

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*Free Data Analytics Webinar + Certificate by HCL GUVI: an HCL Group company.* ๐Ÿ’ป๐Ÿš€ Learn Python, SQL, Excel, Power BI, & more! ๐Ÿ“ˆ *Perfect for everyone* โ€“ No experience needed! โœ… Only *500 seats* left! ๐Ÿ˜ณ For *Working Professionals*, Job Aspirants and *Final Year Students* ๐Ÿš€ *Register now (100% Free)*๐Ÿ‘‡ https://link.guvi.in/codehubb02493

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*Free Data Analytics Webinar + Certificate by Guvi & HCL!* ๐Ÿ’ป๐Ÿš€ Learn Python, SQL, Excel, Power BI, & more! ๐Ÿ“ˆ *Perfect for everyone* โ€“ No experience needed! โœ… Only *500 seats* left! ๐Ÿ˜ณ For *Working Professionals*, Job Aspirants and *Final Year Students* ๐Ÿš€ *Register now (100% Free)*๐Ÿ‘‡ https://link.guvi.in/codehubb02493

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Get started with Python quickly using this easy cheatsheet! ๐Ÿš€ From comments and operators to data structures, loops, and fil
Get started with Python quickly using this easy cheatsheet! ๐Ÿš€ From comments and operators to data structures, loops, and file handling, this guide covers the most useful Python basics, making coding faster and simpler for beginners and pros alike. Save this post and level up your Python journey! For more tips, follow and share with friends interested in learning Python! Check out WhatsApp channels for more updates. ๐Ÿ‘‡ https://whatsapp.com/channel/0029Vb6r6218kyyQgBDNjm26

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โœ…Python Interview Questions with Answers ๐Ÿง‘โ€๐Ÿ’ป๐Ÿ‘ฉโ€๐Ÿ’ป 1๏ธโƒฃ Write a function to remove outliers from a list using IQR.
import numpy as np

def remove_outliers(data):
    q1 = np.percentile(data, 25)
    q3 = np.percentile(data, 75)
    iqr = q3 - q1
    lower = q1 - 1.5 * iqr
    upper = q3 + 1.5 * iqr
    return [x for x in data if lower <= x <= upper]
2๏ธโƒฃ Convert a nested list to a flat list.
nested = [[1, 2], [3, 4],]
flat = [item for sublist in nested for item in sublist]
3๏ธโƒฃ Read a CSV file and count rows with nulls.
import pandas as pd

df = pd.read_csv('data.csv')
null_rows = df.isnull().any(axis=1).sum()
print("Rows with nulls:", null_rows)
4๏ธโƒฃ How do you handle missing data in pandas? โฆ Drop missing rows: df.dropna() โฆ Fill missing values: df.fillna(value) โฆ Check missing data: df.isnull().sum() 5๏ธโƒฃ Explain the difference between loc[] and iloc[]. โฆ loc[]: Label-based indexing (e.g., row/column names) Example: df.loc[0, 'Name'] โฆ iloc[]: Position-based indexing (e.g., row/column numbers) Example: df.iloc ๐Ÿ’ฌ Tap โค๏ธ for more!

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๐Ÿš€ Welcome to PythonAdvisor โ€” your ultimate hub for mastering Python programming and AI technologies! ๐Ÿ‘จโ€๐Ÿ’ป Whether youโ€™re a beginner or an advanced developer, join our community for: โ€ข Daily Python tutorials and coding tips โ€ข Latest AI insights and projects โ€ข Interactive quizzes and challenges โ€ข Support from fellow learners and experts ๐Ÿ”ฅ Start your programming journey with PythonAdvisor today. Subscribe now and unlock the power of coding! ๐Ÿ‘‰ Join us on Telegram: [https://t.me/pythonadvisor] #Python #AI #Programming #LearnPython #PythonAdvisor

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โœ…Python Interview Questions with Answers ๐Ÿง‘โ€๐Ÿ’ป๐Ÿ‘ฉโ€๐Ÿ’ป 1๏ธโƒฃ Write a function to remove outliers from a list using IQR.
import numpy as np

def remove_outliers(data):
    q1 = np.percentile(data, 25)
    q3 = np.percentile(data, 75)
    iqr = q3 - q1
    lower = q1 - 1.5 * iqr
    upper = q3 + 1.5 * iqr
    return [x for x in data if lower <= x <= upper]
2๏ธโƒฃ Convert a nested list to a flat list.
nested = [[1, 2], [3, 4],]
flat = [item for sublist in nested for item in sublist]
3๏ธโƒฃ Read a CSV file and count rows with nulls.
import pandas as pd

df = pd.read_csv('data.csv')
null_rows = df.isnull().any(axis=1).sum()
print("Rows with nulls:", null_rows)
4๏ธโƒฃ How do you handle missing data in pandas? โฆ Drop missing rows: df.dropna() โฆ Fill missing values: df.fillna(value) โฆ Check missing data: df.isnull().sum() 5๏ธโƒฃ Explain the difference between loc[] and iloc[]. โฆ loc[]: Label-based indexing (e.g., row/column names) Example: df.loc[0, 'Name'] โฆ iloc[]: Position-based indexing (e.g., row/column numbers) Example: df.iloc ๐Ÿ’ฌ Tap โค๏ธ for more!

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๐Ÿš€ ๐—•๐—ฒ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—ฎ๐—ป ๐—”๐—œ/๐—Ÿ๐—Ÿ๐—  ๐—˜๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ: ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐˜๐—ต๐—ฒ ๐˜€๐—ธ๐—ถ๐—น๐—น๐˜€ ๐˜๐—ฒ๐—ฐ๐—ต ๐—ฐ๐—ผ๐—บ๐—ฝ๐—ฎ๐—ป๐—ถ๐—ฒ๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐—ต๐—ถ๐—ฟ๐—ถ๐—ป๐—ด ๐—ณ๐—ผ๐—ฟ: fine-tune large language models and deploy them to production at scale. ๐—•๐˜‚๐—ถ๐—น๐˜ ๐—ณ๐—ฟ๐—ผ๐—บ ๐—ฟ๐—ฒ๐—ฎ๐—น ๐—”๐—œ ๐—ท๐—ผ๐—ฏ ๐—ฟ๐—ฒ๐—พ๐˜‚๐—ถ๐—ฟ๐—ฒ๐—บ๐—ฒ๐—ป๐˜๐˜€. โœ… Fine-tune models with industry tools โœ… Deploy on cloud infrastructure โœ… 2 portfolio-ready projects โœ… Official certification + badge ๐Ÿ“˜ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—บ๐—ผ๐—ฟ๐—ฒ & ๐—ฒ๐—ป๐—ฟ๐—ผ๐—น๐—น โฌ‡๏ธ https://www.readytensor.ai/llm-certification/?utm_medium=tel&utm_source=cert-513&utm_campaign=llmed

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๐Ÿ”ฅ Master These 30 Algorithms to Boost Your Coding Skills! ๐Ÿš€ - Binary Search ๐Ÿ•ต๏ธโ€โ™‚๏ธ - Quick Sort โšก - Merge Sort ๐ŸŒŠ - Heap Sort ๐Ÿฐ - BFS ๐ŸŒ - DFS ๐ŸŒฒ - Dijkstraโ€™s Shortest Path ๐Ÿš— - Bellman-Ford ๐Ÿšฆ - Floyd-Warshall ๐ŸŒ‰ - Kruskalโ€™s Minimum Spanning Tree ๐ŸŒณ - Primโ€™s Algorithm ๐ŸŒฟ - KMP Pattern Matching ๐Ÿ” - Rabin-Karp Search ๐Ÿงฎ - Dynamic Programming ๐Ÿง  - Kadaneโ€™s Max Subarray Sum ๐Ÿ’ฅ - Floydโ€™s Cycle Detection ๐Ÿ”„ - Topological Sort ๐Ÿ—‚๏ธ - Backtracking ๐ŸŽฏ - Binary Tree Traversals ๐ŸŒณ - Segment Tree ๐Ÿ“Š - Union-Find Disjoint Set ๐Ÿ”— - Greedy Algorithms ๐ŸŽฏ - Bit Manipulation ๐Ÿ’ก - Sliding Window โฑ๏ธ - Two Pointers ๐Ÿ”€ - Hashing ๐Ÿ”‘ - Recursion ๐Ÿ” - Divide & Conquer โš”๏ธ - Graph Coloring ๐ŸŽจ - A* Search ๐Ÿ—บ๏ธ Master these, ace interviews, and become a problem-solving pro! ๐Ÿ’ช๐Ÿ”ฅ ***

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๐Ÿš€ Dreaming of a UI/UX Design Career with No Experience? This is your moment! ๐ŸŒŸ *FREE MASTERCLASS* ๐Ÿ“… October 10, 2025 ยท 7:0
๐Ÿš€ Dreaming of a UI/UX Design Career with No Experience? This is your moment! ๐ŸŒŸ *FREE MASTERCLASS* ๐Ÿ“… October 10, 2025 ยท 7:00 PM ยท 90 mins (English) Hereโ€™s what youโ€™ll walk away with: โœ… Understand design fundamentals & usability โœ… Build a portfolio recruiters will notice โœ… Networking tips to break into the industry โœ… How to keep your skills & tools up-to-date Final Year Students, Job Aspirants and Working Professionals can Attend for FREE ๐Ÿ‘‰ Register here before spots run out: https://link.guvi.in/fresherjobs02408

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+1
Pandas Part 1.pdf3.52 KB

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*Free Data Analytics Webinar + Certificate by Guvi & HCL!* ๐Ÿ’ป๐Ÿš€ Learn Python, SQL, Excel, Power BI, & more! ๐Ÿ“ˆ *Perfect for e
*Free Data Analytics Webinar + Certificate by Guvi & HCL!* ๐Ÿ’ป๐Ÿš€ Learn Python, SQL, Excel, Power BI, & more! ๐Ÿ“ˆ *Perfect for everyone* โ€“ No experience needed! โœ… Only *500 seats* left! ๐Ÿ˜ณ *Register now (100% Free)* ๐Ÿ‘‡ https://link.guvi.in/codehubb02217 *Join us on WhatsApp:* https://chat.whatsapp.com/JyQhFtCaaWKFRqDAzME1so?mode=ac_t

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*Free Data Analytics Webinar + Certificate by HCL & Guvi!* ๐Ÿ’ป๐Ÿš€ Learn Python, SQL, Excel, Power BI, & more! ๐Ÿ“ˆ *Perfect for e
*Free Data Analytics Webinar + Certificate by HCL & Guvi!* ๐Ÿ’ป๐Ÿš€ Learn Python, SQL, Excel, Power BI, & more! ๐Ÿ“ˆ *Perfect for everyone* โ€“ No experience needed! โœ… Only *500 seats* left! ๐Ÿ˜ณ *Register now (100% Free)* ๐Ÿ‘‡ https://link.guvi.in/codehubb02138 *Join us on WhatsApp!* ๐Ÿ‘‡ https://chat.whatsapp.com/KKSJWFTklUyCnPD9MW2tKZ?mode=ac_t

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*Free Data Analytics Webinar + Certificate by HCL & Guvi!* ๐Ÿ’ป๐Ÿš€ Learn Python, SQL, Excel, Power BI, & more! ๐Ÿ“ˆ *Perfect for everyone* โ€“ No experience needed! โœ… Only *500 seats* left! ๐Ÿ˜ณ *Register now (100% Free)* ๐Ÿ‘‡ https://link.guvi.in/codehubb02138 *Join us on WhatsApp!* ๐Ÿ‘‡ https://chat.whatsapp.com/KKSJWFTklUyCnPD9MW2tKZ?mode=ac_t

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index.html0.47 KB

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// Update timeline data document.getElementById('monthsToGoal').textContent = results.monthsToGoal; document.getElementById('weeksToGoal').textContent = results.weeksToGoal; document.getElementById('weightToLose').textContent = results.weightToLose; document.getElementById('targetWeight').textContent = results.targetWeight; document.getElementById('caloriesPerDay').textContent = results.caloriesPerDay; document.getElementById('tdee').textContent = results.tdee; } // Update body composition (shown for both cases) document.getElementById('currentBodyFat').textContent = results.currentBodyFat + '%'; document.getElementById('targetBodyFat').textContent = results.targetBodyFat + '%'; document.getElementById('leanBodyMass').textContent = results.leanBodyMass; } function resetCalculator() { currentStep = 1; formData = { name: '', gender: '', weight: '', height: '', age: '', activityLevel: '', bodyFat: '' }; // Clear all form inputs document.querySelectorAll('input').forEach(input => input.value = ''); // Clear all selections document.querySelectorAll('.selected').forEach(el => el.classList.remove('selected', 'male', 'female', 'activity') ); // Show first step showStep(1); } // API Integration Function (for when you want to connect to PHP backend) async function saveToDatabase(calculationData) { try { const response = await fetch('api/calculate.php', { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ ...formData, session_id: getSessionId(), ...calculationData }) }); const result = await response.json(); if (result.success) { console.log('Calculation saved with ID:', result.calculation_id); } else { console.error('Failed to save calculation:', result.message); } } catch (error) { console.error('API Error:', error); } } function getSessionId() { let sessionId = localStorage.getItem('abs_calc_session'); if (!sessionId) { sessionId = 'session_' + Math.random().toString(36).substr(2, 9) + Date.now(); localStorage.setItem('abs_calc_session', sessionId); } return sessionId; } </script> </body> </html>

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if (gender === 'male') { return 88.362 + (13.397 * weight) + (4.799 * height) - (5.677 * age); } else { return 447.593 + (9.247 * weight) + (3.098 * height) - (4.330 * age); } } function calculateResults() { const weight = parseFloat(formData.weight); const height = parseFloat(formData.height); const age = parseInt(formData.age); const bodyFatPercent = parseFloat(formData.bodyFat); const gender = formData.gender; const activityLevel = formData.activityLevel; // Calculate lean body mass const leanBodyMass = weight * (1 - bodyFatPercent / 100); // Target body fat for visible abs const targetBodyFat = gender === 'male' ? 12 : 16; // Calculate target weight for abs const targetWeight = leanBodyMass / (1 - targetBodyFat / 100); // Weight to lose const weightToLose = Math.max(0, weight - targetWeight); // Calculate BMR and TDEE const bmr = calculateBMR(weight, height, age, gender); const tdee = bmr * activityLevels[activityLevel].multiplier; // Time estimation (0.5 kg per week) const weeksToGoal = weightToLose > 0 ? Math.ceil(weightToLose / 0.5) : 0; const monthsToGoal = Math.ceil(weeksToGoal / 4.33); // Calories for fat loss (500 calorie deficit) const caloriesPerDay = tdee - 500; // Check if already lean enough const isAlreadyLean = bodyFatPercent <= targetBodyFat; // Update results display displayResults({ currentWeight: weight, targetWeight: targetWeight.toFixed(1), weightToLose: weightToLose.toFixed(1), leanBodyMass: leanBodyMass.toFixed(1), currentBodyFat: bodyFatPercent, targetBodyFat: targetBodyFat, bmr: Math.round(bmr), tdee: Math.round(tdee), weeksToGoal: weeksToGoal, monthsToGoal: monthsToGoal, caloriesPerDay: Math.round(caloriesPerDay), isAlreadyLean: isAlreadyLean }); } function displayResults(results) { // Update titles if (results.isAlreadyLean) { document.getElementById('resultsTitle').textContent = '๐ŸŽ‰ You already have abs!'; document.getElementById('resultsSubtitle').textContent = (formData.name ? formData.name + ', ' : '') + "you're already lean enough to see abs!"; // Show already lean section, hide timeline document.getElementById('alreadyLeanSection').classList.remove('hidden'); document.getElementById('timelineSection').classList.add('hidden'); document.getElementById('alreadyLeanText').textContent = Congratulations! At ${results.currentBodyFat}% body fat, your abs should already be visible.; } else { document.getElementById('resultsTitle').textContent = '๐ŸŽฏ Your Abs Journey'; document.getElementById('resultsSubtitle').textContent = (formData.name ? formData.name + ', ' : '') + "here's your personalized timeline"; // Show timeline section, hide already lean document.getElementById('timelineSection').classList.remove('hidden'); document.getElementById('alreadyLeanSection').classList.add('hidden');