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*Free Data Analytics Webinar + Certificate by HCL GUVI: an HCL Group company.* ๐ป๐
Learn Python, SQL, Excel, Power BI, & more! ๐
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33 844
๐ *How to Learn Python Programming in 2025 โ Step by Step* ๐ปโจ
โ
*Tip 1: Start with the Basics*
Learn Python fundamentals:
โข Variables & Data Types (int, float, str, list, dict)
โข Loops (`for`,
while`) & Conditionals (`if, `else`)
โข Functions & Modules
โ
*Tip 2: Practice Small Programs*
Build mini-projects to reinforce concepts:
โข Calculator
โข To-do app
โข Dice roller
โข Guess-the-number game
โ
*Tip 3: Understand Data Structures*
โข Lists, Tuples, Sets, Dictionaries
โข How to manipulate, search, and iterate
โ
*Tip 4: Learn File Handling & Libraries*
โข Read/write files (`open`, `with`)
โข Explore libraries: math, random, datetime, os
โ
*Tip 5: Work with Data*
โข Learn pandas for data analysis
โข Use matplotlib & seaborn for visualization
โ
*Tip 6: Object-Oriented Programming (OOP)*
โข Classes, Objects, Inheritance, Encapsulation
โ
*Tip 7: Practice Coding Challenges*
โข Platforms: LeetCode, HackerRank, Codewars
โข Focus on loops, strings, arrays, and logic
โ
*Tip 8: Build Real Projects*
โข Portfolio website backend
โข Chatbot with NLTK or Rasa
โข Simple game with pygame
โข Data analysis dashboards
โ
*Tip 9: Learn Web & APIs*
โข Flask / Django basics
โข Requesting & handling APIs (`requests`)
โ
*Tip 10: Consistency is Key*
Practice Python daily. Review your old code and improve logic, readability, and efficiency.
๐ฌ *Tap โค๏ธ if this helped you!*33 844
FREE FREE FREE FREE FREE
๐ Welcome to PythonAdvisor โ your ultimate hub for mastering Python programming and AI technologies!
๐จโ๐ป Whether youโre a beginner or an advanced developer, join our community for:
โข Daily Python tutorials and coding tips
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Hand Written Notes , Ebook.
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33 844
*Free Data Analytics Webinar + Certificate by HCL GUVI: an HCL Group company.* ๐ป๐
Learn Python, SQL, Excel, Power BI, & more! ๐
*Perfect for everyone* โ No experience needed! โ
Only *500 seats* left! ๐ณ
For *Working Professionals*, Job Aspirants and *Final Year Students* ๐
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33 844
*Free Data Analytics Webinar + Certificate by Guvi & HCL!* ๐ป๐
Learn Python, SQL, Excel, Power BI, & more! ๐
*Perfect for everyone* โ No experience needed! โ
Only *500 seats* left! ๐ณ
For *Working Professionals*, Job Aspirants and *Final Year Students* ๐
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33 844
Get started with Python quickly using this easy cheatsheet! ๐
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. ๐
https://whatsapp.com/channel/0029Vb6r6218kyyQgBDNjm26
33 844
โ
Python Interview Questions with Answers ๐งโ๐ป๐ฉโ๐ป
1๏ธโฃ 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๏ธโฃ Convert a nested list to a flat list.
nested = [[1, 2], [3, 4],]
flat = [item for sublist in nested for item in sublist]
3๏ธโฃ 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๏ธโฃ 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๏ธโฃ 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
๐ฌ Tap โค๏ธ for more!33 844
๐ Welcome to PythonAdvisor โ your ultimate hub for mastering Python programming and AI technologies!
๐จโ๐ป Whether youโre a beginner or an advanced developer, join our community for:
โข Daily Python tutorials and coding tips
โข Latest AI insights and projects
โข Interactive quizzes and challenges
โข Support from fellow learners and experts
๐ฅ Start your programming journey with PythonAdvisor today. Subscribe now and unlock the power of coding!
๐ Join us on Telegram: [https://t.me/pythonadvisor]
#Python #AI #Programming #LearnPython #PythonAdvisor
33 844
โ
Python Interview Questions with Answers ๐งโ๐ป๐ฉโ๐ป
1๏ธโฃ 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๏ธโฃ Convert a nested list to a flat list.
nested = [[1, 2], [3, 4],]
flat = [item for sublist in nested for item in sublist]
3๏ธโฃ 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๏ธโฃ 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๏ธโฃ 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
๐ฌ Tap โค๏ธ for more!33 844
๐ ๐๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ๐ป ๐๐/๐๐๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ: ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ
๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ต๐ฒ ๐๐ธ๐ถ๐น๐น๐ ๐๐ฒ๐ฐ๐ต ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐ฎ๐ฟ๐ฒ ๐ต๐ถ๐ฟ๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ: fine-tune large language models and deploy them to production at scale.
๐๐๐ถ๐น๐ ๐ณ๐ฟ๐ผ๐บ ๐ฟ๐ฒ๐ฎ๐น ๐๐ ๐ท๐ผ๐ฏ ๐ฟ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ๐บ๐ฒ๐ป๐๐.
โ
Fine-tune models with industry tools
โ
Deploy on cloud infrastructure
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2 portfolio-ready projects
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Official certification + badge
๐ ๐๐ฒ๐ฎ๐ฟ๐ป ๐บ๐ผ๐ฟ๐ฒ & ๐ฒ๐ป๐ฟ๐ผ๐น๐น โฌ๏ธ
https://www.readytensor.ai/llm-certification/?utm_medium=tel&utm_source=cert-513&utm_campaign=llmed
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๐ฅ Master These 30 Algorithms to Boost Your Coding Skills! ๐
- Binary Search ๐ต๏ธโโ๏ธ
- Quick Sort โก
- Merge Sort ๐
- Heap Sort ๐ฐ
- BFS ๐
- DFS ๐ฒ
- Dijkstraโs Shortest Path ๐
- Bellman-Ford ๐ฆ
- Floyd-Warshall ๐
- Kruskalโs Minimum Spanning Tree ๐ณ
- Primโs Algorithm ๐ฟ
- KMP Pattern Matching ๐
- Rabin-Karp Search ๐งฎ
- Dynamic Programming ๐ง
- Kadaneโs Max Subarray Sum ๐ฅ
- Floydโs Cycle Detection ๐
- Topological Sort ๐๏ธ
- Backtracking ๐ฏ
- Binary Tree Traversals ๐ณ
- Segment Tree ๐
- Union-Find Disjoint Set ๐
- Greedy Algorithms ๐ฏ
- Bit Manipulation ๐ก
- Sliding Window โฑ๏ธ
- Two Pointers ๐
- Hashing ๐
- Recursion ๐
- Divide & Conquer โ๏ธ
- Graph Coloring ๐จ
- A* Search ๐บ๏ธ
Master these, ace interviews, and become a problem-solving pro! ๐ช๐ฅ
***
33 844
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This is your moment! ๐
*FREE MASTERCLASS*
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October 10, 2025 ยท 7:00 PM ยท 90 mins (English)
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Final Year Students, Job Aspirants and Working Professionals can Attend for FREE
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33 844
*Free Data Analytics Webinar + Certificate by Guvi & HCL!* ๐ป๐
Learn Python, SQL, Excel, Power BI, & more! ๐
*Perfect for everyone* โ No experience needed! โ
Only *500 seats* left! ๐ณ
*Register now (100% Free)* ๐
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33 844
*Free Data Analytics Webinar + Certificate by HCL & Guvi!* ๐ป๐
Learn Python, SQL, Excel, Power BI, & more! ๐
*Perfect for everyone* โ No experience needed! โ
Only *500 seats* left! ๐ณ
*Register now (100% Free)* ๐
https://link.guvi.in/codehubb02138
*Join us on WhatsApp!* ๐
https://chat.whatsapp.com/KKSJWFTklUyCnPD9MW2tKZ?mode=ac_t
33 844
*Free Data Analytics Webinar + Certificate by HCL & Guvi!* ๐ป๐
Learn Python, SQL, Excel, Power BI, & more! ๐
*Perfect for everyone* โ No experience needed! โ
Only *500 seats* left! ๐ณ
*Register now (100% Free)* ๐
https://link.guvi.in/codehubb02138
*Join us on WhatsApp!* ๐
https://chat.whatsapp.com/KKSJWFTklUyCnPD9MW2tKZ?mode=ac_t
33 844
// 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 = '๐ 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 = '๐ฏ 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');
Available now! Telegram Research 2025 โ the year's key insights 
