Git Commands

🛠 git init – Initialize a new Git repository
📥 git clone <repo> – Clone a repository
📊 git status – Check the status of your repository
➕ git add <file> – Add a file to the staging area
📝 git commit -m "message" – Commit changes with a message
🚀 git push – Push changes to a remote repository
⬇️ git pull – Fetch and merge changes from a remote repository


Branching

📌 git branch – List all branches
🌱 git branch <name> – Create a new branch
🔄 git checkout <branch> – Switch to a branch
🔗 git merge <branch> – Merge a branch into the current branch
⚡️ git rebase <branch> – Apply commits on top of another branch


Undo & Fix Mistakes

⏪ git reset --soft HEAD~1 – Undo the last commit but keep changes
❌ git reset --hard HEAD~1 – Undo the last commit and discard changes
🔄 git revert <commit> – Create a new commit that undoes a specific commit


Logs & History

📖 git log – Show commit history
🌐 git log --oneline --graph --all – View commit history in a simple graph


Stashing

📥 git stash – Save changes without committing
🎭 git stash pop – Apply stashed changes and remove them from stash


Remote & Collaboration

🌍 git remote -v – View remote repositories
📡 git fetch – Fetch changes without merging
🕵️ git diff – Compare changes


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🔥 Master These 30 Algorithms to Boost Your Coding Skills! 🚀

- Binary Search 🕵️‍♂️
- Quick Sort ⚡
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- DFS 🌲
- Dijkstra’s Shortest Path 🚗
- Bellman-Ford 🚦
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- Prim’s Algorithm 🌿
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Master these, ace interviews, and become a problem-solving pro! 💪🔥

***

✅Python Interview Questions with Answers 🧑‍💻👩‍💻

1️⃣ 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]

2️⃣ Convert a nested list to a flat list.

nested = [[1, 2], [3, 4],]
flat = [item for sublist in nested for item in sublist]

3️⃣ 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)

4️⃣ How do you handle missing data in pandas?
⦁ Drop missing rows: df.dropna()
⦁ Fill missing values: df.fillna(value)
⦁ Check missing data: df.isnull().sum()

5️⃣ Explain the difference between loc[] and iloc[].
⦁ loc[]: Label-based indexing (e.g., row/column names)
Example: df.loc[0, 'Name']
⦁ iloc[]: Position-based indexing (e.g., row/column numbers)
Example: df.iloc

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Get started with Python quickly using this easy cheatsheet! 🚀

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!

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🐍 *How to Learn Python Programming in 2025 – Step by Step* 💻✨

✅ *Tip 1: Start with the Basics*
Learn Python fundamentals:
• Variables & Data Types (int, float, str, list, dict)
• Loops (`for`, while`) & Conditionals (`if, `else`)
• Functions & Modules

✅ *Tip 2: Practice Small Programs*
Build mini-projects to reinforce concepts:
• Calculator
• To-do app
• Dice roller
• Guess-the-number game

✅ *Tip 3: Understand Data Structures*
• Lists, Tuples, Sets, Dictionaries
• How to manipulate, search, and iterate

✅ *Tip 4: Learn File Handling & Libraries*
• Read/write files (`open`, `with`)
• Explore libraries: math, random, datetime, os

✅ *Tip 5: Work with Data*
• Learn pandas for data analysis
• Use matplotlib & seaborn for visualization

✅ *Tip 6: Object-Oriented Programming (OOP)*
• Classes, Objects, Inheritance, Encapsulation

✅ *Tip 7: Practice Coding Challenges*
• Platforms: LeetCode, HackerRank, Codewars
• Focus on loops, strings, arrays, and logic

✅ *Tip 8: Build Real Projects*
• Portfolio website backend
• Chatbot with NLTK or Rasa
• Simple game with pygame
• Data analysis dashboards

✅ *Tip 9: Learn Web & APIs*
• Flask / Django basics
• Requesting & handling APIs (`requests`)

✅ *Tip 10: Consistency is Key*
Practice Python daily. Review your old code and improve logic, readability, and efficiency.

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✅ *Python Scenario-Based Interview Question* 🧠🐍

You have a list:

numbers = [1, 2, 3, 2, 4, 1, 5, 2]

*Question:*
Find the number that appears most frequently in the list.

*Expected Output:*

2

*Python Code:*

from collections import Counter

most_common_num = Counter(numbers).most_common(1)[0][0]
print(most_common_num)

*Explanation:*
– Counter() counts occurrences of each element
– most_common(1) returns the most frequent item
– Access [0][0] to get just the number

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Would you like the next one to be slightly more advanced (e.g., involving strings or list comprehensions)?

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✅ *Python Logic Building Interview Question* 🧠🐍

You have a list of numbers:  nums = [3, 5, 7, 9, 12, 17, 20, 21]

*Question:*
Find and print all numbers in the list that are prime.

*Expected Output:*  [3, 5, 7, 17]

*Python Code:*  def is_prime(n):
    if n < 2:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

prime_nums = [n for n in nums if is_prime(n)]
print(prime_nums)

*Explanation:*
– Checks each number with is_prime() logic
– Uses list comprehension for concise filtering
– Prints list of all prime numbers

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