Sgive

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[Tech_P @Tech_p001]

🚀 Python Basics Part-3 🐍🔥

✅ Functions in Python

Functions help you write reusable and organized code.

Instead of writing the same code again and again, you can put it inside a function and call it whenever needed. 💻

In this part, you’ll learn:
✅ Functions
✅ Parameters & Arguments
✅ Return Statement
✅ Lambda Functions
✅ Recursion Basics
✅ Scope

🧠 1. What is a Function?
A function is a block of code that performs a specific task.

Example:
def greet():
print("Welcome to Python")

greet()

📌 Output:
Welcome to Python

✔ `def` is used to create functions.

📥 2. Function Parameters
Parameters allow functions to accept values.

Example:
def greet(name):
print("Hello", name)

greet("Python")

📌 Output:
Hello Python

⚡ 3. Multiple Parameters
Functions can take multiple inputs.

Example:
def add(a, b):
print(a + b)

add(10, 20)

📌 Output:
30

🔄 4. Return Statement
`return` sends a value back from a function.

Example:
def square(num):
return num * num

result = square(5)

print(result)

📌 Output:
25

✔ `return` is very important in real projects.

🎯 5. Default Parameters
You can assign default values to parameters.

*Example:*
def country(name="India"):
print(name)

country()
country("Japan")

📌 Output:
India
Japan

⚡ 6. Lambda Functions
Small anonymous functions written in one line.

Example:
square = lambda x: x * x

print(square(4))

📌 Output:
16

✔ Mostly used in data analysis and automation.

🔁 7. Recursion Basics
A function calling itself is called recursion.

*Example:*
def countdown(n):
if n == 0:
return

print(n)
countdown(n - 1)

countdown(5)

📌 Output:
5
4
3
2
1

🌍 8. Variable Scope
Scope decides where variables can be used.

📌 Local Variable
Created inside a function.

Example:
def test():
value = 100
print(value)

test()

📌 Global Variable
Created outside a function.

Example:
message = "Python"

def show():
print(message)

show()

📦 9. Modules in Python
Modules help organize code into separate files.

*Example:*
import math

print(math.sqrt(25))

📌 Output:
5.0

📥 10. Import Statements
Used to use built-in or external libraries.

Example:
from random import randint

print(randint(1, 10))

🛠 Practice Programs
✅ Calculator using Functions
✅ Factorial Program
✅ Prime Number Checker
✅ Password Generator
✅ Simple Quiz App

🔥 Common Beginner Mistakes
❌ Forgetting indentation
❌ Missing `return` statement
❌ Confusing parameters and arguments
❌ Infinite recursion

💡 Pro Tip
✔ Write small functions
✔ Keep functions reusable
✔ Use meaningful function names

Good functions make code cleaner and easier to debug.

📌 *Double Tap ❤️ For Part-4

[Ezekiel @ezekiel_aleke]

POWER BI ROADMAP
│
├─ 📁 Getting Started
│ ├─ 📁 Power BI Desktop Setup
│ ├─ 📁 Understanding the Interface
│ ├─ 📁 Report vs Dashboard vs Dataset │ └─ 📁 Connecting to Data Sources
│
├─ 📁 Data Transformation
│ ├─ 📁 Loading & Importing Data
│ ├─ 📁 Removing Duplicates & Errors
│ ├─ 📁 Splitting & Merging Columns
│ ├─ 📁 Changing Data Types
│ └─ 📁 Appending & Merging Queries
│
├─ 📁 Data Modelling
│ ├─ 📁 Understanding Relationships
│ ├─ 📁 Star Schema Basics
│ ├─ 📁 Fact vs Dimension Tables
│ └─ 📁 Managing Table Relationships
│
├─ 📁 DAX (Data Analysis Expressions) │ ├─ 📁 Calculated Columns vs Measures │ ├─ 📁 SUM, COUNT, AVERAGE, DIVIDE │ ├─ 📁 IF, SWITCH Logics
│ ├─ 📁 CALCULATE & FILTER
│ └─ 📁 Time Intelligence (MTD, YTD)
│
├─ 📁 Data Visualisation
│ ├─ 📁 Bar, Line, Pie Charts
│ ├─ 📁 Cards & KPI Visuals
│ ├─ 📁 Slicers & Filters
│ ├─ 📁 Tables & Matrix Visuals
│ └─ 📁 Conditional Formatting
│
├─ 📁 Dashboard Building
│ ├─ 📁 Report Layout & Design
│ ├─ 📁 Drill Through & Drill Down
│ ├─ 📁 Bookmarks & Page Navigation
│ └─ 📁 Building an End-to-End Report
│
└─ 📁 Sharing & Productivity
├─ 📁 Publishing to Power BI Service
├─ 📁 Sharing Reports & Dashboards
├─ 📁 Scheduling Data Refresh
└─ 📁 Daily Practice Workflow

Tap ❤️ For More

[Tips Excel @gudanglifehack]

🚨 Stop using AI like a beginner.

This single ChatGPT prompt can generate an institutional-level crypto trading report similar to what professional hedge funds use.

Save this thread before you lose it. 👇

[Tips Excel @gudanglifehack]

📊Excel Tutoring | Complete Course 2026
Complete Excel Course: Beginner to Advaned in only 2 hours!

[Tips Excel @gudanglifehack]

🚀 SQL Scenario-Based Interview Questions with Answers (Part 9)

💼 FAANG & Product Company SQL Interview Scenarios

📌 Question 81: Find Users Who Were Active on 3 Consecutive Days
*Table:* `user_activity (user_id, activity_date)`

WITH activity AS (
SELECT DISTINCT
user_id,
activity_date
FROM user_activity
),
groups AS (
SELECT
user_id,
activity_date,
activity_date -
ROW_NUMBER() OVER (
PARTITION BY user_id
ORDER BY activity_date
) * INTERVAL '1 day' AS grp
FROM activity
)
SELECT
user_id,
COUNT(*) AS consecutive_days
FROM groups
GROUP BY user_id, grp
HAVING COUNT(*) >= 3;

📌 Question 82: Find the Top 5 Selling Products in Each Category
*Tables:* `products (product_id, category)` `sales (product_id, quantity)`

WITH product_sales AS (
SELECT
p.category,
s.product_id,
SUM(s.quantity) AS total_quantity
FROM sales s
JOIN products p
ON s.product_id = p.product_id
GROUP BY p.category, s.product_id
)
SELECT *
FROM (
SELECT *,
DENSE_RANK() OVER (
PARTITION BY category
ORDER BY total_quantity DESC
) AS rnk
FROM product_sales
) t
WHERE rnk <= 5;

📌 Question 83: Find Customers Whose Latest Order Is Their Highest Value Order
Table: `orders (customer_id, order_date, amount)`

WITH customer_orders AS (
SELECT *,
ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY order_date DESC
) AS latest_order,
RANK() OVER (
PARTITION BY customer_id
ORDER BY amount DESC
) AS highest_order
FROM orders
)
SELECT
customer_id,
order_date,
amount
FROM customer_orders
WHERE latest_order = 1
AND highest_order = 1;

📌 Question 84: Calculate Rolling 30-Day Revenue*
Table: `sales (sale_date, amount)`

SELECT
sale_date,
SUM(amount) OVER (
ORDER BY sale_date
RANGE BETWEEN INTERVAL '29 days' PRECEDING
AND CURRENT ROW
) AS rolling_30_day_revenue
FROM sales;

📌 Question 85: Find Products Purchased by Exactly One Customer
Table: `orders (customer_id, product_id)`

SELECT
product_id
FROM orders
GROUP BY product_id
HAVING COUNT(DISTINCT customer_id) = 1;

📌 Question 86: Find the Longest Inactive Period for Each Customer*
Table: `orders (customer_id, order_date)`

WITH gaps AS (
SELECT
customer_id,
order_date,
order_date -
LAG(order_date) OVER (
PARTITION BY customer_id
ORDER BY order_date
) AS inactive_days
FROM orders
)
SELECT
customer_id,
MAX(inactive_days) AS longest_gap
FROM gaps
GROUP BY customer_id;

📌 Question 87: Calculate Revenue Share by Product Category
*Tables:* `products (product_id, category)` `sales (product_id, amount)`

SELECT
category,
SUM(amount) AS revenue,
ROUND(
100.0 * SUM(amount) /
SUM(SUM(amount)) OVER (),
2
) AS revenue_share
FROM sales s
JOIN products p
ON s.product_id = p.product_id
GROUP BY category
ORDER BY revenue DESC;

📌 Question 88: Find Customers Who Purchased in Every Month of a Year
Table: `orders (customer_id, order_date)`

SELECT
customer_id
FROM orders
WHERE EXTRACT(YEAR FROM order_date) = 2025
GROUP BY customer_id
HAVING COUNT(
DISTINCT EXTRACT(MONTH FROM order_date)
) = 12;

*📌 Question 89: Find the Most Frequently Bought Product After Product A*
*Table:* `order_items (order_id, product_id, sequence_no)`

SELECT
b.product_id,
COUNT(*) AS purchase_count
FROM order_items a
JOIN order_items b
ON a.order_id = b.order_id
AND b.sequence_no = a.sequence_no + 1
WHERE a.product_id = 'Product_A'
GROUP BY b.product_id
ORDER BY purchase_count DESC
LIMIT 1;

[Tips Excel @gudanglifehack]

Build a dynamic search bar in Excel that filters data instantly as you type.

This tutorial shows how to use FILTER, SEARCH, and ISNUMBER to create a searchable table and find data faster.

(Save and share this video).

#Excel #ExcelTutorial #ExcelTips #SpreadsheetTips #MicrosoftExcel

[Dhanian 🗯️ @e_opore]

DSA (Data Structures & Algorithms) Complete Roadmap
|
| | |-- Fundamentals
| |-- Programming Basics
| | |-- Variables and data types
| | |-- Loops and conditions
| | |-- Functions and recursion
| |-- Complexity Analysis
| | |-- Big O notation
| | |-- Time complexity
| | |-- Space complexity
| |-- Problem Solving Basics
| | |-- Pattern recognition
| | |-- Dry runs
| | |-- Edge cases

| | |-- Arrays & Strings
| |-- Array operations
| |-- Prefix and suffix sums
| |-- Sliding window
| |-- Two pointers
| |-- String manipulation
| |-- Hashing basics

| | |-- Linked Lists
| |-- Singly linked list
| |-- Doubly linked list
| |-- Circular linked list
| |-- Fast & slow pointers
| |-- Reversing linked lists

| | |-- Stacks & Queues
| |-- Stack implementation
| |-- Queue implementation
| |-- Monotonic stack
| |-- Priority queue (Heap)
| |-- Deque

| | |-- Trees
| |-- Binary Trees
| |-- Binary Search Trees
| |-- Tree Traversals
| | |-- DFS
| | |-- BFS
| |-- AVL Trees
| |-- Segment Trees
| |-- Fenwick Trees

| | |-- Graphs
| |-- Graph Representation
| |-- BFS & DFS
| |-- Topological Sorting
| |-- Shortest Path Algorithms
| | |-- Dijkstra
| | |-- Bellman-Ford
| | |-- Floyd-Warshall
| |-- Minimum Spanning Tree
| | |-- Prim's Algorithm
| | |-- Kruskal's Algorithm
| |-- Union Find (DSU)

| | |-- Searching & Sorting
| |-- Binary Search
| |-- Merge Sort
| |-- Quick Sort
| |-- Heap Sort
| |-- Counting Sort
| |-- Radix Sort

| | |-- Recursion & Backtracking
| |-- Recursive thinking
| |-- Backtracking patterns
| |-- N-Queens
| |-- Sudoku Solver
| |-- Permutations & combinations

| | |-- Dynamic Programming
| |-- Memoization
| |-- Tabulation
| |-- Knapsack problems
| |-- Longest Common Subsequence
| |-- Longest Increasing Subsequence
| |-- Matrix DP

| | |-- Greedy Algorithms
| |-- Activity selection
| |-- Huffman coding
| |-- Fractional knapsack
| |-- Interval scheduling

| | |-- Advanced Algorithms
| |-- Bit Manipulation
| |-- Trie
| |-- Suffix Array
| |-- KMP Algorithm
| |-- Rabin-Karp
| |-- Manacher's Algorithm

| | |-- Competitive Programming
| |-- Fast I/O
| |-- Modular arithmetic
| |-- Number theory basics
| |-- Combinatorics
| |-- Mathematical algorithms

| | |-- Interview Preparation
| |-- Coding interview patterns
| |-- Blind 75 problems
| |-- LeetCode Top 150
| |-- Mock interviews
| |-- Whiteboard problem solving

| | |-- Real World Projects
| |-- Build a spell checker using Trie
| |-- Route finder with Graph algorithms
| |-- Cache system using LRU Cache
| |-- Search engine indexing demo
| |-- Task scheduler with Priority Queue

| | |-- Community and Growth
| |-- Practice on LeetCode
| |-- Solve Codeforces problems
| |-- Participate in coding contests
| |-- Share solutions on GitHub
| |-- Contribute to open source

Grab Master Data Structures & Algorithms ebook:
https://t.co/Y2uMlOqQZl

[Decode Python @DecodePython]

Python Notes

[Tips Excel @gudanglifehack]

🧵 Thread: Excel Formulas Every Beginner Should Know

Most people only use about 10% of Excel’s capabilities.

If you learn these essential formulas, you’ll work faster, make fewer mistakes, and stand out at work.

Here’s your beginner-friendly guide. 🧵👇

[TechiesVerse @TechiesVerse]

DAY 7 of 20 Days Series of #SQL Basics for Beginners.

Raw data is noisy.

GROUP BY turns thousands of rows into meaningful insights.

Learn how to summarize data with:
✅ COUNT()
✅ SUM()
✅ AVG()
✅ Multiple Groups

📌 Save this cheat sheet — it's one of the most important SQL skills you'll learn.

[Zen | kode diskon Zenius: GOPTN26 @zenbelajar]

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[Khushi Patil @KhushiPatilx]

20 most common SQL Interview Questions.

Do save and Bookmark it for later ✅

#sql

[Tips Excel @gudanglifehack]

📊 If your Excel files are difficult to manage, slow to update, or full of errors, you’re probably making one of these five common mistakes.

(Save this thank me later).

[Tips Excel @gudanglifehack]

🚨 This Formula Just Killed XLOOKUP

[Excel Dictionary @exceldictionary]

How to create a personal progress tracker in Excel. ✅

Download this free step-by-step guide I created 👉🏼 https://t.co/SEhgwtkftx

#excel #exceltips #scribehow #progresstracker #tutorial

[vey @blondq]

BELAJAR BASIC EXCEL DARI 0

Episode 1 : Kenalan dengan Excel

[Jesse--The Data Guy @Mbadiwejesse]

🐍Python Data Structures

[Pooja Pawar, PhD @SheExploresData]

Statistics helps you move beyond reporting to making informed decisions. Understanding concepts like mean, median, variance, correlation, confidence intervals, and hypothesis testing gives you the confidence to analyze data with accuracy and explain insights clearly. #Statistics #DataAnalytics #DataScience #Python #SQL