Python Projects & Resources
الذهاب إلى القناة على Telegram
Perfect channel to learn Python Programming 🇮🇳 Download Free Books & Courses to master Python Programming - ✅ Free Courses - ✅ Projects - ✅ Pdfs - ✅ Bootcamps - ✅ Notes Admin: @Coderfun
إظهار المزيد
الشبكة:📈 نظرة تحليلية على قناة تيليجرام Python Projects & Resources
تُعد قناة Python Projects & Resources (@pythondevelopersindia) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 63 488 مشتركاً، محتلاً المرتبة 2 010 في فئة التكنولوجيات والتطبيقات والمرتبة 5 244 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 63 488 مشتركاً.
بحسب آخر البيانات بتاريخ 05 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 64، وفي آخر 24 ساعة بمقدار 3، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 5.44%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً N/A% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 3 456 مشاهدة. وخلال اليوم الأول يجمع عادةً 0 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 25.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل learning, object, module, string, loop.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Perfect channel to learn Python Programming 🇮🇳
Download Free Books & Courses to master Python Programming
- ✅ Free Courses
- ✅ Projects
- ✅ Pdfs
- ✅ Bootcamps
- ✅ Notes
Admin: @Coderfun”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 06 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
63 488
المشتركون
+324 ساعات
+257 أيام
+6430 أيام
جاري تحميل البيانات...
القنوات المماثلة
77.7K
56.0K
40.7K
29.4K
25.0K
1
سحابة العلامات
الإشارات الواردة والصادرة
---
---
---
---
---
---
جذب المشتركين
أكتوبر '26أكتوبر '26
أكتوبر '26
+36
في 1 قنوات
سبتمبر '26
+222
في 5 قنوات
Get PRO
أغسطس '26
+399
في 7 قنوات
Get PRO
يوليو '26
+479
في 10 قنوات
Get PRO
يونيو '26
+363
في 6 قنوات
Get PRO
مايو '26
+479
في 8 قنوات
Get PRO
أبريل '26
+396
في 9 قنوات
Get PRO
مارس '26
+261
في 6 قنوات
Get PRO
فبراير '26
+781
في 11 قنوات
Get PRO
يناير '26
+936
في 10 قنوات
Get PRO
ديسمبر '25
+882
في 2 قنوات
Get PRO
نوفمبر '25
+1 113
في 6 قنوات
Get PRO
أكتوبر '25
+966
في 9 قنوات
Get PRO
سبتمبر '25
+1 206
في 14 قنوات
Get PRO
أغسطس '25
+1 294
في 30 قنوات
Get PRO
يوليو '25
+1 200
في 33 قنوات
Get PRO
يونيو '25
+1 458
في 36 قنوات
Get PRO
مايو '25
+3 544
في 33 قنوات
Get PRO
أبريل '25
+5 846
في 33 قنوات
Get PRO
مارس '25
+2 109
في 34 قنوات
Get PRO
فبراير '25
+1 437
في 26 قنوات
Get PRO
يناير '25
+1 887
في 37 قنوات
Get PRO
ديسمبر '24
+824
في 13 قنوات
Get PRO
نوفمبر '24
+1 470
في 16 قنوات
Get PRO
أكتوبر '24
+1 518
في 18 قنوات
Get PRO
سبتمبر '24
+1 933
في 12 قنوات
Get PRO
أغسطس '24
+2 152
في 9 قنوات
Get PRO
يوليو '24
+5 254
في 15 قنوات
Get PRO
يونيو '24
+3 642
في 13 قنوات
Get PRO
مايو '24
+1 910
في 17 قنوات
Get PRO
أبريل '24
+3 971
في 15 قنوات
Get PRO
مارس '24
+2 368
في 11 قنوات
Get PRO
فبراير '24
+4 090
في 5 قنوات
Get PRO
يناير '24
+2 023
في 3 قنوات
Get PRO
ديسمبر '23
+2 242
في 5 قنوات
Get PRO
نوفمبر '23
+670
في 10 قنوات
Get PRO
أكتوبر '23
+298
في 3 قنوات
Get PRO
سبتمبر '23
+512
في 0 قنوات
Get PRO
أغسطس '23
+776
في 0 قنوات
Get PRO
يوليو '23
+866
في 0 قنوات
Get PRO
يونيو '23
+43
في 0 قنوات
Get PRO
مايو '23
+226
في 0 قنوات
Get PRO
أبريل '23
+269
في 0 قنوات
Get PRO
مارس '23
+188
في 0 قنوات
Get PRO
فبراير '23
+85
في 0 قنوات
Get PRO
يناير '23
+256
في 0 قنوات
Get PRO
ديسمبر '22
+171
في 0 قنوات
Get PRO
نوفمبر '22
+230
في 0 قنوات
Get PRO
أكتوبر '22
+213
في 0 قنوات
Get PRO
سبتمبر '22
+205
في 0 قنوات
Get PRO
أغسطس '22
+217
في 0 قنوات
Get PRO
يوليو '22
+187
في 0 قنوات
Get PRO
يونيو '22
+281
في 0 قنوات
Get PRO
مايو '22
+361
في 0 قنوات
Get PRO
أبريل '22
+92
في 0 قنوات
Get PRO
مارس '22
+241
في 0 قنوات
Get PRO
فبراير '22
+280
في 0 قنوات
Get PRO
يناير '22
+370
في 0 قنوات
Get PRO
ديسمبر '21
+96
في 0 قنوات
Get PRO
نوفمبر '21
+55
في 0 قنوات
Get PRO
أكتوبر '21
+89
في 0 قنوات
Get PRO
سبتمبر '21
+711
في 0 قنوات
| التاريخ | نمو المشتركين | الإشارات | القنوات | |
| 06 أكتوبر | +3 | |||
| 05 أكتوبر | +8 | |||
| 04 أكتوبر | +10 | |||
| 03 أكتوبر | +2 | |||
| 02 أكتوبر | 0 | |||
| 01 أكتوبر | +13 |
منشورات القناة
Sure! Here's the text with the asterisks replaced by double asterisks:
✅ Python Basics: Part-1
Data Types & Variables 🐍📚
🎯 What is a Variable?
A variable stores data in memory to be used and modified later.
Example:
name = "Alice"
age = 25
🔹 Common Python Data Types:
● String (str) – Text data
message = "Hello, World"
● Integer (int) – Whole numbers
count = 42
● Float (float) – Decimal numbers
price = 19.99
● Boolean (bool) – True or False
is_valid = True
● List (list) – Ordered, mutable sequence
fruits = ["apple", "banana", "cherry"]
● Tuple (tuple) – Ordered, immutable sequence
coords = (10.5, 20.7)
● Set (set) – Unordered collection of unique elements
colors = {"red", "green", "blue"}
● Dictionary (dict) – Key-value pairs
person = {"name": "Alice", "age": 25}
🔑 Dynamic Typing:
Python automatically detects the type, so you don’t need to declare it.
💬 Double Tap ❤️ for Part-2!
| 2 |  🎯 GigaChat 3.5 Reasoning: 5 Key Features
1️⃣ Advanced Reasoning: Explores multiple step-by-step paths, using automated verification to reinforce correct answers and self-correct
2️⃣ Autonomous Tool Usage: Independently decides when to call external APIs or revise earlier steps
3️⃣ Linear Attention: Proprietary architecture retains key context points without re-matching from scratch
4️⃣ Token Economy: Uses 37% fewer tokens than DeepSeek V4 Flash Preview on math problems
5️⃣ Proven Performance: Open-source LLM (built on GigaChat 3.5 Ultra) with massive benchmark gains:
• IFBench: 44 → 77
• Natural Plan: 64 → 80
• LiveCodeBench v6: 56 → 85
🔗 MIT License. Weights on Hugging Face: fp8 | bf16 | 3 400 |
| 3 |  +9Python Interview Questions with Answers | 3 672 |
| 4 | A-Z of essential data science concepts
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊 | 4 838 |
| 5 |  🌈 2026 Job-Seeker Toolkit – Free Interview & IT Cert Resources
🔥The 2026 hiring market is shifting fast. We've put together a 100% free resource bundle covering #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity — including:
✅Q&A banks & mock exams
✅Behavioral interview guides
✅Technical deep-dives for coding & infrastructure roles
✅Real-world project scenarios
Perfect for Software Developer Jobs, IT Internships, and Python Projects practice.
🎯 Interview Question Bank → https://bit.ly/3UP1fah
🪜 Online Free Course For Python & Excel→ https://bit.ly/4zJXCmh
📘 Free Cert E‑Book → https://bit.ly/4zLyDip
☁️ Free AI Materials → https://bit.ly/3Utk3vM
📊 Cloud Study Guide → https://bit.ly/4zO1ORT
🧠 Free Mock Exam → https://bit.ly/4gyN3ta
Tag a friend who's job-hunting or grinding Python projects — let's ace it together! 💪
🧠 Join Study Community:
https://chat.whatsapp.com/FQOG04r9xSiIa2ElhaNUJU
❄️ 1-on-1 support: https://wa.link/fp3nd9 | 4 170 |
| 6 |  📊 Pandas Cheatsheet Every Data Analyst Should Save
Pandas is one of the most important tools for data analysis. Master these core operations to work faster and more efficiently:
🔹 Read & Inspect Data
head(), shape, dtypes, describe()
🔹 Select & Filter Data
Extract relevant rows and columns with ease.
🔹 Row Selection
Use loc[] (labels) and iloc[] (positions).
🔹 Handle Missing Values
isnull(), dropna(), fillna()
🔹 Group & Aggregate
Summarize data using groupby() and aggregation functions.
🔹 Merge & Join Data
Combine datasets with merge() using different join types.
💡 Key Insight :
Strong Pandas skills help transform raw data into actionable insights faster and more effectively.
🚀 Whether you're a beginner or an experienced analyst, mastering these fundamentals is essential for data analytics success. | 5 198 |
| 7 |  Your Data Science degree just got an AI update.
Yeah.
Things are moving fast.
Python. SQL. Machine Learning. Deep Learning. MLOps.
And now GenAI, LLMs, RAG & AI-powered workflows.
An 8-month program with 20+ industry projects and live weekend classes.
Maybe Data Science was just the beginning.
https://lp.pwskills.com/data-science-ai-online-program-pw-skills?utm_source=telegram&utm_medium=influencer&utm_campaign=deepakAugDS | 5 325 |
| 8 | Today, lets understand Machine Learning in simplest way possible
What is Machine Learning?
Think of it like this:
Machine Learning is when you teach a computer to learn from data, so it can make decisions or predictions without being told exactly what to do step-by-step.
Real-Life Example:
Let’s say you want to teach a kid how to recognize a dog.
You show the kid a bunch of pictures of dogs.
The kid starts noticing patterns — “Oh, they have four legs, fur, floppy ears...”
Next time the kid sees a new picture, they might say, “That’s a dog!” — even if they’ve never seen that exact dog before.
That’s what machine learning does — but instead of a kid, it's a computer.
In Tech Terms (Still Simple):
You give the computer data (like pictures, numbers, or text).
You give it examples of the right answers (like “this is a dog”, “this is not a dog”).
It learns the patterns.
Later, when you give it new data, it makes a smart guess.
Few Common Uses of ML You See Every Day:
Netflix: Suggesting shows you might like.
Google Maps: Predicting traffic.
Amazon: Recommending products.
Banks: Detecting fraud in transactions.
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like for more ❤️ | 6 567 |
| 9 |  🚨 BREAKING: PW Skills x Microsoft just launched The Complete Live Gen AI Engineering Program
Generative AI isn't the future anymore, it's the present. And now you can master it live, with Microsoft's backing behind you.
Learn Agentic AI, LLMOps & real-world AI Development, taught through live interactive classes, in Hinglish, over a structured 5-month journey.
🎓 Bonus: Includes a Premium Microsoft Module, added credibility, added skills, added career value.
🎁 Use code GENAI20 and get 20% OFF instantly.
💰 Starting at just ₹4,999.
📅 Batch starts 20th August 2026, seats are limited, and this launch price won't last.
Don't just watch the AI wave. Build it.
👉 Reserve your seat now: https://pwskills.com/generative-ai/gen-ai-engineering-course-654105/?source=pwskills.com&position=course_dropdown&from=course_description | 5 845 |
| 10 | Google Free Certificate Courses — No Fees, No Experience Needed
Google offers genuinely free certificate courses across three platforms: Digital Garage, Skillshop, and Cloud Skills Boost.
Who can apply:
• Anyone with a Gmail account, no fixed eligibility criteria
• No prior experience or technical background required
• Open globally, including India
What you get:
• Google Digital Garage: Free courses on digital marketing, career development, and data skills, with certificates on completion
• Google Skillshop: 26+ official certifications in Google Ads, Analytics, and Search Ads 360, most requiring recertification every 12 months
• Google Cloud Skills Boost: 1,300+ free courses, learning pathways, and hands on labs, with completion and skill badges
• Note: Google Career Certificates on Coursera (Data Analytics, IT Support, UX Design, etc.) are not fully free, they require a paid Coursera subscription unless you qualify for financial aid or a scholarship
Documents needed: None, just a Google account
How to apply:
1. Choose your platform based on your interest: Digital Garage for marketing/career skills, Skillshop for Ads/Analytics certifications, or Cloud Skills Boost for cloud/data skills
2. Sign in with your Gmail account
3. Enroll in your chosen course
4. Complete the video lessons, quizzes, and assignments
5. Download your certificate or badge upon completion
Deadline: None, self-paced and available anytime
Apply here:
Digital Garage: https://grow.google/digitalgarage
Skillshop: https://skillshop.withgoogle.com
Cloud Skills Boost: https://cloudskillsboost.google
If you need more such type of content then do let me know by responding to this message. | 4 556 |
| 11 | ⏳ Your Python already clears half the bar.
The other half is a 60-min aptitude test - tomorrow.
Certification in AI & ML - Vishlesan i-Hub, IIT Patna ML → PyTorch → LLMs, RAG & Agents → Docker deployment ₹99 · Sunday · one attempt
🔗 https://tinyurl.com/DS-29JUL-009 | 4 147 |
| 12 | ✅ Python Exception Handling! 🐍✨
Exception handling allows your program to handle errors gracefully instead of crashing unexpectedly.
num = 10
print(num / 0)
❌ Output → ZeroDivisionError
💡 Without exception handling, the program stops immediately when an error occurs.
1. Basic Syntax:
› Use try and except to handle errors.
try:
num = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero")
✔ Output → Cannot divide by zero
2. Catch Any Exception:
Use Exception to handle all types of errors.
try:
number = int("Hello")
except Exception:
print("Something went wrong")
✔ Output → Something went wrong
3. Catch Multiple Exceptions:
try:
num = int(input("Enter a number: "))
print(10 / num)
except ValueError:
print("Invalid number")
except ZeroDivisionError:
print("Cannot divide by zero")
💡 Different errors can be handled separately.
4. Using else:
The else block runs only if no exception occurs.
try:
num = 10 / 2
except ZeroDivisionError:
print("Error")
else:
print("Division Successful")
✔ Output → Division Successful
5. Using finally:
The finally block always executes, whether an exception occurs or not.
try:
print(10 / 2)
except ZeroDivisionError:
print("Error")
finally:
print("Program Finished")
✔ Output →
5.0
Program Finished
💡 Commonly used to close files or database connections.
6. Using raise:
Manually raise an exception.
age = -5
if age < 0:
raise ValueError("Age cannot be negative")
✔ Output → ValueError: Age cannot be negative
7. Get the Error Message:
try:
print(10 / 0)
except Exception as e:
print(e)
✔ Output → division by zero
💡 e stores the actual error message.
8. Nested Exception Handling:
try:
try:
print(10 / 0)
except ZeroDivisionError:
print("Inner Exception")
except:
print("Outer Exception")
✔ Output → Inner Exception
9. Common Python Exceptions:
✔ ZeroDivisionError → Dividing by zero: 10 / 0
✔ ValueError → Invalid value: int("Hello")
✔ TypeError → Invalid data type: 10 + "20"
✔ IndexError → Invalid list index:
nums = [1, 2]
print(nums[5])
✔ KeyError → Missing dictionary key:
student = {"name": "Alex"}
print(student["age"])
✔ FileNotFoundError → File doesn't exist: open("data.txt")
10. Practice Examples:
✔ Handle invalid input
try:
age = int(input("Enter age: "))
print(age)
except ValueError:
print("Please enter a valid number")
✔ Handle list index error
try:
nums = [10, 20]
print(nums[5])
except IndexError:
print("Index out of range")
✔ Handle dictionary key error
try:
student = {"name": "Alex"}
print(student["age"])
except KeyError:
print("Key not found")
💡 Exception handling makes your programs more reliable by preventing unexpected crashes and providing meaningful error messages.
💬 Tap ❤️ if this helped you! | 4 220 |
| 13 |  You already know Python.
That’s 20% of an AI career. Here’s the other 80%.
ML with Scikit-learn & XGBoost → Deep Learning with PyTorch → LLMs, RAG & AI Agents → Deployment with Docker.
That’s the exact roadmap of the Certification in AI & ML - Vishlesan i-Hub, IIT Patna.
✅ 9 Months | Online | IIT faculty & industry mentors
✅ Ship deployed projects: churn predictor, image classifier + capstone
✅ Placement support through Masai's network of 5000+ companies
Your Python already clears half the entry bar. The rest is a ₹99 test this Sunday.
🗓 2nd August - slot booking closing soon
🔗 https://tinyurl.com/DS-29JUL-009 | 3 651 |
| 14 | Last 6 Hours Remaining!
Before the application closes for E&ICT IIT Roorkee AI & ML Program.
Don't miss out on the chance to:
• Learn live from IIT professors & industry experts
• Build real AI projects
• Get Placement Support from Masai.
Register NOW | 4 294 |
| 15 |  You already know Python.
Now learn how companies actually use it for AI.
Applications are open for TiHAN IIT Hyderabad's AI & ML Program.
✅ Learn from TiHAN scientists, IIT professors & industry experts
✅ Build projects from Flipkart & Mamaearth
✅ Assured interview at TiHAN IIT Hyderabad with 9+ CGPA
✅ Placement support across 5000+ companies through Masai
🗓 Entrance Exam: 19th July
🔗 Register: https://tinyurl.com/DS-26Jul-009 | 4 569 |
| 16 | List of Python Project Ideas💡👨🏻💻🐍 -
Beginner Projects
🔹 Calculator
🔹 To-Do List
🔹 Number Guessing Game
🔹 Basic Web Scraper
🔹 Password Generator
🔹 Flashcard Quizzer
🔹 Simple Chatbot
🔹 Weather App
🔹 Unit Converter
🔹 Rock-Paper-Scissors Game
Intermediate Projects
🔸 Personal Diary
🔸 Web Scraping Tool
🔸 Expense Tracker
🔸 Flask Blog
🔸 Image Gallery
🔸 Chat Application
🔸 API Wrapper
🔸 Markdown to HTML Converter
🔸 Command-Line Pomodoro Timer
🔸 Basic Game with Pygame
Advanced Projects
🔺 Social Media Dashboard
🔺 Machine Learning Model
🔺 Data Visualization Tool
🔺 Portfolio Website
🔺 Blockchain Simulation
🔺 Chatbot with NLP
🔺 Multi-user Blog Platform
🔺 Automated Web Tester
🔺 File Organizer | 4 254 |
| 17 | ✔ Print all values
print(student.values())
✔ Add a new key
student["country"] = "India"
print(student)
✔ Update a value
student["age"] = 23
print(student)
💡 Dictionaries are one of the most powerful data structures in Python and are widely used to store structured data like JSON, APIs, and database records.
💬 Tap ❤️ if this helped you learn Python faster!
-----
1.32 ₽ · /balance_help | 4 108 |
| 18 | ✅ Python Dictionaries! 🐍✨
Dictionaries are used to store data in key-value pairs. They are ordered, mutable, and do not allow duplicate keys.
student = {
"name": "Alex",
"age": 22,
"city": "Mumbai"
}
1. Basic Syntax:
› Dictionaries use curly braces {}.
› Each item consists of a key: value pair.
person = {
"name": "John",
"age": 25
}
💡 Keys must be unique, but values can be duplicated.
2. Access Dictionary Values:
Access values using their keys.
student = {
"name": "Alex",
"age": 22
}
print(student["name"])
print(student["age"])
✔ Output
Alex
22
3. Using get() Method:
Safely access a value without getting an error if the key doesn't exist.
student = {
"name": "Alex",
"age": 22
}
print(student.get("name"))
✔ Output
Alex
💡 If the key doesn't exist, get() returns None by default.
4. Change Dictionary Values:
student = {
"name": "Alex",
"age": 22
}
student["age"] = 23
print(student)
✔ Output
{'name': 'Alex', 'age': 23}
5. Add New Items:
student = {
"name": "Alex"
}
student["city"] = "Mumbai"
print(student)
✔ Output
{'name': 'Alex', 'city': 'Mumbai'}
6. Remove Items:
Using pop()
student.pop("age")
Using del
del student["city"]
Remove all items
student.clear()
7. Dictionary Length:
student = {
"name": "Alex",
"age": 22
}
print(len(student))
✔ Output
2
8. Loop Through a Dictionary:
Loop through keys
for key in student:
print(key)
✔ Output
name
age
Loop through values
for value in student.values():
print(value)
✔ Output
Alex
22
Loop through key-value pairs
for key, value in student.items():
print(key, value)
✔ Output
name Alex
age 22
9. Check if a Key Exists:
student = {
"name": "Alex",
"age": 22
}
print("name" in student)
✔ Output
True
10. Common Dictionary Methods:
✔ keys() → Returns all keys
print(student.keys())
✔ values() → Returns all values
print(student.values())
✔ items() → Returns key-value pairs
print(student.items())
✔ update() → Updates dictionary
student.update({"age": 24})
✔ Output
{'name': 'Alex', 'age': 24}
11. Nested Dictionaries:
students = {
"student1": {
"name": "Alex",
"age": 22
},
"student2": {
"name": "John",
"age": 25
}
}
print(students["student1"]["name"])
✔ Output
Alex
12. Practice Examples:
✔ Print all keys
student = {
"name": "Alex",
"age": 22
}
print(student.keys()) | 3 783 |
| 19 | Final 6 Hours Left!
To register for TiHAN IIT Hyderabad's AI & ML Program.
Don't miss your chance to:
• Learn from India's best scientists at TiHAN, IIT Professors and industry experts
• Direct Interview at TiHAN IIT Hyderabad with 9+ CGPA
Register before the Admission Closes! | 4 225 |
| 20 |  You already know Python.
Now learn how companies actually use it for AI.
Applications are open for TiHAN IIT Hyderabad's AI & ML Program.
✅ Learn from TiHAN scientists, IIT professors & industry experts
✅ Build projects from Flipkart & Mamaearth
✅ Assured interview at TiHAN IIT Hyderabad with 9+ CGPA
✅ Placement support across 5000+ companies through Masai
🗓 Entrance Exam: 19th July
🔗 Register: https://tinyurl.com/datasimplifier-17jul-tihan-009 | 5 003 |
