Here are some of the top Python frameworks for web development:
1. Django: A high-level framework that encourages rapid development and clean, pragmatic design. It includes a built-in admin interface, ORM, and many other features.
2. Flask: A micro-framework that is lightweight and easy to set up, making it a popular choice for small to medium-sized projects. It provides the essentials and leaves the rest to extensions.
3. FastAPI: Known for its high performance and ease of use, FastAPI is ideal for building APIs. It supports asynchronous programming and is built on standard Python type hints.
4. Pyramid: A flexible framework that can be used for both small applications and large-scale projects. It provides a minimalistic core with optional add-ons for added functionality.
5. Tornado: Designed for handling large numbers of simultaneous connections, making it a good choice for applications that require real-time capabilities.
6. Bottle: A very lightweight micro-framework that is perfect for small web applications. It is contained in a single file and has no dependencies other than the Python Standard Library.
7. CherryPy: An object-oriented framework that allows developers to build web applications in a similar way to writing other Python programs. It is minimalistic and easy to use.
8. Web2py: A full-stack framework that includes an integrated development environment, a web-based interface, and a web server. It emphasizes ease of use and rapid development.
9. Sanic: An asynchronous framework built for speed. It is designed to handle large volumes of traffic and is well-suited for building fast APIs.
10. Falcon: Another framework focused on building fast APIs. Falcon is lightweight and focuses on performance and reliability.
Free Resources to learn web development https://t.iss.one/free4unow_backup/554
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING ๐๐
1. Django: A high-level framework that encourages rapid development and clean, pragmatic design. It includes a built-in admin interface, ORM, and many other features.
2. Flask: A micro-framework that is lightweight and easy to set up, making it a popular choice for small to medium-sized projects. It provides the essentials and leaves the rest to extensions.
3. FastAPI: Known for its high performance and ease of use, FastAPI is ideal for building APIs. It supports asynchronous programming and is built on standard Python type hints.
4. Pyramid: A flexible framework that can be used for both small applications and large-scale projects. It provides a minimalistic core with optional add-ons for added functionality.
5. Tornado: Designed for handling large numbers of simultaneous connections, making it a good choice for applications that require real-time capabilities.
6. Bottle: A very lightweight micro-framework that is perfect for small web applications. It is contained in a single file and has no dependencies other than the Python Standard Library.
7. CherryPy: An object-oriented framework that allows developers to build web applications in a similar way to writing other Python programs. It is minimalistic and easy to use.
8. Web2py: A full-stack framework that includes an integrated development environment, a web-based interface, and a web server. It emphasizes ease of use and rapid development.
9. Sanic: An asynchronous framework built for speed. It is designed to handle large volumes of traffic and is well-suited for building fast APIs.
10. Falcon: Another framework focused on building fast APIs. Falcon is lightweight and focuses on performance and reliability.
Free Resources to learn web development https://t.iss.one/free4unow_backup/554
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING ๐๐
๐2โค1
Python Roadmap for 2025: Complete Guide
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
๐ Python Interview ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐
https://t.iss.one/dsabooks
๐ ๐ฃ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ : https://topmate.io/coding/914624
๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.iss.one/getjobss
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
๐ Python Interview ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐
https://t.iss.one/dsabooks
๐ ๐ฃ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ : https://topmate.io/coding/914624
๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.iss.one/getjobss
โค4๐4
Top 10 unique project ideas for freshers โ
1. Fitness Routine Generator: Develop a tool where users can input their fitness goals, time availability, and equipment, and the app generates a customized workout plan. This project will involve dynamic form handling and personalized recommendations.
2. Music Festival Planner:
Create a platform for planning large music events. It could feature ticket booking, artist lineups, venue information, and an interactive map for stages. Add real-time updates for artist schedules using APIs.
3. Travel Budget Calculator:
Develop a tool for travelers to plan trips, set a budget, and see a breakdown of costs like flights, accommodation, and activities. Integrate with APIs for live airfare and hotel prices. This project will teach you cost breakdown algorithms and API consumption.
4. Smart Recipe Suggestion App:
Build an app that suggests recipes based on what ingredients users currently have at home. Add features like dietary preferences, cooking time, and ingredient substitutions. Youโll practice complex filtering and database management.
5. Automated Career Path Advisor:
Design a platform where users input their current skills and career goals, and the app recommends a path of courses, certifications, or career advice. Youโll learn to build recommendation engines and integrate APIs for educational platforms.
6. Remote Workspace Organizer:
Build a web app for organizing tasks, meetings, and projects for remote teams. Include collaborative features like shared to-do lists, a team calendar, and a file-sharing system. This project will help you practice team collaboration tools and scheduling APIs.
7. Book Tracker for Avid Readers:
Create a personalized book tracker where users can log the books they've read, rate them, and set reading goals. You can integrate with external APIs to fetch book details and cover images. This would involve database management and user-generated content.
8. Nutrition Planner for Athletes:
Develop a platform where athletes can input their training regimen, and the app suggests a customized nutrition plan based on calories, macros, and workout intensity. This involves complex calculations and data visualization for nutritional charts.
9. Meditation Timer with Music Integration:
Create a web app for meditation with a built-in timer and background music integration. Allow users to choose different meditation lengths and calming background sounds. Integrate APIs from music platforms to stream music for meditation.
10. Charity Event Volunteer Scheduler:
Design a volunteer scheduling app for charity events. Volunteers can sign up for specific time slots and roles, while event organizers can track and manage the availability of each volunteer. This will require calendar integration, user authentication, and scheduling.
Best Programming Resources: https://topmate.io/coding/886839
ENJOY LEARNING ๐๐
1. Fitness Routine Generator: Develop a tool where users can input their fitness goals, time availability, and equipment, and the app generates a customized workout plan. This project will involve dynamic form handling and personalized recommendations.
2. Music Festival Planner:
Create a platform for planning large music events. It could feature ticket booking, artist lineups, venue information, and an interactive map for stages. Add real-time updates for artist schedules using APIs.
3. Travel Budget Calculator:
Develop a tool for travelers to plan trips, set a budget, and see a breakdown of costs like flights, accommodation, and activities. Integrate with APIs for live airfare and hotel prices. This project will teach you cost breakdown algorithms and API consumption.
4. Smart Recipe Suggestion App:
Build an app that suggests recipes based on what ingredients users currently have at home. Add features like dietary preferences, cooking time, and ingredient substitutions. Youโll practice complex filtering and database management.
5. Automated Career Path Advisor:
Design a platform where users input their current skills and career goals, and the app recommends a path of courses, certifications, or career advice. Youโll learn to build recommendation engines and integrate APIs for educational platforms.
6. Remote Workspace Organizer:
Build a web app for organizing tasks, meetings, and projects for remote teams. Include collaborative features like shared to-do lists, a team calendar, and a file-sharing system. This project will help you practice team collaboration tools and scheduling APIs.
7. Book Tracker for Avid Readers:
Create a personalized book tracker where users can log the books they've read, rate them, and set reading goals. You can integrate with external APIs to fetch book details and cover images. This would involve database management and user-generated content.
8. Nutrition Planner for Athletes:
Develop a platform where athletes can input their training regimen, and the app suggests a customized nutrition plan based on calories, macros, and workout intensity. This involves complex calculations and data visualization for nutritional charts.
9. Meditation Timer with Music Integration:
Create a web app for meditation with a built-in timer and background music integration. Allow users to choose different meditation lengths and calming background sounds. Integrate APIs from music platforms to stream music for meditation.
10. Charity Event Volunteer Scheduler:
Design a volunteer scheduling app for charity events. Volunteers can sign up for specific time slots and roles, while event organizers can track and manage the availability of each volunteer. This will require calendar integration, user authentication, and scheduling.
Best Programming Resources: https://topmate.io/coding/886839
ENJOY LEARNING ๐๐
๐5โค2
PROGRAMMING LANGUAGES YOU SHOULD LEARN TO BECOME ๐ฉโ๐ป๐งโ๐ปโ๏ธ[ Web Developer]
PHP, C#, JS, JAVA, Python, Ruby
โ๏ธ[ Game Developer]
Java, C++, Python, JS, Ruby, C, C#
โ๏ธ[ Data Analysis]
R, Matlab, Java, Python
โ๏ธ[ Desktop Developer]
Java, C#, C++, Python
โ๏ธ[ Embedded System Program]
C, Python, C++
โ๏ธ[ Mobile Apps Development]
Kotlin, Dart, Objective-C, Java, Python, JS, Swift, C#
Join this community for FAANG Jobs : https://t.iss.one/faangjob
๐6
Here are some essential data science concepts from A to Z:
A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
Credits: https://t.iss.one/free4unow_backup
Like if you need similar content ๐๐
A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
Credits: https://t.iss.one/free4unow_backup
Like if you need similar content ๐๐
๐5โค2
Working under a bad tech lead can slow you down in your career, even if you are the most talented
Hereโs what you should do if you're stuck with a bad tech lead:
Ineffective Tech Lead:
- downplays the contributions of their team
- creates deadlines without talking to the team
- views team members as a tool to build and code
- doesnโt trust their team members to do their jobs
- gives no space or opportunities for personal / skill development
Effective Tech lead:
- sets a clear vision and direction
- communicates with the team & sets realistic goals
- empowers you to make decisions and take ownership
- inspires and helps you achieve your career milestones
- always looks to add value by sharing their knowledge and coaching
I've always grown the most when I've worked with the latter.
But I also have experience working with the former.
If you are in a team with a bad tech lead, itโs tough, I understand.
Hereโs what you can do:
โฅdonโt waste your energy worrying about them
โฅfocus on your growth and what you can do in the environment
โฅfocus and try to fill the gap your lead has created by their behaviors
โฅtalk to your manager and share how you're feeling rather than complain about the lead
โฅtry and understand why they are behaving the way they behave, whatโs important for them
And the most important:
Donโt get sucked into this behavior and become like one!
You will face both types of people in your career:
Some will teach you how to do things, and others will teach you how not to do things!
Coding Projects:๐
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING ๐๐
Hereโs what you should do if you're stuck with a bad tech lead:
Ineffective Tech Lead:
- downplays the contributions of their team
- creates deadlines without talking to the team
- views team members as a tool to build and code
- doesnโt trust their team members to do their jobs
- gives no space or opportunities for personal / skill development
Effective Tech lead:
- sets a clear vision and direction
- communicates with the team & sets realistic goals
- empowers you to make decisions and take ownership
- inspires and helps you achieve your career milestones
- always looks to add value by sharing their knowledge and coaching
I've always grown the most when I've worked with the latter.
But I also have experience working with the former.
If you are in a team with a bad tech lead, itโs tough, I understand.
Hereโs what you can do:
โฅdonโt waste your energy worrying about them
โฅfocus on your growth and what you can do in the environment
โฅfocus and try to fill the gap your lead has created by their behaviors
โฅtalk to your manager and share how you're feeling rather than complain about the lead
โฅtry and understand why they are behaving the way they behave, whatโs important for them
And the most important:
Donโt get sucked into this behavior and become like one!
You will face both types of people in your career:
Some will teach you how to do things, and others will teach you how not to do things!
Coding Projects:๐
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING ๐๐
๐6โค1
Guys, Big Announcement!
Weโve officially hit 2 MILLION followers โ and itโs time to take our Python journey to the next level!
Iโm super excited to launch the 30-Day Python Coding Challenge โ perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python โ bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Hereโs what youโll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile script)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic โ Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs โ Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract titles from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer script)
- Final Project (Choose, build, and polish your app!)
React with โค๏ธ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
Weโve officially hit 2 MILLION followers โ and itโs time to take our Python journey to the next level!
Iโm super excited to launch the 30-Day Python Coding Challenge โ perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python โ bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Hereโs what youโll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile script)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic โ Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs โ Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract titles from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer script)
- Final Project (Choose, build, and polish your app!)
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To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
๐7โค1
๐ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐๐ฅ๐ญ ๐ข๐ฆ๐ฉ๐จ๐ฌ๐ฌ๐ข๐๐ฅ๐ ๐๐ญ ๐๐ข๐ซ๐ฌ๐ญ, ๐๐ฎ๐ญ ๐ญ๐ก๐๐ฌ๐ ๐ ๐ฌ๐ญ๐๐ฉ๐ฌ ๐๐ก๐๐ง๐ ๐๐ ๐๐ฏ๐๐ซ๐ฒ๐ญ๐ก๐ข๐ง๐ !
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1๏ธโฃ ๐๐๐ฌ๐ญ๐๐ซ๐๐ ๐ญ๐ก๐ ๐๐๐ฌ๐ข๐๐ฌ: Started with foundational Python concepts like variables, loops, functions, and conditional statements.
2๏ธโฃ ๐๐ซ๐๐๐ญ๐ข๐๐๐ ๐๐๐ฌ๐ฒ ๐๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.
3๏ธโฃ ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ๐๐ ๐๐ฒ๐ญ๐ก๐จ๐ง-๐๐ฉ๐๐๐ข๐๐ข๐ ๐๐๐ญ๐ญ๐๐ซ๐ง๐ฌ: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.
4๏ธโฃ ๐๐๐๐ซ๐ง๐๐ ๐๐๐ฒ ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
5๏ธโฃ ๐ ๐จ๐๐ฎ๐ฌ๐๐ ๐จ๐ง ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.
6๏ธโฃ ๐๐๐ญ๐๐ก๐๐ ๐๐ฎ๐ญ๐จ๐ซ๐ข๐๐ฅ๐ฌ: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.
7๏ธโฃ ๐๐๐๐ฎ๐ ๐ ๐๐ ๐๐๐ ๐ฎ๐ฅ๐๐ซ๐ฅ๐ฒ: Made it a habit to debug and analyze code to understand errors and optimize solutions.
8๏ธโฃ ๐๐จ๐ข๐ง๐๐ ๐๐จ๐๐ค ๐๐จ๐๐ข๐ง๐ ๐๐ก๐๐ฅ๐ฅ๐๐ง๐ ๐๐ฌ: Participated in coding challenges to simulate real-world problem-solving scenarios.
9๏ธโฃ ๐๐ญ๐๐ฒ๐๐ ๐๐จ๐ง๐ฌ๐ข๐ฌ๐ญ๐๐ง๐ญ: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.
I have curated the best interview resources to crack Python Interviews ๐๐
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#Python
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1๏ธโฃ ๐๐๐ฌ๐ญ๐๐ซ๐๐ ๐ญ๐ก๐ ๐๐๐ฌ๐ข๐๐ฌ: Started with foundational Python concepts like variables, loops, functions, and conditional statements.
2๏ธโฃ ๐๐ซ๐๐๐ญ๐ข๐๐๐ ๐๐๐ฌ๐ฒ ๐๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.
3๏ธโฃ ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ๐๐ ๐๐ฒ๐ญ๐ก๐จ๐ง-๐๐ฉ๐๐๐ข๐๐ข๐ ๐๐๐ญ๐ญ๐๐ซ๐ง๐ฌ: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.
4๏ธโฃ ๐๐๐๐ซ๐ง๐๐ ๐๐๐ฒ ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
5๏ธโฃ ๐ ๐จ๐๐ฎ๐ฌ๐๐ ๐จ๐ง ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.
6๏ธโฃ ๐๐๐ญ๐๐ก๐๐ ๐๐ฎ๐ญ๐จ๐ซ๐ข๐๐ฅ๐ฌ: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.
7๏ธโฃ ๐๐๐๐ฎ๐ ๐ ๐๐ ๐๐๐ ๐ฎ๐ฅ๐๐ซ๐ฅ๐ฒ: Made it a habit to debug and analyze code to understand errors and optimize solutions.
8๏ธโฃ ๐๐จ๐ข๐ง๐๐ ๐๐จ๐๐ค ๐๐จ๐๐ข๐ง๐ ๐๐ก๐๐ฅ๐ฅ๐๐ง๐ ๐๐ฌ: Participated in coding challenges to simulate real-world problem-solving scenarios.
9๏ธโฃ ๐๐ญ๐๐ฒ๐๐ ๐๐จ๐ง๐ฌ๐ข๐ฌ๐ญ๐๐ง๐ญ: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.
I have curated the best interview resources to crack Python Interviews ๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
#Python
๐7โค2
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Python project-based interview questions for a data analyst role, along with tips and sample answers [Part-1]
1. Data Cleaning and Preprocessing
- Question: Can you walk me through the data cleaning process you followed in a Python-based project?
- Answer: In my project, I used Pandas for data manipulation. First, I handled missing values by imputing them with the median for numerical columns and the most frequent value for categorical columns using
- Tip: Mention specific functions you used, like
2. Exploratory Data Analysis (EDA)
- Question: How did you perform EDA in a Python project? What tools did you use?
- Answer: I used Pandas for data exploration, generating summary statistics with
- Tip: Focus on how you used visualization tools like Matplotlib, Seaborn, or Plotly, and mention any specific insights you gained from EDA (e.g., data distributions, relationships, outliers).
3. Pandas Operations
- Question: Can you explain a situation where you had to manipulate a large dataset in Python using Pandas?
- Answer: In a project, I worked with a dataset containing over a million rows. I optimized my operations by using vectorized operations instead of Python loops. For example, I used
- Tip: Emphasize your understanding of efficient data manipulation with Pandas, mentioning functions like
4. Data Visualization
- Question: How do you create visualizations in Python to communicate insights from data?
- Answer: I primarily use Matplotlib and Seaborn for static plots and Plotly for interactive dashboards. For example, in one project, I used
- Tip: Mention the specific plots you created and how you customized them (e.g., adding labels, titles, adjusting axis scales). Highlight the importance of clear communication through visualization.
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1. Data Cleaning and Preprocessing
- Question: Can you walk me through the data cleaning process you followed in a Python-based project?
- Answer: In my project, I used Pandas for data manipulation. First, I handled missing values by imputing them with the median for numerical columns and the most frequent value for categorical columns using
fillna(). I also removed outliers by setting a threshold based on the interquartile range (IQR). Additionally, I standardized numerical columns using StandardScaler from Scikit-learn and performed one-hot encoding for categorical variables using Pandas' get_dummies() function.- Tip: Mention specific functions you used, like
dropna(), fillna(), apply(), or replace(), and explain your rationale for selecting each method.2. Exploratory Data Analysis (EDA)
- Question: How did you perform EDA in a Python project? What tools did you use?
- Answer: I used Pandas for data exploration, generating summary statistics with
describe() and checking for correlations with corr(). For visualization, I used Matplotlib and Seaborn to create histograms, scatter plots, and box plots. For instance, I used sns.pairplot() to visually assess relationships between numerical features, which helped me detect potential multicollinearity. Additionally, I applied pivot tables to analyze key metrics by different categorical variables.- Tip: Focus on how you used visualization tools like Matplotlib, Seaborn, or Plotly, and mention any specific insights you gained from EDA (e.g., data distributions, relationships, outliers).
3. Pandas Operations
- Question: Can you explain a situation where you had to manipulate a large dataset in Python using Pandas?
- Answer: In a project, I worked with a dataset containing over a million rows. I optimized my operations by using vectorized operations instead of Python loops. For example, I used
apply() with a lambda function to transform a column, and groupby() to aggregate data by multiple dimensions efficiently. I also leveraged merge() to join datasets on common keys.- Tip: Emphasize your understanding of efficient data manipulation with Pandas, mentioning functions like
groupby(), merge(), concat(), or pivot().4. Data Visualization
- Question: How do you create visualizations in Python to communicate insights from data?
- Answer: I primarily use Matplotlib and Seaborn for static plots and Plotly for interactive dashboards. For example, in one project, I used
sns.heatmap() to visualize the correlation matrix and sns.barplot() for comparing categorical data. For time-series data, I used Matplotlib to create line plots that displayed trends over time. When presenting the results, I tailored visualizations to the audience, ensuring clarity and simplicity.- Tip: Mention the specific plots you created and how you customized them (e.g., adding labels, titles, adjusting axis scales). Highlight the importance of clear communication through visualization.
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๐ Roadmap to Master Python Programming ๐ฐ
๐ Python Fundamentals
โโ๐ Learn Syntax, Variables & Data Types
โโโ๐ Master Control Flow & Functions
โโโโ๐ Practice with Simple Projects
๐ Intermediate Concepts
โโ๐ Object-Oriented Programming (OOP)
โโโ๐ Work with Modules & Packages
โโโโ๐ Understand Exception Handling & File I/O
๐ Data Structures & Algorithms
โโ๐ Lists, Tuples, Dictionaries & Sets
โโโ๐ Algorithms & Problem Solving
โโโโ๐ Master Recursion & Iteration
๐ Python Libraries & Tools
โโ๐ Get Comfortable with Pip & Virtual Environments
โโโ๐ Learn NumPy & Pandas for Data Handling
โโโโ๐ Explore Matplotlib & Seaborn for Visualization
๐ Web Development with Python
โโ๐ Understand Flask & Django Frameworks
โโโ๐ Build RESTful APIs
โโโโ๐ Integrate Front-End & Back-End
๐ Advanced Topics
โโ๐ Concurrency: Threads & Asyncio
โโโ๐ Learn Testing with PyTest
โโโโ๐ Dive into Design Patterns
๐ Projects & Real-World Applications
โโ๐ Build Command-Line Tools & Scripts
โโโ๐ Contribute to Open-Source
โโโโ๐ Showcase on GitHub & Portfolio
๐ Interview Preparation & Job Hunting
โโ๐ Solve Python Coding Challenges
โโโ๐ Master Data Structures & Algorithms Interviews
โโโโ๐ Network & Apply for Python Roles
โ ๏ธ Happy Coding
React "โค๏ธ" for More ๐จโ๐ป
๐ Python Fundamentals
โโ๐ Learn Syntax, Variables & Data Types
โโโ๐ Master Control Flow & Functions
โโโโ๐ Practice with Simple Projects
๐ Intermediate Concepts
โโ๐ Object-Oriented Programming (OOP)
โโโ๐ Work with Modules & Packages
โโโโ๐ Understand Exception Handling & File I/O
๐ Data Structures & Algorithms
โโ๐ Lists, Tuples, Dictionaries & Sets
โโโ๐ Algorithms & Problem Solving
โโโโ๐ Master Recursion & Iteration
๐ Python Libraries & Tools
โโ๐ Get Comfortable with Pip & Virtual Environments
โโโ๐ Learn NumPy & Pandas for Data Handling
โโโโ๐ Explore Matplotlib & Seaborn for Visualization
๐ Web Development with Python
โโ๐ Understand Flask & Django Frameworks
โโโ๐ Build RESTful APIs
โโโโ๐ Integrate Front-End & Back-End
๐ Advanced Topics
โโ๐ Concurrency: Threads & Asyncio
โโโ๐ Learn Testing with PyTest
โโโโ๐ Dive into Design Patterns
๐ Projects & Real-World Applications
โโ๐ Build Command-Line Tools & Scripts
โโโ๐ Contribute to Open-Source
โโโโ๐ Showcase on GitHub & Portfolio
๐ Interview Preparation & Job Hunting
โโ๐ Solve Python Coding Challenges
โโโ๐ Master Data Structures & Algorithms Interviews
โโโโ๐ Network & Apply for Python Roles
โ ๏ธ Happy Coding
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Python for Data Analysis: Must-Know Libraries ๐๐
Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.
๐ฅ Essential Python Libraries for Data Analysis:
โ Pandas โ The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.
๐ Example: Loading a CSV file and displaying the first 5 rows:
โ NumPy โ Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.
๐ Example: Creating an array and performing basic operations:
โ Matplotlib & Seaborn โ These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.
๐ Example: Creating a basic bar chart:
โ Scikit-Learn โ A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.
โ OpenPyXL โ Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.
๐ก Challenge for You!
Try writing a Python script that:
1๏ธโฃ Reads a CSV file
2๏ธโฃ Cleans missing data
3๏ธโฃ Creates a simple visualization
React with โฅ๏ธ if you want me to post the script for above challenge! โฌ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.
๐ฅ Essential Python Libraries for Data Analysis:
โ Pandas โ The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.
๐ Example: Loading a CSV file and displaying the first 5 rows:
import pandas as pd df = pd.read_csv('data.csv') print(df.head()) โ NumPy โ Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.
๐ Example: Creating an array and performing basic operations:
import numpy as np arr = np.array([10, 20, 30]) print(arr.mean()) # Calculates the average
โ Matplotlib & Seaborn โ These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.
๐ Example: Creating a basic bar chart:
import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [5, 7, 3]) plt.show()
โ Scikit-Learn โ A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.
โ OpenPyXL โ Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.
๐ก Challenge for You!
Try writing a Python script that:
1๏ธโฃ Reads a CSV file
2๏ธโฃ Cleans missing data
3๏ธโฃ Creates a simple visualization
React with โฅ๏ธ if you want me to post the script for above challenge! โฌ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค5๐1