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🔰 Comprehensions in python with example

✅ Complete Roadmap to Learn Python Programming 🐍💻

Week 1: Python Basics
• Install Python and VS Code
• Learn variables, data types, input, output
• Practice arithmetic and string operations
• Write 10 small programs
Example: Calculator, temperature converter

Week 2: Control Flow
• Learn if, else, elif
• Learn for and while loops
• Use break and continue
• Solve 20 logic problems
Example: Number guessing game

Week 3: Data Structures
• Lists, tuples, sets, dictionaries
• Indexing, slicing, methods
• Loop through collections
• Solve real problems
Example: Student marks analysis

Week 4: Functions and Modules
• Define functions
• Use parameters and return values
• Learn lambda functions
• Import built-in modules
Example: Reusable math utility

Week 5: Strings and File Handling
• String methods and formatting
• Read and write files
• Handle CSV and text files
• Build small file-based programs
Example: Log file analyzer

Week 6: Error Handling and Debugging
• Learn try, except, finally
• Understand common errors
• Use print and debugger
• Fix broken programs
Example: Robust input validator

Week 7: Object-Oriented Programming
• Classes and objects
• Constructors and methods
• Inheritance and encapsulation
• Build simple class-based apps
Example: Bank account system

Week 8: Standard Libraries
• datetime, math, random
• os and sys basics
• Work with JSON
• Write utility scripts
Example: Automated folder organizer

Week 9: Working with External Packages
• Learn pip and virtual environments
• Use requests library
• Basic API calls
• Handle API responses
Example: Weather app using API

Week 10: Data Handling Basics
• Intro to NumPy
• Intro to Pandas
• Read CSV and Excel files
• Basic data cleaning
Example: Sales data summary

Week 11: Mini Projects
• Build 2 small projects
• Focus on logic and structure
• Write clean, readable code
Examples:
• To-do list app
• Expense tracker

Week 12: Final Project and Revision
• Build one end-to-end project
• Revise core concepts
• Practice interview-style questions
Example projects:
• Simple automation tool
• Data analysis mini project

Daily Rule for You:
• Code at least 60 minutes
• Solve 5 problems daily
• Rewrite old code weekly

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🔰 Comprehensions in python with example

🐍 Python Roadmap

1️⃣ Basics: 📝📜 Syntax, Variables, Data Types
2️⃣ Control Flow: 🔄🤖 If-Else, Loops, Functions
3️⃣ Data Structures: 🗂️🔢 Lists, Tuples, Dictionaries, Sets
4️⃣ OOP in Python: 📦🎭 Classes, Inheritance, Decorators
5️⃣ File Handling: 📄📂 Read/Write, JSON, CSV
6️⃣ Modules & Libraries: 📦🚀 NumPy, Pandas, Matplotlib
7️⃣ Web Development: 🌍🔧 Flask, Django, FastAPI
8️⃣ Automation & Scripting: 🤖🛠️ Web Scraping, Selenium, Bash Scripting
9️⃣ Machine Learning: 🧠📈 TensorFlow, Scikit-learn, PyTorch
🔟 Projects & Practice: 📂🎯 Create apps, scripts, and contribute to open source

React ❤️ for more

Roadmap to become a data analyst

1. Foundation Skills:
•Strengthen Mathematics: Focus on statistics relevant to data analysis.
•Excel Basics: Master fundamental Excel functions and formulas.

2. SQL Proficiency:
•Learn SQL Basics: Understand SELECT statements, JOINs, and filtering.
•Practice Database Queries: Work with databases to retrieve and manipulate data.

3. Excel Advanced Techniques:
•Data Cleaning in Excel: Learn to handle missing data and outliers.
•PivotTables and PivotCharts: Master these powerful tools for data summarization.

4. Data Visualization with Excel:
•Create Visualizations: Learn to build charts and graphs in Excel.
•Dashboard Creation: Understand how to design effective dashboards.

5. Power BI Introduction:
•Install and Explore Power BI: Familiarize yourself with the interface.
•Import Data: Learn to import and transform data using Power BI.

6. Power BI Data Modeling:
•Relationships: Understand and establish relationships between tables.
•DAX (Data Analysis Expressions): Learn the basics of DAX for calculations.

7. Advanced Power BI Features:
•Advanced Visualizations: Explore complex visualizations in Power BI.
•Custom Measures and Columns: Utilize DAX for customized data calculations.

8. Integration of Excel, SQL, and Power BI:
•Importing Data from SQL to Power BI: Practice connecting and importing data.
•Excel and Power BI Integration: Learn how to use Excel data in Power BI.

9. Business Intelligence Best Practices:
•Data Storytelling: Develop skills in presenting insights effectively.
•Performance Optimization: Optimize reports and dashboards for efficiency.

10. Build a Portfolio:
•Showcase Excel Projects: Highlight your data analysis skills using Excel.
•Power BI Projects: Feature Power BI dashboards and reports in your portfolio.

11. Continuous Learning and Certification:
•Stay Updated: Keep track of new features in Excel, SQL, and Power BI.
•Consider Certifications: Obtain relevant certifications to validate your skills.

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Let’s dive into our first mini project using Python:

Project 1: Number Guessing Game 🎯

Goal: 
The computer randomly picks a number between 1 and 100.

The user has to guess it — and the program gives hints like “Too High” or “Too Low” until the user gets it right.

Code:

import random

number_to_guess = random.randint(1, 100)
guess = None
attempts = 0

print("Welcome to the Number Guessing Game!")
print("I'm thinking of a number between 1 and 100.")

while guess != number_to_guess:
    guess = int(input("Enter your guess: "))
    attempts += 1

    if guess < number_to_guess:
        print("Too low! Try again.")
    elif guess > number_to_guess:
        print("Too high! Try again.")
    else:
        print(f"Congratulations! You guessed it in {attempts} tries.")

Concepts used:

• random.randint() for generating numbers

• Taking user input using input() & converting input to integers

• Using while loops and if-else logic

React with ♥️ for the next project

Important Python Concepts: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1441

For all resources and cheat sheets, check out our Telegram channel 
👇👇 
https://t.me/pythonproz

Hope it helps :)

🎯 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

Data Analytics A-Z