I'm a beginner in programming, so far I've only grasped the fundamental concepts of languages such as python...



Every time I delve into the fundamentals of a language, it becomes tedious and at times, incomprehensible; it can be quite frustrating.

I am eager to jump right into learning python and have a comprehensive understanding suitable for beginners.

So, how do I go about learning python? And which framework should I consider utilizing?

Learning a new programming language can indeed seem overwhelming at times, especially when trying to understand the fundamentals. Here are some general guidelines to help you learn Python more effectively:

Acquaint Yourself with Python Syntax: Be comfortable with what Python code looks like. Understand basic concepts like variables, types, functions, loops, conditionals, and error handling. You should also learn about Python-specific concepts, like the use of indentation instead of brackets for blocks of code.

Hands-on practice: It's very important to write code by yourself. You may start with small pieces of code, solving interesting yet simple problems. Gradually, you can move to more complex problems.

Undertake a Project: Choose a simple project based on your interest - be it web scraping, data analysis or even building a simple web application. This makes learning much more fun and practical. You will encounter real issues and learn to solve them.

Python Tutorials and Courses: There are many free and paid resources available for learning Python. Websites like Codecademy, Coursera, edX, and Python's official website all offer Python tutorials suited for beginners. You might also consider referring to a book such as "Learn Python the Hard Way" or "Python Crash Course".

Explore Python Libraries: Python has a wide range of libraries. Get your hands dirty with standard ones like os, math, datetime, etc. Then venture into domain-specific ones like NumPy and Pandas for data science, Flask or Django for web apps, Pygame for games etc.

Community Participation: Join Python forums or communities such as Stack Overflow, GitHub, or Reddit where you can ask doubts, answer others' questions, and learn together.

As for which Python framework to consider, it depends on what you want to do:

For web development, you might want to look into Django and Flask. Django is a high-level Python web framework that enables rapid development of secure and maintainable websites. Flask, on the other hand, is a micro web framework which is more flexible and simpler to start with.

If you're interested in data science, machine learning, or anything related to numerical processing, then Pandas, NumPy and SciPy are good libraries to start with. For visualisation you have Matplotlib and Seaborn. For machine learning, you may consider scikit-learn, TensorFlow, or PyTorch.

For desktop application development, you can look into frameworks like Tkinter or PyQt.


 I can provide some more tips to help you get further in your Python journey:

Understand Data Structures: Python's built-in data structures include lists, tuples, sets, and dictionaries. Understanding their usage and the right scenarios to use them can greatly power up your Python skills.

Object-Oriented Programming (OOP): Python is an object-oriented language, so do take some time to familiarize yourself with concepts like classes and objects, inheritance, polymorphism, and encapsulation.

Error Handling: Learn how to handle errors using try, except, else and finally keywords in Python to manage exceptions at runtime.

Debugging: Get familiar with Python's pdb, the interactive source code debugger. Debugging skills are invaluable when it comes to fixing and preventing bugs.

File Operations: Learn how to read from and write into files, and perform operations like copy, delete, or move files using Python's os, shutil, and pathlib libraries.

Working with databases: Python has several libraries to interact with databases. SQLite comes built-in with Python and serves as a good starting point to understand SQL queries through Python.

Regular Expressions: Python's re library can help you match or search for patterns in strings. Understanding it is important for text processing tasks.

Tests: Consider learning to write Python tests. A common framework for doing so is pytest. This will also introduce you to the concept of test-driven development (TDD).

Understanding Pythonic ways: Pythonic means code that doesn't just get the syntax right but that follows the conventions of the Python community and uses the language in the way it is intended to be used. Concepts like list comprehensions, generator expressions, and the usage of "Pythonic" tools in-built in the standard library like itertools, functools can make your code more elegant and efficient.

Learn to use Jupyter Notebooks: Especially if you're going into data science or numerical calculations with Python. Jupyter notebooks offer a good environment to write code and view the results, which can be particularly handy for visualizing data.

Exploring Other Paradigms: Apart from OOP, Python supports procedural and functional programming paradigms as well. Learning them will broaden your understanding of Python and programming as a whole.

Set up a Developer Environment: Learn how to set up a development environment using IDEs like PyCharm or text editors like VS code or Atom. Learning how to use virtual environments, understanding Python package managers like pip or conda will be very useful when working on larger projects.

Advanced topics and tips that can help in building more robust applications and mastering Python:

Asynchronous Programming: Python's asyncio library provides tools for building concurrent applications using coroutines. This is particularly useful when coding applications that require high performance with I/O bound tasks.

Decorators and Context Managers: Decorators allow you to wrap a function to extend its behavior without permanently modifying it. Context managers (the with keyword) help manage resources that need set-up/tear-down stages, such as file reading/writing or network connections.

Metaclasses: Metaclasses are deeper magic that most Python developers will never need to touch, being the classes that create classes. However, they can provide powerful programming constructs when used judiciously.

Multi-threading and Multiprocessing: For CPU bound tasks, threads and processes can help you achieve increased speed through parallelism. Learn about the threading, multiprocessing libraries, and their quirks and limitations.

Profiling Python Code: Profiling helps you understand where your code's bottleneck is. Python provides the cProfile module for such purposes, although alternative tools exist.

Scripting: Python's simplicity makes it natural for automating repetitive tasks in your system. Learning to script Python code for automation purposes would be useful.

Development Practices: Good code practices are also an essential part of a developer's journey. Understand best practices for writing clean, efficient, and maintainable code.

Package Distribution: If you're interested in contributing to the community, learn how to package your Python utility so others can install it easily using pip.

Web Scraping: Python is well-regarded for its web scraping capabilities. Libraries such as BeautifulSoup and Scrapy enable you to harvest data from web pages.

GUI Programming: Learn about GUI programming in Python. Tkinter is provided in the standard library. However, more robust options like PyQt and Kivy do exist for more complex applications.

Stay Updated: Python, like any other language, keeps evolving. Stay updated with changes, new features, and deprecated features by reading Python Enhancement Proposals (PEPs) etc.

Lastly, don't forget to ask for help when you get stuck. The Python community is very supportive and there are many forums like StackOverflow and GitHub where solutions for many problems already exist or can be found. One of Python's greatest strengths is its community and the wealth of open-source libraries and frameworks available. Exploring these resources will further deepen your Python knowledge. Happy learning!

If you're interested in programming, the first thing I would suggest is taking a break from coding and focusing on learning English. Having an intermediate level of English (B1) with a vocabulary of 2-3 thousand words and a good understanding of grammar will suffice for understanding most programming books. Without English, you'll struggle when trying to find solutions to complex problems on platforms like StackOverflow, where many explanations are in English. Additionally, if you encounter errors while running your code, the error messages will typically be in English as well. Learning English, even at an intensive pace, might take a couple of years, but it's worth it because it will greatly facilitate your programming journey.

Once you've gained a good grasp of English, you can start exploring beginner courses on platforms like Coursera. For example, if you're interested in Python, you can find suitable courses there. Of course, Coursera isn't the only option; there are other educational websites available as well. A simple Google search will open up a world of possibilities.

Not knowing English can put you at a significant disadvantage, as books translated into Russian often lag behind their original versions. As mentioned earlier, English proficiency will also make it much easier for you to find online solutions, regardless of the programming language you choose, whether it be Java, Ruby, Go, or any other language.

Of course, you can simultaneously learn English and study Python. In that case, there are specific books that are often recommended for beginners. Starting with Dawson's book is a good choice, as Lutz's book might be overwhelming due to its level of detail. Nevertheless, regardless of your decision to learn Python or any other programming language, English should be your first priority. It truly is essential.

On a more personal note, I would encourage you to reflect on whether you truly have a passion for programming, regardless of the language. If you find yourself growing bored during the initial stages of learning and feel the need to accelerate the process, it's possible that further training might not bring you joy. Programming can be a challenging and sometimes monotonous endeavor, especially when it comes to debugging and fixing errors in code. Even as someone who isn't professionally trained as a programmer, I've spent countless hours and days debugging programs and searching for elusive errors. Testing a program often takes much longer than actually writing the code, regardless of the programming language involved. And once you move past syntax and start delving into algorithms, the fun factor can decrease significantly. However, attention to detail, precision, and perseverance are vital attributes to possess in this field.

Therefore, if you're not keen on spending months simultaneously learning English and diving into programming languages, algorithms, and so on, it might be worth considering other pursuits.

I have a few questions that may not be directly related, but I would still like to raise them:

1. Learning a language, even Python, takes a long time and can be boring. It requires daily commitment for at least a year. It is important to understand this before starting the journey. Is it worth it? Is it necessary? What is the end goal? You might struggle for six months and realize that you haven't made any progress. Giving up means losing that half-year. It's crucial to have a clear understanding of why you're doing it and when to expect results.

If you decide to pursue programming as a career rather than just a hobby, your approach needs to change. Instead of aiming to "learn quickly without English," you should focus on "doing everything in three months to be able to submit a resume for a junior position."

You shouldn't tell yourself "I don't know English, unfortunately." Instead, say "I'll push myself to learn English, no matter what." Replace "I want quick results" with "I will work hard for three months and become a junior programmer at xхx company!"

For such serious goals, serious commitment is required. For example, imagine someone quitting their job to become a programmer. They have enough savings to sustain themselves for four months. After four months, they either land their dream job as a junior programmer or have to go back to being a welder. This kind of determination and urgency is necessary.

3. The ideal approach is to teach yourself the language and then find an internship or junior position to start working in the field. This is the fastest and most effective way to learn, while also getting paid for your work. It's also an opportunity to completely change your life if you're currently in a different profession.

In cities with over a million inhabitants, there are plenty of internship positions available. One year of working as an intern will provide you with three times more knowledge and experience than eight months of self-study through books and courses.

4. It's not necessary to learn English before learning Japanese, or vice versa. These two tasks can be pursued simultaneously and even complement each other. English is learned more effectively when there is a need or interest in a specific topic. For example, if you come across an incredibly interesting book but it's only available in English, or if all the relevant materials and tutorials are in English, then your motivation to learn the language will increase. Therefore, it's advisable to learn both languages concurrently.

5. In my humble opinion, you should dedicate at least 3-4 hours a day to programming languages and 4-5 hours a day to English. This could include 2 hours of studying grammar, writing, and reading, as well as another 2 hours of watching TV series and movies with subtitles to improve listening skills. Additionally, you can listen to background audio while cooking food. If you follow this routine consistently for 6 months, it should be sufficient to reach a junior level.

Typically, only students or unemployed individuals have the luxury of having that much time to dedicate to learning.

To gain a comprehensive understanding of Python, I recommend starting with the basics and gradually building your way up. Begin with the official documentation and tutorials, and then move on to online courses and resources like Codecademy, DataCamp, and Python.org. For a framework, consider Flask or Django, both of which are popular and well-documented.

Additionally, explore libraries like NumPy, pandas, and scikit-learn for data science and machine learning applications, and libraries like requests and BeautifulSoup for web scraping and automation. Don't be afraid to experiment and try out different libraries and tools to find what works best for you.