Getting started with Python 🐍
DataBites #16
Hey you all!
It’s Josep here, one more week! 👋🏻
Today, I’m writing from my new home in Rotterdam! It’s been a stressful process, but finally, it’s come to an end!
And I couldn’t be more happy about it.
🚀 Major Milestone Alert!
We’ve hit a HUGE milestone: DataBites now has 5,000 subscribers! 🎉
Honestly, I never imagined the newsletter would grow this quickly, and I’m beyond grateful.
To celebrate, I’ll be introducing a few new changes to improve the format and make it even more valuable for each of you.
Plus… keep an eye out next Wednesday for a special gift to all of you! 😋
Now that we’ve caught up on life updates, let's dive into the important stuff 👨🏻💻.
Following up on my previous DataBites issue, this week, I’ve crafted a detailed roadmap to help you navigate the complex world of Python, from the very basics to the most advanced concepts.
Why Learn Python?
Python is widely regarded as one of the most versatile and beginner-friendly programming languages. It’s used in data science, machine learning, web development, and countless other fields.
You can read why you should start learning it now in a previous issue👇🏻
… now that you understand why this language should be part of your toolkit.
The final question, then, is:
How to get started with Python?
This roadmap will help you to get started, build a strong foundation, and progress through more advanced topics.
Let’s break down the steps to mastering Python!
Navigate Your Way Through Python
Whether you're a data enthusiast, a budding analyst, or an experienced developer looking to sharpen your data science coding skills, understanding Python is crucial.
So let’s start by…
1. Learning the Basics
Before diving into data-heavy tasks, you need to understand Python fundamentals:
Basic Syntax and Variables
Data Types: Strings, Integers, Floats, etc.
Conditionals and Type Casting
Error Handling and Functions (including built-in functions)
Data Structures: Lists, Tuples, Sets, Dictionaries
Loops: for and while loops
2. Data Structures
Once you have a solid foundation of the basics, a good grasp of data structures is crucial for efficient programming:
Arrays and Linked Lists
Heaps, Stacks, and Queues
Hash Tables
Trees, including Binary Search Trees
3. Algorithms
With data structures covered, it’s time to explore algorithms:
Sorting Algorithms: Bubble, Merge, Quick Sort, etc.
Searching Algorithms: Binary Search and Linear Search
Recursion and its applications
Already proficient with Python’s algorithms?
Now it’s turn to further understand Python’s world with its modules and functions.
4. Modules
Python's extensive library of modules can make your life easier:
Built-in Modules: os, sys, math, etc.
Creating Custom Modules for reusability
5. Advanced Topics
Dive deeper with advanced Python techniques:
Lambdas: Anonymous functions
Decorators: Enhancing functions
Iterators and Generators
Regular Expressions for pattern matching
If you have arrived here… congrats! You are already a mid-level user. Now it’s turn to spice up your coding abilities with OOP 🔥
6. Object-Oriented Programming (OOP)
Python is an object-oriented language, so understanding OOP is essential:
Classes and Objects
Inheritance
Dunder (Magic) Methods to customize class behavior
Now it’s turn to start understanding how to install (and why!) Python’s best libraries.
7. Package Managers
To install libraries and manage dependencies, you'll need to know:
PyPI and pip
Conda for data science environments
Poetry for project management
Already bored of essentials concepts? Then Python still has a lot more to offer ✨
8. Comprehensions
Python offers elegant ways to create data structures:
List Comprehensions
Generator Expressions
9. Learn a Framework
Choose a framework based on your goals:
Web Development: Django, Flask, Pyramid
Data Science/Visualization: Plotly Dash
Asynchronous: FastAPI, Aiohttp, Tornado
10. Concurrency
For high-performance applications, learn concurrency:
Global Interpreter Lock (GIL)
Threading and Multiprocessing
Asynchrony with async/await
11. Environments
Managing different Python environments is critical:
Virtualenv and Pyenv
Pipenv for dependency management
12. Static Typing
Add type hints to improve readability and catch errors early:
Pydantic and Mypy
Pyright and Pyre
13. Code Formatting
Maintain clean, readable code with formatting tools:
Black, YAPF, and Ruff
14. Documentation
Document your code for clarity:
Sphinx for generating docs
15. Common Packages
Familiarize yourself with essential packages:
Typing and Tox
16. Testing
Testing is crucial for reliable code:
Pytest and Unittest
Doctest and Nose
17. DevOps
Finally, integrate Python into DevOps practices for deployment and scaling.
Phew, that was a lot to cover!
This roadmap is just a summary to help you start your Python journey.
We’ll dive deeper into each topic in upcoming issues, so stay tuned for more!
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Cool,I want to learn python, under environment, I can't see,anaconda environment there,I have been having is with activating python interpreter in conda environment? any help at all?