Want to be a Python developer? Here is a quick guide to help you get started
This article guides you on the basics of Python to help you start your career as a Software Developer.
Fundamentals
When delving into the world of programming, there are fundamental concepts and skills you should grasp early on. These foundational principles will pave the way for your learning journey and empower you to tackle more complex challenges as you progress.
Understand variables' concepts, scope, functions, methods, how code is executed, returning values, and how to comment on your code.
Functions vs. Methods
Learn Data Types: int, float, booleans, string manipulation, Lists, Tuples, Sets, Dictionaries, and type casting.
Explore math operations.
Master conditionals, while loops, and for loops.
Errors and exception handling.
Object Oriented Programming
OOP is a programming paradigm that organizes software design based on the concept of classes and objects. An object possesses unique attributes and behavior, offering a systematic approach to code organization that enhances modularity, maintainability, and reusability.
Here are the key aspects beginners should know about OOP:
What are objects, and how they work
Classes
Instances
Methods
Overriding vs. overwriting
Abstraction
Encapsulation
Inheritance
Polymorphism
Public vs. non-public attributes in Python
Properties, Getters, and Setters
Objects in memory - How objects work
Aliasing, cloning, mutable and immutable Data Types
Use of import statements: working with multiple files
Data Structures & Algorithms
Data structures are organized arrangements of data in the memory of a device. Their primary purpose is to enable efficient and secure operations on the data.
Algorithms are structured and ordered operations designed to perform specific tasks or functions. In data structures, algorithms are responsible for various operations, such as inserting, deleting, updating, sorting, and searching data.
While there are numerous applications for data structures and algorithms, let's focus on the main concepts you should master to solve common algorithmic problems effectively.
Big O notation: How to measure the performance or complexity of an algorithm.
Arrays: Vectors that contain elements that belong to the same data type.
Sorted Array
Unsorted Array
Hash Tables: It allows you to store and retrieve values based on a unique key associated with each value. They are also known as dictionaries in Python.
Linked List: Ordered collection of objects. They differ from lists in how they store elements in memory, using references as part of their own elements.
Double-ended linked list
Doubly linked list
Singly-linked list
Recursion: It is a process in which a function calls itself.
Sorts: The process of arranging the data in a particular format or order
Bubble sort
Insertion sort
Merge sort
Quick sort
Selection sort
Shell sort
Stacks and Queues: They are simple data structures that allow us to store and retrieve data sequentially. Stack works on the principle of “last in, first out”. Queue works on the principle of “first in, first out”.
Stack
Circular queue
Priority queue
Deque
Trees: It is a data structure that inherits the characteristics of tree topologies. The data is arranged in a hierarchical way in trees.
- Binary search tree
Graphs: A finite set of nodes and edges connecting them. It is like a cyclic tree, where these nodes maintain any complex relationship among themselves instead of parent-child relationships.
A* search
BFS and DFS
Dijkstra
Greedy search
Package Manager and Venv concepts
In Python, understanding how to utilize virtual environments is of utmost importance. It is an indispensable tool for creating isolated environments. It creates a folder containing all the necessary executables to use the packages that a Python project would need.
Package managers allow you to manage the dependencies in your virtualenv that your project needs to work correctly. You can use several options on your projects, like Pip, PyPi, Poetry, pipx, conda.
But for simplicity, I recommend sticking with Pip to keep it simple:
Make sure you have Python and Pip installed:
pip install virtualenv
Once you have them installed on your machine, create a virtualenv on your project folder and activate it
mkdir project_folder
cd project_folder
virtualenv venv
source venv/bin/activate
Now you can install packages and mitigate your dependency management issues
Make sure your venv is always activated while you're coding. Deactivating is simple:
deactivate
It's a good practice to create a requirements.txt in your project folder. This file will store information about all the libraries, modules, and packages that you installed.
pip freeze > requirements.txt
If you need to recreate the environment, or if another developer needs to use your project with identical versions in their machine, just run the following command:
pip install -r requirements.txt
It will install all the dependencies in the Python project.
IDE vs. Code Editor
You can surely use your command line to code your first projects. Indeed, I strongly recommend it because every programmer should know how to use the terminal. But using a code editor or an IDE will make your life much easier after a while.
A code editor is a text editor used by developers that specifically helps write a programming language for a computer program.
Here are some friendly options that emphasize speed and usability:
Atom
Sublime Text
IDE stands for "Integrated Development Platform," they are programs developers can write, compile, and debug code. It is a code editor with extra features to help your project to be error-free, reducing some efforts when you're programming.
There are many Python IDEs to use. Here are the most popular:
PyCharm
Visual Studio Code
Jupyter Notebook (emphasis on data science)
Debugging
As with coding, debugging is equally essential for your projects. You need to understand what's going on in your code to ensure the quality of your work.
Especially when you're working on a team, you eventually will need to debug other developers' code to know what you're doing and facilitate your collaboration.
The debugging process saves time and effort by identifying issues early to fix them and proceed with further development. Whether you're using pdb module (python debugger), or an integrated debugging environment, you must master debugging in Python.
Conclusion
Some of the terms covered here may not be completely clear if you are a beginner. Don't worry, this is entirely normal at first. Just remember to focus on understanding these terms and putting them into practice. You'll soon be more familiar with and comfortable moving on to the following topics.
Mastering these topics will empower you to embark on a successful and rewarding journey to becoming a Python Software Developer. Remember that continuous learning and practice are vital to becoming a skilled programmer. Good luck with your studies!