What Are Python Concepts?

Python, renowned for its simplicity and readability, is one of today's most popular programming languages. It encompasses many concepts catering to beginners and seasoned professionals, making it highly versatile for various applications, from web development to data science.

First released in 1991 by Guido Van Rossum, Python offers a design that focuses on simplicity and readability for faster learning and more efficient coding [1]. Its relevance across industries, including software development, machine learning, and scientific computing, underscores Python's utility in tackling modern computational challenges.

Discover the core concepts you’ll need to start with Python and explore careers in the field. 

Python concepts

At its core, Python centers around several key concepts that ensure its ease of use and broad applicability. Explore these concepts, from basic syntax to object-oriented programming, in more detail to better understand this versatile programming language.

Basic syntax

Python's clear syntax and dynamic typing system form the foundation of the language, enabling developers to write clear and logical code for various applications. Syntax in a language refers to the rules that govern the arrangement of words to be understood. Like the English language or any other natural language, Python’s syntax sets the rules that describe how to arrange the symbols so the computer can understand them. Python’s intuitive, easy-to-read syntax makes it accessible no matter your programming skill level. 

Variables

You can use variables to store information within a Python program. For example, you might set the variable x to 1000 with the command x=1000. You can reference that variable later, and Python can access the information you stored there. Variables can store many different formats of information, including whole numbers, numbers with decimal points, strings (or a sequence of numbers, letters, or words), Booleans (or true/false values), lists of numbers or words, or tuples (ordered collections of values). 

Control structures

Concepts such as loops and conditional statements allow code execution based on specific conditions, making programs dynamic and responsive. You can set the conditions for the programs and determine the structure for how your program will flow based on the feedback from the program itself. For example, suppose you want to create a program to calculate your payroll costs and each employee’s paycheck. You might offer a bonus program where employees qualify for higher pay if they meet certain criteria. You could use a conditional structure to write a program that checks if each employee meets that criteria before determining the amount you need to pay them. 

Data structures

Python is rich in integrated data structures, including lists, dictionaries, sets, and tuples, which helps increase the efficiency of working with and storing data. Each of these data formats requires you to structure the data in a different way that signals to the computer how to work with the data, determining whether the numbers can be moved around, rearranged, or changed. For example, you might want to create a list that you can manipulate so the numbers inside will change as you complete calculations, or you might need to create a list that must remain the same for the entirety of the program. Here, choosing a list or a tuple will offer different functionalities based on your needs. 

Functions and modules

These concepts promote code reusability and modularity, allowing for organized and scalable code development. A function is a subset of code that runs a program. Complex computer programs run many individual functions to create the entire program, and you can reuse them as needed when the program repeats the same task. A module is similar to a function in that you can create scripts (longer programs that include functions and variables) and save them to a plug-and-play module system to quickly return to the program you created. You can access functions and modules others created in Python to quickly begin coding using prebuilt pieces. 

Object-oriented programming (OOP)

Object-oriented programming is a type of programming language that groups variables and functions into objects that contain both the data and the instructions for what the program should do with that data. Python's support for OOP principles enables developers to create objects with attributes and methods, leading to more intuitive and maintainable code.

Python uses

Python's versatility lies in its wide range of applications. It also features many different libraries and frameworks to enhance its usability across domains. For example, you can perform web development with Django and numerical computations with SciPy.

Some applications of Python include the following: 

• Web and internet development: Frameworks such as Django, Pyramid, Flask, and Bottle can help you build websites and applications more easily and quickly. 

• Data science: Python offers useful packages and libraries for data science, such as SciPy and Pandas. Python can quickly analyze large data sets and is a time-saving tool for data scientists. 

• Software and application development: Python is useful for prototyping new apps or software because it allows you to easily and quickly start coding a minimum viable product. 

• Game development: Python is also useful for game development. Using Python, you can visualize your game coming together more quickly so that you can focus on gameplay mechanics, story, and other game design mechanics. 

• Artificial intelligence: Python’s vast offering of libraries and frameworks with data science capabilities also lends itself to machine learning and artificial intelligence applications. 

Who uses Python?

Python's accessibility and wide-ranging applications make it a favored language among software engineers, data scientists, AI researchers, and hobbyists. Explore careers that may require working with Python, including software development, data science, or web development. 

Software developer

Average annual US salary (Lightcast™): $132,466.34 [2]

Job outlook (projected growth from 2023 to 2033): 17 percent [3]

Educational requirements: To begin working as a software developer, you typically need a bachelor’s degree in computer science, engineering, or mathematics. 

As a software developer, you will work with clients to understand their software needs and design, build, test, and integrate software to help them meet their goals. You may also recommend upgrades and changes to your client’s software systems. In this role, you may specialize in specific types of software, such as mobile applications or systems software. 

Data scientist

Average annual US salary (Lightcast™): $114,282.59 [4]

Job outlook (projected growth from 2023 to 2033): 36 percent [5]

Educational requirements: To start a career as a data scientist, you must earn a bachelor’s degree. Common areas of study include mathematics, statistics, computer science, or a similar field. 

You will categorize, visualize, and analyze data as a data scientist. Once you draw insights from data, you can use data visualization to present your findings to the stakeholders of your projects. Data science has applications in many industries, so you could work in machine learning, research, and academic settings, or business strategy in a corporate context. 

Web developer

Average annual US salary (Lightcast™): $77,304.62 [6]

Job outlook (projected growth from 2023 to 2033): 8 percent [7]

Educational requirements: While some employers prefer candidates with a bachelor’s degree in computer science or a related field, you may be able to start a career as a web developer with a high school diploma and relevant skills or certifications. 

As a web developer, you will work with clients to create or improve websites to help them meet their business goals. In this role, you could work to help create content and decide what information will go on each page of the website, write code that powers the look and functionality of the website, design graphics, or monitor live websites for metrics that offer insights into performance. 

Limitations of Python

Although Python is popular for many reasons, such as its versatility, intuitive syntax, and the resources and documentation available for beginners, it also has limitations you should be aware of. For example, unlike other programming languages, Python requires a lot of memory to be flexible and easy to use.

It also runs more slowly than other programming languages. As a result, you may choose to use Python for prototyping applications and another language for your finished, published project. Another limitation of Python is that because of its versatility and adaptability, you may use it for purposes that another coding language can fulfill.

Python is a powerful tool in your programming skill set, but you should also feel encouraged to look for better solutions when available. 

Discover more Python concepts on Coursera

Understanding variables, functions, modules, and syntax helps you build your Python knowledge. Take the next step in exploring Python with the wide range of courses and Specializations available on Coursera, such as Python for Everybody Specialization from the University of Michigan. You’ll learn to program and analyze data with Python and develop programs to gather, clean, analyze, and visualize data. Join the global community of learners and professionals to unlock new opportunities in the tech industry with Python.