What is Python? Everything You Need to Know to Get Started

What do NASA, Spotify, Google, and JP Morgan Chase have in common? These companies all use Python on a daily basis.

Python is a powerful and versatile programming language that plays a critical role in a wide variety of technological solutions. From web applications, search engines, and games to animation software and even other programming languages, Python is at the heart of innovation.

In recent years, Python has seen a surge in popularity, becoming one of the most widely used programming languages across the globe. Its applications are expanding into new and exciting areas, such as artificial intelligence, machine learning, and data science.

In fact, Python occupies the number one position in the TIOBE index due to its consistent growth and usage. Given its widespread adoption and versatility, understanding Python is more important than ever.

In this comprehensive guide, we will explore the world of Python, its history, its rise to popularity, the various career paths it supports, and much more.

What Is Python?

Python is a powerful, high-level programming language known for its readability and simplicity. It follows the object-oriented programming paradigm, which means it's organized around objects rather than actions, making it intuitive and efficient for developers.

Python's design philosophy emphasizes code readability and simplicity, allowing developers to write clear, logical code for small—and large-scale projects. As a high-level language, Python abstracts away much of the complexity involved in programming, enabling developers to focus on solving problems rather than worrying about underlying technical details.

Python is at the core of many technologies and applications we use daily. For instance, YouTube uses it for video processing and search engines to handle vast amounts of data.

Why Is Python So Popular?

Python is consistently rated as one of the world's most popular programming languages. In fact, Python claimed the top spot in the TIOBE Programming Community index multiple times, including 2023, solidifying its position as a preferred language among developers.

In the Stack Overflow Developer Survey 2024, Python was ranked as the most commonly used and desired programming language. This consistent ranking highlights Python's growing influence and widespread adoption across various fields.

Stack Stack Overflow Developer Survey 2024 programming languages section results.

Python's popularity can be attributed to several factors:

1. Python is versatile and flexible

Python is a general-purpose language, which means it can be used to create a wide variety of applications. From web development to data analysis, from artificial intelligence to scientific computing, Python's versatility is unmatched.

For example, data scientists use Python to generate visualizations and manipulate data, while web developers use it to build dynamic websites.

2. Python is simple and easy to learn

Python's simple and clean syntax makes it an ideal language for beginners. Its commands are English-based, and its straightforward layout helps new programmers understand code easily. This simplicity also makes Python suitable for rapid development and prototyping, reducing the time it takes from concept to implementation.

If you want to start learning Python, check out our Introduction to Python course. 

3. Python is open-source

Python's open-source nature has led to the development of a vast ecosystem of libraries and frameworks. Whether you need tools for web development (Django, Flask), data analysis (pandas, NumPy), machine learning (TensorFlow, scikit-learn), or any other task, Python has a library for it.

This extensive collection of resources speeds up development and allows developers to focus on solving problems rather than reinventing the wheel.

4. Python has strong community support

Python boasts a large and active community of developers contributing to its continuous improvement. This community support means that there are countless tutorials, forums, and documentation available to help newcomers and experienced developers alike.

The thriving community also fosters the creation of new tools, libraries, and frameworks, further enhancing Python's capabilities.

5. Python is used everywhere

Python's widespread use in various industries makes it a valuable skill for developers. Companies across the globe, from tech giants like Google and Facebook to financial institutions like JP Morgan Chase, rely on Python for their technological solutions.

This ubiquity ensures that Python developers are in high demand, making it a smart career choice.

6. Python is in continuous evolution

Python is continually evolving to meet the needs of modern developers. Recent versions, such as Python 3.10 and 3.11, have introduced significant performance improvements and new features, keeping the language relevant and efficient.

The result is that more people know Python and are more likely to use it for their own projects or suggest it to others.

Comparison of Python with other programming languages

When Was Python Created?

Python was first conceived in the late 1980s as a successor to the ABC programming language, which, despite its high-level capabilities and simplicity, had limitations that hindered its widespread adoption.

ABC was designed to replace BASIC, a programming language that dates back to 1964. Although ABC offered many useful features, it was not extensible and lacked the flexibility needed for broader use, serving primarily as an instructional language. These limitations inspired its creator to develop a new language that retained ABC's strengths while addressing its weaknesses.

The first version of Python, 0.9.0, was released in 1991 on alt.sources, an early internet forum for sharing source code. This initial release featured many of the core aspects that define Python today, including an object-oriented design, a module system, functions, exception handling, and essential data types like lists, dictionaries, and strings.

Since its humble beginnings as a project to improve upon ABC, Python has evolved significantly, growing into one of the world's most popular and widely used programming languages. Its development has been guided by a strong community of contributors and an emphasis on simplicity and readability, making it a favorite among developers across various domains.

Who Invented Python?

Python was invented by Guido van Rossum while he was working at the Centrum Wiskunde & Informatica (CWI) in the Netherlands. Van Rossum had been actively involved with the development of the ABC language but found several limitations and a lack of extensibility frustrating:

“I had a number of gripes about the ABC language, but also liked many of its features. It was impossible to extend the ABC language (or its implementation) to remedy my complaints – in fact, its lack of extensibility was one of its biggest problems.” - Guido van Rossum

Driven by the desire to create a more flexible and powerful language, van Rossum began developing Python as a side project during the holiday season in 1989. This extracurricular endeavor eventually led to the creation of Python, which he named after the British comedy group Monty Python, reflecting his irreverent sense of humor.

Throughout his career, van Rossum remained deeply involved in the development of Python. He served as the Benevolent Dictator for Life (BDFL), guiding the language's evolution until he stepped down from the role in 2018. His contributions to the software development field extend beyond Python, including the creation of Mondrian, a code-review tool used by Google.

Although his name may not be as widely recognized as those of tech giants like Steve Jobs or Bill Gates, Guido van Rossum's work has had a profound impact on software development and technology worldwide.

How Python Has Evolved Over the Years

Python has gone through many changes over the course of its lifetime, which is not surprising given that the language started as van Rossum's hobby project and became one of the world's most prominent programming languages.

Here are a few of the ways that we've seen Python evolve to match the needs of developers and advancing technologies:

A timeline of Python versions and features

Python ongoing developments

Python's development has not stopped with major releases. Intermediate updates continually introduce new features, performance improvements, and security enhancements. Recent versions, such as Python 3.9, 3.10, and 3.11, have brought significant optimizations and new syntax features that make Python code more expressive and efficient.

The Python community plays a crucial role in the language's evolution. The Python Software Foundation (PSF) and countless volunteers contribute to Python's development, ensuring it remains relevant and powerful. The community-driven approach has led to a thriving ecosystem of third-party packages available via the Python Package Index (PyPI), further extending Python's capabilities.

Python libraries, frameworks, and packages

Since its initial release, the Python community has grown exponentially, leading to the development of a vast array of libraries and frameworks.

These tools have broadened Python's applicability across numerous fields, including web development, data science, artificial intelligence, and more. For example, libraries like TensorFlow and scikit-learn have made Python a cornerstone of AI and ML research and applications.

If you are interested in Python advanced libraries, head to our Machine Learning courses section.

Python and the rise of data science

In today's data-driven world, the importance of data cannot be overstated. The field of data science has emerged as a crucial discipline, combining mathematics, statistics, and programming to extract meaningful insights from vast amounts of data. These insights help businesses make informed decisions, drive innovation, and solve complex problems.

Python has become a cornerstone of data science due to its versatility, ease of use, and powerful libraries. Alongside SQL and R, Python is one of the most popular programming languages in this field

Who Uses Python?

Python is used by companies and professionals across a wide range of industries to create websites, develop software components, build applications, and work with data, AI, and machine learning technologies. Its versatility and ease of use make it a popular choice for both startups and established enterprises.

Companies using Python

Python is utilized by some of the world's leading companies, demonstrating its widespread adoption and versatility. Here are a few notable examples:

• Google: Python is one of the official languages at Google and is used extensively for system building, code evaluation tools, and various services.
• NASA: Python is used for various scientific and engineering applications, including data analysis and simulation.
• Spotify: The music streaming giant uses Python for data analysis and backend services.
• Netflix: Python powers various aspects of Netflix’s operations, from recommendation algorithms to data analytics.
• JP Morgan Chase: Python is used in the financial sector for quantitative analysis and trading strategies.
• Facebook: Utilizes Python for infrastructure management, data analysis, and various backend services.
• Instagram: The popular social media platform uses Python for its backend, leveraging its simplicity and scalability.

Professional roles using Python

Python’s flexibility means it’s valuable in numerous professional roles, including but not limited to:

• Data Scientists: Use Python for data analysis, visualization, and building machine learning models.
• Web Developers: Use frameworks like Django and Flask to build robust web applications.
• Software Engineers: Develop a variety of software solutions, from system scripts to full-scale applications.
• Machine Learning Engineers: Leverage Python's machine learning libraries to build and deploy models.
• Data Analysts: Manipulate and analyze large datasets using tools like Pandas and NumPy.
• DevOps Engineers: Use Python to automate workflows, manage infrastructure, and deploy applications.
• Researchers: Employ Python for scientific computing and research simulations.
• Game Developers: Use Python for scripting and building game logic.
• SEO Specialists: Automate tasks and analyze web data to improve search engine rankings.

Python career paths and average salaries

Check out Python Developer Salaries blog post to learn more about compensation for Python professionals.

Python’s broad range of applications and the diversity of its user base underscore its status as a leading programming language.

What Can Python Do?

Perhaps the better question is, what can't Python do?

Although Python is most often thought of as a coding language for websites, apps, data science, AI, and ML projects, its applications extend far beyond these areas.

Let's explore some of the (sometimes surprising) ways Python is used:

1. Data analysis and visualizations

Python is well suited to data science tasks in general, and this includes data analytics and visualizations. With Python, analysts can sort, manipulate, and glean high-level insights from data. They can also use the language to create powerful visuals highlighting their findings.

There is a growing number of Python libraries and frameworks for data analytics and visualization, including Pandas Visualization, Plotly, and Matplotlib, to name just a few. Whether it's a simple diagram or a complex statistical report, Python has tools that can help.

Another reason why Python is a preferred language for data science is because anyone can use it. Analysts and business intelligence professionals aren't always programmers or developers, but Python is user-friendly enough that people without a computer science background can adapt to it easily.

DataCamp's specialty is teaching individuals and employees at major companies such as Google how to use Python and other data science languages.

2. Programming applications

Because Python is a general-purpose programming language, it can be used to create all sorts of web and mobile applications, from advanced financial service products to components in an F1 racing game.

Python is also frequently used to program file directories, create graphical user interfaces (GUIs) and application programming interfaces (APIs), and much more.

If you can think of it, there's a good chance you can build it (or at least many key components) with Python.

Interested in learning how to create Python applications? Check out our Python Programmer career track.

3. AI and machine learning

Python is the ideal language for AI and ML applications due to its stability, flexibility, and simplicity. It allows developers to write reliable, readable code and prototype quickly.

Libraries such as scikit-learn, TensorFlow, and Keras provide powerful tools for building and deploying machine learning models, making Python indispensable for cutting-edge technology development.

If you're interested in working on the cutting edge of technology, DataCamp's Machine Learning Scientist with Python career track can help get you there.

4. Financial analysis and fintech

In the financial world, Python is favored for quantitative and qualitative analysis and handling large datasets.

It helps automate tasks such as calculating risk, managing stock portfolios, tracking market trends, and visualizing stock data. Python is also integral to developing fintech products, with companies like Venmo, Robinhood, and Affirm using Python in their tech stacks.

Would you like to become the next finance guru? Check out DataCamp's Intermediate Python for Finance course.

5. Marketing and search engine optimization (SEO)

Python is increasingly used in digital marketing and SEO. It helps automate tasks, categorize keywords, extract and analyze data, and implement changes across multiple web pages.

Natural language processing (NLP) libraries like SpaCy assist SEO professionals in optimizing content and analyzing search trends.

Django, a popular web framework, simplifies the process of technical SEO optimization.

6. Game development

Python isn't the most common or popular programming language for game development, and not many games are written entirely in Python. But it's often used by developers for other tasks, such as linking C and C++ modules.

That's not to say you can't build a full game with Python—check out Unknown Horizons if you'd like to see a game that only uses Python. Most games use multiple languages; for example, famous games like The Sims 4 and Battlefield 2 all use Python code for critical elements such as game logic.

PyGame, a cross-platform set of Python modules designed for the creation of video games, helps developers with Python-related tasks.

7. Graphic design

Python is a helpful language when it comes to developing graphic design applications. It's used in 2D imaging software, including the well-known programs Gimp and Paint Shop Pro. There's also DrawBot, a popular open-source application that helps users create 2D graphics using Python code.

Graphic designers who work with websites or digital images may make use of Python on a regular basis.

As further proof of Python's versatility, 3D animation software such as Blender and Lightwave use Python, too.

8. Give rise to other programming languages

Python's simplicity and clear syntax have inspired the creation of other programming languages like Go (Golang) and Cobra. It is also an excellent starting point for learning coding, as its ease of use makes transitioning to other programming languages simpler once the basics of Python are mastered.

Python's diverse applications and user-friendly nature make it an invaluable tool for many tasks. Whether you're analyzing data, developing applications, or exploring new technological frontiers, Python has the tools and libraries to support your endeavors.

Python in Action: Code Samples

Here are some code examples that showcase Python's capabilities in data manipulation, visualization, and machine learning using modern libraries. This section is meant to give you a taste of the language.

1. Finding the average of a list of numbers

import numpy as np

# Creating an array consisting of numbers from 1 to 10
a = np.arange(1, 11)

print("The generated array looks like:")
print(a)

print("The average of the numbers in the array:")
print(np.mean(a))

# The above code in just one line
print("The result of the average for the short version:")
print(np.mean(np.arange(1, 11)))

Output:

The generated array looks like:
[ 1  2  3  4  5  6  7  8  9 10]
The average of the numbers in the array:
5.5
The result of the average for the short version:
5.5

2. Multiplying matrices using NumPy

import numpy as np

# Creating matrices using NumPy
b = np.array([[2, 3], [4, 5]])
c = np.array([[6, 7], [8, 9]])
d = np.array([1, 10])

print("The matrices look like:")
print("b =\n", b)
print("c =\n", c)
print("d =", d)

# Multiplication of 2-D arrays
bc = np.matmul(b, c)
print("Result of b * c =\n", bc)

# Multiplication of 2-D and 1-D arrays
cd = np.dot(c, d)
print("Result of c * d =", cd)

Output:

The matrices look like:
b =
 [[2 3]
 [4 5]]
c =
 [[6 7]
 [8 9]]
d = [ 1 10]
Result of b * c =
 [[36 41]
 [64 73]]
Result of c * d = [76 98]

3. Visualizing data with Matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Generating some data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Creating a simple plot
plt.plot(x, y, label='Sine Wave')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Sine Wave Example')
plt.legend()
plt.show()

Output:

A plot displaying a sine wave.

4. Basic machine learning with scikit-learn

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Loading the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Splitting the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Training a RandomForestClassifier
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

# Making predictions
y_pred = clf.predict(X_test)

# Evaluating the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy of the RandomForest model: {accuracy:.2f}")

Output:

Accuracy of the RandomForest model: 1.00

Python libraries by use case

Learning Python: From Basics to Advanced

Python's simplicity means that even those with no programming experience can start using the language immediately. Whether it's an office worker automating routine tasks, a marketer sending emails at set intervals, or a student learning to code, Python is accessible to everyone.

However, there's a significant difference between what you can achieve with basic Python skills and what you can do with advanced or intermediate-level expertise. Much like learning to play a musical instrument, mastering Python requires practice and gradual skill-building. While you may start with simple scripts, with time and effort, you can progress to developing complex algorithms, creating sophisticated web applications, or conducting advanced data analysis.

Python experts are involved in a variety of complex tasks, from building AI systems that generate their own algorithms to developing new APIs and solving real-world problems. Whether you are new to Python, looking to deepen your understanding, or aiming to achieve mastery, resources are available to help you reach your goals.

Summing Up

Python is powerful, flexible, and incredibly versatile. Its user-friendly and intuitive nature, combined with rapid development capabilities and ease of learning, make it one of the world's most popular programming languages.

Python's applications span across industries, powering technologies in web development, data science, artificial intelligence, finance, and more. Its popularity and use are expected to grow, making Python an essential skill for modern professionals.

Learning Python is a smart investment for anyone looking to enhance their career prospects. Whether you aspire to be a well-rounded programmer, a data scientist, an AI or ML engineer, or another technology professional, Python opens doors to numerous high-demand career opportunities. Moreover, careers requiring Python skills often come with impressive salaries due to the high demand and relatively low supply of qualified professionals.

Ready to start your Python journey? Check out comprehensive resources and courses tailored to all skill levels. Whether you're a complete beginner or an advanced user, there’s a path for you to follow.