Introduction

Python is the most popular and widely used programming language of 2025, and according to the TIOBE Index it’s been in the top 5 programming languages for almost a decade. By allowing developers to deliver web applications and software quickly and efficiently, Python has become the go-to language for many industries, supporting devs from startups to multinational companies.

But despite its popularity, challenges remain when hiring Python developers. Supply and demand poses the biggest challenges, with the demand for qualified, experienced Python engineers outweighing the domestic supply within the US. Businesses often amplify this challenge by restricting their search to local tech talent; however, in recent years, the rise of remote, global work has begun to solve this issue.

What is Python?

Python is an incredibly popular programming language. Created in 1991, Python is the preferred language for many leading corporations, including Netflix, Spotify, NASA, and Google.

Python is unique for its object-oriented, semantic-infused, and high-level programming dynamics, offering developers increased speed, productivity, and efficiency. It’s a general-purpose language, making it versatile and easy to learn. Though broadly adaptable, Python is commonly used to create websites and software as well as for task automation, data visualization, and analysis.

Why use Python?

Praised for its simplicity and versatility, Python allows for rapid software development and testing. Its readability and ease of use make it a preferred choice for startups and small businesses, allowing them to quickly turn ideas into functional solutions. Furthermore, its extensive libraries and frameworks can speed up the development process, providing vital tools for developers working in different industries and sectors.

Python is also instrumental in data-driven decision-making, due to its adaptability to data analysis, visualization, and machine learning applications. With Python, companies can automate repetitive tasks to optimize operations as well as perform reliable predictive modeling.

As a highly versatile programming language, Python can be used for web development, data science, machine learning, automation and scripting, DevOps, Infrastructure-as-Code, CI/CD pipelines, software development, game development, and more.

To fully grasp Python’s versatility, consider these famous applications:

• Google: Python runs on many Google internal systems and shows up in many of Google’s APIs.
• YouTube: Uses Python for view video, control templates for the website, administer video, and many other purposes.
• Netflix: “We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members.”
• Stripe: Uses Python for their web and mobile APIs due to its efficiency for huge calculations and data processing required by fintech products.
• Reddit: Moved its entire platform from Lisp to Python when its traffic started to grow, allowing it to handle large amounts of users while keeping the site fast.

Benefits of Python

Python offers developers many unique benefits. Its clean and straightforward syntax enhances code readability, increasing productivity and development cycles. Additionally, Python’s abundant resources, libraries, frameworks, and tools expedite both development and troubleshooting.

For data science and machine learning, Python libraries like Pandas, NumPy, SciPy, and scikit-learn allow developers to handle and analyze data efficiently, enabling data-driven decision-making.

For web development, Python’s web frameworks like Django and Flask make Python well-suited to building robust and scalable web applications.

With applications across automation, scripting, game development, machine learning, data science, back-end and front-end development, full-stack development, and mobile application development, Python’s versatility and robust frameworks have made it increasingly popular across various uses:

• Automation & Scripting: Python is widely used for simplifying repetitive tasks, testing frameworks and handling data analysis, simplifying processes and increasing efficiency.
• Game Development: Python allows developers to quickly experiment with game mechanics, test ideas, and iterate efficiently on their designs.
• Data Science: Due to its ease of use and low barrier to entry, Python is popular among data scientists and analysts.
• Machine Learning: Python allows developers to easily build and prototype machine learning models and perform data analysis quickly and efficiently.
• DevOps: Python can be embedded in CI/CD platforms to automate build, test and deployment stages, ensuring faster delivery cycles.
• Back-End Development: Python frameworks like Django and Flask are popular tools for streamlining the backend development process, ensuring a strong foundation for web applications.
• Front-End Integration: While Python is not a front-end language, it easily interacts with front-end technologies, aiding in full-stack development and ensuring seamless integration.
• Mobile Applications: Python frameworks like Kivy are used in mobile application development, providing a unified cross-platform experience.

What does a Python developer do?

Python developers build software, applications, websites, and other services, alongside testing code and correcting errors in Python environments. They write reusable, effective code to develop robust backends.They may also be responsible for implementing security and data protection best practices.

Python Skills and Qualifications

Python developers will often hold a degree in computer science or similar, though this is not essential. When hiring a Python developer, it’s most important to look for strong experience in Python development, including frameworks like Django and Flask.

There are also many certifications and qualifications that a Python developer may have. Popular certifications include:

• Microsoft Certified: Python Developer Associate
• Google IT Automation with Python Professional Certificate
• OpenEDG Python Institute’s PCPP1 and PCPP2 certifications
• DataCamp Python Data Associate
• Harvard’s Professional Certificate in Computer Science for Python Programming (CS50)

Beyond technical skills, soft skills are vital when choosing your Python developer. Problem-solving is crucial, aiding with troubleshooting and continuous learning. Communication is also essential when looking for any new team member, particularly in facilitating clear and open communication between technical and non-technical stakeholders.

Popular Python-Related Languages

As Python is a backend language, it’s helpful to have a strong grasp on frontend technologies to facilitate fullstack development. Alternatively, for backend developers, understanding other popular backend languages is essential to effective development.

• JavaScript: A high-level programming language popular for front-end development, allowing developers to create interactive elements on sites.
• R: Python developers working in data science are likely to encounter R, a go-to language for data analysis and statistical computing.
• SQL: Another popular language in data science and analytics which is commonly used in conjunction with Python.
• C/C++/C#: Python is frequently integrated with C/C++/C# to optimize performance, allowing Python developers to write extensions or work with system-level tasks more easily.
• BASH: A helpful language to use in conjunction with Python for automation.

Popular Python Libraries

Python is well-known for its active community and vast array of helpful libraries. Some of the most popular python libraries include:

• NumPy: A Python library for scientific computing with support for multi-dimensional arrays and matrices, along with a collection of mathematical functions.
• TensorFlow/PyTorch: Leading libraries for deep learning, developed by Google and Meta and largely used for building neural networks and other ML models.
• Scikit-learn: Also called sklearn, this is an open source machine learning library for Python that can be used to streamline artificial intelligence (AI) and statistical modeling.
• Ramp: A Python library used for rapid prototyping of machine-learning models, with a simple, declarative syntax for exploring features and algorithms.
• Pipenv: The “Python Development Workflow for Humans,” this library provides users with a working environment that’s easy to set up and use.
• Bob: A free signal processing and machine learning toolbox for Python and C++.
• Keras: An open-source neural network Python library designed for fast experimentation with deep neural networks.
• Dash: The “original low-code framework for rapidly building data apps in Python”, perfect for data visualization.
• Pandas: Adapts Python for data analysis and modeling with the provision of easy data structure and quicker data analysis.
• Matplotlib: The most popular option for dimensional plotting.
• SymPy: A Python library designed for symbolic mathematics and computer algebra systems (CAS) that keeps code as simple as possible.

Popular Python ​​Programming Frameworks

Frameworks are collections of reusable code, templates and tools that provide a foundational structure for building applications effectively and efficiently. Some of the most popular Python frameworks include:

• Django: A powerful, high-level framework for building robust web applications quickly and with less code, providing a more streamlined development process.
• Flask: A lightweight ‘micro-framework’ used to build simple web applications quickly and efficiently, with request handling and application signaling.
• CherryPy: A minimalistic object-oriented framework (OOP) for web app development. Used by giants like Netflix and Hulu.
• FastAPI: A modern web framework designed to build robust backend APIs.
• Tkinter: A built-in Python GUI framework, perfect for creating various widgets such as buttons and menus.
• asyncio: An asynchronous framework that allows developers to write concurrent code using the async/await syntax, designed to handle thousands of concurrent connections.

Popular Python ​​Programming Tools

There are many tools available to enhance the experience of using Python. Some of the most popular tools include:

• Git: The go-to version control system that helps developers track changes in their code during development, facilitating collaboration.
• Jupyter Notebook: Allows Python developers to create and share documents that contain live code, equations, visualizations, and explanatory text, often used in data science.
• PyCharm: A popular Integrated Development Environment (IDE) that provides smart coding assistance, code analysis, autocomplete and other helpful tools.
• Virtualenv: This lets developers create isolated Python environments, allowing you to manage dependencies for each project separately.
• Anaconda: An open-source data science, scientific computing and AI distribution platform for Python and R.
• Pytest: A Python testing framework that can be used to write various software tests including unit tests, integration tests, end-to-end tests, and functional tests.
• Pylint: A static code analysis tool for Python that analyzes code for various errors, potential bugs, and stylistic inconsistencies.
• flake8: Another popular linter that checks Python code for style and quality issues to maintain code consistency.

How does Python fit into backend development in 2025?

With robust frameworks like Django and Flask, Python simplifies backend development, including handling database interactions and building robust server-side logic. It continues to be used by leading companies like Instagram/Meta to handle their high traffic and data volume.

Python also supports various databases and works well in conjunction with frontend technologies, making it a popular choice for building the back end of web applications looking for efficiency, scalability, and security.

How much does it cost to hire a Python developer?

When hiring traditionally in the US for a local, mid-level Python developer, the average annual compensation is $127,649, with a range of $85,000 to $160,000 according to Built In. However, even by just expanding to other parts of the United States, salaries can be reduced significantly.

On average, companies hiring Python developers with HireDevelopers can expect to save between 50% and 80% on salaries and hiring costs, with exact salaries varying based on location and experience. The greatest savings can be found by hiring in Africa and Asia, while companies looking for aligned time zones and a closer cultural match can hire from our extensive pool of Latin American developers.

How does Python integrate with Cloud platforms?

Python makes cloud engineering significantly easier, integrating simply with tools like Apache Airflow, AWS Glue and Google Dataflow for managing cloud data pipelines for cloud platforms like AWS, Azure, and Google Cloud. Libraries like Pandas, PySpark and Dask also help to analyze cloud-stored data efficiently.

With Google Cloud in particular, Python integrates with Cloud Monitoring, Cloud Trace, Cloud Logging and Error Reporting functions, allowing developers to quickly diagnose performance bottlenecks and bugs.

Why do developers choose Python for backend development?

The main factors for developers choosing to develop in Python include its readability, which enables rapid development, and its range of libraries and frameworks, like Django and Flask, which allow for greater scalability. In general, Python is versatile, easy to use, and backed by a robust community of developers contributing essential resources, tools and support for effective web development.

How do developers build security into Python applications?

Python is a popular programming language for building secure web applications. Common best practices for ensuring security in Python include using separate environments for each project; encrypting sensitive information; sanitizing user input to prevent SQL injections; and conducting regular security audits with tools like Bandit and PyUp.

Why do developers use Python in DevOps automation?

Python is an essential tool for DevOps engineering, providing scripts and tools for version control and CI/CD pipelines with libraries such as Fabric, Ansible and Paramiko. Its simplicity, readability, and extensive libraries make it a great choice for DevOps, with quick scripting, automation of repetitive processes, and integration with various tools and systems.

How is Python used for scalable web development?

Python is a robust technology for scalable web development, with frameworks like Django, Pyramid and Flask offering built-in features for handling database management, routing and security. Other popular Python frameworks for scalable web development include FastAPI, Tornado, and CherryPy.

Why is Python used for API development?

APIs, or Application Program Interfaces, are how two different software components communicate.

Python offers a number of advantages when it comes to developing APIs, including its simplicity and readability. Python also offers a range of frameworks for developing APIs, including Flask, FastAPI, Django, web2py, CherryPy, Jam.py, Pyramid, Falcon, Bottle, Eve, Sanic, Tornado and Hug.

What are the most common Python frameworks for microservices architecture?

Microservices architecture lets developers build modular, autonomous and self-contained services they can reuse across applications. It structures an application around loosely-coupled services communicating with each other via APIs or messaging protocols.

The most popular Python frameworks for developing microservices architecture are Flask, Django, and FastAPI. Other useful frameworks include Spring Boot, Bottle, Tornado, Falcon, Sanic, Pyramid and CherryPy.

How can I optimize Python for performance?

To optimize Python performance, developers should focus on using proper data structures and data types, reducing global variables, and using built-in, performance-focused functions and libraries like NumPy.

Developers can also consider CPython alternatives like Jython, IronPython and PyPy to boost performance for specific use-cases.

How is Python used for data processing?

Python is popular for data science, data analysis and data processing due to its straightforward syntax and readability.

When Python is used for data processing, it is often with a Python library tailored to data science such as Pandas, NumPy or Dask. These libraries provide functions designed specially for large data sets and are generally more versatile, integrating well with Machine Learning and statistical analysis workflows.

How is Python’s dynamic typing used for software development?

Unlike static typing languages like Java and C, Python’s dynamic typing allows the language to determine the data type of a variable dynamically, at runtime, without requiring explicit type declarations for variables.

Python’s dynamic typing functionality provides increased flexibility and efficiency for developers, allowing for faster prototyping and development. Typically, dynamic typing is used in conjunction with type hints or a tool like mypy to make code more robust.

How does Python handle concurrency?

Concurrency refers to the ability to execute and manage multiple tasks at the same time, without needing to finish one before starting the next.

In Python, concurrency is achieved through threading, multiprocessing, and asynchronous programming. For I/O-bound tasks, Python developers can use libraries like asyncio for asynchronous programming

How is Python used in machine learning (ML)?

Python is one of the most popular programming languages for machine learning due to its readability, scalability, compatibility with other programming languages, and extensive libraries and frameworks.

The Python libraries most often used for machine learning include TensorFlow, Keras, LightGBM, Fastai, Scikit-learn, NumPy, Pandas, Seaborn, Matplotlib, SciPy and PyTorch. For computer vision, OpenCV, Scikit-Image and Pillow/PIL are popular and highly useful Python libraries.

How to hire Python developers

The first step to hiring a Python developer is to decide on a hiring method. Traditional hiring methods include local job adverts, LinkedIn job posts and using your professional network to identify potential candidates.

However, these methods are often time consuming and can be expensive. The median time to hire a developer is around 41 days, with the slowest 10% taking up to 82 days and 38% of companies reporting project delays due to recruitment problems, according to the Linux Foundation’s 2024 State of Tech Talent Report.

Alternatively, many companies are now turning to services like HireDevelopers to cut costs and speed up the hiring process. With HireDevelopers, you can hire Python developers from anywhere in the world, at any price point, in just 24 hours.

When putting together a job description or job interview for a Python developer, prioritize technical skills, like familiarity with key frameworks such as Django and Flask and libraries such as TensorFlow and Numpy.

You should also consider using coding challenges alongside a traditional interview during the hiring process – if you’re using a pre-vetted talent pool like HireDevelopers, this has already been done for you. Coding challenges allow you to assess problem-solving skills and coding expertise in a realistic, real-time environment.

If you need help putting together a Python job description or conducting a Python job interview, you can check out our helpful and comprehensive guides below. Alternatively, to cut your time-to-hire by 90% and unlock savings of up to 80%, get in touch and book your free HireDevelopers consultation today.

Why hire Python developers with HireDevelopers?

Hiring the right Python developer for your team is a huge undertaking, and the process can be intimidating, especially for startups and small businesses.

At HireDevelopers, our pre-vetted, global tech talent marketplace streamlines your hiring journey and cuts time-to-hire by over 90% without compromising on quality or stretching your budget. Receive a custom shortlist of top Python developers at any seniority level, at any price point and located anywhere in the world within just 24 hours of your consultation.

Plus, we’ll handle all payroll, legal compliance and HR completely for free, giving you more time to start building with your new Python dev.

Get in touch today and start hiring top-notch Python developers in just 24 hours.