Python has steadily risen in popularity among the ranks of programming languages, rivalling Java and C. Based on Github statistics, Python is the second most used language across publically accessible repositories in 2021. It is the third most used programming language among developers worldwide as of 2021. This rise is partly due to data science and the ecosystem of software libraries for machine learning. Machine learning and artificial intelligence-based projects are ever-present in society and provide us with improved personalization, search functionality, recommendations, and intelligent hardware that can see, hear and respond to us and the world. From development to deployment and code maintenance, Python is a functional programming language for assured software development. Python is the best fit for machine learning and AI-based projects. The language provides simplicity, access to a wide range of libraries, platform independence and a wide-open source community.
Table of contents
- Who Is Using Python?
- Advantages of Using Python
- Disadvantages of Using Python
- How Long Does It Take To Learn Python?
- Concluding Remarks
Who Is Using Python?
Python is used heavily in academic research, including machine learning, bioinformatics, biology, and mathematics. Google has been a staunch supporter of Python since its inception. The founders of Google decided to use Python whenever possible and C++ when they could not use Python, for example, where memory control was necessary or low latency was preferred. Netflix uses Python for machine learning algorithms which analyze user data and behaviour to enhance recommendations. Spotify is one of the most famous examples of an application that uses Python for data analysis and backend development. Spotify leverages Hadoop for extensive data management and the Python package Luigi to build complex pipelines of batch jobs, as well as machine learning libraries for their recommendation engine. Meta (Facebook) actively uses Python for its ease of use and availability of libraries, limiting the amount of code to write and maintain. Reddit was written in Lisp but then rewritten in Python. Amazon uses Python in several areas of its platform. The language is implemented in the product recommendation system where, with machine learning, Amazon analyzes customer buying habits and recommends products. Generally, tech startups are drawn to how easy a language is to learn and how scalable the language is. Given the lifeblood of tech startups is the developers and data scientists, Python is very actively adopted as part of their tech stacks.
Advantages of Using Python
Easy to Read, Learn and Write
One of the more striking qualities of Python is how readable it is. It has English-like syntax, which makes it easier to read and understand the code. Generally, experienced developers will recommend Python to beginners because it is straightforward to pick up and learn. Python requires fewer lines of code to perform the same task compared to other popular languages like C/C++ and Java. Python has minimal syntax meaning developers can write less code to finish tasks. Python is dynamically typed, which means it does not know the variable type until the code is run. It automatically assigns the data type during execution. This quality further minimizes code writing as the developer does not need to declare variables with their data types. Python is an interpreted language, which means that it executes code line by line. If any error is encountered, any further execution stops and the error is reported. By only reporting one error, the developer has a more manageable task of debugging. As of Python 2.5, the ternary operator is in use, which enables you to write conditional statements with a single line of code instead of multiple lines.
Immense Number of Libraries and Frameworks
Python developers have access to many statistical, scientific and visualization libraries, ideal for machine learning. Having access to libraries, which is pre-written code, allows for complex software algorithms to be built without reproducing significant lines of code, reducing development time. Here are some of the libraries available:
- Keras, Tensorflow, Scikit-learn, and PyTorch for machine learning development
- NumPy and Theano for high-performance scientific computing and data analysis
- Pandas for high-level data structures and analysis
- Matplotlib and Seaborn for data visualization
- NLTK for computational linguistics and natural language processing
- Beautiful Soup and Scrapy for extracting HTML and XML files from the web
Installing libraries is incredibly easy with its dedicated package manage pip. Alongside libraries, Python grants developers the ability to create virtual environments, isolated spaces for Python projects. Each project can have its dependencies and set of libraries. Some data science platforms like Anaconda provide tailor-made, prebuilt sets of packages for data scientists that can be installed in one step.
Some of Python’s most popular frameworks include:
- Django for web apps and services that works very well with database services such as MySQL and MongoDB. For further reading on installing MySQL to use with Python go to the article: How to Install MySQL on Mac.
- PySpark for distributed data management and machine learning
- Flask as a micro web framework, which is in use with popular applications such as Pinterest and LinkedIn.
For a more in-depth discussion on the best Python libraries for data science and machine learning, you can read through my post titled “Top 12 Python Libraries for Data Science and Machine Learning“.
Although this point is not a property of Python as a language, its immense popularity and ease of use have led to the creation of Integrated Development Environments (IDEs) like PyCharm. IDEs effectively allow for better software development by providing autocomplete and library integration options, debugging tools, PEP8 style suggestions and error highlighting in the code. Using PyCharm, developers can also create virtual environments and switch between them and integrate with GitHub. By choosing Python for machine learning development, its deep ecosystem can make completing tasks much more straightforward, increasing productivity.
Google’s Colaboratory or Colab, for short, enables Python code to be written and executed in a browser with no configuration, free access to GPUs and easy sharing. The developer can write code in a Colab notebook, which are Jupyter notebooks hosted by Colab which can be combined with images, HTML, LaTeX and other augmentations. Colab is used extensively in the machine learning community to develop and train neural networks, experiment with TPUs, share AI research, and create tutorials.
Prototype creation with Python is very effective. Developers can build an application prototype to showcase the core functionalities without coding up the entire application. Refactoring prototype code into the complete application is simple with Python. With frameworks like Jupyter that enable a notebook approach to development, the steps taken to build an application are documented together with examples and visualization. Dynamic analysis is a valuable tool for prototyping, and it is the ability to track what is happening during program execution. The Python settrace() mechanism allows the developer to follow all the code lines, variables and values as the program executes.
Open Source Community and Widely Supported
Python is completely free to download under the PSF License Agreement and the Zero-Clause BSD license, meaning developers have permission to use, copy, modify, and distribute the software for any purpose with or without a fee. Python has a massive user community, to the effect that members of the community call themselves Pythonistas and convene in the thousands at PyCon conferences. Having a large community means a high likelihood of any problem encountered during development seen before, and some skilled developers have built or are working on a solution. Most applications will have extensive documentation taking advantage of automatic Python API documentation generation tools, tutorials, and examples. With the Python ethos of open-source, there are thousands of development tools and open-source packages available to extend Python and enhance any developer’s solution.
Platform Independence and Portability
Internet of Things
Internet of Things (IoT) has been a hot topic in recent years. It refers to the network and connection of different devices with data-taking hardware, for example, sensors, that can exchange information. IoT allows individual devices to interact with each other and work together. Raspberry Pis are single-board computers that can serve as DIY IoT devices. The Raspberry Pi Foundation specifically selected Python as the primary language because of its power, versatility, and ease of use. Python and several IDEs, like Mu, come preinstalled on the Raspbian operating system. Raspberry Pi can now be used to develop embedded machine learning applications and train custom models using Edge Impulse, a cloud-based development platform for machine learning on edge devices. Raspberry Pi’s machine learning software development kits are available for Python and C++, Go, and Node.js. Raspberry Pis can be programmed for various data types and use cases, including object detection using camera data. Google is also developing a Tensorflow Lite framework, allowing users to build machine-learning models for deep-learning tasks like image and speech recognition on the Raspberry Pi.
Synergy with Serverless Technology
Serverless solutions enable businesses to improve efficiency and save money and time. Moving to a system like Amazon Web Services (AWS) Lambda, which has costs paid by the function processing time or the number of invocations rather than paying for entire servers, the solution will be very scalable and cost-effective. Functions can be easily programmed, and without the need to set up servers or hardware, systems can be implemented significantly faster. Python code is simple and less verbose than other popular languages like Java, which is ideal for Serverless designs as it keeps each function concise and self-contained. The synergistic effect manifests in improved speed and efficiency. Python works well on Serverless platforms because it can load very quickly, eliminating extended cold starts on AWS Lambdas, allowing for as much time as possible for the function to execute.
Disadvantages of Using Python
As much as Python is a boon for machine learning, there are limitations or disadvantages that we will describe to give a balanced view.
Python is an interpreted language. The code is executed line by line. This makes Python slower than compiled languages like C and C++, which are closer to hardware. Python is dynamically typed, every time the variable is read, written or referenced, its data type is checked, and the memory is allocated accordingly. Python also has a Global Interpreter Lock (GIL), which only allows one thread to execute at a time, even in a multithreaded architecture with more than one CPU core. As a result, multithreaded CPU-bound programs can be slower than single-threaded programs. Many use a different implementation of Python, such as Jython or PyPy, to achieve true multithreading. Although Python is not the “fastest to execute” programming language, it is arguably the “fastest to write” programming language.
Mobile and Game Development
Python is strong for both simple and complex web and desktop applications but is limited in mobile app development due to its high memory usage and slow speed. Modules like Kiwi can make Python more mobile-friendly. 3D rendering is computationally intensive; compiled languages closer to hardware like C++ are better for speed and efficiency at the level gamers expect.
Heavy memory consumption using Python is a trade-off for the many advanced functionalities the language offers.
Compared to popular technologies like JDBC and ODBC, the database access layer of Python is considered to be underdeveloped. This makes Python less suitable for large enterprise solutions, which need smooth and efficient interaction with complex legacy data.
We presented dynamic typing as a positive; however, this can lead to runtime errors as the data type can change. Python developers will need to do more testing.
The ease of Python is a double-edged sword. Python developers will tend to become accustomed to the readability and dynamic typing and avoid other languages due to increased difficulty. Not everything can is possible with Python, and shifting to a new language from one as accessible as Python can be difficult.
How Long Does It Take To Learn Python?
A complete beginner can learn the basics of Python in two to six months. The amount of time taken depends on how much time you have to dedicate to learning. There are straightforward online courses for Python that typically take four months to complete if you spend six hours per week on the course. The more frequently you practice using Python, the faster you will learn. Python users have access to hundreds of thousands of libraries which make coding in Python very easy. However, mastering Python is an ongoing process as it is dependent on what mastery means to you and what you need to learn for your tasks as you progress as a developer.
Python is a powerful, easy-to-use language that sets itself apart with its unrivalled open source community and colossal ecosystem. With AI-driven companies like Google and Meta making Python part of their core software base, many advances in support, frameworks and environments have been made that make the programming language incredibly accessible and suited for machine learning-focused developers. Many of the most popular machine learning, data analysis and visualization libraries are Python native. Python is synonymous with data science and machine learning, to the point where job posts for data science roles will ask for experience in Python as a priority and knowledge of at least a handful of the mentioned libraries and frameworks described in this article. Python is not without its shortcomings, particularly with speed, memory usage and mobile application development. However, the speed of code development in Python and its extensive set of libraries overcome these limitations. Python is very flexible in terms of the level of complexity in development. For machine learning, a developer can use a more straightforward interface such as Keras to build neural networks or can go deeper and customize their network layers using Tensorflow or PyTorch. With notebook environments like Jupyter, it is effortless for data scientists and machine learning engineers to brainstorm ideas and iteratively work towards a solution, which is essential for work that calls upon creativity and dynamic problem-solving.
Other languages are used today for machine learning, including R and Java, each with its ecosystem and dedicated community. Still, Python is the most entwined with the data science and machine learning community, provides the most accessibility to the tools and libraries necessary, and has the matching ethos of rapid understanding, prototyping, and community-driven advancements.
Thank you for reading to the end of this article. We hope you have enjoyed it and appreciate how well suited Python is for machine learning. If you are interested in learning right away, you can click through to our shortlist of the best online courses to learn Python specifically for machine learning. If you are motivated to become involved in machine learning and become a research scientist, you can learn more about what a research scientist does. The differences between that role, data scientists and machine learning engineers by clicking through to the article titled “Key Differences Between Data Scientist, Research Scientist, and Machine Learning Engineer Roles” Stay tuned for more articles about Python.