Further, python libraries which are the new JIT compiler helps to accelerate CPython by boosting its stock interpreter. The language is also evolving fast within the data science space. This changes the whole dynamic of the average Python user. Will Python become the most popular high level coding language that will eventually replace Java? Probably not. Speaking of Java development , this language will probably get replaced by Swift.
Custom Software Leverage our software development expertise to build custom applications, modernize legacy systems, and build powerful API integrations.
Learn More. Software Development Blogs. Check out a related article:. I give my consent to Intersog to process and retain my personal data as set out in the retention section of the Privacy Policy.
Andrew Zola. Take, for example, Dropbox. Dropbox started when Drew Houston kept forgetting his flash drive as a student. It was initially a solution he could use for himself, a party of one. By November of , million people were using Dropbox, which was no big deal because… Dropbox was built on Python.
In , HackerRank released a survey of various industries, revealing which programming languages they were prioritizing when hiring developers, programmers, and engineers. When it came to FinTech, Python dominated the pack:. If you look at finance technologies, big banks like Bank of America have worked hard to transform their tech stack from legacy code to Python.
Talk about massive real-world applications! So now you know what Python is used for, how do you go about learning it? Our Break Into Tech course is a comprehensive program designed to help total beginners in tech start a new and fulfilling career.
You can read FutureLearn's Cookie policy here. Category: Digital Skills , Tech , What is. Despite starting out as a hobby project named after Monty Python, Python is now one of the most popular and widely used programming languages in the world. Besides web and software development, Python is used for data analytics, machine learning, and even design. As we outlined in our summary post on what different programming languages are used for , Python is an object-oriented based around data , high-level easier for humans to understand programming language.
According to the TIOBE index , which measures the popularity of programming languages, Python is the third most popular programming language in the world, behind only Java and C. There are many reasons for the ubiquity of Python, including:. So, we know why Python is so popular at the moment, but why should you learn how to use it? Aside from the ease of use and versatility mentioned above, there are several good reasons to learn Python:. Many of our short courses, such as Getting Started with Python , take weeks to complete, with only a few hours of learning required each week.
Our deep learning and Python programming ExpertTrack takes 21 weeks to complete, with hours of study needed every week. Clearly, Python is a popular and in-demand skill to learn. But what is python programming used for? Python can be used for:. In fact, Python is among the favourite languages among data scientists, and there are many Python machine learning and AI libraries and packages available.
You can discover the uses of Python and deep learning while boosting your career in AI. Much like AI and machine learning, data analytics is another rapidly developing field that utilises Python programming.
Python for data science and analytics makes sense. You can learn about data analytics using Python with our ExpertTrack, which will help you develop practical data analytics skills. It can be used to predict outcomes, automate tasks, streamline processes, and offer business intelligence insights.
Besides, Python integrates well with most cloud and PaaS providers, and supports numerous file export and sharing options. By now, you surely know that Python is incredibly flexible. In the context of data processing, it means it allows you to build data models, systematize data sets and create ML-powered algorithms with ease.
Here are just some of the most common use cases and popular libraries. The accelerating pace of technological change is one of the most creative—and destructive—forces shaping the financial services industry. The disruption caused by fast-moving, up-and-coming start-ups, has forced the traditionally conservative sector to innovate, or risk falling behind.
Coupled with rising customer expectations, security and data protection concerns, as well as evolving regulations, financial services companies need robust technologies to rely on.
With its flexibility, high performance, and access to a large ecosystem of scientific libraries, Python is the most reached-for programming language to help companies in this sector not just adapt to the changes but also spearhead them. Financial operations, such as risk analytics, creditworthiness assessment, or fraud detection, involve processing huge amounts of data. Using Python, a leading language in machine learning and data science, financial service professionals can analyze and visualize data faster than ever to recognize patterns, spot opportunities, and reduce risk.
Due to tight competition and strict regulations, financial institutions need technologies that enable them to rapidly release new products or updates. They allow organizations to build sophisticated tools without the need to create every solution from scratch.
It can integrate seamlessly into trading and other financial services thanks to its ability to handle complex applications and real-time analysis while maintaining its ease of use. This makes the prototyping and development of financial services solutions time- and cost-effective. Financial institutions need tools that easily adapt to the evolving industry regulations. Since Python is one of the fastest-growing programming languages in the world, it can be used to build solutions that are flexible and scalable.
As the go-to language for data scientists, Python is also a great alternative for specialized languages such as R for machine learning. Over the years, Python has managed to incentivize cloud platform providers to use it for implementing and controlling their services.
CSVs , other files used by companies such as spreadsheets , external systems, APIs, and web documents. One of the libraries that we find particularly interesting is Pandas. Pandas is based on other scientific and calculationally optimized packages, and it offers a rich programming interface with a large number of functions that are needed to process and transform data.
Apache Spark is an open-source engine used to process large amounts of data. It leverages the parallel computing principle in a very efficient and fault-tolerant way. Even though it was originally implemented in Scala and natively supported the language, it now has a popular and commonly used interface in Python, named PySpark. As a result, PySpark is a powerful tool to help you transform and aggregate huge volumes of data. It also makes it ready for consumption by end users, such as business analysts, or by further components, for instance by involving machine learning.
Launched in , it facilitates the use of Airflow in certain AWS zones. Written in Python, Apache Airflow is an open-source workflow management platform that allows you to programmatically author and schedule workflow processing sequences, and then monitor them via a built-in Airflow user interface. The alternatives to Airflow include the Python-based workflow orchestrators Perfect and Dagster. The tools go a certain way toward addressing the problems in Airflow, and workflows in both of them can be managed with Python.
The language has become one of the most popular tools for performing ETL tasks thanks to its access to countless libraries and ease of use. In fact, many data engineers use Python instead of ETL tools, as it proves to be more flexible and effective for these tasks.
Finally, since most of the latest and relevant technologies can be implemented and controlled with Python, the language has a plethora of uses in data engineering. Like, say, whether to choose Python or a different programming language for your software project. We also have other free resources you may benefit from:. Hire Us. What Is Python Used for? Table of Contents Why Use Python? Python is everywhere. You may not even realize how widespread it is. Download the PDF.
Why Use Python? Python is popular and widely supported Python is quickly ascending to the forefront of the most popular programming languages in the world. The incredible growth of Python is shown very clearly by StackOverflow: Its continuous rise in popularity is reflected in the TIOBE index and Coding Dojo identifies Python as one of the most in-demand programming languages of Writing Python code is easy, which speeds up development Python is accessible by design, making it one of the fastest languages in terms of speed of development.
Python gives you tried and tested scalability No one can really predict when your user numbers will start surging and scalability will become a priority. Takeaways Why Python, then? If you really need to find the one language to rule them all, the choice is clear. Python for Web Development. Moreover, the trends are pushing for more and more impressive web apps that, among others, include: flawless mobile and desktop versions, asymmetrical layouts, Progressive Web Apps, integrated animations, ML-powered chatbots.
Advantages of Python for web development There are many advantages of Python that help you get results fast within the field of web development: Python has a large selection of pre-built libraries for just about anything. Scientific computing, image processing, data processing, machine learning , deep learning—you name it, Python has it. Python code takes less time to write due to its simple and clean syntax.
Because of this, code written in Python lends itself very well to creating quick prototypes. Python accelerates the ROI of commercial projects.
The reason behind this is similar to the previous point: you can write and ship your code faster. This is especially important for startups. Python has a built-in framework for unit tests. This helps you ship bug-free code. Top Python web frameworks Django The most widely used Python web framework— at least until recently.
Pyramid The maturity of Pyramid stems from the legacy of two previous frameworks: Pylons and repoze. Python for the Internet of Things. Python fits these criteria very well. The language is supported by a large, helpful community, which has led to the creation of an extensive set of pre-written libraries, making it easier to implement and deploy working solutions.
Python is portable, expandable, and embeddable. This makes Python not system-dependent and allows it to support many of the single-board computers currently available on the market, regardless of the architecture or operating system. Python works great for managing and organizing complex data. For IoT systems that are particularly data-heavy, this is especially useful.
Python is easy to learn without forcing you to get acquainted with many formatting standards and compiling options The most immediate consequence of this are faster results. Python code is easily readable and compact thanks to its clean syntax.
This is helpful on small devices with limited memory and computational power. Python is the language of choice for the Raspberry Pi. It matters a great deal, since the Raspberry Pi is one of the most popular microcontrollers on the market. Python offers tools that streamline the IoT development process, such as webrepl. This gives you the option to use your browser to run Python code for IoT.
Since Python is an interpreted language, you can easily test your solution without compiling the code or flashing the device. Think of it as the cherry on top of an already delicious cake. What tools are available for Python in IoT? Raspberry Pi Have you ever seen an interesting IoT project around the web?
0コメント