Did you know that some of the most popular digital services in the world today – including Google, Instagram, and Reddit – all use Python?
Python is an interpreted, object-oriented programming language with dynamic semantics, which makes it perfect for rapid application development and scripting. It was invented back in 1991 and has been gaining traction across various industries since the early 2000s.
In fact, according to the 2020 Stack Overflow Developer Survey, Python skills demand annual salaries of $59k, globally. This puts it in the top ten most lucrative languages to know.
With this in mind, in this article, we’ll take a closer look at some of the ways Python helps developers create value across verticals, use cases, and industries. Along the way, we’ll share some real-world applications.
Back-End Web Development
When it comes to back-end web development, Python is used to create many popular websites and web apps that we use today. According to W3Techs, 1.4% of websites use Python as their server-side programming language. As mentioned earlier, Google, YouTube, Reddit, Instagram, Netflix, Quora, and Spotify all use Python.
One of the main reasons why Python is incredibly popular in the back-end web development space is its compatibility with cloud server technologies. In addition to this, it’s perfect for database interactions and writing APIs. This is precisely why tech giant Google uses it extensively in enterprise-level software solutions including Google AdWords, Google Sites, and Google Code.
Python has a large selection of well-supported frameworks for just about everything, including back-end web development. Django and Flask are perhaps the most popular Python web development frameworks in use today. Django is perfect for multi-layer, full-stack applications, whereas Flask is ideal for smaller software solutions that are developed to be integrated into other, larger applications.
In the business services industry, Python is primarily used for providing API access to financial information. For example, a financial data company that provides partners and stakeholders access to financial information through different electronic resources might benefit from using Python. Developers can also create a Python SDK for the company’s financial information API, which would allow them to incorporate their customers’ APIs into their own. As a result, they would be able to deliver better customer experiences.
Python significantly simplifies data regulation and compliance for finance and banking industries through its wide selection of supported libraries. Venmo, for example, uses Python.
Data Analytics and Machine Learning
Since Python is a high-level programming language, it’s easy to see why it’s often the language of choice for developing data analytics, machine learning, business intelligence, and data science-powered solutions.
Its inherent compatibility with machine learning technologies makes it perfect for modernizing legacy applications by adding machine learning capabilities to them. This has widespread use cases and demand in the finance and healthcare sector, among others. In addition to this, it integrates seamlessly with other popular programming languages. For example, Python is extensible in C and C++ which means it can also be used to run large-scale data mining operations, faster.
For example, if a financial services corporation wants to mine customer behavioral data, its analytics team is likely to use Python to process the structured data the company collects and correlate it with fresh, unstructured data from social media to identify and increase viable cross-selling and acquisition targeting opportunities. Python has a number of use cases in the data analytics and machine learning space ranging from sales forecasting and business insights to detecting product defects to quality monitoring in retail and manufacturing.
Popular Python libraries including OpenCV and scikit-learn are extremely effective for building and implementing machine learning algorithms. Keras is used for developing and evaluating deep learning models, and it allows you to define and train neural networks models. Seaborn and Matplotlib are incredibly useful for data analysis, manipulation, and visualization. Python’s NLTK platform is widely used by data scientists and data analysts when working with human language data and natural language processing.
Python is also widely used for data transformation and manipulation. For example, many retail store chains collect data centrally in a warehouse and share it with multiple applications. This allows them to enable their supply chain, banking, and reporting needs in a standardized manner, from a single source of truth. From a technical standpoint, this is easily possible with Python’s create table commands in combination with the right libraries and frameworks.
Scripting and Automation
It’s no secret that Python is one of the most robust scripting languages out there.
In the context of automation, Python is great for writing scripts with only a few lines of code that can parse text files, scrape content from the web, and generate inputs for testing applications. Its built-in framework for running unit tests allows developers to quickly ship bug-free code. This makes Python essential for a variety of different development projects as well as an ideal replacement for Bash scripts.
Fabric and Salt are two popular Python tools for scripting and automation. Fabric is an orchestration tool which means you can use it to coordinate commands on multiple machines. And Salt is a configuration management tool that handles configuration files and installing packages.
For example, you might consider using these tools to automate repetitive tasks like sending out emails en masse or scraping structured data off the web.
Internet of Things
As you probably already know, Internet of Things (IoT) projects typically involve some sort of physical objects (with IP addresses) interacting with other physical objects over a wireless network. Python helps developers create value in the IoT space in a variety of different ways, since it’s incredibly lightweight and scalable.
Aside from the fact that Python is easy to learn and supported by a large community, it’s also portable and embeddable. The key benefit of this in the context of IoT is that the programming language becomes non-system-dependent. In this way, it’s perfect for microprocessors and single-board computers as well as products commonly used for IoT like Raspberry Pi, Home Assistant, and Zerynth.
For instance, with Python and Raspberry Pi alone, developers can build a variety of smart agriculture solutions, manufacturing optimization command centers, home automation products, and robots.
In addition to this, Python tools like webrepl streamline IoT development processes, giving developers the flexibility to use their browser to run code. This also makes it easier (and faster) to test different solutions as compared to using an outdated programming language like C or C++.
Conclusion
It goes without saying that developers need to know Python to future proof their careers and continue to create value in their respective industries.
Python is used by all sorts of developers and data scientists across a variety of industries ranging from a scripting solution for sysadmins to back-end web development for full-stack developers to the driving force behind IoT and machine learning.
