What Are Python Developers Capable of?
Python has been used for more than 30 years, being recognized as a highly demanded programming language. Statista reveals that as of 2022, 48.07% of software engineers go down the path of learning and applying Python, which makes it one of the five most sought-after programming languages.
But whatever versatile and efficient Python is, you may be interested in how industries and particular businesses benefit from its usage, and what Python developers can create to cultivate the digitalization of modern organizations while addressing cumbersome challenges. And to find answers to these questions, look no further than in this article.
Python: Industries & Products
Fintech sector
Financial institutions — from Paypal to Goldman Sachs, to Zopa — include Python in the tech stack of their products. For instance, Goldman Sachs, known as a global company working at the intersection of investment banking and management, has introduced Python into their ecosystem. Engineers who work in the company use it for data analysis, web application development, and DevOps. It’s notable that Python is gaining popularity among DevOps teams since it allows reducing the time and effort required for automation and orchestration and has important out-of-the-box features.
As for other solutions designed by fintech companies, they are inextricably linked to the volume of information at their disposal. And having a lot of customer-related data, financial companies can translate that into value by infusing it at scale for Artificial Intelligence (AI). Innovative AI solutions based on Python can assist in solving arduous challenges:
- Smart assessment of borrowers to enable smooth loan decision-making
- Analysis of underwriting decisions
- Managing risks
- Algorithmic trading processes
- Quick ranking for stocks
This list is not exhaustive and may vary significantly. But to give you a better picture of why financial companies and banks hire Python core developers, we’ll consider a real-life example.
Imagine that a non-bank credit institution intends to predict the behavior of its existing and potential customers in the following year. The organization can opt for traditional forecasting methods or apply available data, including issued loans and current market trends, to build AI models that can assess past data, find patterns, and forecast future outcomes. Choosing a time series forecasting method such as ARIMA, SARIMA, Autoregression, Moving Average, or others, is the responsibility of Python developers, though it should partially be a business solution.
Entertainment services
When it comes to entertainment services (or however they are treated by users), most of us immediately think about Netflix, Youtube, and Spotify. What do all these services have in common? They require a recommendation system that analyzes users’ preferences and makes suggestions on what they might like to watch or listen to next. The Netflix recommender system is probably one of the most sophisticated in the area of generating playlists. It substitutes a big data project empowered by data obtained from ratings, popularity metrics, context-related information about the duration of playing and used devices, search queries, etc.
Netflix supposes that without this recommender system the company may lose $1B yearly because subscribers would shun their product in favor of other services for watching series and movies. Currently, 83% of Netflix users agree to share their personal information to receive a personalized service. And as of the core under the hood of recommendation systems, it can be based on TensorFlow, Keras, PyTorch (open source ML framework for Python), and so on.
Large tech companies
In this section, we’ll briefly examine how companies operating in absolutely different niches gain advantages from adding Python to their tech stack. In other terms, we’ll explain what Python developers are capable of when it comes to complicated high-tech solutions. So, let’s start with Uber.
Being a famous transportation company, Uber is expected to have a fascinating application for drivers and passengers. The company preferred Python instead of Ruby because of the following reasons:
- Highly-loaded mathematical operations on the backend (to predict demand, arrival time and other figures Python suits better than Ruby)
- The Simplicity of Python (this feature enables to onboard new engineers easily, thus being able to support and improve the code)
- Flexibility (ability to use Python not only for the backend but for the user interface and marketplace)
In addition to Uber, Python is being used by Dropbox, the American file hosting company with a market value of $8B. Dropbox has built an API for its usage in third-party apps, making possible automation via Python. The team has written several millions of lines of code in Python, thus having a chance to explore the challenges of scaling, testing, and refactoring.
Other famous products, the tech stack of which includes but is not limited to Python, are Twilio (CMS relies on Python and Django), Reddit, Meta, Instagram, and Quora. Moreover, Google perceives Python as one of three core languages, on par with Golang and Java.
Governmental institutions
NASA, Central Intelligence Agency (CIA), Consumer Financial Protection Bureau (CFPB), and SEC are listed among governmental institutions that have decided to add Python to their tech stack. It serves as a core programming language for Consumer Financial Protection Bureau applications, which are partially open-source projects available to other agencies. The reason why these organizations use Python often comes down to the simplicity of the code maintenance. At least, a senior engineer named Robin Friedrich mentioned this in Python’s blogPython’s blog by a senior engineer named Robin Friedrich.
Concluding the article, we have to mention that the reasons for using Python may differ (from building ML solutions to designing software to control self-driving cars), but its relevance is confirmed by top-notch companies, including S&P 500. Actually, Python engineers are capable of web development, data science, financial analysis, testing, and prototyping. And that’s just the tip of the iceberg since Python is suitable for much more and has proved to be a relevant tool for real estate intelligence solutions, career management advisory, and multi-cloud networking platforms.
However, as with any other technology, it’s not all roses — Python has its downsides. That’s why you may need a competent consultation to make an informed decision about going with Python or any other technology to fulfill your intentions and address business needs.
