Google. YouTube. Instagram. Spotify. Reddit.
Aside from being some of the most popular software services in the world, what else do they have in common?
That’s right: they all use Python.
Python is everywhere. You may not even realize how widespread it is.
The prominence of Guido van Rossum’s creation can be attributed to a number of factors.
Most of all, Python is easy to learn, clear to read, and simple to write in. This speeds up development without sacrificing reliability or scalability.
Thanks to the high demand for Python, it’s also very well supported in the community and keeps on growing in popularity.
But why exactly is Python such a good choice? And what areas of technology or business does Python benefit the most?
Read on if you’re looking to break into any of the following fields and are considering whether to choose Python for your tech stack.
When you’re looking to build something, you want to be sure you have the right tools. In many cases, the choice of tools will decide your entire experience.
Choose the wrong tools, and you’ll pay the price down the line. Your work may be slower, your competition may outpace you, or your end result may fall below expectations.
When you choose the right tool, you’ll be thankful for it every step of the way.
If what you’re building is a software project for your company, your core tool is your programming language. Your choice of language at the start of the project will impact it profoundly in the future.
For your software project to be a success, we recommend that you choose Python as your tool. Here are the main reasons why.
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 2019.
How can you benefit from Python’s popularity? By using a popular language, you have a much higher chance of finding a solution to any problem you may encounter. In fact, if your issue is common enough, there’s probably a ready-made solution available in Python right now.
Python has a healthy community of enthusiasts that strive every day to make the language better by fixing bugs and opening new possibilities. And, as mentioned before, it also enjoys strong support from the world’s largest corporations. One of them is Google. They are actively working on guides, tutorials, and other resources to get the most out of Python.
Python is accessible by design, making it one of the fastest languages in terms of speed of development.
A user-friendly environment in the hands of your development team means less time wrestling with your building tool and more time spent actually building.
Additionally, you can get a headstart with the rich selection of frameworks and libraries. They will save you the hassle of coding features by hand, accelerating your time-to-market. (Check out the most popular Python web frameworks and scientific libraries in the sections below.)
But wait—isn’t Python slow? You’ve probably heard the rumor somewhere.
While it’s true that Python’s runtime is slower compared to other languages, Python is still the language of choice for companies such as Google. Why?
Because CPU time is rarely the limiting factor.
The limiting factor is your employee’s time.
You want to optimize your most expensive resources—and developer hours don’t come cheap. So you need all the help you can get to shorten time-to-market, even if it leads to slower runtime execution.
But that’s not all. For situations where performance really does matter, Python offers tried and tested solutions to incorporate other, faster languages into the code—like Cython, for example.
Python’s syntax is clear and concise. The language is designed to be readable and close to actual English, making it easy to decipher. Python also requires fewer lines of code to achieve results compared to languages such as C or Java.
The simplicity of Python helps greatly when you have to read the code you’ve written or code from another developer. Code review goes much faster when you have fewer lines of code to actually review, and the code reads like English. There’s also less “catch-up” involved when the code changes hands—you can figure out pretty quickly what each function is supposed to do.
With code that’s easier to understand and navigate, you can reduce the amount of work needed to maintain and expand your code base.
No one can really predict when your user numbers will start surging and scalability will become a priority. This is why it’s a good idea to use a language that scales great and, as we’ve mentioned above, is easy to maintain.
Some of the most ambitious projects around the web—YouTube, Reddit, EVE Online—all use Python to serve their user base reliably.
Why Python, then? There are a few reasons:
If you really need to find the one language to rule them all, the choice is clear.
In the current market, a business without a website might as well not exist. Moreover, the trends are pushing for more and more impressive web apps that, among others, include:
Nowadays, more than ever, it’s important to choose the right tools when the time comes to build (or, quite likely, rebuild) your own website or web app.
There are many advantages of Python that help you get results fast within the field of web development:
In addition to Python’s standard features, one of its major strengths in web development is the variety of web frameworks it offers.
With a large selection of well-supported frameworks, you can find the right starting point for any project. Python gives you the tools to get the job done reliably, no matter whether you’re looking to focus on:
The most widely used Python web framework—at least until recently.
Django’s trademark is its completeness, as it aims to offer all the tools you need to build a web app in a single package.
It’s the perfect choice if your app is fairly standard, since it allows you to skip most of the initial steps and get a working solution faster.
Compared to Django, Flask is much more geared towards microservices, which may be the reason why it’s the new #1 in popularity according to JetBrains.
Contrary to the all-in-one-package philosophy of Django, Flask works more like the glue that allows you to combine libraries with each other.
Flask lends itself well to an iterative approach of adding new features and services “one drop at a time.”
Bottle is another framework that would rather stay out of the way than overwhelm the user with every single thing they might need.
The framework is lightweight and has no external dependencies other than the Python standard library (stdlib).
It works great for prototyping, as a learning tool, or for building and running simple personal web apps.
The maturity of Pyramid stems from the legacy of two previous frameworks: Pylons and repoze.bfg. Now merged into Pyramid, Pylons used to be one of the top Python frameworks.
Pyramid’s prime advantage over Django is that it’s very easy to customize, whereas Django is more “opinionated.” This makes Pyramid a great choice for non-standard projects that could be more complex.
We’ve barely scratched the framework surface here, so to speak. Head over to our article for more exotic examples of Python web frameworks.
Python’s simplicity and wide selection of pre-existing libraries and frameworks make it a great fit for both garden-variety web projects and highly complex web apps.
The Internet of Things can be variously understood, depending on your perspective.
For the sake of this explanation, let’s assume we’re talking about physical objects fitted into an embedded system that connects them to the Internet.
These “things” now have their own IP addresses and can interact with other “things,” remote or local, using the network.
IoT often plays a role in projects involving wireless sensor networks, data analytics, cyber-physical systems, big data, and machine learning. Additionally, IoT projects often involve real-time analytics and processes.
Ideally, your programming language for an IoT project should already be a strong choice for the aforementioned fields, while also being lightweight and scalable.
Python fits these criteria very well. Here’s how.
Have you ever seen an interesting IoT project around the web? If so, there’s a good chance the Raspberry Pi was likely involved. The Raspberry Pi:
Most importantly, it has a Linux distro on board, which means it also uses Python, making coding for the Raspberry Pi simple and straightforward. The Raspberry Pi is an incredibly versatile device you can use to build anything: a media center, a retro gaming machine, a time-lapse camera, a robot controller, an FM radio station, a web server, a motion-capture security system, a Twitter bot, a mini-desktop PC. It’s also one of the most popular tools for teaching programming.
The Raspberry Pi is an incredibly versatile device you can use to build anything:
It’s also one of the most popular tools for teaching programming.
When it comes to Python solutions for IoT, it doesn’t get much smaller than MicroPython: a small microcontroller optimized to run Python on a board that’s only a few square inches in size.
The kit includes a software package, so if you’re just starting out in IoT with Python, you don’t need to look a lot further.
One feature of MicroPython that’s especially enticing is WebREPL (read-evaluate-print loop), which is similar to a command line and accessible through a webpage. With WebREPL, you can use a simple terminal in your browser to run Python code on your IoT device without the need for a serial connection.
To make the deal even sweeter, you don’t need to connect the board to WiFi, since it can create its own network.
Zerynth hails itself as “the middleware for IoT and Industry 4.0.”
It provides developers with a full ecosystem of tools, including an IDE, a toolchain for development, a multithreaded RTOS (real-time operating system), a device manager, and a convenient mobile app to monitor and control Zerynth-powered devices.
Zerynth speeds up IoT development by allowing you to write in Python or a hybrid of C and Python.
You can use Zerynth to program the most popular 32-bit microcontrollers, connect them to Cloud infrastructures, and keep your devices running the latest version of your software with Firmware Over-the-Air updates. It’s also very compact, requiring just 60–80 kB of Flash and 3–5 kB of RAM.
Home Assistant is an open-source Python project for intelligent home automation. You can install it on a PC or a Raspberry Pi.
Home Assistant drives automation; for example, it can control the lights in your house and measure the temperature in each room.
On top of that, Home Assistant is compatible with a variety of drivers and sensors.
To Python’s great advantage, IoT programming is inching closer to desktop programming every day. As the capabilities of “smart things” continue to grow, their similarity to desktop computers is sure to follow.
We’ve already mentioned that a device like the Raspberry Pi could even serve as a miniaturized desktop PC. This trend works in favor of using Python for IoT, because with greater memory and computational power comes greater freedom of choice in terms of picking the right programming language.
Therefore, when developers and project managers are looking to choose a language that brings results fast and makes life easier, they tend to go with Python.
Writing in Python is as quick, easy, and painless in IoT as in other fields.
In the current IoT environment, you choose your programming language just like you would choose it for any other project. Ease of writing matters more than the choice of language, and Python has that in droves.
Machine learning is the latest craze in the software development world. It’s been steadily rising in popularity due to its seemingly limitless possibilities—and rightly so.
The very idea that computers can actively learn instead of operating in strict accordance with codified rules is simply exhilarating. It offers a whole new approach to problem solving.
At the forefront of machine learning is Python. Multiple studies unequivocally hail Python as the most popular language for machine learning and data science.
But why is that? What is Python’s secret?
There are several reasons why Python is a perfect fit for machine learning:
But there is one more argument to be made for Python here, which in the case of machine learning is greater than all the others combined: extensive open-source library support.
Python is famous for rich selection of libraries, especially for data science. It’s the root cause why Python is considered the go-to solution for machine learning.
Here are some of the most popular Python libraries for machine learning.
Scikit-learn is the best known and arguably most popular Python library for machine learning. Built on SciPy and NumPy—and designed to interoperate with them—scikit-learn is open source, accessible to all, and reusable in a number of contexts.
The library features a wide variety of algorithms for: classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. These algorithms include: support-vector machines, random forests, gradient boosting, k-means, and DBSCAN.
Yet despite the barrage of options scikit-learn provides, the data mining and data analysis tools it offers are both simple and efficient.
TensorFlow was originally developed by engineers and researchers at Google to meet their needs for a system that can build and train neural networks to find and decipher correlations and patterns. The process was designed as analogous to the ways humans reason and learn.
The flexible, high-performance architecture of the open-source library makes it easy to deploy numerical computation across multiple platforms, as well as from desktops to server clusters to mobile devices.
TensorFlow is used by companies such as Uber, Dropbox, eBay, Snapchat, or Coca Cola—not to mention Google, of course. This pretty much speaks for itself.
Nilearn is a high-level Python library for easy and fast statistical learning on neuroimaging data. The library leverages scikit-learn for a plethora of advanced machine learning techniques, such as pattern recognition or multivariate statistics. The applications of this include predictive modelling and connectivity analysis, among others.
Constructing domain-specific feature engineering is the highest value nilearn holds for machine-learning experts. This means shaping neuroimaging data into a matrix of features perfect for statistical learning, or the other way around.
Mlpy is a high-performance Python library for predictive modeling, built on top of SciPy, NumPy, and the GNU Scientific Libraries. Multiplatform and open source, mlpy aims to provide solutions for supervised and unsupervised problems, offering an extensive range of cutting-edge methods.
Finding a sensible compromise between efficiency, modularity, reproducibility, maintainability, and usability is the prime goal of mlpy.
Supervised machine learning is one of the most widely-applied uses of AI. In supervised learning, an algorithm learns from a labeled dataset with the output already being known. Two of the main techniques within this category are classification and regression.
Classification is used to categorize data into desired, distinct classes and predict a discrete value. It can serve to assess creditworthiness or help with medical diagnostics.
Regression is used in problems that involve continuous numbers, including demand and financial forecasting, as well as property price estimation. The predicted outcome here is an estimation of a numeric value.
Both classification and regression problems can be solved thanks to a large number of Python libraries, including:
In unsupervised machine learning, the algorithm relies on its own ability to solve problems after it’s been handed an unlabeled dataset without training instructions and a known outcome.
Clustering and matrix factorization are two of the most common unsupervised machine learning methods. Frequently applied in user segmentation and recommender systems, both methods are used to group elements based on the similarity between object properties.
Some of the most popular libraries used in clustering and recommendation system engines are:
Reinforcement learning algorithms learn to modify their behavior to make the right decisions after receiving feedback. They have been tested in self-learning solutions, including video games and traffic light control systems.
Problems posed by reinforcement learning are often highly specific and finding solutions to them may prove quite challenging. These Python libraries can help:
Python is the top choice for machine learning because its myriad of pre-prepared, tried-and-true libraries does most of the heavy lifting during the development process.
The tools are all there, simple and ready to use, complemented by extensive documentation and a vibrant community to go with it.
If you’re looking to get into machine learning in Python yourself, we have a comprehensive tutorial covering all the bases, written by our very own machine learning experts.
And if you’re not new to machine learning and wish to expand your Python skills in ML, maybe you’ll be interested in a more advanced resource: an extensive overview of tree-based ensemble models in Python.
Startups are very special cases. Building them from the ground up is a substantial undertaking, as they require a different approach than an established company. Making the process easier in any way you can is essential.
That’s why when you choose the programming language for your project, you need one that will let you get started quickly and deliver the results you expect efficiently. At the same time, you can’t compromise on high quality.
Python can give you all of that.
There are plenty of reasons why Python is a dream come true for any startup. Just to name a few, it’s because the language is:
We understand that theory is one thing and practice is another.
Here are just a few startups that have rightly bet on Python as their programming language of choice.
21 Buttons is the leading European fashion social network, with a user base of over 6 million bloggers and influencers. The startup provides a platform to share clothing designs and outfits with other like-minded fashion enthusiasts.
21 Buttons built their product using Python and a variety of Python frameworks, like Django or Flask.
Deuce Tennis is a startup connecting tennis players with courts and clubs near them. Their goal is to grow the popularity of the sport and make playing tennis easy, enjoyable, and affordable for everyone.
By leveraging trending technologies such as React Native together with Python, the previously tedious process of booking and managing tennis sessions and courts becomes effortless with Deuce.
Deuce Tennis is available both as a mobile and web application. They have already made a splash on the worldwide tennis scene after they received backing from tennis legend Andy Murray.
TravelPerk made headlines a while back as one of the hottest Barcelonian startups—and for good reason. They offer an all-in-one solution for business travel booking and management.
The startup hopes to change the way businesspeople travel for good. And the best part? It’s totally free. The first and so far only one of its kind to be this bold.
TravelPerk is built in Python and React, putting code quality and user experience over anything else, with the backing of top tech investors like Spark Capital.
Like the previous entries, Zappi also enables individuals to get in contact with others. However, they stand out from the startup crowd, since their focus is putting substitute teachers in contact with schools.
The startup’s mission is to revolutionize the painstaking and stressful process of finding supply staff. Aware of the advantages Python offers, Zappi was adamant about using the language for their software product.
Their platform consists of a mobile solution for teachers, a web application for schools, and an administration platform for the Zappi hub that contains a centralized database of both the substitute teachers and the schools.
The Zappi app is available on Google Play and the App Store.
Startups are both exciting and terrifying endeavors.
They need to launch and grow fast, it takes a while before they turn a profit, and their financial foundations are often shaky.
There’s a lot at stake and the risk of failure is high. That’s why research and choice of programming language are crucial before you set out to launch a startup of your own.
Thankfully, the development speed, high code quality, and efficient scalability of Python make the language seem almost custom-made to help your startup overcome every challenge in its way.
While Python may not be a new technology, the rise in popularity it enjoys among the hedge fund and investment banking industries is a fairly recent development. But the fact that Python is the fastest-growing language in finance should come as no surprise.
If your company is about to venture into the world of fintech, you need a programming language that is high performing, easy to scale, and mature. The tech stack you choose should also have a wealth of ready-made solutions and libraries to fall back on.
This makes Python and fintech a match made in heaven.
Hedge fund and investment banking industries have long decided that Python is an ideal choice for fintech because the language addresses many of their highly specific needs:
Fintech belongs with Python for a number of reasons:
Clean syntax
Python code is easy to understand, because it resembles actual English. This allows developers to learn it quickly and to become fairly competent in it within a short time.
Fast time to market
Python is a dynamically typed language, making development in it quicker than in statically typed languages such as Java.
When writing in Python, you need less code, which in turn allows for faster deployment.
Helpful libraries
Python boasts an extensive array of libraries for a plethora of purposes; a lot of those are excellent for fintech and finance.
Do you need an algorithmic trading library? Try pyalgotrade. Something for scientific and technical computing? There is SciPy. How about quantitative economics? Check out quantecon.py.
Whatever your question, Python has the answer.
Many fintech companies have chosen to use the many benefits Python offers to their advantage.
Here are some examples.
Venmo started out as a fintech startup. These days, it’s more of a social media network. Either way, Venmo is an intuitive tool for people to split bills or pay one another.
Like PayPal, Venmo is an easy way to move money around. Linking a credit card, debit card, or checking account is required to receive or send payments.
Originally owned by Braintree, Venmo was acquired by PayPal in 2013 for $800 million. In Q2 of 2017 alone, Venmo processed a staggering $8 billion in payments.
Backend software developers at Venmo are expected to have experience working in Python and Django.
Newable Business Finance is a platform that can be used to apply for business loans. They offer financial services to clients unwilling or unable to receive loans from traditional banking institutions.
The platform does away with tedious reliance on paperwork and bureaucracy using printed documentation, replacing it with a seamless and efficient online alternative. Business owners get their money quickly and reliably.
Newable Business Finance was built under tremendous time constraints. Within 2 months, the development team delivered the complete product, having started from nothing. Since day one, the platform has been running smoothly and pitfall-free.
How do we know that? We had a hand in building the platform.
Zopa was founded on a bold principle: cut out the middleman and let borrowers and lenders deal directly with each other. The clients’ investments are distributed across multiple loans, meaning not one of them holds more than 1% of a given investment.
The experiment paid off. In 2018, Zopa surpassed £3 billion in lending, the first consumer peer-to-peer lending platform to do so. As regulators put increasing pressure on traditional peer-to-peer lending, Zopa set out to launch its digital challenger bank.
Zopa uses a tech stack that is combination of Python, Java, and C#. Relying on a variety of frameworks, from Django to Pandas, they consider Python to be the key weapon in their arsenal.
Vyze is a leading fintech for manufacturers and retailers. They provide a comprehensive lending service, combining support, technology, and supply to elevate businesses to brand new financing heights.
Vyze offers extraordinary financing to clients of all shapes and sizes. Their solutions are smart, adaptable, and most of all simple in an effort to give you a more satisfying purchasing experience, wherever you shop.
STX Next supported Vyze in building their cutting-edge platform. Some of the solutions Vyze needed to build were nowhere to be found in other fintech products. This meant that the developers had to use every trick in Python’s proverbial book to create the solutions from scratch.
The languages and frameworks you choose for your fintech will to a large extent decide the longevity and ultimate fate of your product. They determine what you can build, who you can hire to build it with, and how long the market validation will take.
This is what you’ll get if you go with Python for your fintech:
We think the choice should be clear.
Since Python is the go-to programming language for domains such as artificial intelligence, machine learning and deep learning, it’s no surprise that it’s also a fundamental tool for any data scientist.
With data often described as the new oil, the success of any business that works with it depends on the ability to extract insights from large databases and make strategic decisions based on them.
Python allows organizations to analyze and visualize data in meaningful ways to identify patterns, track down relationships, and help solve complex business problems.
Since it was designed with ease of use in mind, Python is a great tool for every data scientist, from a beginner without tech background to a pro with years of professional experience. Its flexibility and high code readability are some of the reasons 87% of data scientists reported using it daily.
Python’s popularity for data science is, in a large part, due to the power of its multiple libraries.
Data scientists typically manage complex problems that can be solved with four general steps: data collection and cleaning, data exploration, data modeling, and data visualization. Python provides libraries that help with each stage.
They also make the language compatible with high-performance algorithms that allow it to combine with technologies such as artificial intelligence, machine learning, and predictive analysis.
A lot of data science tasks, such as web scraping, can be done using dedicated Python libraries, for instance Scrapy, Beautiful Soup, or Requests.
On top of that, Python gives you access to many deep learning frameworks, including Caffe, TensorFlow, PyTorch, and Keras.
The great thing about Python is that there are plenty of ways it can be applied without compromising on the quality of the final result and without the need to make use of any other languages.
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.
Even though it’s feasible to use Python on its own to work with data, the job becomes much easier with some of its useful libraries. 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.
Knowledge of Python is one of the most popular skills that banks require when they hire developers, which points to the sector’s reliance on it.
Given its ease of use, it’s not uncommon to see Python skills outside of software development roles in finance. It’s a great skill to have by professionals such as asset managers, financial analysts, business analysts, and risk managers to help them better understand the technology underpinning the operations. It’s often more effective than Excel for collecting, presenting, and analyzing data.
Python can be used to create solutions in virtually any area of financial services, including:
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. Python offers access to a number of frameworks and scientific libraries, such as NumPy, Pandas, SciPy or Matplotlib. They allow organizations to build sophisticated tools without the need to create every solution from scratch.
Python’s syntax is the closest to the mathematical syntax that is used to describe scientific problems or financial algorithms. 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. It reduces the potential error rate and helps ensure that the software won’t become obsolete.
Python doesn’t just make it easier for data engineers to do their work; in many cases, it makes it possible in the first place.
As the go-to language for data scientists, Python is also a great alternative for specialized languages such as R for machine learning. Often branded the language of data, it’s indispensable in data engineering.
What are some of the most common use cases of Python in data engineering? Read on to find out.
Python is one of the few programming languages supported by the serverless computing services of all three platforms (AWS Lambda Functions, GCP Cloud Functions, and Azure Functions).
Over the years, Python has managed to incentivize cloud platform providers to use it for implementing and controlling their services.
The largest cloud providers, including Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, keep Python users in mind when designing solutions to a variety of problems.
Business data may originate from various sources, such as databases (both SQL and noSQL), flat files (e.g. CSVs), other files used by companies (such as spreadsheets), external systems, APIs, and web documents.
Thanks to how popular Python is, you can pick from a large number of libraries and modules, including those used to access data, such as SQLAlchemy for some SQL databases, Scrapy, Beautiful Soup, and Requests.
One of the libraries that we find particularly interesting is Pandas. It allows you to read data into “DataFrames” from a variety of different formats, such as CSVs, TSVs, JSON, XML, HTML, LaTeX, SQL, Microsoft, open spreadsheets, and several other binary formats.
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.
To facilitate DataFrame operations, AWS Labs maintains the aws-data-wrangler library called “Pandas on AWS.” It can be used as a Layer for Lambda Functions, which helps make the deployment of serverless functions much easier.
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.
What makes developing ETL jobs really straightforward for adepts of Pandas is the fact that PySpark supports most of Spark’s features, including Spark SQL, DataFrame, Streaming, MLlib (Machine Learning), and Spark Core.
PySpark is also supported by the largest cloud providers, namely Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.
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.
The fact that there are very popular Python-based tools for on-premise systems has prompted platform cloud providers to commercialize them as “managed” services that, as a result, are easier to set up and work with.
This applies to Amazon’s Managed Workflows for Apache Airflow, among others. Launched in 2020, 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.
If you’re a data engineer, Python’s versatility can help you maintain high velocity across all the tasks you need to perform.
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.
These days, we’re coexisting with machines on a daily basis. This is remarkable in its own right, but also pretty mind-boggling, considering how much we differ from them. The language barrier alone is quite steep between us, though we still managed to come up with a solution to that particular issue.
Natural Language Processing, or NLP, is the bridge that can help us close that gap. And as usual, Python comes in as our main tool to that end, offering a number of libraries that can help you make the most of NLP.
Natural language processing comes in two main types: rule-based and statistical. The names are pretty self-explanatory—rule-based NLP operates mostly on a set of established rules, while statistical NLP uses large amounts of data to make judgments.
One is more intuitive, while the other gives you more control. That is, if you have the right skills. However, both are just alternatives to helping you accomplish the same thing.
First of all, humans and machines operate on completely different wavelengths. The latter is very orderly, while the former is comparatively chaotic and unpredictable—and that’s just one gap we’re still struggling to bridge.
Human communication isn’t always easy to parse for machines, since we often speak using terms that aren’t even remotely literal. Not to mention the fact that all our languages are constantly evolving!
Natural language processing libraries are essentially databases that make it much easier to get an NLP to work as needed. Python comes with a fair share of them, so we’d like to showcase some of the more impressive ones for you now.
When it comes to teaching machines how to understand human communication, NLP is the way forward—there’s no doubt about it.
We’re hoping that, with the help of Python, taking the plunge and reaching out for all the opportunities that come with the technology will be a bit less daunting for you.
The applications of Python are numerous and so are its benefits.
It’s perfect for web development, the Internet of Things, machine learning, startups, and fintech—among many others.
We’ve discussed at length why Python is such a good fit for all of these purposes.
However, just to give you a quick recap:
This page gives you an overview of what Python is most useful for, but it doesn’t nearly exhaust the subject, so expect more updates to come in the future.
Meanwhile, maybe you’re looking for answers to different questions. Like, say, whether to choose Python or a different programming language for your software project.
If that’s the case, we have another resource that will be perfect for you: a detailed look on how Python compares to other programming languages.
We also have other free resources you may benefit from:
.svg){kind=icon}
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