Python: The Definitive Business Guide

The official definition that we’ll find on Python’s website reads:

But what does that mean? And what difference does that make to your business? Let’s analyze this statement one-by-one. 

Interpreted

Compilation is the process of translating human-readable code into machine code and generating an executable file, which runs the operations defined by the programmers. 

Interpreting involves an interpreter instead, i.e., a piece of software that executes a program line by line on the fly without producing a file to be run by the operating system. 

Python technology is classified as an interpreted language, although there is some compilation involved in its execution. Instead of compiling into machine code, Python code is translated into a simple set of instructions called bytecode. These instructions are executed by a virtual machine and not a computer’s CPU. This has some advantages. 

For example, interpreted languages tend to be more flexible, which enables easier updates and quicker releases. They are usually more compact than their compiled counterparts. The code is portable, which means you can reuse it in different platforms and deployment environments. Examples of interpreted languages include Ruby, Perl, and BASIC. 

Object-oriented

The most popular programming paradigm, the object-oriented approach, uses the concepts of classes and objects. They are used to structure the code into self-contained, reusable modules that can be easily stacked together. This modality enables code reuse, which speeds up development and usually facilitates troubleshooting and problem-solving. 

Object-oriented programming (OOP) also leverages inheritance to avoid excessive re-work and speed up development. Java, C++, PHP, Ruby, and Swift are just a few examples of object-oriented languages. It is worth noting that while it’s object-oriented by design, Python can also support other programming paradigms.

High-level

As a high-level language, Python has strong abstraction from machine language, being quite easy to understand – and use – by programmers. High-level languages are close to human language, which lowers the learning curve for users and facilitates coding. They are also machine-independent, portable, and easier to debug than low-level technologies. High-level languages in common use today include JavaScript, Java, Visual Basic, and C#.

Dynamic semantics

Python is also dynamically typed. Without going into technical details, this roughly means that it executes many operations (such as code extensions and updates) at runtime and doesn’t require as many data type declarations as statically typed languages. As a result, Python code is usually more succinct and tolerant to change. It can also be tested and debugged on the fly. Apart from Python, popular dynamic programming languages include R, PHP, JavaScript, Groovy, and Erlang.

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We should also add that Python is open source and can be freely distributed, also for commercial use. This helps reduce project costs through lower license fees, enhances compatibility with other standards and systems, and opens the door for custom code extensions that are sometimes impossible with a licensed technology.

As a modular language, Python easily integrates with other languages and technologies, a worthy feature in complex projects that involve the development of hundreds of components. Code readability is another intrinsic characteristic of Python that’s highly advantageous from the project management perspective. 

Python: basic concepts

As a business-oriented professional, you don’t need to know how to code in Python (unless you want to, which we highly encourage you to do). However, some basic understanding of the language’s fundamental concepts will help you sail through project meetings attended by technical staff. 

Python module vs. package

You may have come across Python modules and packages; these two Python-related concepts are often confused. Let’s spell out the difference between them.

Python module

A module is simply a Python file containing statements and definitions (classes, functions, and variables). 

Developers use modules to break the code into small and manageable chunks that make it easier to read and understand. A module’s definitions can be imported to other modules, eliminating the need to write the same code again. 

An example of a Python module is acme.py, where acme is the module’s name, and .py signifies a Python extension.

Python package

A package is a hierarchical collection of modules. Think of it as a folder on your PC that groups related files. 

Just like your folders, packages help maintain a readable, well-organized project structure. They serve as a directory that facilitates module tracking and management. Each package can be split into sub-packages, and modules can be imported across particular packages.

There’s one important condition that every package must meet to be considered a package – it must hold a file __init__.py. The file can be empty, but without it, the code won’t initialize.

Objects and classes in Python

Another important distinction to be made in Python is that between objects and classes.

As pointed out in the first section, many Python implementations subscribe to the object-oriented programming paradigm. In this approach, objects are a fundamental concept. 

Each object is a unique collection of attributes and functions that act on these variables. These properties are defined in a class, which serves as a template for the object. The template is used to create specific object instances.

Imagine a dog object. Class defines what attributes and functions an object needs to be classified as a dog. It will have a name, age, size, breed, color, etc. An object instance corresponds to an individual dog which has specific parameters of each of the attributes defined in the class.

At this point, we will also need a construct that describes the dog’s behavior. If you want the dog to fetch a ball, what instructions do you need to provide? Let’s find out in the next section. 

What are Python functions?

Objects, classes, and instances deal with the attributes of an object. Functions and methods have to do with its behavior.

A function is a block of code that a developer can run to carry out a specific task or return data as the result. In our case, the function fetch a ball would include instructions to be executed by your dog. 

Each function may consist of parameters (variable names), and arguments (variable values) passed to the function when it’s called. Functions are reusable and can be called anywhere as they aren’t tied to particular objects or classes. 

Methods also consist of a set of instructions to be executed in a given order. However, unlike functions, they cannot be called by their names. Each method is implicitly used for a specific object and class.

Who created Python?

Python was conceived in the 1980s and was first released on February 20, 1991, by Guido van Rossum. At that time, Van Rossum was a programmer at Centrum Wiskunde & Informatica (CWI) in the Netherlands, a government-funded research lab. His team was working on the ABC programming language that focused on user-friendliness and programmer efficiency. 

The project turned out unsuccessful, but even as Van Rossum moved to other ventures, he hadn’t abandoned it altogether. He and his colleagues needed an easy-to-use scripting language for Amoeba, an operating system they were developing. That’s when the idea of Python hatched. The successor to ABC took after many of its core concepts and features, including:

• high-level data structures
• dynamic typing capabilities
• focus on programming efficiency

Want to learn more about Python backstory? Hear it from Guido van Rossum himself:

Later on, Van Rossum developed some of these characteristics into a set of principles underlying Python’s design philosophy. According to them, Python has to be:

• “an easy and intuitive language just as powerful as those of the major competitors
• open-source, so that anyone can contribute to its development
• as understandable as plain English
• suitable for everyday tasks, allowing for short development times.”

Despite numerous updates and releases (the most recent major release of Python is version 3.9.2, with Python 4.0 expected to launch in the next few years), these goals still hold.

KEY TAKEAWAYS:

• Python has been around since the late 1980s, yet it remains in the top tier of the most popular and used programming languages.
• The language was conceived to be highly readable, and it emphasizes programming efficiency and speed. 
• As a high-level language, Python resembles human language, which reduces the learning curve and facilitates development.
• Versatile and flexible, Python can be used in various applications. The interpreter is supported by mainstream OSs. 
• Python is open source, which enables greater innovation and reduces software development project costs.

Several advantages of Python for your business should already come into view in the first section. Let’s bring them together and use some more compelling reasons why you should consider Python in your next software project.

Python advantages for business

If we revert to the official programming language’s website, we’ll find out that:

That almost sounds too good to be true; nevertheless, Python does stand out among other programming languages in many respects. How? We will analyze the core Python advantages in this section, considering how they translate into added value for your business.

Affordability and cost savings

Developed under an open-source license, Python costs your business nothing to use. Anyone can download its source code, update it, and distribute it without obtaining costly licenses. Furthermore, most tools and libraries necessary for Python development (see section  Python: technology environment) are also free. As you don't need to cover any license fee, the project's total cost greatly diminishes.  

Fast and efficient development 

Efficiency is intrinsic in Python. Simple syntax translates to fewer code lines, while a high-level approach, code readability, and uncluttered formatting make life easier for programmers. Additionally, Python has an extensive collection of libraries, which facilitate and speed up development. All in all, these Python features mean your team needs to write less code to achieve brilliant results faster. 

Mild learning curve

Because of its relative simplicity and developer focus, Python is considered a great language to kick off a programming career. Close to natural language, it is quite easy to pick up, understand, and code – at least up to a certain proficiency level. Along with that, resources for learning Python, including free courses and tutorials, are widely available. 

The mild learning curve allows us, developers, to pick up the language faster. But the fact that Python is so approachable also allows project managers or product owners to make more informed business decisions as they gain a better understanding and visibility of the developed software. Non-programmers like data scientists, statisticians, or financial analysts, also benefit from getting familiar with Python, which underlies many applications they deal with on a daily basis.

Extensive range of applications

We’ve already mentioned it a few times, and we’ll say that again. Python’s key business advantage is probably its adaptability. As a general-purpose language, Python is extremely productive, and it covers a wide array of uses across industries. 

Python is commonly used in data science, AI, and machine learning projects. It can serve as a scripting language for web applications or as a standard component of most Linux and macOS distributions. Academic employees use it for their research. Python has also become a crucial tool for scientific computing, IT security applications, and the gaming industry. Enterprise software naturally also makes heavy use of the language. 

→ → To learn more about Python’s diverse applications, jump to the section Which industries use Python?.

Flexible integrations

Another advantage of Python is that it easily extends to other languages, like C++, PHP, or Java. This comes in handy, especially in distributed projects that span multiple teams and technologies. Python’s flexibility makes it possible to stick large software components together without timely and costly code rewrites. Additionally, you can leverage the speed and efficiency of Python to build quick product prototypes. Once they’ve been tested and approved, you can easily translate them into another supported language (like Java or C++) to release a production-ready version.

Code portability

Python follows the cross-platform philosophy, which describes a program's capability to launch on various platforms with no code extensions or modifications. The fact that you can write the Python code once and run it (almost) anywhere saves a significant amount of time and provides you with out-of-the-box multi-platform support. 

Vibrant developer ecosystem

The 19th edition of SlashData’s State of the Developer Nation report, published in October 2020, estimates that there are ca. 9 million Python developers globally; this is more than Java users. The report also suggests that more than 2.2 million members have joined the Python community within a year, a telltale sign of the rapid growth of the language's ecosystem.  

What does it mean to your business? First of all, easier access to expert Python developers. Secondly, a robust community keeps the language’s technology stack relevant and updated. This positively reflects on the quality, security, and stability of your Python-based software. 

→ → If you’re interested in finding out more about Python developers’ community, check out the section Python community – who, what, where, and how?.

Strong enterprise backing

Here’s one more benefit of Python that may influence your decision about giving it a try in your next software project. Namely, it is a fully-fledged enterprise programming language. 

Jump to the section Python for enterprises if you want to know what exactly makes Python an ideal choice for building mission-critical business applications. Here, let us just casually drop a few names that make extensive use of Python: MIT, Google, Facebook, NASA, AstraZeneca, Amazon, Instagram, Revolut, PayPal, Goldman Sachs, Spotify, Netflix, Udemy, Uber… and the list goes on.  

Python disadvantages

We hate to admit that, but everything has its pros and cons. Even Python. As much as we admire the language, there are certain limitations to it that we have to account for:

Speed limitations

Code speed is probably Python’s greatest vulnerability. As Python needs an interpreter to execute, it tends to be slower than Java or C++. However, while we’re at it, it’s essential to distinguish between the CPU performance, a technical parameter, and code delivery time, a key indicator for you as a business person. 

When we look at hard data, yes, despite regular optimizations, Python can still not crunch numbers as fast as some of the best-performing programming languages. But this rarely affects what probably matters the most to you: time-to-market. 

In terms of the capability of turning your software idea into reality, Python tends to be even twice as fast and productive as “faster” languages. Several factors account for that, including Python’s compact code size, the language’s accessibility, or the abundance of 3rd party libraries. 

Not native to mobile development

Another aspect that often crops up in discussions over Python’s deficiencies is the lack of native mobile support. Indeed, Python is not officially supported by Android or iOS, and it doesn’t have inbuilt mobile development capabilities. 

This doesn’t mean, however, that there aren’t quality mobile apps using Python. The Python community has bypassed these limitations by releasing several libraries and packages like Kivy, PyQt, and Toga, which enable creating mobile apps with Python. 

As our CTO demonstrates in his post, Python mobile development is possible, and with outstanding results, provided the development team has the requisite tools and skills. 

Versioning headache

Did anyone say: “Programming pet peeves?” December 3rd, 2008 was a breakthrough moment in Python’s history – that’s when Python 3.0 came to life. It brought some monumental changes, rectifying major flaws in the previous releases and massively simplifying the syntax. There was one problem, though. Py3K, as it was dubbed, was not backward compatible with its forerunner. This triggered a deluge of problems in development. For example, even though some versions became obsolete and the official support ceased, key libraries were not updated to work with the new version. 

Compatibility issues may often result in extra development effort and require increased coding awareness. As a result, Python developers need to be well-versed on the feature compatibility to understand which version of Python to use, and make an informed choice of libraries, managers, and other tools.   

A tendency for run-time errors

Coding with Python is fast and efficient. Regretfully, it also creates more opportunities for developers to overlook potentially critical errors. Without explicit data type declaration, Python tends to be more vulnerable to run-time errors exposed when a program is already running. 

This propensity of Python sometimes requires more detailed or additional testing, which may lengthen the QA stage of the project. Again, a solution exists. Python comes with a range of tools and libraries to create automated tests, which help speed up debugging.

KEY TAKEAWAYS

• Python was designed to be easy, fast, user-friendly, and intuitive.
• It is an open-source technology, which cuts down project costs.
• It’s also extremely versatile, which means you can use it in a range of applications. 
• A myriad of frameworks, libraries, and tools facilitate and speed up the development.
• Python blends well with other programming languages, and its code is portable.
• While it’s a start-ups' favorite, Python is also widely used by global enterprises.
• Python tends to be slower than Java or C++, and it’s more vulnerable to run-time errors.
• It isn’t a mobile technology by design. Even so, you can still use it to create mobile apps.
• Python’s biggest downside is the lack of backward compatibility between major releases.

One of the key reasons behind Python’s immense popularity, besides the ease-of-use and rich library support, is its cross-domain, cross-industry application. Python is a multi-purpose language; thanks to its inherent structure and open-source nature, it applies to many fields, which we’ll explore in this section.

Python for web and Internet development

As of January 2021, there were nearly 1.2 billion active websites around the world. Carrying out successful business activities in the 21st century without an online presence is virtually impossible. For some industries, like eCommerce, every minute of website downtime translates to thousands of dollars in lost revenues. Additionally, being down for more than 15 minutes already affects your website ranking. And reindexing a website to make up for the lost rank is always arduous and, at times – unfeasible. 

This explains why modern companies invest in reliable, tried-and-tested technologies to build their websites and web applications. Many of them chose Python as the backend technology for its diverse support libraries, organized data structure, and fast development capabilities. Python technology also allows creating data-intensive, dynamic websites with custom animations and an elegant look and feel.

JavaScript is typically executed on the client-side. On the other hand, Python is primarily used to build server-side web applications that handle URL routing, managing HTTP requests, database access, web security, and other critical tasks underlying website/web app processes. The abundance of development tools, libraries, and packages that come with the language encourages rapid development. 

→→ Do you find the distinction between packages, frameworks, and libraries confusing? Jump to the section Python: technology environment, which explains these basic definitions and examines the most popular Python tools.

Python for data science and machine learning

When it comes to data science, ML, and AI, Python’s a top pick. The language has a leading role in implementing machine learning algorithms, predictive analytics solutions, and data engineering scenarios.

One reason for that is the profusion of data-oriented tools and packages that Python extends practically for any scenario. Numpy, Matplotlib, Pandas – these are just a few foundational libraries that simplify and accelerate data processing projects. They are instrumental for performing advanced mathematical and statistical functions, manipulating and visualizing data, and working with voluminous data sets. These resources are also well-suited to numerical programming and statistics. 

When tackling machine learning and AI tasks, developers and data scientists alike may leverage tools like scikit-learn, Theano, and TensorFlow. These and other Python ML libraries facilitate work with neural networks, enable natural language processing, and streamline common machine learning and data mining tasks.

Another factor contributing to the wide application of Python in scientific computing and machine learning is the language’s high accessibility. With its clear syntax and readability and an extensive set of support libraries, Python lowers the entry bar for beginners and non-programmers. This makes it the most widely used programming language in data science.

Python for audio/video applications

If Python is a general-purpose language, why not use it for developing audio/video apps? Python offers a wide range of multimedia libraries, such as GStreamer, FUPlayer, and Boxtream, that allow developers to work with images and sound. They are used, in particular, to:

• record, edit, mix, and export audio,
• play audio and video files in different formats,
• edit voice and video material, 
• stream sound.

Additionally, there are also several Python modules libraries for audio/video conversion. These include soundfile, wavio, and pydub.

Python GUI development

Python is commonly used for developing extensible and usable desktop user interfaces with a minimum amount of code. Game developers can choose from a variety of open-source GUI toolkits and libaries, including PyQt, PySide, Kivy, and Tkinter, among other tools. The choice depends on the project scope and requirements, app type, and individual developer’s preferences. 

The available features and platform/device support vary across frameworks and libraries. For instance, the Qt framework and related packages are cross-platform, so they greatly reduce the time needed to roll out applications to particular operating systems. Other tools, like Tinker, support developers with customizable in-built widgets to streamline the app delivery and reduce code size. Certain extension modules allow Python developers to create native user interfaces well-suited for modern touch-based devices.

Python for business applications 

Business applications need to be scalable and extensible and ensure fast and secure access to sensitive corporate data. And despite a common misconception that dynamically typed languages cannot deliver on these features, Python is well-suited for developing enterprise-grade software. 

• As a highly flexible language with a huge catalog of tools and libraries for any use case, Python can easily adapt to changing business requirements. 
• Python is compatible with numerous platforms and blends well with other programming languages (including Java, C++, Rust, and Go), an invaluable asset from the perspective of a large organization that works with hundreds of legacy and modern applications that need to communicate seamlessly. 
• Besides, Python’s vast and well-maintained open source ecosystem attests to the language’s reliability and stability, which are essential ingredients of any business application.

Python for ERP platforms

Several recognized ERP systems (like Tryton and Odoo) rely on Python in their backend, leveraging its capabilities for fast development and effective code reuse. There are many advantages of this approach.

Let’s start with time and budget savings. Coding in Python takes less time than using Java, a traditional technology for ERP implementations. Secondly, Python has a neat, organized architecture, where each business function is implemented as a separate module. This enables the rapid development of additional features as a business need arises. The clean, highly readable Python code is also easier to debug and troubleshoot if issues occur. Finally – a major advantage over Java – Python is a fully-fledged language that can be used at all levels, from core functions to scripting. Such an approach helps reduce system complexity, making it more flexible and resilient.  

Python game development

The Sims 4, Battlefield 2, World of Tanks, Civilization IV – all these record-breakers use Python to perform different tasks. Dozens of Python game development libraries like PyGame, Pycap, and Pyglet have made it possible, extending crucial resources for fast game prototyping, testing, and debugging, among other capabilities.  

The vast arsenal of Python tools for game creation is also very useful for budding developers who are yet to build their first game. They can take advantage of countless GUI elements, widgets, themes, and controls to facilitate the development of visually rich games in Python.

Python for blockchain

Versatile, flexible, and modern, Python is also an excellent choice to implement blockchain. By employing standard and extension packages (like Flask), Python developers may create individual blocks, code them into an immutable chain, and test and expose the chain by means of a REST API. Python’s simple syntax and concise code eliminate complexities and ease linking the blocks together in a secure and firm manner. 

Drilling down into a specific use of blockchain for cryptocurrencies, Python also supports developers with a range of dedicated libraries. One of them is CryptoCompare, a leading service for crypto news and market information and statistics. Cryptofeed is another useful library. It allows streaming market data from various exchanges directly into the user’s app. 

Python for microservices

Considered a disruptive technology barely a few years ago, microservices have entered the mainstream, and many companies are rewiring their platforms to this architectural pattern. To do that, or to build their microservices-based platforms from scratch, they may use Python. 

Python comes with a wide range of open-source tools to facilitate microservices’ implementation, the top one being Nameko. This dedicated and extensible microservices framework makes it possible to create a service that can respond to RPC messages, listen to events, and dispatch events on certain triggers, among other functions. The tool takes care of all connections, transports, and concurrency, allowing developers to create the service using regular Python classes and methods.

KEY TAKEAWAYS

• Python is a versatile, general-purpose language.
• It offers a rich set of frameworks and libraries used for a host of different applications.
• Data science, business analytics, and machine learning are key areas of Python use.
• The language is also applied in the backend of business apps, including ERP systems.
• Python comes with an arsenal of tools for the implementation of modern technologies.
• For instance, it is commonly used for blockchain and microservices-based applications.
• Many games and multimedia apps utilize Python to handle dynamic content. 
• Cross-platform Python frameworks are increasingly used in mobile app development.

A jack of all trades and a master of many, Python doesn’t limit its usage to one specific area. Multi-purpose, highly accessible, and user-friendly, the language is applied across various industries. Considering its strong inclinations towards data science, ML, and AI technologies, it’s hardly surprising that it dominates verticals such as insurance, banking, healthcare, or digital marketing, all of which heavily rely on data. 

The core strengths of Python make it also an in-demand language for other applications. Thus, before we dive deep into Python’s technicalities and examine particular tools and libraries for machine learning, data analytics or data visualization, let’s have a quick look at industries that use Python in their solutions.

Python for finance and banking

Python has made it big in fintech, becoming one of the industry’s most widespread technologies. It belongs to the core languages at several huge-scale financial institutions, including Bank of America, Citigroup, and JP Morgan, powering financial analytics and accounting systems, banking platforms, and digital wallets and payments. 

The language offers an assortment of tools and libraries that come in handy when building financial applications. They are primarily used for data acquisition, manipulation, and visualization and provide insurance brokers, bankers, and financial advisors with deep data insights. 

NumPy, for example, is a basic Python package for scientific computing often applied in systems that perform statistical computations and risk analyses. The Pandas library is a frequent choice for financial data visualization and blockchain implementations. Other Python tools often leveraged in finance projects include pyrisk (to evaluate financial risk and performance), scikit-learn (to apply machine learning to banking and financial software), and Zipline (a dedicated library for trading applications).

Common uses: financial analysis, calculations, data manipulation, business insights, payments handling, blockchain-based solutions

Companies that use Python: ING Bank, Bank of America, Citigroup, Revolut, PayPal

Python for eCommerce

eCommerce companies make extensive use of large data sets to streamline the buyer’s path to purchase and optimize inventory levels. Additionally, faced with an increased shopper demand for a personalized experience, they increasingly leverage AI and automation techniques to provide product recommendations, enable omnichannel experience, and deliver bespoke services. To achieve these goals, many of them turn to Python.

Apart from the data science libraries like NumPy, Pandas, or SciPy, which help with data aggregation, cleansing, and manipulation, eCommerce platforms benefit from the deep learning and AI capabilities delivered by Python tools and packages (including PyTorch, Keras, or MXNet). They use them to build powerful recommendation systems, analyze buyer behavior, and implement chatbot support. Some companies also employ natural language processing capabilities to add multi-language support to their websites and apps or enable voice search for a more convenient end-user experience.

Common uses: buyer data analytics, product recommendations, cross-selling and up-selling, image recognition, speech detection, chatbot service

Companies that use Python: Amazon, Walmart, Shopify, American Eagle, Zalando, eBay

Python for real estate

Millions of listings, thousands of clients, extensive sales histories; the real estate industry produces copious amounts of data. Data that – if handled properly – can lead to more informed, relevant, and data-driven decisions. Python provides a vast collection of libraries like PyDoop, PySpark, or scikit-learn that support real estate agencies and realtors. For example, Python-based software helps scrape data from property listings, analyze it and shortlist the right properties (take a look at this neat example of a simple Python web scraping bot that helps automate real estate deal screening). 

Developers enhance real estate platforms with Python AI and ML libraries to deliver predictive analytics capabilities that combine historical data, behavioral analysis, and real-time information. Altogether, they enable real estate stakeholders to assess market demand accurately, develop accurate price valuations, and predict customer behavior to tailor the offering accordingly. Python is also used in the real estate context to cater to less elaborate scenarios. For instance, Python-based APIs enable smooth communication and data exchange between websites, databases, mobile apps, and other platforms used by agents, landlords, tenants, and potential buyers. Python software can also be integrated with IoT sensors and devices to power home automation and improve property management.

Python’s use by real estate organizations is long-standing, as shown in this video (which dates back to 2015). In the clip, Dr. Aleksandar Velkoski, Director of Data Science at the National Association of Realtors, demonstrates how Python and Elasticsearch can be combined to build a powerful real estate search platform. 

Common uses: data scraping, property and buyer insights, risk evaluation and price forecasting, streamlined property management, IoT and smart home

Companies that use Python: Zillow, Opendoor, Insurami, Remarkably 

Python for healthcare and pharmaceutical

Since Python is the programming language of choice in data science and research, its use naturally extends to medicine. Again, it's impossible to list all its healthcare applications due to the abundance of practical tools, libraries, and packages (like pyhealth, MedPy, PythonMed, or Seaborn). Thus, we’ll name only a few that come to the fore. 

First of all, Python presents the healthcare industry with predictive capabilities. Using Python AI and ML libraries, scientists develop sophisticated analytical tools that can detect diseases based on early symptoms or even predict them before any symptoms develop, based on individual patient’s medical data and history. The same solutions are leveraged to come up with an efficient, personalized treatment plan. Python libraries are also frequently used in bed occupancy management platforms and solutions that help predict the length of stay.

Computer-aided analysis of medical images is another popular area for Python programming. Programmers and data scientists use Python code to implement deep learning in image visualization systems. Machine learning models enhance image diagnostics' speed, accuracy, and output, accelerating case review and reducing diagnostic errors. 

Python is also frequently used to build health management platforms, from appointment scheduling software to self-service patient health portals. When we look at pharmaceutical companies, they often use Python-based neural network models for drug discovery. To “invent” a drug, AI algorithms sift through potential drug compounds and validate them against a massive database of parameters. Many extraordinary drug discoveries were made by applying this method. One example is MIT’s antibiotic compound, which can kill antibiotic-resistant bacteria strains. AstraZeneca is another well-known enthusiast of Python. The pharmaceutical giant uses it in its smart software for collaborative drug discovery.

Common uses: outcomes prediction, disease detection, triage automation, image-based diagnostics, patient management, healthcare apps, drug discovery

Companies that use Python: AstraZeneca, UnitedHealth Group, Philips, Sema4, Novartis

Python for digital marketing

Digital marketing is a significant beneficiary of Python, which is natural, considering that data analytics lie at the core of any digital marketing strategy and activities. Depending on the scope of work, digital marketers might leverage Python-based apps and services for data mining and analytics, campaign planning and automation, report automation, customer insights, and many, many more tasks. 

Python's data science capabilities are used across various digital marketing fields, from website traffic analysis, through SEO and SEM campaigns, down to personalized email campaigns. Python improves marketing automation with readily available libraries that help manage repetitive data management processes and in-built modules that handle complex data science, AI, and engineering formulas.

If you look for specific use cases, here’s a handful. You can use Python code to program solutions that gather survey data, collect lead or subscriber information, or track competitor products, prices, and promotions. Python scripts are suitable for automating massive file operations or repetitive data formatting. They may also help analyze campaign efficiency on different media and make predictions on customer lifetime value, churn, conversion, and other key marketing metrics. 

A rich collection of Python libraries, such as Pandas, Matplotlib, Advertools, and Selenium, are useful for SEO tasks. They streamline activities related to data extraction and analysis, web scraping, and data visualization.

Common uses: campaign scheduling and automation, lead analytics, campaign insights, reporting, audience targeting, SEO

Companies that use Python: Facebook, HubSpot, Zapier, SEMrush, Mailchimp 

KEY TAKEAWAYS

• Python is extensively used in industries that rely on vast volumes of data.
• The language is favored by non-programmer users due to its high readability.
• Healthcare, pharmaceuticals, and finance and banking all benefit from Python.
• A large number of eCommerce and digital marketing platforms are Python-based.
• One of the greatest beneficiaries of Python technology is the real estate industry.
• Python is popular among startups who take advantage of its speed and flexibility.
• Its use for corporate, enterprise-grade applications is constantly increasing.

As of Q1 2021, The Python Package Index, the official repository for Python, lists nearly 300,000 Python projects, 2.5 million Python releases, and 4 million files. These numbers prove two main things. One, Python is ubiquitous. Two, it verges on the impossible to count the ever-growing number of its frameworks, libraries, and packages. 

Therefore, we’ll not go there. Instead, we will briefly go over the most popular and extensively used Python frameworks, tools, and libraries. The ones used by huge organizations to enhance large-scale application delivery and by agile startups to roll out MVPs and bring amazing apps to market in no time. 

Without further ado, here’s all you need to know about Python tech stack when you talk to product devs and system architects. Enjoy!

Python web frameworks

Before we look through particular frameworks, there’s one small distinction to be aware of at this point. Python frameworks may generally fall into two categories: micro-stack frameworks and full-stack frameworks. 

• Micro-stack frameworks – also known as micro frameworks. They are compact, simple, easy to use, and best suited to quickly build small apps, MVPs, and programs that are a part of a larger project. A few examples of micro frameworks include Flask, CherryPy, or Web.py.
• Full-stack frameworks – these are far more powerful and complex by nature. They provide everything developers need to build fully-fledged applications from the bottom up. Apart from delivering an extensive set of libraries, they also comprise other services, such as templating engines and data management components. Popular full-stack Python frameworks include Django, Web2py, and TurboGears.

Django. An all-around “batteries included” framework that has everything developers need to build web applications in Python.

Best for: creating end-to-end web applications fast and with less code
Features: routing, templating, database administration, built-in authentication, forms 
Advantages: comprehensive, fast, scalable
Full-stack or micro-stack: full-stack
Who uses it: The Washington Post, Udemy, YouTube, Instagram, NASA, Pinterest

Web2py was created to enable the rapid development of scalable, secure, and portable database-driven web applications.

Best for: data gathering and analytics applications
Features: debugger, code editor, ticketing framework, deployment tool, HTTP requests management, cookies, internationalization engine
Advantages: ease of deployment, cross-platform, handles uploading/downloading huge files
Full-stack or micro-stack: full-stack
Who uses it: PikHotel, Groupthink, Formatics, Tech Fuel

Turbogears enables Python engineers to facilitate web development and reduce coding effort using a range of plug-ins and standard components. 

Best for: blogs, wikis, maintainable database-driven applications
Features: templating, mapper, debugger,  authorization, CRUD, sessions, caching, plug-ins
Advantages: multi DB support, scalability, speed
Full-stack or micro-stack: full-stack (but can also act as a micro framework)
Who uses it: SourceForge, 1000 Corks, Bisque, The Voice of Turkey

Flask is a great choice for smaller, agile, and less complex applications, as it doesn’t impose the project structure on developers.

Best for: small and medium projects, urgent development
Features: URL routing, request and error handling, templating, unit testing, support for Google App Engine, debugger, development server
Advantages: simple, flexible, extensible
Full-stack or micro-stack: micro-stack
Who uses it: Disqus, Eventbrite, Netflix, Uber

A Python web framework and networking library, Tornado has been designed to handle thousands of asynchronous processes.

Best for: building applications that involve multiple simultaneous clients
Features: web templates, user authentication, translation and interpretation, networking library, coroutine library, 3-rd party authentication and authorization support 
Advantages: high performance, concurrent connections, small size
Full-stack or micro-stack: micro-stack
Who uses it: Zalando, Facebook, Delivery Hero, Uploadcare

Originally developed for building web APIs, Bottle is distributed as a single file module allowing fast and easy development of simple web applications.

Best for: simple, personal-use apps, prototype projects
Features: built-in web server, utilities, templating, directing
Advantages: lightweight, simple, can run standalone
Full-stack or micro-stack: micro-stack
Who uses it: Netflix, Scommerce, Paysa

CherryPy is “a minimalist Python web framework” that can function on its own or launch on any working framework with Python support.

Best for: startups, prototyping
Features: encoding, caching, sessions, authentication, static content, built-in profiling and testing support
Advantages: highly customizable, flexible, cross-platform
Full-stack or micro-stack: micro-stack
Who uses it: Netflix, Hulu, YouGov Global, Learnit

In its creators' words, Falcon is suitable for building high-performance microservices, responsive app backends, and higher-level frameworks.

Best for: RESTful APIs and microservices
Features: routing, unit testing, exception handling, HTTP error responses 
Advantages: extensibility, cross-platform compatibility, superb performance
Full-stack or micro-stack: micro-stack
Who uses it: Rackspace, LinkedIn, Leadpages, Wargaming

Pyramid’s greatest assets are flexibility and scalability. Developers can instantly build the skeleton architecture and take it from there, gradually adding features and components.

Best for: special-purpose, domain-specific apps of all sizes
Features: URL generation, templating, HTML structure validation, authentication, events and subscribers  
Advantages: minimalist, customizable, stable
Full-stack or micro-stack: micro-stack
Who uses it: SurveyMonkey, Mozilla, Yelp, Dropbox, Substance-D

Estimating the cost of Python projects

Below, we have listed the key cost areas that you need to factor in when evaluating the budget for your next Python-based application. 

Project type and scope

The first thing to consider when evaluating any software development project is the scope of work. To come up with a high-level overview of the necessary investment, clearly flesh out the desired end-product, together with its core features. This will also prove instrumental in outlining the project timeline and duration, which directly impact the budget. 

The type of application also determines the project cost. For example, banking or healthcare platforms will require additional features that ensure enhanced security and regulatory compliance. A powerful eCommerce platform needs AI integration for personalized recommendations and other customer-centric features. And so on.

Features and complexity

Once you get a ballpark estimate, you can drill down into particular features for an accurate cost breakdown. Put together a comprehensive list of end features. Their scope and complexity directly impact the time needed for delivery, the team size, and, consequently, the project cost. Standard functionalities involve less effort and investment than custom options. The more sophisticated and unique the features, the higher Python expertise they require to develop. The seniority of your development team will also impact the total cost. 

Don’t forget about prioritization. It will make it easier to trim down the requirements if the total cost estimation exceeds your planned budget. Drawing up a detailed feature specification at the start will help you minimize sudden expenses as you proceed. 

Tools and licenses

Accurate estimation of a Python project (or any software project, for that matter) must factor in the cost of tools used for its delivery. Unlike some other programming technologies, Python is a free software license; generic classes cost nothing to customize, and a vast majority of Python tools and libraries are cost-free. Nevertheless, it’s useful to determine the required toolkit at the start, at least when it comes to developing core features.

At this stage, you’ll also need to remember about external cost factors related to the development of any web- or mobile-based application. These include the domain name, a hosting server, and design templates (unless you wish to create a custom-built design from scratch, which will raise the cost even further). 

Design, development, and QA

This factor covers several crucial stages, from research, through architecture design and wireframing, up until the delivery of project mockups and the final project launch on the production environment. Depending on the established project size and scope, your Python team will include three to +12 people to handle these activities. 

Why is three the minimum? Unless we’re talking about delivering one or two extra features or a small extension project, you’ll need a project manager, a developer, and a QA specialist to efficiently proceed with your work. 

As your project gets bigger and more complex, it will require new roles to be fulfilled, including UI/UX designer, architect, business analyst, and more developers and testers. Obviously, the rates charged by each of these Python professionals bear upon the project cost. Considering that most of them won’t be involved full-time, outsourcing some of these roles is the preferred option for many companies.

→→ To check the standard rates charged by Python specialists worldwide, jump to Python developers’ rates. 

It’s also worth noting that the large selection of Python libraries, frameworks, and other built-in resources further reduces development complexity and drives down the final project costs. Thus, Python’s development speed makes it a perfect candidate for rolling out prototypes and MVPs fast and cost-effectively. However, this benefit becomes less prominent in huge, multi-team projects where other project factors increasingly come into play.

Software development pricing model 

If you decide to hire Python developers to bring your project to fruition, another cost factor to consider would be the contract type. Depending on your requirements and project scope, you may choose between three standard types of partner engagements or go for a hybrid variant:

• Fixed-price. You set the total project price based on the defined and approved work scope and the agreed deadline. In this arrangement, you pay the hired team for the exact work you ordered, nothing less and nothing more. Fixed-price is generally good for small, short-term projects with clearly defined scope, well-developed requirements, and limited budget. However, it gives you little to no control over the process, in addition to being inflexible.  
• Time and material (T&M). Here, you compensate the hired team for the number of hours spent on the project and any expenses incurred (such as licenses, third-party tools, travel costs, etc.). It’s a highly flexible model that makes an ideal match for Agile projects, granting you complete control on the project scope and alterations. This approach enables dynamic decision-making, but in return, it requires you to be much more involved. Due to its highly flexible nature, time and material are usually more demanding in the timeline and budget tracking.
• Outsourcing (dedicated team). Finally, the third engagement model boils down to extending your team without hiring and HR hassle. As in T&M, the billing is also based on the contractors’ hourly rates, with the addition of the management fee. The hired team works under your direction, and it can be scaled up and down easily, depending on the current project needs. Although it typically requires the biggest budget, this contract type is an excellent choice for long-term projects with a broad scope. Instead of providing ad-hoc, random people to take charge of your development, it serves you with a handpicked, dedicated team of specialists who quickly become familiar with your company goals and expectations and constitute a reliable extension to your core employees.

Scope of integrations

Software integration is one of the most labor-intensive and high-cost phases in software development. This process alone may set you back from $10,000 upwards. Large-scale, enterprise integrations can amount to $50,000-$100,000! Apart from integrating with multiple external systems and platforms, robust Python apps need to provide a database component and ensure swift and secure authorization and authentication for access.

Fortunately, Python’s ingenious design makes it extremely effective for integrating with other languages (such as Java, C++, Rust, and others). Usually, Python developers don’t need to rebuild their code to communicate with them. Instead, they can simply leverage the existing Python APIs and Foreign Language Interfaces. 

The same goes for third-party apps and interfaces. Python supports a variety of web protocols, including JSON, HTML, and XML. If you need to plug your app into PayPal or Amazon, provide support for the invoicing system, or get access to Dropbox or OneDrive files, Python will use secure APIs for that. As far as database connections are concerned, the language integrates with them very efficiently through readily available libraries.

Maintenance

Often overlooked, maintenance is among the key factors influencing the cost of any software project. Web and mobile applications need to be regularly updated for seamless user experience and tight security. This entails extra costs following the software launch. 

At the minimum, you’ll need to account for regular application updates, fixing any issues that may arise in the updated versions, and design and usability enhancements essential to meet the changing user preferences and reflect evolving design trends. Additionally, remember that if yours is a scalable app, any future updates and extensions will need to be budgeted individually. 

Contingency

Last but not least, don’t forget to include an adequate budget reserve in your estimate. It should be sufficiently large to account for both, the “known unknowns” and the “unknown unknowns” that may arise throughout the project. Depending on your project's complexity and duration, the contingency on your software project can amount to 20%-50% of the total estimated budget. 

Python libraries

Python tools’ ecosystem is so vast that listing all or even a majority of libraries verges on the impossible. Instead, here’s a rundown of top Python libraries and packages that you will likely encounter in your projects according to their field of application. 

Python data analysis libraries

One area where Python-based libraries find broad application is data analysis, or data science in general. Time and time again, Python technology comes up as the most popular programming language among data scientists. There are several reasons why Python and data science mesh so well, including the language’s flexibility, versatility, user-friendliness, and coding efficiency.

Python offers heaps of purpose-specific packages and libraries for data collection, exploration, manipulation, and visualization. Together, they enable users to play with all sorts of data available in various formats. Here are a few established Python libraries that allow complex data operations:

NumPy

NumPy is a Python library used for working with large, multi-dimensional arrays. It accelerates array processing and supports developers in dealing with complex mathematical implementations. Notably, a myriad of other libraries, including TensorFlow, PyTorch, Matplotlib, or Pandas, leverage it as the essential base underlying their computations. 

Characteristics:

• Broadly used across data science as well as machine learning projects.
• Finds an increasing use in the data visualization realm.
• Open-source, developed and maintained publicly by the GitHub community.
• Highly interoperable with a wide range of hardware, platforms, and libraries.
• Created for top performance, it retains simple and accessible Python syntax.

Typical applications: quantum computing, statistics, graphs and networks, Bayesian inference, simulation modeling

Industries: physics, mathematics, cognitive psychology, chemistry, bioinformatics, sports analytics, astronomy

Pandas

Sometimes called “the SQL of Python,” Pandas is a data analysis and manipulation tool optimized for performance and designed for easy use. It handles two-dimensional data tables in Python, allowing the user to load, select, group, and filter data, and perform various data functions.

Characteristics:

• It’s a foundational Python library for data analysis and statistics.
• Provides a wide array of built-in data input and output tools, which reduces coding. 
• Supports various file formats, from text and Excel files to SQL databases and JSON.
• Intelligent labeling enables data slicing, indexing, and subsetting of huge data sets.
• Pandas is free to use and released under the BSD license.

Typical applications: quantitative data analysis, data manipulation, big data processing 

Industries: finance, accounting, economics, neuroscience, statistics, advertising, web analytics

Pillow

A Python Imaging Library, Pillow enables image opening, saving, and processing. It supports different image file formats and provides all the basic image processing functionalities. These include image filtering and enhancements, transparency handling, format conversion, text manipulation, and so on.

Characteristics:

• Pillow forked off the Python Imaging Library repository for Python 3 support.
• It runs on all major operating systems and supports a variety of display interfaces. 
• In addition to basic functions, Pillow also delivers histograms for statistical analysis. 

Typical applications: image manipulation, statistical analysis

Industries: research and development, higher education, ITC, financial services, accounting

Matplotlib

When it comes to more sophisticated data visualization, matplotlib is the way to go for many projects. This comprehensive library built on NumPy arrays enables creating complex, quality, and interactive visualizations in Python. While matplotlib itself is huge and comprises thousands of code lines, it reduces the creation of quality plots down to a few commands.

Characteristics:

• Matplotlib can generate line plots, 3D plots, contour plots, and histograms, among others. 
• It offers broad customization options, e.g., for line styles, font, color schemes, etc.
• It’s extensible with third-party tools for added features.
• To ensure reliable performance, the library builds on NumPy.
• Matplotlib can interact with a variety of backends and platforms.

Typical applications: data visualization and modeling, quick data analysis, GUI

Industries: publishing, insurance, research, healthcare, eCommerce, social media

Python machine learning libraries

The next group of Python libraries helps developers build robust predictive analytics and machine learning platforms. It also comprises tools that are instrumental in developing natural language processing software and components. Let’s review the most popular of them.

TensorFlow

One of the most popular Python tools, TensorFlow, is an end-to-end deep learning library that eases the development and training of machine learning models. It was created to define and run computations using tensors, so neural networks' primary data structure. This broad and flexible ecosystem of tools, libraries, and community resources is mainly used by researchers and developers to power ML algorithms and perform complex computational operations. 

Characteristics:

• Intuitive and quick to use thanks to high-level APIs and frequent updates and releases.
• Highly flexible, with modules that can be used standalone or stacked together.
• Designed to use various backends, with models deployable on-prem and in the cloud.
• Developed by Google, the library is open and boasts a huge community of contributors.
• Immensely popular and used by Twitter, Uber, LinkedIn, Google, Coca-Cola, Lenovo, Xiaomi, Airbnb, and PayPal, among thousands of other companies.  

Typical applications: image captioning, data classification, voice recognition, text-based applications, video and motion detection, deep neural networks

Industries: manufacturing, healthcare, e-commerce and retail, telecommunications, ITC, transport and logistics, FinTech and banking, social media

Scikit-learn

Another essential Python library in the context of machine learning is Scikit-learn. It provides a selection of dedicated methods for predictive data analysis, including clustering, regression, data classification, model selection, and more. Provided that a developer uses quality data that has been properly selected, cleansed, and formatted, running machine learning algorithms with Scikit-learn to perform the required operations is pretty straightforward.

Characteristics:

• Scikit-learn focuses on data modeling and supports most supervised and unsupervised learning algorithms.  
• It is an ideal tool for advanced image processing, such as extracting features.
• The library leverages NumPy, sciPy, and matplotlib.
• It prioritizes UX by minimizing dependencies and delivering out-of-the-box features.
• Users include betaworks, Spotify, Change.org, JPMorgan, Evernote, and Booking.com

Typical applications: predictive analytics, image recognition, data plotting, decision trees, outlier detection, topic extraction, data modeling, prototyping

Industries: academia, finance and banking, eCommerce, digital media, healthcare and medical imaging, online learning, software engineering, sales & marketing

Keras

Keras is a simple and flexible Python library designed to reduce the user’s cognitive load and simplify coding while creating high-level artificial neural networks. The highly productive library acts as an interface for TensorFlow, facilitating the development and evaluation of deep learning models. 

Characteristics:

• Keras’s guiding principles include minimalism, modularity, and flexibility.
• Designed to enable fast experimentation, the library’s a great fit for prototyping.
• Keras can be easily extended with custom building blocks.
• It allows deep models to be run on mobile, the web, and virtual machines.
• NASA, CERN, Facebook, Verizon, and JPMorgan are among Keras users.

Typical applications: low-level tensor operations, scaling computations, deep learning, computer vision, NLP, prototyping

Industries: higher education and research, medicine, biotechnology, software manufacturing, telecom, startups 

PyTorch

Originally developed by Facebook’s AI Research lab, PyTorch shares many similarities (and components) with TensorFlow. Robust yet flexible, it offers a quick and agile solution for fast-paced deep learning and NLP projects. A native Python technology by design, it takes what’s best after the programming language – modularity, simplicity, and lean approach to software development. 

Characteristics:

• Like many other Python ML libraries, PyTorch emphasizes flexibility and ease of use.
• It can be extended with other libraries and packages to get more functionalities.
• PyTorch enables a smooth transition between CPU and GPU. 
• It also provides a robust platform for the generation of dynamic computational graphs.
• Companies using PyTorch include Facebook, Microsoft, Udacity, Salesforce, Airbnb.

Typical applications: computer vision, NLP, machine translation, dialog assistance, neural networks, AI prototyping

Industries: education, SaaS development, mathematics, linguistics, cancer medicine, car manufacturing 

Other Python libraries worth knowing

• Requests – A neat and simple standard Python library for making and customizing HTTP requests. 
• Theano – A library and compiler built on top of NumPy for defining, optimizing, and evaluating mathematical expressions that involve multi-dimensional arrays. 
• StatsModels – A module handling statistical module analysis and estimation, statistical data exploration, and statistical testing. 
• Scipy – An ecosystem comprising a bundle of packages for scientific computing, 2D plotting, data structures and analysis, and high-performance computation. 
• Plotly – This library is used for creating interactive graphs and dashboards in 
• Beautiful Soup – A Python HTML parser used to easily scrape information from web pages. 
• Scrapy – As the name suggests, this tool is quite similar in its use to Beautiful Soup. However, it offers some extended features. 

KEY TAKEAWAYS

• Python has a colossal ecosystem of frameworks, libraries, and packages.
• Developers use them to ease and speed up coding and reduce code size.
• The abundance of dedicated tools makes Python a great fit for machine learning projects.
• Another area where the language excels is data analytics and visualization.
• Python also comes with a standard set of testing tools and GUI toolkits.
• A sweeping majority of Python packages, libraries, and tools is completely free.

Why does Python live on land? Because it’s above the C-level. 

Behind every bad joke, there’s always some truth. As one of the top languages used worldwide, Python stands above many programming technologies in terms of ease of coding, library support, popularity, code size, and readability. 

But while it excels in many areas, Python is not an answer to all project needs. The language gives way to its counterparts in some respects. Understanding the core differences between Python and other popular programming languages will make it easier for you to decide which of them meets your specific business requirements.

Comparing Python to other languages

We already know Python’s strengths and weaknesses. So let’s see how it stacks up against other technologies. To determine this, we’ve assessed factors like performance and stability, ease of use, coding speed, available talent pool, and a bunch of more in our comparison below.

Python vs. Java 

We start from the most common technology comparison, i.e., that of Python versus Java. Both languages have been around for decades, and they share many similarities (such as strong cross-platform support, vast communities, and rich libraries). So how to decide which of them better fits your project?

Key features

Python and Java are both primarily object-oriented languages. As a result, their code is reusable, efficient to build, and relatively easy to maintain. However, one major difference is that Python is an interpreted and dynamically typed language. As a result, it benefits from highly readable and compact code, which boosts programmers' productivity. On the minus side, this feature of Python makes it slower than compiled languages, like Java. 

Java’s performance depends on various factors, including version, hardware architecture, vendor, and OS. Overall, the language tends to demonstrate slightly better performance than Python. However, it still lags behind natively-compiled technologies like C or C++. Additionally, Java development requires significantly more code than Python, resulting in more time needed to review and maintain the code.

Performance and speed of coding

The requirement to use an interpreter instead of a compiler adds overhead to Python’s processing rate. Additionally, Python 2 is incompatible with Python 3, affecting coding speed, particularly for junior developers. Still, in many projects, the performance delay is insignificant, especially if we factor in other Python advantages, such as the low entry point and cost-efficiency.

As a compiled language, Java launches slower but runs faster than Python. Since its code is checked before running, fewer errors usually occur at runtime. On the flip side, Java usually requires more work and code than Python to get things working. Its code is rather verbose and hard to read, which affects all stages of project development, from programming down to testing and updates. This also means that in many cases, when an issue emerges, Java developers will program things from scratch rather than trying to debug code. 

Learning curve

Clean code, simple syntax, robust community, and extensive library support add to Python's mild learning curve. The language has been designed for quick and easy learning, making it a suitable choice for beginners. Additionally, Python is a technology that business professionals such as data analysts or project managers can easily understand and analyze, which can considerably streamline project delivery.

With a complex and broad-ranging programming environment, Java sits on the other end of the learning curve. As a result, it presents many challenges for junior developers. Learning it involves gaining a deepened understanding of APIs and design patterns, which takes time. 

Fortunately, aspiring Java experts can rely on numerous free courses, tutorials, and interactive resources for learning. Additionally, even though the entry point for Java is pretty high, once the initial concepts have been grasped, the learning curve becomes milder.

Applications

Large developer teams working on enterprise-grade software frequently turn to Java due to its stability and resilience. The language can also support AI and machine learning projects, but Python is much more common - and advantageous - in these applications. 

While Java often powers web development and desktop GUI applications, Python makes the top choice in data science projects. It is also widespread in rapid prototype creation, MVPs, and quick ideation projects.

Community 

The gigantic community of 12.5 million developers makes Java the most popular programming language in the world. Nearly 68% of software professionals surveyed by Stack Overflow know it. Consequently, the language has an enormous ecosystem of well-tested frameworks and libraries that cater to thousands of implementation use cases.

However, Python treads on Java’s heels. With 9 million users worldwide, it is one of the most popular, loved, and used technologies. In addition, the current count for projects developed in Python is ca. 300,000, which further attests to the language's extensive use. 

The verdict

Python and Java are both influential languages, which provide superior results in their chosen fields of use. Overall, Java offers greater stability and is better suited to handle large, enterprise-grade projects spanning multiple development teams. On the other hand, Python is agile, dynamic, and intuitive - ticking all boxes to build prototypes fast and iterate quickly.

If you follow agile practices or need to build software related to data science, AI, or ML, then go with Python. The same applies to small-scale apps with quick turnaround cycles. However, for most enterprise solutions and desktop GUIs, Java would be the standard choice.

Python vs. Ruby/Ruby on Rails

Next on our list is Python versus Ruby comparison. Ruby is a high-level, interpreted, general-purpose programming language developed in the mid-1990s by Yukihiro Matsumoto. In the words of its creators, Ruby “focuses on simplicity and productivity. It has an elegant syntax that is natural to read and easy to write.”

Key features

Ruby supports object-oriented programming, among other paradigms. Like Python, it is open source, so no license fee is required to use it. And similarly, it is a cross-platform and multi-purpose technology with a broad spectrum of applications, ranging from web development, data analysis, prototyping, and MVPs. Another common feature of the two languages is prioritizing programmer convenience and coding speed through easy-to-read syntax.

Performance and speed of coding

Python and Ruby are interpreted languages, which means they are inherently slower than compiled technologies like Java or C#. Nevertheless, they offer a range of optimizations (CPU enhancements, memory optimizations, garbage collector improvements) to make the code run orders of magnitude faster. And when it comes to development speed, that’s where both Python and Ruby get a head start.  

Ruby is easy and fun to use (especially Ruby on Rails). The “convention over configuration” approach applies defaults in the code, accelerating development and reducing developers' efforts. Another Ruby-related concept is Rails "magic," a collection of abstract patterns that handle complex configurations such as setting up a server environment or wiring models and controllers. These patterns help reduce manual coding and make project development more efficient.

Compared to Python, Ruby grants developers greater freedom. Nevertheless, while the fact that there are always several ways to achieve the same result often positively affects the developer’s experience, it may hinder efficiency. Too much autonomy can lead to unnecessary code complexity and project delays. Additionally, being one of the most permissive programming technologies means that Ruby can occasionally be unstable. 

Learning curve

Ruby's focus on simplicity and productivity streamlines the learning process and lowers entry barriers, especially for the Rails framework. In addition, the “convention over configuration” philosophy provides essential guidance for new developers and helps them build quality code from the start.

However, because the language uses a unique approach to coding, understanding the principles of Ruby magic may initially take some time. The modest community size also means that budding Ruby developers may have it harder to start than Python novices. Hands down, Python is much easier to grasp for beginners.

Applications

Ruby was built from the ground up to power front-end and back-end web applications. And it comes with a variety of frameworks that support this goal. The principal one is called Ruby on Rails, and it facilitates the development of database-backed web apps.

Yet, as already mentioned, the language serves various purposes. Common examples include data analysis, prototyping, and proof of concepts. Sounds familiar? Yes, these are the chief areas of application for Python, too. 

And when we think of companies taking advantage of Ruby and Ruby on Rails, their list has no end. From hospitality services (Couchsurfing, Airbnb), through fundraising and financial sites (Kickstarter, Indiegogo), to social media and streaming apps (Twitter, Hulu, Twitch) - hundreds of leading organizations build websites and web apps with Ruby.

Community 

When we look at the community, though, Ruby doesn’t stand a chance with Python. Only about 7% of developers worldwide know Ruby. This number is insignificant when compared to more than 44% of professionals using Python. At the same time, although small, the Ruby community is well-knit and faithful to the technology. That’s because the language has been focused predominantly on web development and functional programming from the get-go. 

Most Ruby experts work with the Ruby on Rails framework, but less popular tools like dry-rb, mRuby, or Hanami have been getting traction lately. All in all, it’s fair to say that while Python supporters are thick on the ground and diverse, Ruby followers seem to be more loyal and committed.

The verdict

In a nutshell, Python and Ruby are like a pair of siblings - they share some DNA but follow their own, separate paths in life. Ruby makes a great fit for web app development. Python, on the other hand, offers a much wider range of applications. 

Thanks to fast performance, Ruby deals better with high-traffic sites and apps. On the other hand, Python stands out in applications that involve math and scientific operations conducted on massive data volumes. 

Python vs. Julia

Julia is a rising star in numerical analysis, data science, and machine learning programming. It was built from the ground up for high performance, offering blazing execution speed. What else makes it a worthy contestant to Python, and how to determine which language makes a better choice in your case? Let’s see.

Key features

Composable, dynamically typed, compiled before runs, and open source - this is Julia in essence. The language offers reproducible environments where you can repeat experiments without having to build code from scratch. Julia combines the benefits of dynamic typing and static typing; it is extremely interactive; and follows the same simple, easy-to-read syntax as Python. 

Performance and speed of coding

Designed to be ultra-fast, Julia beats unoptimized Python to the punch when it comes to the speed of execution in its target domains (i.e., data mining, machine learning, and scientific computing). In addition, code compilation allows Julia to solve many of the performance issues frequently encountered by Python. 

The language has a full release cycle, including LTS (long-term support) releases, stable, and beta/nightly builds. So far, there have been no breakthrough releases that would cause any major disruptions to Julia’s stability. This may - though doesn't have to - change with version 2 in the future.

Learning curve

Top speed, robust performance, and ease of use make Julia the favorite language for 73% of data science developers. Nevertheless, the language has rudimentary documentation, which focuses strictly on data science and machine learning applications. As a result, applying it to other fields is incredibly ambitious.

Besides, using Julia even for its dedicated uses requires much effort on the developers’ part. Unfortunately, the nano community also doesn't help aspiring Julia developers, as there aren't many colleagues to learn from if you’re a newbie. 

Applications

One of Python’s core strengths is that it’s extremely versatile. It can support web development, automation, web scripting, and even enterprise app programming. 

In theory, Julia is capable of the same. But while it’s true that Julia comes with a range of non-scientific packages, in practical terms, it is meant for use in scientific programming and not much beyond it. 

Community 

Julia's supporters are steady, committed, but in short supply. Data scientists are the top users; hence, the language’s adoption in commercial sectors remains insignificant. And while two in three Julia users love it, their total count is drastically low. For now, a humble 0.9% of developers know Julia, making it the least popular programming language. It will take a long time before it turns into a computational science mainstay if this is ever going to happen.

The verdict

Created specifically for conducting scientific calculations and machine learning operations, Julia outshines Python in performance in these fields of application. That’s why many believe it might be ultimately replacing it in the long term. 

Still, the technology is not recommended for projects outside of the data science and ML scope, as it requires explicit customizations and coding from scratch. On top of that, the meager community size and lacking documentation are the main obstacles to Julia’s widespread adoption. Also, Python is far more mature. It has been around for three decades now and is not likely to disappear any time soon. 

Python vs. C#

Python and C# are an entirely different kettle of fish. The former was created as open-source code from the onset, it powers agile, dynamic websites and web apps, and it’s a dynamically interpreted language built for developers’ convenience. The latter originated in the Microsoft solutions ecosystem and went open merely a few years ago. 

C# was developed as a general-purpose programming language for the CLI, and it is statically compiled for maximum performance and stability. But, is it enough to beat Python’s merits?

Key features

C# is a well-established, powerful programming language that inherits from C & C++ while retaining simplicity. Developed by Microsoft, it shares many similarities with Java. 

The language encompasses static typing, imperative, and functional programming disciplines. It can be compiled on different platforms and finds a broad application in mobile and desktop apps, websites, cloud-based services, enterprise software, and game development. Like Python, C# is object-oriented.

Performance and speed of coding

Microsoft-backed and mature (it was released over 20 years ago), C# is remarkably stable and consistent. The language imposes constraints on developers to create reliable and robust code. As a compiled language, it is also faster than its interpreted counterparts, including Python.

That said, C#, like Java, achieves stability at the expense of code size and readability. Despite a highly organized structure, its syntax can be quite complex; for example, C# uses indentation, semi-colons, and curly brackets, and it is case-sensitive. These features generate hundreds of lines of extra code that take up valuable space - and require additional time to produce.

Another factor affecting the implementation speed with C# is that the language is a stickler for rules. Miss a bracket or use the wrong variable type, and you're in for big trouble. As a result, developers need to validate each coded line. The added workload mounts the pressure to meet the deadline.

Learning curve

Although not as straightforward as Python, C# retains a relatively mild learning curve. The language offers lots of learning materials available for free, its community of developers is quite extensive, and many complex tasks are abstracted, so the aspiring programmers don't need to figure out how they work. 

Still, C# has its unique challenges. For example, one needs a thorough understanding of certain principles and design patterns before starting to code. Besides, mastering C# goes beyond getting familiar with its syntax. To use the language, developers need to comprehend the entire Microsoft ecosystem, which may be overwhelming. Especially in the beginning.

Applications

Based on its background, C# is the language of choice for developing Microsoft Windows apps. Large corporate teams especially favor it when building robust, extended business applications. But when we look at science computing, C# yields to Python and its enormous number of frameworks and libraries for data science, machine learning, AI, and computation. 

Community 

Mature tooling, extensive library ecosystem, and active developers' community - C# has it all. About one-third of the world's developers use the language in their projects, which places it among the most popular programming languages (after Java and Python). 

Additionally, C# developers can count on Microsoft support if they get stuck with programming or addressing code issues. That assistance may turn out priceless when project deadlines are nearing. Unfortunately, that’s something Python cannot provide.

The verdict

C# performs best in complex, enterprise-grade corporate software. The language is extremely powerful, allowing developers to build sophisticated, high-performing programs and web apps. Of course, for projects involving Microsoft integration or reliance on enterprise standard syntax and libraries, C# is the way to go.

Theoretically, C# can most likely do whatever Python can do, and with better performance. However, its barrier to entry is significantly higher than that of Python. Many developers also find Python easier and faster to code. Therefore, for fast-paced, experimental, or showcase projects, we suggest going with Python. Also, when it comes to machine learning and data science, Python would be the winner.

Python vs. C#

Python and C# are an entirely different kettle of fish. The former was created as open-source code from the onset, it powers agile, dynamic websites and web apps, and it’s a dynamically interpreted language built for developers’ convenience. The latter originated in the Microsoft solutions ecosystem and went open merely a few years ago. 

C# was developed as a general-purpose programming language for the CLI, and it is statically compiled for maximum performance and stability. But, is it enough to beat Python’s merits?

Key features

C# is a well-established, powerful programming language that inherits from C & C++ while retaining simplicity. Developed by Microsoft, it shares many similarities with Java. 

The language encompasses static typing, imperative, and functional programming disciplines. It can be compiled on different platforms and finds a broad application in mobile and desktop apps, websites, cloud-based services, enterprise software, and game development. Like Python, C# is object-oriented.

Performance and speed of coding

Microsoft-backed and mature (it was released over 20 years ago), C# is remarkably stable and consistent. The language imposes constraints on developers to create reliable and robust code. As a compiled language, it is also faster than its interpreted counterparts, including Python.

That said, C#, like Java, achieves stability at the expense of code size and readability. Despite a highly organized structure, its syntax can be quite complex; for example, C# uses indentation, semi-colons, and curly brackets, and it is case-sensitive. These features generate hundreds of lines of extra code that take up valuable space - and require additional time to produce.

Another factor affecting the implementation speed with C# is that the language is a stickler for rules. Miss a bracket or use the wrong variable type, and you're in for big trouble. As a result, developers need to validate each coded line. The added workload mounts the pressure to meet the deadline.

Learning curve

Although not as straightforward as Python, C# retains a relatively mild learning curve. The language offers lots of learning materials available for free, its community of developers is quite extensive, and many complex tasks are abstracted, so the aspiring programmers don't need to figure out how they work. 

Still, C# has its unique challenges. For example, one needs a thorough understanding of certain principles and design patterns before starting to code. Besides, mastering C# goes beyond getting familiar with its syntax. To use the language, developers need to comprehend the entire Microsoft ecosystem, which may be overwhelming. Especially in the beginning.

Applications

Based on its background, C# is the language of choice for developing Microsoft Windows apps. Large corporate teams especially favor it when building robust, extended business applications. But when we look at science computing, C# yields to Python and its enormous number of frameworks and libraries for data science, machine learning, AI, and computation. 

Community 

Mature tooling, extensive library ecosystem, and active developers' community - C# has it all. About one-third of the world's developers use the language in their projects, which places it among the most popular programming languages (after Java and Python). 

Additionally, C# developers can count on Microsoft support if they get stuck with programming or addressing code issues. That assistance may turn out priceless when project deadlines are nearing. Unfortunately, that’s something Python cannot provide.

The verdict

C# performs best in complex, enterprise-grade corporate software. The language is extremely powerful, allowing developers to build sophisticated, high-performing programs and web apps. Of course, for projects involving Microsoft integration or reliance on enterprise standard syntax and libraries, C# is the way to go.

Theoretically, C# can most likely do whatever Python can do, and with better performance. However, its barrier to entry is significantly higher than that of Python. Many developers also find Python easier and faster to code. Therefore, for fast-paced, experimental, or showcase projects, we suggest going with Python. Also, when it comes to machine learning and data science, Python would be the winner.

Python vs. Golang

Publicly released by Google in 2012, Go (Golang) emerged to rectify the defects of other widespread programming languages. It was supposed to retain C’s static typing and runtime efficiency, match Python’s readability and usability, and add a dash of high-performance networking and multiprocessing. Was this attempt successful?

Key features

Go is a statically typed, compiled programming language created and maintained by Google.  It takes after the C-family programming languages, adding a garbage collector on top to handle memory leaks. One of the principal assumptions behind Go’s creation was to ensure superior readability and simplify code development. The goal was to get a performance and speed boost and make programming easier. 

Performance and speed of coding

Go is quite a new technology. As such, it includes plenty of modern features to improve stability, such as garbage collector, memory safety, or structural type system. As a compiled language, Go is also extremely performant. Evidence shows that it can work up to four times faster than Python or Java. Small syntax and support for concurrency additionally further boost its execution speed. 

But when we examine coding speed, things don’t look quite as rosy anymore. Compared to Python, Go’s code is much more verbose, requiring more development work. For example, a function that takes a few lines in Python eats up much more space when delivered in Go. For that reason, Go is not suitable for experimental development and quick project validation.

Learning curve

For developers who are familiar with C or Java, Go will be quite easy to master. However, complete beginners may initially struggle with understanding some of its concepts. Still, Go's syntax is compact and clean, and like Python, the language prioritizes code readability. This makes it learner-friendly and lowers the barrier to entry.

Applications

Originally, Go was created for system programming, but the range of its applications has expanded since. The language provides great support for scalability, which makes it suitable for working with large data sets. It is also a popular choice for implementing microservices-based software architectures, cloud computing apps, and cluster computing software. Many high-performing e-commerce sites also make use of Golang.

Community 

Barely a teenager, Go is still a relatively young language, so understandably, it cannot match Python in community size and access to resources. As a result, developers may occasionally find it hard to get the required tools.

But Go's young age also speaks to its advantage. All the existing code and resources are relevant and up-to-date, and they are well-suited for modern project needs. And the language's community, though compact (only about 9% of developers worldwide use Go), is very active. When we consider documentation, Golang comes with a comprehensive programming ecosystem comprising editors, IDEs, and plugins that speed up and facilitate coding.

The verdict

On the surface, Go and Python are quite dissimilar. But, in reality, they share some fundamental approaches. Both languages serve the purpose of making programming as fast and efficient as it gets. They also focus on streamlining the developer’s experience and supporting emerging technologies.

Go takes the win when we consider code execution performance and speed. By contrast, Python comes out ahead in community support and language versatility. In many cases, Go makes a valid alternative to using Python. The ultimate choice between the two depends on your project size, goal, and priorities.

Python vs. SQL

You may think that setting Python and SQL side by side is like comparing apples to oranges. That’s because Python brings associations with data science software, software prototypes, or full-scale web apps, whereas SQL is a query language. 

But one of Python’s greatest merits is that it can be used for conducting relatively simple tasks, like retrieving and presenting data in various formats from a collection. And that’s precisely the perspective we will assume to make the Python versus SQL comparison.

Key features

SQL, or Structured Query Language, dates back to the 1970s (!). It was created to serve one main goal: to define queries that retrieve specific data from database tables. This standardized language is an essential tool for anyone working with relational databases, reporting, and dashboard analytics. SQL statements perform tasks such as data update and retrieval from common relational database management systems (including Oracle, Microsoft SQL, or Amazon RDS).

Performance and speed of coding

As a powerful query technology used to communicate with databases, SQL is highly performant by design. It allows retrieving huge volumes of data quickly and efficiently and conducting various data operations in no time. Moreover, the language can handle a large number of transactions simultaneously and allows to carry out complex database queries comprising multiple steps.

At the same time, SQL’s predefined and inflexible data model requires introducing major adjustments on every data migration or data model change. The same applies to managing data conversions between SQL and other languages - it can get tough, so the process requires some expert skills.

Learning curve

SQL is a language that needs a whole different mindset than most 'typical' programming languages. It doesn't generate countless lines of code. Instead, it uses a handful of operators and basic syntactic rules. 

While extensions of the main queries exist, to conduct most database operations, it’s enough to use a handful of operators, like “Select,” “Insert,” “Update,” “Delete,” etc. From this perspective, SQL is fairly straightforward, even for non-programmers. Additionally, as a standardized language, SQL offers robust support to all learners and users. 

But while we may risk a statement that SQL is SIMPLER than Python (fewer concepts, smaller grammar, user-friendly syntax), it doesn’t mean it’s EASIER to learn. Surely, up to a certain level, the effort required to get familiar with a few SQL commands is much lower than getting into the nitty-gritty of Python. But getting proficient at SQL is certainly not a walk in the park.

Applications

Examples of industries that rely on SQL include finance: banking apps, payment processors, reconciliation engines; social media: managing profile information, applying database updates (e.g., when a new photo is uploaded); media & entertainment: retrieving songs/books from the library, storing and updating user preferences, and many more.

But even though SQL is one of the most widely-used languages in business, it rarely comes standalone in applications. Business intelligence tools, database queries, and back-end data science apps all build on SQL, but not as the sole technology.

Community 

Well-known, popular, and established, SQL has a gigantic worldwide community. Nearly 57% of developers know it. The most popular SQL databases like MySQL, Oracle, PostgreSQL, or Microsoft SQL Server have millions of independent and corporate users. 

The SQL ecosystem is used by over 60% of organizations globally. So despite recurring rumors about the technology's demise, it looks like SQL has retained business relevance, especially as it is increasingly applied in big data analytics and data science projects.

The verdict

As of today, SQL remains the go-to language for database queries. Using Python or another language just to mimic the SQL functions in most cases would be redundant. However, SQL is only capable of pulling data and combining it from multiple tables. Suppose you need to conduct more complex operations, like manipulating data or transforming it to other formats. In that case, the language won’t meet your needs, as it offers a limited set of commands. 

That’s where Python steps in, with its ability to experiment with large pools of data. It offers many purpose-built frameworks and libraries for data analysis, manipulation, and visualization; they work particularly well with structured data sets. As a result, the sky's the limit when it comes to Python-based data management. Of course, provided that you have a team of expert Python developers on board.

Python vs. Rust

Compared to Python, Rust is a completely different beast, not only because it is a compiled language. Indeed, the principles underlying Rust are anything like Python paradigms. But despite core differences, some commonalities between the two programming languages do exist. What are they? Find out below.

Key features

According to its creators, “Rust is blazingly fast and memory-efficient.” Certainly, the language promises to solve pain points present in many other languages, such as performance lags and high memory consumption. At first glance, its syntax is very similar to that of C/C++  but with improved memory safety, easier concurrency, and pedantic compiler checks. 

As a result, Rust enables writing stable and secure code very fast. Additionally, the language is memory-efficient. Also, by reducing the risk of obscure code, it encourages long-term maintainability. All of that positively impacts your project.

Performance and speed of coding

Rust takes pride in being a highly productive language. It offers diverse integrated tooling that speeds up code development with features like auto-completion and multi-editor support. 

Like Go, Rust belongs to the younger generation of programming languages, and it applies similar methods to achieve top reliability and performance. These include the garbage collector, for more performant memory access, and compilation directly to machine code, for increased coding speed. Besides, Rust makes it possible to add performance optimizations. For that reason, it’s a perfect candidate for projects that prioritize speed and rely on CPU-intensive operations.  

At the same time, Rust is still a low-level language. Consequently, it generates much more code than Python, which lengthens development time. Also, the need for external dependencies can be a potential issue for developers working with Rust. Finally, Rust’s framework and library documentation can be very confusing. 

Learning curve

Rust has its quirks, and some of its concepts like ownership and aliasing control are completely new to most developers, even those experienced in other technologies. These robust features require much more time to understand than Python basics. Even elementary things like splitting code into different files require getting familiar with long pages of Rust documentation. It can probably take weeks before a newbie feels comfortable using the language. 

For that reason, Rust may be quite hard to pick up on the go. It is more suited for professional programmers with a solid grasp of other languages. That’s the exact opposite of Python, which doesn’t require any coding skills. 

Applications

Rust code conforms to four main concepts in programming: procedural, parallel, functional, and OOP. Therefore, Rust is a universal language that can be applied in many areas. These include programming client applications and web servers, blockchain, building own operating systems, writing engines for browsers and games, and many others.

Community 

Even though Rust remains a niche technology - known by only about 5% of developers worldwide - most of its users are die-hard fans. A staggering 86.1% of Rust users love it. This makes it the most loved programming language in the world, surpassing TypeScript and Python. 

Furthermore, the language’s adoption rate is steadily growing. Considering the support Rust provides for cutting-edge implementations, we might see it permeating the mainstream shortly.

The verdict

Because they are quite different, we wouldn’t consider Python and Rust as competing technologies. Instead, both languages have their specific fields of application and unique strengths.

For example, Python shines at scientific computing, which isn’t something that Rust is likely to be doing any time soon. Likewise, it doesn't make much sense to write shell scripts in Rust if you can implement them easily in Python. 

Still, Rust is a very powerful language with a solid foundation, liberal license, supportive community, and a democratic approach to language development. Its future is bright, and if you are interested in developing your project in Rust, we are happy to help!

Python vs. Bash

Bash, available by default on Linux and Mac OS devices, is not really a programming language. Instead, it’s a user shell that interprets user commands and exposes an OS's services to a human user or another program. 

But the reason we included it in this list is automation. Like Python, Bash can be used to automate the execution of tasks and reduce manual effort. So how does it look side by side Python in this context?

Key features

Bash typically runs in a window terminal to read and execute shell script commands. It can also be used as a CLI programming language to perform basic tasks. Bash provides a simple yet powerful way of interacting with the device to perform calculations, manipulate files, and launch programs from specific directories, among other tasks. The shell requires no third-party apps to be installed and does not deal with any frameworks. 

Performance and speed of coding

Bash launches quickly (faster than Python) but has poor execution time performance. However, it is sufficient for the uncomplicated types of operations that Bash traditionally performs. 

The language was first released over three decades ago, and it is pretty stable, as updates occur rather infrequently. It comes preinstalled in most Unix systems and can be directly typed in the command line. 

Bash lacks sophisticated functions, objects, and data structures, which facilitates coding. For the same reason, it’s impossible to accomplish many computations or data tasks using Bash. In any operations that involve managing files and streams of text, Bash is sometimes a quicker and more efficient alternative to Python and other languages. But its simplicity greatly limits its use in more difficult scenarios.

Learning curve

Very simple and practical, Bash is intuitive and easy to use. There are plenty of resources, articles, and tutorials available on the Internet to support learners. Usually, they are sufficient to gain a fundamental understanding of Bash within a few days.

A person with no programming experience may initially struggle with understanding how scripts and commands work. Yet, Bash remains one of the easiest technologies to learn, especially for people with some prior knowledge of programming languages. However, knowing and using Bash are two different things. The syntax for more complex general-purpose programming tasks can be confusing and unintuitive, even for seasoned programmers.

Applications

Bash scripts serve many purposes, like executing shell commands, automating simple tasks, or running multiple commands at once. In addition, they make an admin's life easier by streamlining some activities and dealing with large amounts of text and files. However, it's usually more beneficial to write scripts using a programming language like Python when things get more complex.

Python can be used for writing shell scripts using projects like scriptine. There are several advantages of such an approach. First of all, replacing complex shell scripts with Python code often simplifies coding and makes it more efficient. Secondly, Python offers more granular controls over memory use in apps that go beyond shell programming. Finally, using Python means you can also deploy some of its data calculation or analytics features, which isn’t possible with pure scripting. 

Community 

According to Stack Overflow's developers' survey, one in three developers knows Bash. Nevertheless, Bash knowledge is usually more of a side effect of managing servers and automating processes, so it's hard to speak of the Bash community as such. Normally, there's no need to look for dedicated Bash developers.

The verdict

With Bash and Python, it shouldn’t be either one or the other. Simple administration tasks (like creating a directory or deleting a file) can be handled easily by a Bash script. But when managing complex data structures or running sophisticated calculations quickly, you will be far better off with Python. Additionally, Bash lacks essential features and resources to support agile programming, so it won’t be a good choice if you’re following that approach.

Python vs. R

When it comes to data science programming, Python and R go neck and neck. They both are open source, modern, and loved by their communities. With so much common ground, what are the differentiators?  

Key features

R was developed nearly 30 years ago by academics and statisticians. As a result, it has a rich ecosystem of libraries and packages for deep analytics, statistical analysis, and data visualizations. The language relies heavily on statistical models, offers cross-platform support, and can perform complex operations on various data types, from vectors and arrays to matrices and images.

As an interpreted language, R doesn’t need a compiler. Like Python, it offers full compatibility with many other programming languages. R also interacts smoothly with databases.

Performance and speed of coding

Even though R was created specifically for developing statistical software and carrying out data analytics tasks, its performance leaves a great deal to be desired. For example, packages are much slower than those of MATLAB or Python. Additionally, the language uses much more memory than other technologies, especially when working with large data sets.

Still, thanks to various built-in functions, checking statistical hypotheses often takes only a few lines of code. Moreover, basic statistical methods are implemented as standard functions, which further accelerates development. Comfortable and clear language constructions are a definite advantage for non-professional programmers. Overall, when used in its target domain, R is a highly productive language for development.

Learning curve

Despite all its benefits, R often has a reputation of being difficult to learn. It certainly needs some time to get accustomed to, especially for learners with no prior knowledge of programming and data science. 

Compared to other statistical analysis technologies, R poses a challenge because it requires a more thorough understanding of coding principles. As a programming language, it deviates from some standard conventions that Python or its counterparts often follow. For many learners integrating the realms of software development and data science may create difficulty.

Applications

R comes with a massive collection of packages and test tools for the fast delivery of data analytics, visualization, and processing projects. As a language conceived by statisticians, it excels at statistical computing, predictive analytics, and data science. Like Python, R can scrape data from the web and subsequently cleanse and correct it. 

Additionally, R is extremely robust when it comes to data visualization. It’s capable of creating virtually any type of custom graph, mainly thanks to its ggplot2 library. 

Community 

One of R’s strongest qualities is the vast size of its package ecosystem. The language has gathered a vibrant community of active users, most of whom engage in data science and predictive analytics projects. Think of a use case, and there are high chances that a package supporting its delivery already exists. 

Yet, because the scope of the language is rather limited, R has never entered the mainstream like Python. As a result, only about 5.7% of developers use R.

The verdict

While the Internet is teeming with speculations on R replacing Python or another way around, there is no reason why the two languages couldn’t peacefully coexist. R leans heavily into statistical models, while Python offers a broader approach to data manipulation. Programmers working on machine learning implementations are much more likely to go with Python. Non-programmers who need to conduct deep statistical analysis will probably choose R.

Certainly, Python takes the cake for versatility. It supports all kinds of data formats and integrations and enables pulling data from the web. In contrast, R is limited to working with Excel, CVS, and text files and only allows basic web scraping. But all things considered, the two languages aren’t as much competing as they offer different benefits within the same, broadly defined area of data science.

KEY TAKEAWAYS

• Python tops the ranks for the ease-of-use, and development speed, and productivity.
• It is one of the best languages for data science and analytics projects.
• Like Ruby (and Ruby on Rails), Python is well-suited for rapid prototyping.
• Compiled languages, like Java and C#, exceed Python in performance.
• Python has one of the most powerful and active communities among all languages.

Here’s the part that will probably resonate with you the most – we’ll talk $$. If you ask: ‘How much will I pay for developing my Python project?’, we will tell you that there’s only one answer: ‘It depends.’ Coming up with a specific figure without knowing these details is impossible. 

Python development cost can range from $3,000 for developing a small software modification and $80,000 for creating a medium-size web platform. For enterprise-grade systems, the figure can get much higher, exceeding $100,000.

Multiple factors come into play when gauging any software development project's cost, regardless of the programming language. We explore them below. 

If you’ve fallen in love with Python by now (as we hope you have!), you might be interested to know where to find out more about the technology and its recent trends. Your wish is our command! 

We’ve identified top of the tops among thousands of offline and online Python resources and put together this directory of websites, conferences, courses, and influencers to follow if you want to enhance your knowledge of Python. Some of them will be more suited for software engineers and QA specialists of various seniority. Others will be also beneficial for business stakeholders – entrepreneurs, product owners, CTOs, CIOs, and project managers.

Here’s our shortlist of essential Python resources and must-visit events (many of them virtual as of 2021). 

Python events and conferences

SciPy

Location: online
Date: July 12-18, 2021
Tickets: $30-$75 for the conference only. Tickets that include the tutorials start at $50 for students and $125 standard. 
Audience: Both, technical and business.

SciPy is an annual scientific computing with Python event that follows the standard format of tutorials, sprints, and main conference. According to its website, the conference “brings together attendees from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development.” Specialized tracks take place in parallel to the main event. This year, they feature data visualization, image processing, and scientific applications of ML and data science. A series of mini-symposia on industry-specific tools and libraries is also available. 

EuroPython

Location: online/Europe
Date: July 26 – August 1, 2021
Tickets: €175.00 for business, €95.00 individuals (based on 2020 rates, the pricing for this year is still under development).
Audience: Both, technical and business.

EuroPython is the largest conference for the Python programming language in Europe, running continuously since 2002. It is regularly attended by 1,200-1,400 participants from all over the world. The conference is a combination of keynote talks, poster sessions, and code sprints covering a range of Python-related topics, including Python tools, AI and ML, game programming, network programming, APIs, alternative Python implementations, and more.

Xtreme Python

Location: online
Date: November 24, 2021
Tickets: $20-$80 (depending on the date purchased).
Audience: Technical, intermediate to advanced Python devs.

This is a one-day online event looking into Python software development tools, big data and server-side development, Python libraries and frameworks, and other Python-related topics. The conference will be divided into small, 25-minute sessions. The exact schedule and topics are yet to be confirmed. 

Python Web Conference

Location: online
Date: 2022 dates TBA
Tickets: $199 for professional admission, $99 for student admission (2021 rates).
Audience: Business and technical.

A virtual event that covers a vast range of topics regarding Python web development and includes one day of tutorials, as well as break-out rooms, live demos, chat rooms, online polls, and other activities. In 2021, the program was divided into four streams devoted to application development, PyData, cloud, and Python culture. Despite the fact that the conference mainly targets web developers, it offers plenty of insights for project managers and other business-oriented professionals, with topics around developing Python apps in the cloud, running open source projects, strategies for remote work, and more.

PyCon US

Location: online/USA
Date: 2022 dates TBA
Tickets: $100 for individuals and $150 for corporate visitors. A discounted rate applies to students. A special ‘Patron’ rate available. Tutorials are charged extra, at $50 per tutorial.  
Audience: Both, technical and business.

This is the largest annual gathering of the Python community, underwritten by the Python Software Foundation. It is attended by Python enthusiasts of all levels, including beginners and business-oriented visitors. The program includes the main conference, with talks on particular frameworks and Python deployments, to the principles of writing good software documentation.

The event has its regional editions outside of the USA, in countries across all continents (including Australia, Brazil, Cameroon, China, Czech Republic, Japan, Indonesia, India, Namibia, Poland, Pakistan, and Mexico, to name a few). 

DjangoCon

Location: online/Porto, Portugal
Date: 2022 dates TBA
Tickets: The early bird rates start from €59 for students up to €149 for business and €299 for contributor participants.
Audience: Mostly technical, specifically interested in Django web development.

As the name suggests, DjangoCon focuses on Django, the most widely used Python framework. The conference attracts Django practitioners of all levels and aims to help them enhance the knowledge of the framework and develop new skills. This year’s program is still in the making. Last year, some of the topics covered featured Django security, creating a personal streaming service with Django, and implementing particular libraries and packages.

Python Local User Groups

Location: worldwide
Date: various
Tickets: N/A
Audience: Rather technical.

Python Local User Groups are a network of global meetings open to all Python enthusiasts. The meetings take place in nearly 200 cities worldwide, attracting over 860,000 members with various levels of Python expertise. The sessions are quite informal and promote knowledge-sharing and collaboration and peer exchange of Python experiences.

Python blogs, vlogs, and podcasts

There are scores of Python-themed resources on the Internet. Listing all of them is virtually impossible, but you have to start somewhere. To help you do that, we’ve compiled a list of hand-picked Python blogs, vlogs, and podcasts to boost your knowledge.

Planet Python (blog)

Planet Python is a comprehensive Python knowledge source that brings together recent postings from the leading Python-related blogs. If you don’t want to sign up for dozens of different sources but would rather have all the Python information bundled together in a single place, that’s the blog to go!

Python Insider (blog)

As a Python news aggregator, Python Insider is another great place to consult for a daily dose of Python-related information. In addition to the latest news and updates (available in several language versions), the site also features a collection of links to Python developers’ blogs.

Python Software Foundation (blog)

The next blog is curated and maintained by the Python Software Foundation, an organization with the mission to promote, protect, and advance Python. The blog features news from the Foundation and tracks the latest Python updates, new features, and tutorials.

Real Python (blog)

If you’d like to get familiar with Python programming, start by visiting this blog. It offers a huge collection of free Python courses, tutorials, quizzes, and other resources to learn Python from the ground up. All materials are presented in a visually attractive and enormously engaging format to make the learning process a breeze.

The Python Guru (blog)

This is another great place for anyone who wants to learn the ropes of Python. The learning content is divided into short, manageable chunks that guide the user step-by-step through particular concepts. Even though the blog was last updated in 2020, it is still a valuable resource for Python beginners.

Python Bloggers (blog)

This community blog is appropriate for more advanced Python users. It focuses specifically on data science news and tutorials shared by Python enthusiasts. Most entries are quite technical and discuss the deployment of particular data science features or the application of specific packages and libraries for data analytics, processing, and visualization.

Talk Python To Me (podcast)

Talk Python is a weekly podcast by software developer and entrepreneur Michael Kennedy. It covers a vast array of Python topics. Each episode out of over 300 hundred released to this day is roughly 45-minutes long and features guest interviews with scientists, developers, business owners, and other professionals who discuss their dealings with Python from different perspectives. 

Python Bytes (podcast)

This is another massively successful podcast brought by Kennedy in collaboration with Brian Okken. The two hosts cover newsworthy Python headlines in episodes ranging from 30-60 minutes. 

The Real Python Podcast (podcast)

Available on the Real Python blog, this podcast hosted by Christopher Bailey brings you interviews, coding tips, and conversations with guests from the Python community. The program covers an exhaustive range of topics related to Python programming and breaking into a career in software development. 

Profitable Python (podcast)

Ben McNeill helps Python developers hone their skills in his podcast, offering a variety of Python tips, tutorials, and real-life use cases. The host often delves into the issues related to data science, computer science, mixed reality, and ML and AI projects. He offers plenty of career advice and mentorship in parallel, covering such areas of a software developer’s profession as self-education, profitability, or working with non-technical teammates.

PyData (YouTube)

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. The same organization is in charge of the various worldwide PyData events mentioned before. On its channel, PyData shares clips of talks, keynotes, and interviews that took place on particular events, as well as thematic playlists that group videos focusing specifically on a given topic (e.g., AI, probabilistic programming, Pandas framework).

PyCon session recordings (YouTube)

It is not a single channel but a series of YouTube channels that give you access to all recordings made during Pycon conference talks and sessions. You can see that the videos are grouped according to the location where each discussion or presentation took place. 

Coding for Entrepreneurs (YouTube)

The goal behind this channel is simple: to teach non-technical leaders about developing code in Python. This account does it through a host of user-friendly, self-paced videos covering the breadth and depth of Python-related topics. If you’re a beginner, you might want to start from the series called ‘30 Days of Python’, guiding you through the language’s fundamentals. More advanced users can dig into specific topics that feature creating a Twitter-like app in Django, integrating Django with JavaScript front-ends, or building a Rest API using the framework.

Python books

If you prefer the good, old-school sources of Python knowledge over podcasts and other media formats, here’s a list of 10 curated books that will help you learn the ropes of the language. We’ve gathered some suggestions for beginners and advanced Python users alike so that you can find something well-suited to your level and experience.

Books for Python beginners

Learning Python, 5th Edition | By Mark Lutz

A comprehensive introduction to the core Python, which is a great place to start exploring the language.

This Python guide is geared for developers who are well-versed in other languages, but it’s also well-suited for users with no programming experience, as it covers the basics such as core Python objects, concepts, development tools, and the differences between major Python releases.

👉 Get it here.

Python Crash Course, 2nd Edition | By Eric Matthes

With its claim to be “the world’s best-selling guide to Python,” this book helps beginners learn basic programming concepts and practice writing clean code in a short time. 

In the first part, the book sheds light on essential Python-related concepts, looking into loops, classes, lists, dictionaries, and other basic topic areas. The second part follows a hands-on approach, exploring specific Python projects that cover gaming and data visualizations.

👉  Get it here.

Head First Python | By Paul Barry

If you want to gain a quick grasp of Python’s basics, choose this fast-paced, beginner-friendly guide. It comes with practical coding exercises to let you quickly proceed from the language’s fundamentals to building your own simple web application.

Additionally, the content is delivered in a highly digestible visual format for easier knowledge acquisition, helping the readers learn fast and efficiently.     

👉 Get it here. 

Learn Python in One Day and Learn It Well. Python for Beginners with Hands-on Project | By Jamie Chan

Claiming that you can get familiar with any programming language in a single day is a stretch. But over the top title aside, this useful book takes a practical approach to learning Python with a careful selection of topics and examples that break down complex concepts into digestible bits for faster comprehension. 

The guide covers essentials such as variables, data types, format strings, objects and classes, and inheritance and applies them in hands-on projects to show you how they work in real life. 

👉 Get it here. 

Python Programming: An Introduction to Computer Science | By John M. Zelle

Published as an academic textbook to teach Introduction to Programming, this book uses Python to illustrate the fundamental principles of designing and writing software code.

While it isn’t explicitly a Python course, the book may be quite useful for business professionals with no prior knowledge of programming. It provides a detailed explanation of computer science and programming fundamentals.

👉 You can purchase the latest edition on Amazon.

Books for specific Python applications

Python Cookbook, 3rd Edition | By David Beazley, Brian K. Jones

This hefty Python guide covers all bases on Python 3 code development with much detail. As such, it is recommended for professional Python programmers who need an upgrade on the workings of the language in its 3.X release.

The book provides plenty of complete code samples to use in your development. It also features practical recipes that allow readers to implement a particular task with greater efficiency and fewer code lines.

👉Get it here.

👀  See also: Learn Python 3 the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code | By Zed A. Shaw

Python Machine Learning, 3rd Edition | By Sebastian Raschka, Vahid Mirjalili

Machine learning and deep learning are among Python’s leading areas of application; therefore, the number of books in the field is overwhelming. One suggestion to consider is this best-selling guide with rich visualizations, clear explanations, and working examples of Python being used in the ML and deep learning context.

The book examines key Python frameworks, models, and techniques that enable machine intelligence, teaching you step-by-step how to use the language’s power to develop scenarios in image classification, intelligent web apps, sentiment analysis, and much more.

👉Get it here. 

👀 See also: Introduction to Machine Learning with Python | By Andreas C. Müller, Sarah Guido

Python for Data Analysis, 2nd Edition | By Wes McKinney

If your Python project primarily concerns data visualization, get this book. It uses functional case studies to teach you how to solve a broad set of data analysis problems using Python. 

Dedicated to data scientists, the Python guide relies primarily on Python modules rather than custom software development to cover all the basic steps of data collection, cleansing, analysis, modification, and visualization using Pandas, NumPy, Matplotlib, and other Python tools. 

👉Get it here. 

👀 See also: Python Data Analytics: With Pandas, NumPy, and Matplotlib | By Fabio Nelli

Natural Language Processing with Python | By Steven Bird, Ewan Klein, Edward Loper

If you want to become versed in Python's natural language processing applications, start by reading this book. Packed with practical examples, it explores the ins and outs of Python programs that work with large collections of unstructured texts. 

Among others, the book studies the main algorithms for analyzing and processing written text, covering all stages of NLP projects, from processing raw text, through applying taggers and categories, down to managing linguistic data.

👉 Get the book here. 

👀  See also: Natural Language Processing in Action: Understanding, analyzing, and generating text with Python | By Lane Hobson, Howard Cole, Hapke Hannes 

Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners | By Al Sweigart

This book is a must-read for any business person who wants to optimize work using automation. Once you cover the Python fundamentals in the first few chapters, the book uses hands-on examples to quickly teach you how to automate tedious office tasks, like filling out Excel spreadsheets, sending emails, or organizing and retrieving files.

The best thing is that you need absolutely no programming experience to apply the steps demonstrated in the book, and you can use your newfound skills right away to save hours of menial work.

👉 Get the book for free here or buy it on Amazon. 

👀  Effective Python: 90 Specific Ways to Write Better Python | By Brett Slatkin

Python courses and certificates

Again, there’s a plethora of basic to advanced-level courses, workshops, and certification programs that teach you Python programming. We can’t possibly list all of them, so we’ll focus on those that offer particularly strong value for money. Additionally, we will look into online programs exclusively.

Python experts and influencers

So you are interested in Python and want to keep your finger on the pulse on the latest Python news but have no time to read books, tune in to podcasts, or sign up for a course? No problem! Just check out these social media profiles and follow them to stay updated on Python.

We hope that this report has been useful in establishing where Python stands now. But let’s pause for a moment and think about Python’s future.

What is the future of the Python language?

Already several generations old, Python has a bright future ahead. We’re going through the fourth industrial revolution now, when fields like robotics, automation, nanotechnology, smart healthcare, and other areas impacted by machine intelligence and data science are gaining significance. As computer science has become somewhat of an umbrella term, Python enjoys a comfortable lead as the most popular programming language of 2020. And that state is likely to continue. 

The U.S. Bureau of Labor Statistics expects an increase in computer and information research jobs by 15% by 2029.

The U.S. Bureau of Labor Statistics projects a pronounced growth in data science jobs in the coming years. According to the institution’s Occupational Outlook Handbook, the demand for computer and information research scientists will increase by 15% by 2029, much faster than the average for all occupations. The CareerFitter website anticipates even a stronger trend, foreseeing a 35% surge in demand for statisticians and mathematicians! The outlook for other jobs involving handling of data and working with AI and machine learning systems is equally optimistic.  

According to the World Economic Forum, Python is a top-three skill for data scientists.

The World Economic Forum’s Future of Jobs report lists Python programming among the top three skills for data and AI occupations. Python is an essential area of knowledge on the learning agenda for data science professionals. When it comes to engineering jobs, the language is considered the most in-demand technology, according to the study.  

The world’s most progressive higher education institutions have Python programming classes in their curricula; this makes a strong case for the language’s bright prospects. Python was the most popular introductory teaching language at top-ranked US universities already a few years ago. Today, it retains its leading position, being taught in courses at such elite establishments as Harvard, Yale, Stanford, MIT in the USA, or Cambridge, Oxford, National University of Singapore, University of Tokyo, and University of Sydney if we look elsewhere.

All in all, it is safe to comment that while new technologies are going to emerge, and some of the existing, like R, Swift, or Go, are likely to grow in importance, Python’s position will remain unchallenged in the next few years. The language will continue to dominate AI, machine learning, and data science programming, as well as web development. And who knows, maybe new fields of Python’s application will come to light in the upcoming years?

Thank you for reading our Python guide. If you’ve reached this far, it means that your interest in the Python language is substantial. 

Maybe you have already decided to go with Python but lack resources to move forward? Or are you still hesitating and need to consult your idea with experts?

Either way, we have excellent news for you: we are here to help! Our first-class team of Python developers and consultants will help you:

• Design and build your software application with Python.
• Secure your Python-based software.
• Make your application error-proof.
• Decide whether Python is the right choice for you.

Just say the word, and we’ll be right here to connect you with our Python team.