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What is Python?

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasises readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.

How to hire for Python?

Define your needs

To find a perfect fit for your team, you need to establish who that would be. To give you some examples: a good teammate, excellent level of English, business knowledge, with 3 years of experience, growth-attitude, etc.

Build a process

To filter the best possible hires, you need a set of steps. We call it a hiring funnel (it’s a lot like a sales funnel). Establish how you want to check your candidate’s skills, way of thinking, culture, and team fit. Prepare questions in advance and take a team member to the interview.

Overcome obstacles

Hiring Python developers seems difficult because there are a lot of them in the market (but not everyone as experienced and promising as you wish), and there is also high demand. A well-thought-out process is your key to overcome these obstacles.

Here are 10 common Python development interview questions that can help you hire Python developers that deserve a place on your team

Companies That Use Python

Python is so popular, quite a few major business enterprises are well-acquainted with the software. Companies that use Python include the following:

What Are the Advantages of Using Python?

There are many advantages to using Python. Below are a few of the main advantages leading companies using Python to choose the language for their tech stack in the first place.


Python is easy in more than one way. It’s easy to read, nearly replicating the English language in composition. Likewise, it is easy to write. Of course, developers who can understand the code they’re writing do much better than those who don’t. Global companies using Python can rest assured that their developers are spending less time in Stack Overflow and more time developing. But besides basic comprehension, code that is easier to write is by definition high-yielding. For example, it takes fewer lines of code to perform a task in Python than using another popular, high-level programming language like Java or C. In consequence, businesses can speed up development time and decrease their time-to-market (TTM), giving them more leverage to keep up with the competition.


Like most programming languages, Python is free. It’s also open-source, meaning developers can modify elements of the language to their needs. But more importantly, Python is popular. All the above characteristics contribute to the fact that the language is in high demand. Python is accessible, both in usage and syntax. And all the companies using Python only further this cyclical chain of demand and implementation. Popular technologies strive because of their large and dedicated communities which offer tactical solutions to developers in need. For your own benefit, note that an in-demand language will guarantee that you have a large pool of Python developers to choose from for your next Python project. The larger the pool, the less difficult it will be to find bonafide talent.


Judging from the growing list of global companies using Python, it’s clear that Python is scalable. In this regard, Python’s utility for a wide array of applications truly shines. First of all, Python supports multiple programming paradigms, including structured, procedural, functional, and object-oriented programming (OOP). OOP and class structures often serve as the foundation for large-scale software applications. Python also has sound libraries for machine learning and artificial intelligence, exemplifying just how far-reaching the language is. Data science is another subject in which Python excels, with financial technology routinely relying on the Python language. Not to mention, Python is conducive for game development as well, with big names like Sims 4 and Eve Online owing their existence to the infamous Python. In short, Python can do many things and most of the things Python can do are objectively exceptional.

Disadvantages of Python

Slow Speed

We discussed above that Python is an interpreted language and dynamically-typed language. The line by line execution of code often leads to slow execution.The dynamic nature of Python is also responsible for the slow speed of Python because it has to do the extra work while executing code. So, Python is not used for purposes where speed is an important aspect of the project.

Not Memory Efficient

To provide simplicity to the developer, Python has to do a little tradeoff. The Python programming language uses a large amount of memory. This can be a disadvantage while building applications when we prefer memory optimisation.

Weak in Mobile Computing

Python is generally used in server-side programming. We don’t get to see Python on the client-side or mobile applications because of the following reasons. Python is not memory efficient and it has slow processing power as compared to other languages.

List of Python Alternatives


Go is a statically typed, compiled programming language designed at Google by Robert Griesemer, Rob Pike, and Ken Thompson. Go is syntactically similar to C, but with memory safety, garbage collection, structural typing, and CSP-style concurrency. The language is often referred to as Golang because of its former domain name, golang.org, but the proper name is Go.


Java is a high-level, class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible. It is a general-purpose programming language intended to let programmers write once, run anywhere (WORA), meaning that compiled Java code can run on all platforms that support Java without the need for recompilation.


Either it is web-based applications, complex API’s, real-time development or even micro services. ASP.NET is a once such complete package. This makes it one of the top qualifying contender and an alternative to Node JS.

4. Node JS

Node JS is an open-source, cross-platform, back-endJavaScriptruntime environment that runs on the V8 engine and executes JavaScript code outside a web browser. Node.js lets developers use JavaScript to write command line tools and for server-side scripting—running scripts server-side to produce dynamic web page content before the page is sent to the user's web browser. Consequently, Node.js represents a "JavaScript everywhere" paradigm, unifying web-application development around a single programming language, rather than different languages for server-side and client-side scripts.

5. PHP

PHP is a general-purpose scripting language geared towards web development. It was originally created by Danish-Canadian programmer Rasmus Lerdorf in 1994. The PHP reference implementation is now produced by The PHP Group. PHP originally stood for Personal Home Page, but it now stands for the recursive initialism PHP: Hypertext Preprocessor.

List of top Python Packages

1. NumPy

You can do basic mathematical operations without any special Python packages. However, if you’re going to do any kind of complex math, the NumPy package will make your coding life much easier.NumPy provides tools to help build multi-dimensional arrays and perform calculations on the data stored in them. You can solve algebraic formulas, perform common statistical operations, and much more.While NumPy is a valuable Python package for a variety of general-purpose programming tasks, it’s particularly important if you want to do machine learning, since it provides part of the foundation for libraries like TensorFlow.

2. Pendulum

If you have at least a little Python programming experience, you probably know that you can use the datetime module to manage dates and times within an application. While datetime is great for basic work along these lines, the Pendulum Python package makes it easier to do more complex coding involving dates and times. It’s more intuitive to work with, and it manages time zones automatically. Best of all, Pendulum is designed to be a drop-in replacement for datetime. That means you can use it with code you’ve already written based on datetime. With only a few exceptions, Pendulum will work just as well, without the need to modify the code, while providing extra features not present in plain-old datetime.

3. Python Imaging Library

If your Python application interacts with images in any way, the Python imaging library, also known as PIL or Pillow, is a Python must-have. It makes it easy to write code that opens, modifies, and saves images in a variety of formats.If you’re doing more advanced work with images (like image recognition, in which case OpenCV would be a good package to consider), Pillow won’t cut it on its own. But for basic image importing, manipulation, and exporting, Pillow is your go-to solution.

4. MoviePy

MoviePy is to videos what Pillow is to images. It provides a range of functionality for common tasks associated with importing, modifying, and exporting video files. It also lets you do things like insert titles into videos or rotate videos 90 degrees (if for some reason you decide you want to do that).Like Pillow, MoviePy is not intended as a tool for advanced data manipulation. If you’re writing a video editing app, you’ll probably also need to rely on OpenCV (which can work with videos as well as images) to provide the advanced functionality that MoviePy lacks. But for most standard tasks involving videos in Python code, MoviePy gets the job done quite well.

5. Requests

Writing code that sends HTTP requests can be tricky, due in no small part to the fact that HTTP does not exactly format data in a way that is easy for humans to read. The Requests Python package (motto: “HTTP for Humans”) tackles this problem by automating many of the tedious tasks that you would otherwise need to perform in order to send HTTP requests from your application. It removes the need to add query strings, or do POST form encoding. It also keeps connections with HTTP servers alive automatically, eliminating the need to write a bunch of code for doing that. In short, if your application sends any data over HTTP, Requests is a must-have package.

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