Python development services | Data analysis & ML

Python is a widely-used, general-purpose programming language. The idea behind its creation is remarkably high code readability. Used in many areas, Python works especially well for machine learning, data analysis, scientific computing, and prototyping solutions.

What is Python

Versatility

Python is a dynamic, object-oriented general-purpose programming language, designed with an emphasis on code readability in mind. Highly interpretable & efficient, it is suitable for performing demanding tasks, e.g. data science, thanks to the extensive array of dedicated libraries. Versatile and platform-independent (to some extent), Python allows importing useful modules based on other languages.

Prototyping

Python often serves as a prototyping tool, allowing to concentrate attention on the problem and approach, instead of the infrastructure. The language enables fast code rewriting to improve understanding of problems and considered solutions, which, later on, are translated into the projects’ target language. Python is flexible as an interpreted language and contributes to time savings during project development.

Why you should consider Python

Open-source

Widely available and distributable, also for commercial use

Efficient third-party modules

Easy to integrate external libraries, often built on a C++ basis to improve efficiency

Dynamic typing

Quicker turnaround, smaller source code, easier testing and debugging

Stable

New versions released regularly for 30 years, demonstrating continuous technology development

Productivity

Due to the simple syntax and the number of specialized libraries, many problems can be quickly solved with a few lines of code

Flow control

Although Python is dynamically typed, which makes development faster, there are tools to control types (e.g. mypy)

Easy to learn

With very readable code and easy syntax, Python is nearly ready to use in project development

Asynchronous programming

Parallel computing, where some tasks run independently of the main application thread

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Features of Python

High-level language

Python is a high-level language, requiring less focus on hardware aspects and architecture agnostic – it doesn’t have to run on a particular architecture. Applications built with Python often run as efficiently as those created in low-level languages, while requiring less code.

Extensive standard library

Various open-source libraries streamline the development of solutions without the need to build particular functionalities from scratch. There are over 200 modules in the standard library, just waiting to be used for the most common tasks. Additionally, there are over 130,000 libraries facilitating development, most of which were created for data analytics, data mining, and automation. The most popular are Pandas, Matplotlib, NumPy, BeautifulSoup, SciPy, and Scrapy.

Cross-platform

Python applications run on different operating systems, without the need to build or compile them on each platform individually, as long as devices have the Python interpreter installed (and many operating systems have Python pre-installed). This enables gradual system shifts instead of complete code rewriting while adapting legacy in extensive projects.

Dynamic analysis

Enables all variables and values to be tracked as the program runs, both on real and virtual processors. Also referred to as dynamic code scanning, dynamic analysis facilitates error recognition and repair, resulting in simplified trouble-shooting.

Notebooks

Python comes with the opportunity to build and test code in notebooks like Jupyter. Instead of coding the complete solution, testing it as a whole and re-writing if it turns out to be flawed, once a particular element tested in a notebook is considered correct, it can be implemented into the whole code. The feature is particularly useful in data science and machine learning projects. Notebooks can be accessed from any computer, while calculations happen on computing servers.

Free and open source

There are 7 million Python programmers, a large and constantly growing community developing the technology. With great community support, creating newer and newer libraries, there’s a great chance that projects built in Python will remain up-to-date for much longer, with little to no risk of becoming obsolete any time soon.

Benefits of Python

Convenient prototyping tool

Python has the infrastructure that allows you for testing smaller parts of an application and, once validated, moving them to their destination in the application, rather than creating an elaborate build and testing the whole architecture from start to finish.

It does not require a full rewrite of components to a compilable language (C, C++) after debugging and moving to the target application. Some parts of the final build can remain in Python thanks to their ease in maintenance.

Suitable for data science & machine learning

When it comes to data processing, Python enables leveraging to different scale operations performed previously by companies in Excel sheets but supported with tools like Reportlab, xlwt, xlrt.

It is suitable for processing large data sets in data science solutions due to numerous libraries and frameworks, data structuring, data visualization. Python is more and more widely used in creating various models, including Bayesian networks and decision trees.

Fast development

Python contributes to remarkably faster development than other general-purpose languages, like Java or C. It comes with numerous modules, usually very well documented, easy to use without writing database connectors.

Python allows testing of different paradigms and patterns in the same program, working equally well in the functional, as in object-oriented programming approach.

Where to use Python

Machine learning

Combining the power of dedicated libraries, Python frees programmers’ attention from struggling with code to focus on advanced algorithms

Data science

Standing on the giant’s shoulders (NumPy, Pandas & Matplotlib), Python is the go-to solution for data scientists

Natural language processing (NLP)

Building services able to understand human languages and shell out crucial information from e.g. documents

Cross-platform applications

With popular frameworks such as Django, Flask, and Bottle, Python contributes to rapid app development

Blockchain

Thanks to its versatility and high performance, Python has the potential to build decentralized applications

Prototyping

A fast-track to building and testing elements of applications to improve their time-to-market

They use Python

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At NeuroSYS, we specialize in using the science of tomorrow to power your business today

We offer a range of future-forward solutions to unleash the potential of your business. Stay ahead of your competition with our expertise in Python-powered projects. Our team uses Python to prototype and test applications to build them error-free and market-ready in a fraction of time, compared to more traditional ways of development. We offer more than code, as our process starts with the analysis of business requirements.

Contact us for a free consultation

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Would you like to read more on how Python is utilized in various fields? See more solutions it’s used in:

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