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Python is a high-level, general-purpose programming language that focuses on code readability.

This language is widely applied in software development – from web frameworks and applications to data science, language development, prototyping, and graphic design applications, which provides Python an upper-hand over other languages.

Why is it beneficial to use Python?

Python technology usage

Extensive libraries. Python has a large standard library that includes areas such as OS interfaces and protocols, Internet, web service tools, and string operations. A significant share of highly used tasks are scripted into it that restricts the code lengths.

Integration. Python is a simple way to develop web services by applying COM or COBRA components due to integration of EAI. Python calls via C, C++ and Java due to which the language has effective control capabilities. The language processes all markup languages because it runs on any current OS through similar bytecode.

Enhanced developers’ output. With massive support libraries and object-oriented designs, Python has the potential to increase programmers’ productivity up to ten times while using C,C++, C#, Perl, Java, VB.

Productivity. Python has powerful integration features, increased control capabilities, a unit testing framework, which in most cases results in higher productivity and speed of applications. It’s a great solution when it comes to scalable multi-protocol network applications.

Python’s weak spots

  • Low speed, especially compared to C and C++
  • Requires more testing due to runtime errors
  • Underdeveloped database access layers
  • High memory consumption
  • Not suited for mobile computing

Python’s main application domains:

Web development

Python is one of the three languages (Python, PHP, Ruby) that possess advanced ecosystems for quick development of high-quality web project. Key platforms used for this purpose are:

  • Django – a monolithic, synchronical framework
  • Flask – a micro-synchronic framework
  • Tornado – a monolithic, non-synchronous framework
  • Twisted – a monolithic, non-synchronous framework
  • Aiohttp – a micro non-synchronous framework

Today Django dominates the market while Flask is a runner-up offering simplicity, fast start and integration with microservices.

Django is a high-level Python framework that offers fast development and clean design. Its main goal is to simplify the creation of complex database-driven websites. Choose Django when you are interested in the end product. It’s also the right choice when it comes to working with simple applications such as a news website, an online shop, or a blog. Django allows to solve every task in an understandable way.

Flask is a lightweight but extensible web framework. Developers choose Flask to build web applications in a more flexible manner using a number of popular web development tools and libraries. With a simple, customizable architecture, Flask is easier than Django.


Python is widely used in writing of short scripts for automation of various working operations and processes. An example of such is the system of email processing. To collect data you need to count the number of incoming emails containing specific keywords. This can be done manually or by writing a simple email counting script. Below are a few reasons why Python is used for automation tasks:

  • A simple syntax that allows to write scenarios fast
  • Simplified debugging due to the fact that code doesn’t compile before run

Machine Learning and Data Science

Python and Data

Data Science is a broad term and can be best described by a set of its components.

  • Data exploration and analysis Standard Library, Pandas, SciPy, NumPy.
  • Visualization of data Matplotlib, Seaborn, Datashader.

Deep learning. Deep learning has significantly improved over the past 5 years. It’s a subset of machine learning that is implemented using Keras and TensorFlow among other libraries.

Classical machine learning. It encompasses all supervised and unsupervised tasks that are not related to deep learning. Scikit and StatsModels are two feature-rich tools for classification, dimensionality reduction, regression, and clustering.

Big Data and data storage. Python relies on Apache technologies (Spark, Hadoop, Dask, HDFS, h5py/pytables) to process data when a distributed environment is absent.

Odds and ends. Natural language processing, image manipulation with libraries are the main subtopics. Technologies used are OpenCV, Cython, nltk, Spacy, OpenCV/cv2.

In this domain you need to have advanced knowledge of Mathematics and Statistics. In fact, an academic degree in a technical field is almost always required when you intend to work with Big Data or ML.

Test automation

The primary tool for test automation is Java with a plethora of its frameworks and out-of-the-box solutions. Some small companies apply Python for full-fledged testing or writing scenarios for tools. On average, test automation vacancies take nearly 30% of an overall number, and often knowledge of both Python and Java is required.

NCube specializes is delivering advanced QA testers with deep proficiency in Python and Java.

Data Science and Python developers are within our scope of expertise – contact us via the form.

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