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In the BizUPLab blog we continue to talk about the technologies that our specialists use in their work. Today we are going to talk about Python. It is a high-level, interpreted, general-purpose programming language. It is characterised by efficiency, simplicity and versatility of use. This object-oriented programming language with dynamic typing is widely used in various fields, such as
- Web programming;
- Application software development;
- Artificial intelligence and machine learning;
- Data analysis and visualisation
- Testing;
- Microcontroller programming;
- Process automation;
- Web programming;
- Application software development;
- Artificial intelligence and machine learning;
- Data analysis and visualisation
- Testing;
- Microcontroller programming;
- Process automation;
Python: what makes it different from other programming languages
Pros and Cons
Python, like any other programming language, has its advantages and disadvantages. The main advantage is its simplicity and speed of development. It is this competitive advantage that sets it apart from other languages.
«ecause of its popularity, Python has a huge ecosystem of libraries, frameworks and tools that make writing code easier and more powerful.
The Python interpreter can be integrated with other programming languages - such as Java, C and C++. This allows Python developers to extend the functionality of their project by plugging in their features. Python is a cross-platform language. Programs can be adapted to all existing hardware and software platforms.
Python is easy to learn. Even if you are far from IT, learning this language will not be very difficult. It has a simple structure and syntax. The barrier to entry for Python is low, as it is a very beginner-friendly language. It is not just used by developers. Python is popular with mathematicians, analysts, scientists, network engineers, system administrators and even accountants.
On the downside, performance is poor. For example, iOS applications written in Swift run 8.7 times faster than those written in Python. Because Python is an interpreted programming language, code written in it is executed with a certain delay and consumes additional resources of the computer or device.
Python, like any other programming language, has its advantages and disadvantages. The main advantage is its simplicity and speed of development. It is this competitive advantage that sets it apart from other languages.
«ecause of its popularity, Python has a huge ecosystem of libraries, frameworks and tools that make writing code easier and more powerful.
The Python interpreter can be integrated with other programming languages - such as Java, C and C++. This allows Python developers to extend the functionality of their project by plugging in their features. Python is a cross-platform language. Programs can be adapted to all existing hardware and software platforms.
Python is easy to learn. Even if you are far from IT, learning this language will not be very difficult. It has a simple structure and syntax. The barrier to entry for Python is low, as it is a very beginner-friendly language. It is not just used by developers. Python is popular with mathematicians, analysts, scientists, network engineers, system administrators and even accountants.
On the downside, performance is poor. For example, iOS applications written in Swift run 8.7 times faster than those written in Python. Because Python is an interpreted programming language, code written in it is executed with a certain delay and consumes additional resources of the computer or device.
Python applications
Python is the most versatile of programming languages. Let's learn more about some of its applications.
Web development
This is one of the most popular uses of the language. Python is widely used to develop the server side of web applications and to write the business logic of the application. The most popular web frameworks are Django, Flask and Pyramid. Django is a powerful tool for creating complex web projects. Developers can perform complex tasks related to databases, queries, functions and site structure with minimal code. For simpler applications, the Flask framework can be used.
Machine learning and AI
Powerful libraries are used to solve problems in these areas: TensorFlow, Scikit-learn, Keras and others. However, if you are not looking for easy ways, you can write your own machine learning library. The language itself is well suited to this. Another reason why Python is popular in this environment is that even complex machine learning models can be implemented with 20-40 lines of code.
Python is the most versatile of programming languages. Let's learn more about some of its applications.
Web development
This is one of the most popular uses of the language. Python is widely used to develop the server side of web applications and to write the business logic of the application. The most popular web frameworks are Django, Flask and Pyramid. Django is a powerful tool for creating complex web projects. Developers can perform complex tasks related to databases, queries, functions and site structure with minimal code. For simpler applications, the Flask framework can be used.
Machine learning and AI
Powerful libraries are used to solve problems in these areas: TensorFlow, Scikit-learn, Keras and others. However, if you are not looking for easy ways, you can write your own machine learning library. The language itself is well suited to this. Another reason why Python is popular in this environment is that even complex machine learning models can be implemented with 20-40 lines of code.
Automation and scripting
The Python language is well suited for creating scripts that perform cyclic or algorithmic actions to automate certain processes. Scripts written in Python are also used by programmers to simplify the process and reduce development time. By spending as little as 15-30 minutes writing a script, you can get rid of a routine that takes hours to complete manually.
Data Science
These are mainly data analysis and visualisation tasks. There are many libraries for these tasks, such as Numpy for numerical data or Pandas for tabular data. There are even more libraries for visualisation: Matplotlib, ggplot2, bokeh, the same Pandas and others.
Testing
Python's capabilities are used by testers and developers for finding and fixing bugs, automated builds, software prototyping, project management, etc. In addition, Python unit testing environments are used to test features. Test scripts that simulate various software usage scenarios are also written in this language.
The Python language is well suited for creating scripts that perform cyclic or algorithmic actions to automate certain processes. Scripts written in Python are also used by programmers to simplify the process and reduce development time. By spending as little as 15-30 minutes writing a script, you can get rid of a routine that takes hours to complete manually.
Data Science
These are mainly data analysis and visualisation tasks. There are many libraries for these tasks, such as Numpy for numerical data or Pandas for tabular data. There are even more libraries for visualisation: Matplotlib, ggplot2, bokeh, the same Pandas and others.
Testing
Python's capabilities are used by testers and developers for finding and fixing bugs, automated builds, software prototyping, project management, etc. In addition, Python unit testing environments are used to test features. Test scripts that simulate various software usage scenarios are also written in this language.
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