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.

 

Difference Between Scripting and Compiled Languages

• Languages that are needs to be compiled are compiled languages Ex: C, C++, Java
• Some other languages can be interpreted and don’t need a compilation. Ex: JavaScript, Perl, PHP, Python
• Scripting language can have a big performance problem. Translating on the fly can affect the application’s performance.
• Typically, the pre compiled code is faster than on the fly translating.
• It’s worth noting that languages are not only interpreted or compiled.

 

Comparisons with other Languages

• Java
• Java Script
• Perl
• C++

Java

• Python is slower than Java in general.
• Development period is way shorter than Java.
• In general, Python code is 3-5 times shorter. The reason for this: Python is more advanced and Dynamic Data Type in Python. Ex: variables.
• Because of the run-time typing, Python’s run-time must work harder than Java’s
• For example, when evaluating the expression a+b, it must first inspect the objects a and b to find out their type, which is not known at compile time. It then invokes the appropriate addition operation, which may be an overloaded user-defined method. Java, on the other hand, can perform an efficient integer or floating point addition, but requires variable declarations for a and b, and does not allow overloading of the + operator for instances of user-defined classes.
• For this reasons Python is more like a “glue language”.
• On the other hand, Java is a low level implementation language. So they are perfect couple together.
• Ex: Components can be developed in Java and combined to form applications in Python. Python can also be used to prototype components until their design can be “hardened” in a Java implementation

 

Java script

• Python’s “object-based” subset is roughly equivalent to JavaScript.
• Python supports writing much larger programs and better code reuse through a true object-oriented programming style, where classes and inheritance play an important role.

 

Perl

• Python and Perl come from a similar background (Unix scripting).
• Many similar features, but have a different philosophy.
• Perl emphasizes support for common application-oriented tasks, e.g. by having built-in regular expressions, file scanning and report generating features.
• Python emphasizes support for common programming methodologies such as data structure design and object-oriented programming, and encourages programmers to write readable (and thus maintainable) code by providing an elegant but not overly cryptic notation.
• As a consequence, Python comes close to Perl but rarely beats it in its original application domain; however Python has an applicability well beyond Perl’s niche.

 

C++

• Almost everything said for Java also applies for C++, just more so: where Python code is typically 3-5 times shorter than equivalent Java code, it is often 5-10 times shorter than equivalent C++ code.
• Anecdotal evidence suggests that one Python programmer can finish in two months what two C++ programmers can’t complete in a year. Python shines as a glue language, used to combine components written in C++.

 

Performance Analysis

• https://benchmarksgame-team.pages.debian.net/ benchmarksgame/faster/python.html

Energy Efficiency

• Energy efficiency isn’t just a hardware problem. Your programming language choices can have serious effects on the efficiency of your energy consumption.

 

What exactly can I use Python for?

• There are three main popular applications for Python:
• – Web Development.
• – Data Science – including machine learning, data analysis, and data visualization.
• – Scripting.

 

Web Development

• Web frameworks that are based on Python like Django and Flask have recently become very popular for web development.
• These web frameworks help you create server-side code (backend code) in Python.

 

Data Science – Machine Learning

• Two of the most popular python libraries are scikit-learn and TensorFlow.
• – scikit-learn comes with some of the more popular machine learning algorithms built-in. (For beginners)
• – TensorFlow is more of a low-level library that allows you to build custom machine learning algorithms. (For more efficiency)

 

Data Science – Visualization

• One of the most popular libraries for data visualization is Matplotlib.

 

Scripting

• Scripting usually refers to writing small programs that are designed to automate simple tasks.

 

What about embedded applications, desktop applications or gaming ?

• You can use python on Rasberry Pi. Performance: Average.
• You could use the library called PyGame to develop games, but it’s not the most popular gaming engine out there. You could use it to build a hobby project.
• You could make one with Python using Tkinter, but it doesn’t seem like the most popular choice either. Instead, it seems like languages like Java, C#, and C++ are more popular for this.

 

So, what is the strongest side of python ?

• My personal thought is rapid web development.
• Frameworks such as Django and Pyramid. • Micro frameworks such as Flask and bottle.
• Advanced content management systems such as Plone and django CMS.
• Python’s standard libraries supports many internet protocols. (HTML, XML, JSON, E-mail processing, easy-to-use socket interface …)
• However for backend development Java also should be considered:
• Short Term, Easy, Understanding : Python
• Long Term, Fast, Scalable : Java