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The Python programming language is a foundational framework for data science, machine learning, systems automation, web and API development, and more.
Since 1991, the Python programming language was considered a complementary language only for useless programs. Even a book called “Automate the Boring Stuff” (a popular book that we can translate into Turkish as “Automate Boring Stuff”) has been published.
However, in the last few years, Python has emerged as a world-class programming language for modern software development, infrastructure management, and data analysis. No longer a backdoor generator for hackers, it has earned a reputation as a shining language in web application creation and system administration, data analytics and machine learning.
Important Advantages of Python
The Python programming language has significant advantages for beginners or those who are proficient in Python.
Python Programming Language is Easy to Use and Learn
Other complex programming languages take a lot of time to learn, and it is very difficult to learn to use them because of their wide range of uses. But Python syntax is both readable and forward-looking. Learning is simple thanks to the stable programming language. It is also an ideal choice for beginners. As a result, anyone new to programming using Python can make progress quickly and simply. It is at the forefront when it comes to simplicity over other complex languages.
Python is Applicable and Its Use Framework Is Quite Broad
Although its name is less known, it is both popular and common among software developers. Many Github projects are based on Python. It even shows Python-based programs at the top in systems where programming examples such as Tiobe Index and Github are listed. Its usage area is supported by many OSes, from the smallest operating systems to the largest operating systems. Major software libraries and API-supported services on the Internet have Python-based bindings or wrappers. Therefore, Python freely interfaces with these services or uses these libraries directly. Although it is not the fastest among software languages, its use on multiple platforms has made it one step ahead.
Python Is Not A Simple Language
Although most scripts and automation programs created cover Python code schemes, Python is also used to create professional quality software, both as standalone applications and web services.
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What Is Python Used For?
It also has features that provide system requirements and configuration in tools such as Ansible and Salt, in addition to automating interactions with web browsers or application GUIs. In short, scripting and automation are just the tip of the iceberg for Python.
Application Programming with Python
You can create console applications and multi-platform GUI applications with the Python programming language. You can use them as standalone setup files. A Script created with Python cannot create a binary series on its own, but thanks to third-party programs cx_Freeze and PyInstaller, the situation turns from impossible to possible.
The Place of Python in Data Science and Machine Learning
Many of the libraries in the Python interface are suitable for machine learning and data science. Its high-quality commands in libraries in these areas have greatly helped the continued development of machine learning libraries and other numerical algorithm libraries. Sophisticated data analytics have become the most important topics for IT today. Python has been the most suitable programming language for these situations.
Coding Web Services and RESTful APIs with Python
When the local libraries in RESTful APIs Python are combined with third party web software, a faster website management can be achieved with a few lines of code. The creation of REST APIs with appropriate code blocks allows the site to quickly execute data execution. In particular, the last update of Python has transformed asynchronous operations into a more powerful structure. With the right libraries, it has helped sites provide data flow in response to thousands of code requests per second.
Metaprogramming and Code Compilation
Compared to other languages, all modules and libraries in Python act as objects. Thanks to this, Python stands out as an effective code compiler. By manipulating applications written with their own code features, it makes applications that are very difficult or almost impossible to write in other languages very easily written.
Python’s ability to build multiplatform applications also makes it suitable for generating efficient code in code compiler systems like LLVM.
Python as Sticky Code
Python, often called Glue Code, got its nickname because it creates a common structure with the codes in the C language library and helps to make effective programming. It has made it stand out as a unique option in data science and machine learning.
Python’s Shortcomings
Although Python is generally suitable for writing quality applications, there are some shortcomings and areas where it is not enough.
Because it is a high-level programming language, it is not suitable for system-level programming. However, this category does not include device drivers or operating system kernels.
It’s also not ideal for situations that call for cross-platform standalone binaries. You can develop an application for Windows, MacOS and Linux operating systems. However, the application to be created will not be too magnificent.
In general, Python programming language does not provide much functionality in heavy programs because speed is at the forefront. Therefore, you can use languages such as C / C ++, which is one of the oldest programming languages.
How Python Makes Your Jobs Easy
The Python programming language consists of very short and readable syntaxes. Here’s the standard “Hello World” text in Python’s latest 3.x update.
print(“Hello world!“)
You can use many object elements to explicitly express many common program flows in Python. Consider a standard program that reads lines from a txt file inside a list object and you want it to sort them in array form. The code for this is as follows.
with open(‘apsisx.txt’) as apsisx_file:
file_lines = [x.strip(‘\n’) for x in apsisx_file]
In terms of the code syntax of the program, the “with/as” structure is a context manager that provides an efficient way to instantiate a code block of an object and then throw it out of that block. In this case, our object was apsisx_file, which was called with the open() function. This way, there is no need to write a few extra lines of code to open the file, read individual lines from it, and then close it.
The construct [x.strip(‘\n’) for x in apsisx_file] is another Python language schema. The build containing other elements (here, apsisx_file and other code) allows it to be repeated. It helps each duplicate object (that is, each x) be processed and automatically added to a list.
In general, in Python, you can write the “for…” pattern as a loop, which is also created in other languages. The key point is that Python is a way of economically expressing things like iterative loops over multiple objects. It also helps you perform a simple operation for each item in the loop or work with things that require explicit sampling or disposal.
Code blocks like these allow people who develop programs with the Python language to balance diversity and readability.
Python’s other language features complement common use cases. Most modern object types such as “Unicode strings” can be imported directly into the language. Data structures such as lists, dictionaries (hashmaps), tuples (used to store immutable structures of the object), and sets (used only for certain code strings) are available.
Comparison of Python 2 and Python 3
There are currently two versions of Python. These have the feature to open the horizons of new users. Of these, Python 2.x is referred to as “legacy” and will continue to be supported and receive updates into 2020. However, it can continue unofficially after this period has expired. Python 3.x has many features currently available to operating systems that are not available in version 2.x. The main ones are better concurrency controls and a more efficient build system.
Libraries of Python
Python’s recent success is due to its own rich ecosystem of additional third-party software. Python draws on support from both a powerful standard library and libraries that are easily available from third-party developers, and the knowledge gained is also readily available. This programming language has been enriched with decades of development as well as contributions.
Python’s standard library provides modules for common programming tasks such as math, array manipulation, file and directory access, networking, asynchronous operations, threading, multiprocess management. It also includes modules such as reading and writing structured file formats such as JSON and XML, handling compressed files, working with internet protocols and data formats (web pages, URLs, email) that handle common, high-level programming tasks required by modern applications. includes. Most external code that exposes the C-compatible foreign function interface can be accessed via Python’s ctypes module.
Default Python version be full arak is an undeveloped version. However, it can be used via Tkinter with a multiplatform GUI library and an embedded copy of the SQLite 3 database.
The thousands of third-party libraries included in the Python Package Index (PyPI) are the biggest indicators of Python’s popularity and versatility.
For Example:
The BeautifulSoup library acts as an all-in-one tool that performs full analysis of HTML and extracts all the data out.
Frameworks such as Flask and Django allow rapid development of web services that cover both simple and advanced use cases.
Multicloud services can be managed using Apache Libcloud with Python’s object model.
NumPy, Pandas, and Matplotlib speed up math and statistical operations and make data visualization easy.
Features That Downgrade Python
Like the C#, Java, and Go programming languages, Python also has pooled memory management. That is, it ensures that the programmer does not have to implement code to track and drop objects. Normally, garbage collection happens automatically in the background, but if this introduces a performance issue, you can trigger it manually or disable it entirely.
An important aspect of Python is that it has a dynamism structure. Because it is used as an object-oriented programming language, everything in the language is treated as an object, including functions and modules. In this way, the objects used make it much easier to write high-level code. Developers can perform complex object manipulations with just a few lines of code and even use parts of an application by arranging abstractions that can be modified as needed.
Python’s use of so-called “significant whitespace” can be considered one of Python’s best and worst features. The indentation on the second line below isn’t just for readability; It is part of the code sequence in Python’s use. Python compilation rejects lines of code that don’t use proper indentation to show control flow.
with open(‘apsisx.txt’) as apsisx_file:
file_lines = [x.strip(‘\n’) for x in apsisx_file]
As these spaces are sometimes cumbersome to use, they are considered a problem for beginners. But strict indentation rules are much less compelling than they generally seem. It also helps make it visually more readable.
Another potential annoyance when compared to languages like C or Java is how Python handles variable typing. By default, Python uses dynamic or “duck” intermediate scripting, which is great for fast coding, but can be problematic in large codebases. However, Python recently added support for optional compile-time hinting. In this way, projects that can benefit from static typing can be made usable.
Is Python Too Slow? It Doesn’t Have To Be That Way!
Among the negative rumors about Python is that it is slow. Generally speaking, yes Python is slow. Programs created via Python often run much slower than corresponding programs in C/C++ or Java.
Why so slow? This requires slow synchronization, as build programs analyze code line by line. The fact is that, even when compiled, it makes it difficult to optimize its speed for the language, as the objects in Python are determined through the code used. However, Python’s speed may not be as important as it sounds, and there are ways to mitigate it.
There Are Many Ways To Increase The Speed Of Python
Generally considered slow, Python isn’t always slow. Many Python programs are slow because they cannot properly import tasks from Python or third-party standard libraries. Using libraries like NumPy and Pandas, math and statistics operations can be increased tremendously, and the PyPy runtime provides a noticeable amount of speed for many Python applications.
There is a common saying among software developers. 90% of the program is its functionality, the remaining 10% is the code of the program. When you optimize the 10% slice, the acceleration of the program will be quite successful. With Python, you can convert that 10 percent to C or even assembly language using projects like Cython or Numba. However, the result is a bit slower to use than the program created in C, but it has the potential to compete with C when the memory micromanagement feature of C is ignored.
Developer Time Generally Beats Machine Time
Or to put it another way: For many tasks, development speed beats execution speed.
The running times of the same program, both in Python and in another language, vary. For example, in Python, the program is active in 6 seconds.
