Concept Of Python Language - SKengineers

 

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. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.

Often, programmers fall in love with Python because of the increased productivity it provides. Since there is no compilation step, the edit-test-debug cycle is incredibly fast. Debugging Python programs is easy: a bug or bad input will never cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception. When the program doesn't catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on. The debugger is written in Python itself, testifying to Python's introspective power. On the other hand, often the quickest way to debug a program is to add a few print statements to the source: the fast edit-test-debug cycle makes this simple approach very effective.

What is Python used for?

Python is commonly used for developing websites and software, task automation, data analysis, and data visualization. Since it’s relatively easy to learn, Python has been adopted by many non-programmers such as accountants and scientists, for a variety of everyday tasks, like organizing finances.

“Writing programs is a very creative and rewarding activity,” says University of Michigan and Coursera instructor Charles R Severance in his book Python for Everybody. “You can write programs for many reasons, ranging from making your living to solving a difficult data analysis problem to having fun to helping someone else solve a problem.”

Here’s a closer look at some of the common ways Python is used.

If you’re new to Python (and programming in general), you can begin writing your first Python program in minutes by taking Python for Everybody from the University of Michigan.

Data analysis and machine learning -

Python has become a staple in data science, allowing data analysts and other professionals to use the language to conduct complex statistical calculations, create data visualizations, build machine learning algorithms, manipulate and analyze data, and complete other data-related tasks.

Python can build a wide range of different data visualizations, like line and bar graphs, pie charts, histograms, and 3D plots. Python also has a number of libraries that enable coders to write programs for data analysis and machine learning more quickly and efficiently, like TensorFlow and Keras.

Learn Python for data analysis -

Start building the job-ready skills you’ll need as a data analyst, including Python, SQL, and Excel, with the IBM Data Analyst Professional Certificate on Coursera. You can finish in less than six months with a credential from an industry leader.

Web development -

Python is often used to develop the back end of a website or application—the parts that a user doesn’t see. Python’s role in web development can include sending data to and from servers, processing data and communicating with databases, URL routing, and ensuring security. Python offers several frameworks for web development. Commonly used ones include Django and Flask.

Some web development jobs that use Python include back-end engineers, full stack engineers, Python developers, software engineers, and DevOps engineers.

Automation or scripting -

If you find yourself performing a task over and over again, you could work more efficiently by automating it with Python. Writing code used to build these automated processes is called scripting. In the coding world, automation can be used to check for errors across multiple files, convert files, execute simple math, and remove duplicates in data.

Python can even be used by relative beginners to automate simple tasks on the computer—such as renaming files, finding and downloading online content or sending emails or texts at desired intervals.

Learn Python for automation -

Boost your Python automation credentials with the Google IT Automation with Python Professional Certificate.

Software testing and prototyping -

In software development, Python can aid in tasks like build control, bug tracking, and testing. With Python, software developers can automate testing for new products or features. Some Python tools used for software testing include Green and Requestium.

Everyday tasks -

Python isn't only for programmers and data scientists. Learning Python can open new possibilities for those in less data-heavy professions, like journalists, small business owners, or social media marketers. Python can also enable non-programmer to simplify certain tasks in their lives. Here are just a few of the tasks you could automate with Python:

Keep track of stock market or crypto prices

Send yourself a text reminder to carry an umbrella anytime it’s raining

Update your grocery shopping list

Renaming large batches of files

Converting text files to spreadsheets

Randomly assign chores to family members

Fill out online forms automatically

Why is Python so popular?

Python is popular for a number of reasons. Here’s a deeper look at what makes it so versatile and easy to use for coders.

It has a simple syntax that mimics natural language, so it’s easier to read and understand. This makes it quicker to build projects, and faster to improve on them.

It’s versatile. Python can be used for many different tasks, from web development to machine learning.

It’s beginner friendly, making it popular for entry-level coders.

It’s open source, which means it’s free to use and distribute, even for commercial purposes.

Python’s archive of modules and libraries—bundles of code that third-party users have created to expand Python’s capabilities—is vast and growing.

Python has a large and active community that contributes to Python’s pool of modules and libraries, and acts as a helpful resource for other programmers. The vast support community means that if coders run into a stumbling block, finding a solution is relatively easy; somebody is bound to have run into the same problem before.

Programming examples -

Hello world program -

print('Hello, world!')

Program to calculate the factorial of a positive integer:

 

n = int(input('Type a number, and its factorial will be printed: '))

 

if n < 0:

    raise ValueError('You must enter a non-negative integer')

 

factorial = 1

for i in range(2, n + 1):

    factorial *= i

 

print(factorial)

Python Examples -

Python is a great choice for -

Web and Internet development (e.g., Django and Pyramid frameworks, Flask and Bottle micro-frameworks)

Scientific and numeric computing (e.g., SciPy – a collection of packages for the purposes of mathematics, science, and engineering; Ipython – an interactive shell that features editing and recording of work sessions)

Education (it’s a brilliant language for teaching programming!)

Desktop GUIs (e.g., wxWidgets, Kivy, Qt)

Software Development (build control, management, and testing – Scons, Buildbot, Apache Gump, Roundup, Trac)

Business applications (ERP and e-commerce systems – Odoo, Tryton)

Games (e.g., Battlefield series, Sid Meier\’s Civilization IV…), websites and services (e.g., Dropbox, UBER, Pinterest, BuzzFeed…)

Implementations -

CPython is the reference implementation of Python. It is written in C, meeting the C89 standard with several select C99 features (with later C versions out, it's considered outdated; CPython includes its own C extensions, but third-party extensions are not limited to older C versions, can e.g. be implemented with C11 or C++ It compiles Python programs into an intermediate bytecode which is then executed by its virtual machine. CPython is distributed with a large standard library written in a mixture of C and native Python. It is available for many platforms, including Windows (starting with Python 3.9, the Python installer deliberately fails to install on Windows 7 and 8;  XP was supported until Python 3.5) and most modern Unix-like systems, including macOS (and Apple M1 Macs, since Python 3.9.1, with experimental installer) and unofficial support for e.g. VMS. Platform portability was one of its earliest priorities, during the Python 1 and Python 2 time-frame, even OS/2 and Solaris were supported; support has since been dropped for a lot of platforms.

Other implementations -

PyPy is a fast, compliant interpreter of Python 2.7 and 3.6. Its just-in-time compiler brings a significant speed improvement over CPython but several libraries written in C cannot be used with it.

Stackless Python is a significant fork of CPython that implements microthreads; it does not use the call stack in the same way, thus allowing massively concurrent programs. PyPy also has a stackless version.

MicroPython and CircuitPython are Python 3 variants optimized for microcontrollers, including Lego Mindstorms EV3.

Pyston is a variant of the Python runtime that uses just-in-time compilation to speed up the execution of Python programs.

Cinder is a performance-oriented fork of CPython 3.8 that contains a number of optimizations including bytecode inline caching, eager evaluation of coroutines, a method-at-a-time JIT and an experimental bytecode compiler.

Unsupported implementations -

Other just-in-time Python compilers have been developed, but are now unsupported:

Google began a project named Unladen Swallow in 2009, with the aim of speeding up the Python interpreter fivefold by using the LLVM, and of improving its multithreading ability to scale to thousands of cores, while ordinary implementations suffer from the global interpreter lock.

Psyco is a discontinued just-in-time specializing compiler that integrates with CPython and transforms bytecode to machine code at runtime. The emitted code is specialized for certain data types and is faster than the standard Python code. Psyco does not support Python 2.7 or later.

PyS60 was a Python 2 interpreter for Series 60 mobile phones released by Nokia in 2005. It implemented many of the modules from the standard library and some additional modules for integrating with the Symbian operating system. The Nokia N900 also supports Python with GTK widget libraries, enabling programs to be written and run on the target device.

Cross-compilers to other languages -

There are several compilers to high-level object languages, with either unrestricted Python, a restricted subset of Python, or a language similar to Python as the source language:

Cython compiles (a superset of) Python 2.7 to C (while the resulting code is also usable with Python 3 and also e.g. C++).

Nuitka compiles Python into C++.

Pythran compiles a subset of Python 3 to C++.

Pyrex (latest release in 2010) and Shed Skin (latest release in 2013) compile to C and C++ respectively.

Google's Grumpy (latest release in 2017) transpiles Python 2 to Go.

IronPython allows running Python 2.7 programs (and an alpha, released in 2021, is also available for "Python 3.4, although features and behaviors from later versions may be included"on the .NET Common Language Runtime.

Jython compiles Python 2.7 to Java bytecode, allowing the use of the Java libraries from a Python program.

MyHDL is a Python-based hardware description language (HDL), that converts MyHDL code to Verilog or VHDL code.

Numba uses LLVM to compile a subset of Python to machine code.

Brython, Transcryptand Pyjs (latest release in 2012) compile Python to JavaScript.

RPython can be compiled to C, and is used to build the PyPy interpreter of Python.

Performance -

A performance comparison of various Python implementations on a non-numerical (combinatorial) workload was presented at EuroSciPy '13. Python's performance compared to other programming languages is also benchmarked by The Computer Language Benchmarks Game.

Development -

Python's development is conducted largely through the Python Enhancement Proposal (PEP) process, the primary mechanism for proposing major new features, collecting community input on issues and documenting Python design decisions. Python coding style is covered in PEP 8. Outstanding PEPs are reviewed and commented on by the Python community and the steering council.

Enhancement of the language corresponds with development of the CPython reference implementation. The mailing list python-dev is the primary forum for the language's development. Specific issues are discussed in the Roundup bug tracker hosted at bugs.python.org. Development originally took place on a self-hosted source-code repository running Mercurial, until Python moved to GitHub in January 2017.

CPython's public releases come in three types, distinguished by which part of the version number is incremented:

Backward-incompatible versions, where code is expected to break and needs to be manually ported. The first part of the version number is incremented. These releases happen infrequently—version 3.0 was released 8 years after 2.0.

Major or "feature" releases are largely compatible with the previous version but introduce new features. The second part of the version number is incremented. Starting with Python 3.9, these releases are expected to happen annually. Each major version is supported by bugfixes for several years after its release.

Bugfix releases, which introduce no new features, occur about every 3 months and are made when a sufficient number of bugs have been fixed upstream since the last release. Security vulnerabilities are also patched in these releases. The third and final part of the version number is incremented.

Many alpha, beta, and release-candidates are also released as previews and for testing before final releases. Although there is a rough schedule for each release, they are often delayed if the code is not ready. Python's development team monitors the state of the code by running the large unit test suite during development.

The major academic conference on Python is PyCon. There are also special Python mentoring programmes, such as Pyladies.

Python 3.10 deprecated wstr (to be removed in Python 3.12; meaning Python extensions need to be modified by then), and added pattern matching to the language.

API documentation generators -

Tools that can generate documentation for Python API include pydoc (available as part of standard library), Sphinx, Pdoc and its forks, Doxygen and Graphviz, among others.

Naming -

Python's name is derived from the British comedy group Monty Python, whom Python creator Guido van Rossum enjoyed while developing the language. Monty Python references appear frequently in Python code and culture; for example, the metasyntactic variables often used in Python literature are spam and eggs instead of the traditional foo and bar. The official Python documentation also contains various references to Monty Python routines.

The prefix Py- is used to show that something is related to Python. Examples of the use of this prefix in names of Python applications or libraries include Pygame, a binding of SDL to Python (commonly used to create games); PyQt and PyGTK, which bind Qt and GTK to Python respectively; and PyPy, a Python implementation originally written in Python.