benefits of python programming language

14 attractive benefits of Python Programming language

Before getting into the attractive benefits of Python programming language, let’s look into history of python language. History of python language starts from late 1980s, when it was envisioned. However, it was started to implement in December, 1989 by Dutch programmer Guido van Rossum, Netherlands. He was working as successor to ABC programming language capable of exception interfacing and handling, with Amoeba operating system. He is principal author of python programming language, and at this time he is member of Python steering council. For a long time he played central role in deciding the direction of python. This was the reason he was awarded honorable title by Python community as “Benevolent Dictator For Life” (BDFL). Through this title, he continued to supervise Python expansion and hold final vote in case of dispute in Python community. However, last year i.e. 2018, he steps down from this post.

  • Story behind name “Python”.

Mostly people presume that name of this programming language was choose after a snake. Even the logo of python language portrays the picture of two yellow and blue snakes. However, narrative of its name is rather interesting from its look.

“In 1970s “Month Python’s Flying Circus” was a renowned BBC comedy TV show and Van Rossum was big fan of that show. When he started implementing Python, Guido van Rossum was also studying printed scripts from “Month Python’s Flying Circus”. While developing programming, Guido needed a unique, short and little mysteries name for his language, so he decided name “Python”.”

  • Python is top ranked programming language.

Due to its developer responsiveness and simplicity, Python programming language is one of the top marketable professional skills. It has firm claim of being major fastest-growing programming language, which is somehow true. As per TIOBE Programming Community Index since 2003, Python language has continuously ranked in top ten most popular programming languages. In-fact, in April, 2018 it was ranked fourth position. Even, by IEEE spectrum ranking of 2017 regarding top programming languages, Python language ranked first position. However, in year 2018, Python has been ranked at third position by RedMonk Programming language rankings.

  • Benefits of Python language.

  1. Machine Learning.

In order to learn from dataset and experience, machine learning is an adaptive and effective tool. Many machine-learning techniques and algorithms have been developed which facilitates computers to learn. Machine-learning has its origin in Statistics and Computer Science. On the top of Python scientific built tools like SciPy, Matplotlib and NumPy, a famous Machine Learning tool is Scikit-Learn. Scikit-Learn support different models for Regression, Clustering, Classification, Dimensionality Reduction, Model Selection and Preprocessing. Some benefits of Scikit-Learn, are consistent APIs, integration of parallelization, good documentation and it’s available under license of BSD and commercial license with full support.

  1. Natural Language Processing.

Natural language processing is utilized for reading and understanding text. NLTK (Natural Language Toolkit) is a popular library used in Python for natural language processing. In order to understand text, it has abundant trained algorithms. NLTK has vast corpora of lexical resources like chat logs, journals, movie reviews and much more. Alongwith NLTK is available under Apache License V.2.0.

  1. Data Analyzing (Pandas).

Landscape of data analysis in Python has altogether been changed by Pandas library and available under BSD license. It is built on top of NumPy and it has two fundamental data structures i.e. DataFrame and Series. It can hold any kind of data like floats, integers, strings and objects etc. Every data stored in series, is tagged after the index. DataFrame is structured in tabular data with labeled columns and rows akin like Excel spreadsheet. Actually, data is never in-order. In order to fill-in missing data, slicing, merging, indexing, reshaping of datasets, and joining of dataset, pandas can be used. Moreover, in order to read Microsoft excel, comma-separated values (CSV) files, Hierarchical Data Format (HDF5) and SQL database, pandas can be utilized.

  1. Academia.

In many computer science departments at American Universities, Python is being recommended as introductory programming language. At an accelerated pace, Python programming language is being adapted for research purposes by academia.

It has some advantages over Matlab, e.g. Python is a real programming language, and Matlab is not. It has many scientific tools which are equally good as of Matlab modules.

Nowadays, developers required to accomplish work in various languages, including Python. Every language have index starting from zero while index of Matlab starts from 1, which may cause more syntactical errors. For functions and indexing, Matlab uses parentheses, which confuses codes. However, for clarity of codes, Python uses parentheses for functions and square brackets for indexing. Moreover, Python is open source and free while Matlab is closed with costly licensing agreement.

Raspberry-Pi foundation started Raspberry project in order to bring computer knowledge to kids and elderly people. Furthermore, purpose of this project was to familiarize computer knowledge to lower-stratum of society who is left without computer education. This groundwork brings, low-cost and tiny Raspberry Pi devices, enough powerful to do most work as was doing desktop. In Raspberry Pi Python is recommended as preferred programming language, due to its simplicity of learning.

  1. Excellent Scientific Tools.

For analyzing and simulating complex systems, scientific tools are essential.

Even most of these scientific tools can be used without limitation, available under the license of Berkeley Software Distribution (BSD).

  1. NumPy offers N-dimensional array objects that can be utilized in Fourier transform, linear algebra and other mathematical operations.
  2. SciPy library is largely used for optimization and numerical integration.
  3. Jupyter has played an important role in revolutionizing the programming in Python. It provides an interactive web-based interface which can be invoked from browser. It is also used to create embeddable plots to visualize data and inscribe Python programs.
  4. SymPy library is utilized to produce symbolic mathematics.
  5. Matplotlib is one of the most popular and oldest plotting library available for Python.

These scientific tools, provides better chances to create working prototypes and solve scientific problems, comparatively more quickly.

  1. Statistical Analysis.

For statistical analysis, Python library i.e. Statsmodels is utilized. It also supports various features and models like generalized linear models, discrete choice models and linear aggression models. Statsmodels is tested thoroughly, in order to ensure accuracy of results, by comparing other statistical packages. Alongwith pandas, Statsmodels also can be used to fit statistical models. Under modified BSD license, this package is also available. It is used across various fields like finance, economics and engineering.

  1. HTTP library.

The Requests HTTP library is famously known as library created for humans. Python has standard HTTP library to carry out mostly HTTP operations, known as urllib.request. But the APIs (Application Programming Interfaces of urllib.request are very wordy and difficult to use. In order to resolve these issues, Requests was created as stand-alone library. Common HTTP verbs like GET, PUT, POST AND DELETE which communicate read, update, create and delete operations, are fully supported. Additionally, Requests offers features like support for Connection timeouts, International Domains, thread-safety, Cookie Persistence. This library is available under Apache license 2.0.

  1. NoSQL Connectors/ ORM/ Database Connectors.

Database connectors are drivers which allow to query the database from itself programming language. PostgreSQL and MySQL are most famous open source databases. From Oracle, MySQL-Python-Connector is well known Python connector which is available for MySQL. For PostgreSQL, Psycopg2 is Python connector which is extensively used. ORM (Object-Role-Modeling) is authoritative way of querying database to attain persistence so that data can survive beyond application process.

There is difference between Relational databases and object-oriented language models. This difference leads to several problems like inheritance, navigations, associations, identity and granularity. ORM follows business layer logic, and helps regarding mapping data, from Object-oriented languages to Relational databases.

For popular NoSQL databases like Cassandra and MongoDB (written in Python language), Python connectors are also available.

  1. Web frameworks.

There are two most trendy web-frameworks, and both have different purposes. Flask in micro-framework and Django is developed and full-fledged framework. Flask is used to build little applications with least requirements. Django has integrated support for many web-related services like internationalization, serialization, caching, automatic admin interface and ORM support. On the other hand Flask allows users to organize web-services as per your needs thereby installing external libraries. Under BSD derived licenses, both of these frameworks are available to use.

  1. Python Distributions.

With standard libraries, PSF (Python Software Foundation) releases Python analyst/ interpreter. But, to utilize Python in enterprise or scientific environment, other packages required installation. And to test compatibility of these packages with latest release of Python, is very time-consuming and cumbersome. However, Enthought Canopy Express and Anaconda are two famous distributions which come with core Python interpreter. These are also famous scientific tools which help to start out-of-the-box working.

  1. Availability of IDE (e.g. PyCharm & Spyder).

For Python programming, PyCharm is the much famous IDE. It comes in three editions i.e. Community Edition, Education Edition and Professional Edition. It has advance attributes like code highlighting, code completion, refactoring, support for various web-frameworks and remote development capacities. It is also available for various platforms like Linux, Windows and OS-X. An extension for Visual Studio called PTVS (Python Tools for Visual Studio) has also been released by Microsoft Company. It converts visual Studio IDE into complete Python IDE. Similarly, another IDE is Spyder which comes as part of Anaconda distribution itself.

  1. Cloud Computing (e.g. OpenStack).

OpenStack software, controls large groups of storage, compute and networking resources through datacenter managed through dashboard, is written in Python. It is used to generate a scalable public and private cloud. Its Foundation supervises the development of OpenStack software. It has classy load balancing which is vendor independent, extremely reliable and has built-in security. It uses dashboard as middle unit to manage processing power, network resources and storage in data-center. Linux distributions like Ubuntu and Fedora comprise OpenStack as element of their package. Python application’s hosting on cloud platform is supported by various cloud service providers like Amazon Web-Services (AWB), Microsoft-Azure and Google-App-Engine.

  1. Python Community.

If any credit can be given for the success of Open Source projects, it would be Community. Development of projects is taken advance by adding new elements and role of Community members for testing the software, recommending others and in documenting software. Members of Python community are expected to follow code of conduct adopted by the community and generally considered very helpful. This community is very active and friendly in order to accommodate the inexperienced and newcomers. Python conferences regularly held all over the world, wherein core Python developers are invited to share their work experience. Such sharing with other developers paves the way for further Python adaptation across the world. Python language documentation is famous for its completeness and depth.

  1. Stack in Industry.

Many companies uses Python stack to command their communications.

  1. In application development of the most famous photo sharing service i.e. Instagram Django framework was used.
  2. In development of Firefox web-browser, by Mozilla, majority of development was done using Django framework.
  3. eBay and PayPal where every year transactions in billions of dollars take place, also powered by security-features of Python library.
  4. For their web-development framework, Twilio and Pinterest have used Flask framework.
  5. In major projects of companies like Google, Amazon, Twitter and Washington Post Requests library is used.
  6. In Boeing, NASA ad LinkedIn, Python data analysis and Scientific tools are being used.

 It is not possible to sum-up list of companies, using python, however, to solve challenging problems, many large and well-known companies uses python nowadays.

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