python frameworks

Reasons to Use Python Programming Language

Python is a world-famous simple, powerful and general-purpose computer language. This is an open-source language. It is called a high-level language, dynamic language, graphical user interface language, object-oriented language, interpreter language. Python language can easily create complicated programs. It is easy to write a program in Python.

Big companies like NASA, Google, Yahoo, YouTube, Quora, Instagram, Pinterest, Spotify, Reddit, Facebook, Netflix, Dropbox, PayPal, Uber’s Tech, Amazon, IBM, Mozilla, Walt Disney Feature Animation among others

The Python Programming language runs on the operating system, such as Windows, Linux, UNIX, and Mac. Extensive Coding must be written in languages like C, C ++ and Java.  But in the language of Python, you can write a small coding and create the best program. The program written in a different computer language can be used in Python language. Similarly, a Python language program can be used in a different computer language. This is the most significant feature of Python language. There are a lot of standard libraries in Python. Using this can easily create a program. Using the Python language can create a web application, email parsing, network programming, desktop application, Internet protocol, games, robot program, mobile applications, GUI application, and scientific applications. Python is also widely used in fields such as Artificial Intelligence, Internet scripting, Embedded scripting, Database programming, System utilities, Machine Learning, Image Processing, Data Science, Web Development, Robotics, and Automation. With Python, you can create a simple Chatbot. You can create a computer language with Python.

Python Market Share 2019

Features of Python

There is a lot of python’s specialty. It is the first of which is an open-source language. This is a simple language. Simply learn this. If you learn python, you get the job efficiently. Write less of the coding and create a program. Below are the features of Python Development:

  • Beginner friendly
  • Large Community support
  • Great Employment Opportunities
  • Interpreter (Learning made easy)
  • An easy to learn language
  • Elegant
  • Object-oriented
  • Portable
  • A high-level interpreted language
  • Simple Elegant Syntax
  • Not overly strict
  • The expressiveness of the language
  • Good Language to Teach Programming
  • Great libraries
  • Readable code
  • Beautiful code
  • Rapid development
  • Open-source
  • Powerful
  • Dynamic typing
  • Get Immediately Jobs & Careers
  • Readable and Maintainable Code
  • Multiple Programming Paradigms
  • Compatible with Major Platforms and Systems
  • Scientific computing
  • Great documentation

Reasons to Use Python Programming Language

In the world today, Python is one of the most versatile programming languages liked by both expert and beginner programmers all over the world. However, its versatility alone isn’t enough to make Python the best choice out of all the other different programming languages. So, let’s find out why below:

  • Python is easily understood

Machine learning is mainly about identifying patterns in a given data and being able to improve on it and coming to intelligent decisions unaided. To achieve this, Python is better suited as a programming language because it is easily understood. Its easy readability and relatively less complicated nature make it a plausible option for quick prototyping.

  • Python has a vast array of libraries

Recall in the previous book that Python has an extensive library anyone can contribute to. Many of the built-in libraries can be used in artificial intelligence and machine learning. Some of those libraries are inclusive but not limited to, the following:

Tensorflow: Used as a neural network library of high levels.

Pylearn2: This library can also be used in data mining and machine learning, and is more flexible than scikit-learn.

Pandas: This library was developed using Numerical Python (NumPy) as a base array. Pandas provide quick running speed and several data engineering features for use. In the Python ecosystem, panda is a widely known library used in carrying out analysis of general-purpose data. A few of the data engineering features in panda includes the following; selecting subsets of data, reshaping data into various forms, finding and filling missing days, combing multiple datasets together, calculating down columns and across rows, and reading and writing several data formats, among other things.

Numerical Python (NumPy): This library is a basic package which is required for improved data analysis performance, as well as scientific computing within the Python language. Modified tools such as pandas and scikit-learn are designed using NumPy as a foundation. Many operations in NumPy are implemented in C, meaning NumPy is quite quick, making it an invaluable tool for use in programming.

Scikit-learn: Used in analysis, data mining, and machine learning, it is among the widely used libraries of machine learning. This library supports a range of both supervised and supervised algorithms such as k-means, decision trees, clustering, among other things. Scikit-learn is based on two essential libraries in Python, namely; SciPy and NumPy. Scikit-learn makes the implementation of activities such as ensemble methods, data transformation, feature selection, etcetera, possible in several lines.

Matplotlib and Seaborn: For a machine learning expert, both storytelling and data visualization are key factors necessary to execute dataset analysis before deciphering whether or not to carry out a given command. Matplotlib is widely known for its use in the 2D python visualization library. The availability of a wide range of commands and interfaces lets professionals design graphics with publication-quality using the data given.

  • Python has a flexible syntax and ample readability

Since Python identified as an object-oriented language, it makes use of a friendly syntax and modern scripting. As such, it is designed with a form of readability that is almost at the level of human comprehension. The scripting characteristic of Python helps programmers and coders to try out their hypotheses and execute their algorithms easier and faster. This explains why structural programming languages such as C++, Perl, and Java, which need hard coding are not the preferred alternatives in machine learning. In summary, in the hands of a beginner or expert, many things can be achieved using Python, which is an ideal language for carrying out complex tasks in machine learning.

  • Natural Simplicity

It is easy to read and concise coding with python. It is arguably the best programming language in terms of easy usage and simplicity, even for beginner programmers. In machine learning, there are 2 requirements, namely, sophisticated algorithms and multistage workflows. For professionals in machine learning, a language which promises fewer details of coding means more focus is placed in searching for answers to issues and pursuing the aim of given projects. Furthermore, when collaborative coding is required, or projects have to alternate between teams in machine learning, the easy readability of Python codes play a highly beneficial role in driving business. When the project is equipped with lots of 3rd party elements, python tends to be more useful. The simplicity involved in Python aids in developing projects faster in comparison to different other languages used in programming, making developers easily carry out a test on algorithms without necessarily having to attempt implementation.

  • Implementation becomes easy and more efficient with Python

The simplicity and efficiency of implementation in Python is one factor which makes it a favorite in machine learning. On the part of other programming languages, beginner programmers or learners have first to familiarize themselves with the language before they can be able to apply it in artificial intelligence or machine learning. In the case of Python, the story is different. With only a basic knowledge of Python, one can put it to use in machine learning as a result of the extensive array of libraries, tools, and resources available for use. In addition, debugging errors and writing codes becomes relatively more straightforward and quicker when compared to other programming languages, mainly C++ or Java. Generally, programmers in artificial intelligence and machine learning would prefer to use their time designing heuristics and algorithms instead of debugging their codes for errors in syntax.

Since algorithms are widely used in machine learning, coding in Python becomes relatively easier for developers to carry out testing. Python has the ability to command a singular logic. In conclusion, the integrated approach used in Python allows programmers to analyze the methodology of their codes.

The reasons as mentioned above and more are why Python is a well sought after programming language, and a favorite in machine learning.

Also read: Top python frameworks

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Written by:

Stuti Dhruv

Stuti Dhruv is a Senior Consultant at Aalpha Information Systems, specializing in pre-sales and advising clients on the latest technology trends. With years of experience in the IT industry, she helps businesses harness the power of technology for growth and success.

Stuti Dhruv is a Senior Consultant at Aalpha Information Systems, specializing in pre-sales and advising clients on the latest technology trends. With years of experience in the IT industry, she helps businesses harness the power of technology for growth and success.