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Introduction to Python for Engineers and Scientists Step by Step Guide

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The idea of Python can be defined in a single verdict:

“Python is a multipurpose, portable, object-oriented, high-level programming language that permits an interactive environment to code in a minimalistic way.”

Python appeared as a leader amongst well-established and enhanced languages including C, C++, and Java for very modest reasons. Python includes the principles of the philosophy that difficult tasks can be completed in simple ways. We incline to think that real-world, difficult problems need complex paths to produce complex solutions. The developers of Python comprised the exact opposite philosophy. Python was fashioned to have a tremendously fast and simple learning curve and development procedure for software engineers. Consequently, it is considered the most general-purpose programming language subsequently users can work in almost any study field and still be able to find a valuable piece of code for themselves. Python harnessed the power of the open source movement, which helped it accumulate a huge user base from almost all walks of life. Being open source in nature, Python permitted people to make small programs and share them with each other with comfort. In Python, a collection of programs for accomplishment various tasks makes up a module (package). The great number of modules and developers has permitted Python’s use to jump rapidly within the computer science community and finally grab the number one position as the most favored programming language.

Furthermore, in this guide you’ll get to know details about given topics:

High-Level Programming

A high-level programming language is a language that is separated from the details of a particular computer. A low-level language is one where users directly feed the machine code to a processor for obtaining results.

Interactive Environment

To a large extent, Python derives its philosophy from the ABC language. The syntax structure was largely derived from C and UNIX’s Bourne shell environments. They served as inspiration for the interpretative nature of the working environment. The interpretive nature means that Python presents a REPL-based interactive environment to a developer.

Object Orientation

An object-oriented programming (OOP) language deals with data as an object on which different methods act to produce a desired result. Everything computable is treated as an object. Its nature is defined as its properties. These properties can be probed by functions, which are called methods. The abstract nature of objects makes it possible to invent objects of the user’s choice and apply the programming concepts for a variety of applications.

Multipurpose Nature

As discussed in the preceding section, the OOP-based architecture of Python enables developers from different walks of life to use and enrich the language in their fields of expertise. Virtually all fields of computations have used Python. You can define a module specific for one kind of problem.

Minimalistic Design

The minimalistic design philosophy of Python means that it emphasizes code readability. It also provides a syntax structure that allows programmers to express concepts in fewer lines of code than in languages such as C++ and Java. Moreover, Python provides the means to write programs that can be scaled up easily. Python features a dynamic type system where a Python interpreter guesses the type of an object by its definition, which avoids the user’s need to define the same.

Portability

Since Python belongs to an open source community, it has been ported (that is, adapted to work on) to many many platforms so that Python code written on one platform can run without modification on others (except system-dependent features).

Extensibility

Rather than providing all functionalities in its core program, Python’s creators designed it to be highly extensible. Users can thus choose to have functionality as per their requirements.

Modular Programming

The modular nature of Python programming incorporates the complex tasks being divided into small modules that seamlessly interact with each other. Modules make both development and debugging easier, and they can be simply imported to enable the use of various functions.

Installation

  • Windows
  • Ubuntu
  • Mac OS X

Using the Python Interpreter

Anaconda IDE

Python as a Calculator

Python Environment

  • Installing Virtualenv
  • Activating Virtualenv
  • Deactivating the Virtual Environment

IPython Notebooks

Saving a Jupyter Notebook

Online Jupyter Environment





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