Ask me why Python is so popular amongst the programming languages. The answer to this is quite short, crisp and simple yet very satisfying similar to the code built on it. There are many coding languages, and a handful of those will wake you up from a happy sleep. Python showcases a very smooth outlook to the traditional way of developing a program. Don't believe me? Have a look at the sample code online.
Now compare it with other languages like Perl, Java, Tcl, C++. It seems that my words are entirely justified eh.
The career with Python is sure to meet your expectations.
Either you are looking for a headstart in an aggressively progressing career or waiting for a leaping step in your current stream Python outstands all of its competitors. With its increasing credibility, demand and adoption of learning Python can help in forming a firm base for its coders. Python professionals well paid as per the industry standards. Widely accepted, flexible, compatible and runs swiftly on many operating systems like Mac, Windows, Linux and .net.merging with the back-end processes of organizations worldwide. Its legacy dates from the late 1980's. Python is developed, supported, managed and monitored by the Python Software Foundation(PSF).
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With its intensive set of features supporting the starters and professionals migrating to Python, it stands out as an excellent choice of skillset.
Free to purchase and is an open-source
Python has a free open source license(OSL) which is also valid for commercial purposes. It can be downloaded and installed for free directly from the official site.
Ease of learning, coding, and implementation
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Python is a giant in advanced coding languages with most of its instructions acutely resembling the English language. It can be learned quickly by a rookie compared to complex coding languages like C++ and Java. It adds convenience to a professional with expertise on software other than Python with its resemblance to object-oriented structures with other software.
Fast, flexible and portable
Python is an interpreted language, the code gets checked during the execution unlike compiling and then running the system followed by others. The instructions written in Python by one developer can be understood and modified by another developer easily. Code developed on Windows os can be executed and improvised on another os.
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Python supports various domains
Python Package Index provides with the detailed listings of the catered packages. Python contains several standard libraries for including modules like GUI, Test, Automation, DB, Networking, Web Development, Image processing and Text processing, etc. Part played by these libraries are detailed below.
- Machine learning - Agility is obtained for improvising AI machines with TensorFlow and Keras libraries.
- Hadoop - Employs Pydoop library to support processing of Big data.
- Web Development - Django, Pylons, and Flask frameworks coded in Python are more stable for developing websites.
- Automated testing - Selenium and Splinter automated testing tools have application programming interface that can execute on Python. With Pytest one can also test on cross-platforms and cross-browsers.
- Graphics - With Tkinter library of this software GUI applications can be written and run effortlessly.
- Image processing - Python Image processing library PIL supports imaging files from various formats.
Python's support for Scientific libraries
Dealing with high volumes of data and performing analytics at the same time is a considerable issue these days as it keeps on adding to the vault. Data Science with Python training provides advanced features to hit this targeted arena. Python with its support to scientific libraries is rapidly improving on data processing levels. Python drives into clearing the blocks in statistic data modeling with the help of its Numpy, Scipy, Pandas, and Matplotlib
- Numpy: Some call it a numeric python due to its support for higher level mathematical calculations. The arrays and matrices can be made in multidimensional arrays facilitating the speed.
- Scipy: Scipy supports several scientific, the mathematical calculation as linear algebra, Fourier transforms, interpolation tools, signal processing, and statistics.
- Pandas: Pandas deliver data frame functionality and help in data munging(extraction of data into a proper format from the raw data also indexing it). It is quite intelligent in aligning the messed up or missing data into a readable format. It supports SQL database, CSV, Excel, Text files.
- Matplotlib: It is an advanced library for Python users to develop graphs. These are not just any ordinary graphs they are formed from a set of sophisticated analytics and includes Line Plots, Histograms, Scatter Plots, 3D Plots, Contour Plots and Polar Plots.
It follows both Procedural and OOP Coding patterns
Coding in Python follows procedural functions and Object Oriented programming concepts. Long lines of the program written in procedural pattern with a mix of code and data to feed. It involves functions and program subroutines and is often less manageable. Where as Oops includes the programming with class, objects, and methods. These three pave the way to inheritance, abstraction, and polymorphism functional behaviors.
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- The class is the structural unit of an object consisting of grouped data and function with reuse capability. Its functional process can define as a method. The attribute is a term used for the container of data and capacity. There are of two categories of characteristics; built-in can apply instantly and those defined by users in the class.
- The object is used to create a class instance during runtime or when the code is made operational.
- Abstraction is a process used in class to hide complex procedures simplifying its appearance.
- Inheritance is a phenomenon used by the subclass which inherits and uses the functions and attributes of the primary or parent class thus code can reuse. You can then improvise the derived class or subclass without affecting the main one.
- Polymorphism is employed when using inheritance helping the inherited class to perform the same functions of the parent class differently.
Duration of learning and Mastering Python
The below mentioned curve relates to the learning process in Python. Learning Python may vary from 2 to 6 months depending on the pace and the hours spent on it.
As displayed in the curve a beginner learns from the basics steadily stepping the ladder of expertise. Some may leave the track due to lack of commitment yet most of them are sure to reach a higher mark with their gradual and consistent efforts.
Most of the earning more than their expectations with endless opportunities in Python. We would have quoted a figure but do not want to limit you there.
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