Data science: The big picture and why you should care
Python is extensively used by data scientists. Python language is among the popular data science programming languages not only with the top big data companies but also with the startups.
Organizations of all sizes and industries – from the top financial institutions to the smallest big data start-ups are using the Python programming language to run their business.
Python is uniquely placed in both the list – Technologies which are featured most strongly in job vacancy advertisements as well as in the list of the highest paying jobs. That makes it the best option for software engineers to learn.
Let’s have a look at some of the unique features of Python which places it so useful in the software industry:
Python Unique features
Python can be embedsed within your C/C++ programs to give ‘scripting’ capabilities. Due to Python’s extensive mathematics library, and the third-party library NumPy ,it is frequently used as a scientific scripting language to aid in problems such as numerical data processing and manipulation.
Python, does not need compilation to binary. Program can be directly run from the source code. Python converts the source code into an intermediate form called bytecodes and then translates this into the native language of using computer and then runs it. This actually, makes using Python much easier since there is no compilation dependency, making sure that the proper libraries are linked and loaded etc. This makes the Python programs more portable, and all we need to do just copy Python program onto another computer to make workable!
Runtime error checking:
Python provides two very important features to handle any unexpected error in your Python programs and to add debugging capabilities in them.
Exception Class : Exception class in the base class for all exceptions
The best way to handle exceptions is by using a “try-except” block:
(x,y) = (5,0)
z = x/y
print “divide by zero”
Assertions: An assert is used to check the error condition during program execution which can be turned or off once testing is complete.
When assertion condition is true nothing happens and programs continues to run but when error condition occurs ,Python raises an AssertionError exception.
The syntax for assert is −
assert Expression[, Arguments]
Python Parallel Computing :
Python parallel computing allows to carry out many calculations parallelly, which in turn reduces the amount of time it takes to run your program to completion.
Python supports test frameworks/tools which are supported across platforms and browsers e.g Selenium, Splinter.
Support of UI applications:
Python has a variety number of GUI frameworks which are available for UI , from TkInter (traditionally bundled with Python, using Tk) to a number of other cross-platform solutions, which also has bindings to platform-specific (also known as “native”) technologies.
Some of the examples are below:
- Cross-Browser Frameworks: PyJamas
- Cross-Platform Frameworks: appJar,AVC,CEF ,Python,Dabo,ENAML,formlayout,GnomePython etc.
- Platform-specific Frameworks: Chaquopy,IronPython Studio,MacPython,Ocean,PyMUI etc
GUI Design Tools and IDEs:
Autoglade, Blackadder, Boa Constructor , Gazpacho ,PythonWorkset
Python for AI (Artificial Intelligence) :
Artificial intelligence is the intelligence demonstrated by machines. AI is a way of making a remote-controlled computer, or a software which thinks intelligently. AI is accomplished by statistically and logically studying how human brain thinks and how humans brain learn, decide, and work , and then using those outcomes of this study a basis of developing intelligent software and systems.
Python provides lots of Web programming frameworks for developing websites. Web frameworks supports by python are Pylons, Django, Zope2, TurboGears, web.py, Grok, web2py etc. Most popular framework by python developers is Django.
Python for Machine Learning
Python is now a days most preferred language for computer science research.
Python for Startups
Startups are the kinds of businesses that they have to hit the market when it is hot, because the competition in IT is so fast growing and changing. Python allows you to have a prototype working, of your idea of product in one or two months, with the help of a small team. Start ups have to spend small amount to get proof of concept or even a working prototype straight to clients to get the funds you need so desperately. If one can take learning from the success of other startups that have gone big and successful can also help you find the best solutions for your project.
So, overall Python is simple to learn and use, Free and open source, used by the large community.
Python commands are mostly in simple English which are easy to remember and write, readable. Python has set of standard libraries which offer a lot of functionalities which lets you implement complex applications easily.
Python is designed with the new beginners in mind. The use of white space and common expressions has removed for the need of tedious variable declarations and braces.