1.
Introduction To Python
Biswajeet. D

2.
Python is an interpreted, object-oriented, high-level programming language with
dynamic semantics.
 First released in 1990
 Designed By: Guido van Rossum
 Name came from a 1970s British television show : Reference: https://www.python.org/~guido
Monty Python’s Flying Circus

3.
Why Python ?
 Easy to learn
 Readable
 Simplicity
 Multipurpose
 Errors appear on runtime. . . . . .

4.
A Sample Python Code Snippet……

5.
R Vs Python – Swap 2 Variables Code
Comparison
Below is screenshot of swapping 2 variables without using a third
variable in R & Python

6.
Python Material
Online Materials:
https://docs.python.org/3/
Other Resources:
http://www.sololearn.com/Course/Python/
Popular Python recipes
http://code.activestate.com/recipes/langs/python/
“Sololearn Python“
(A Simple Android
App for Beginners)

7.
What is Python?
Multi Functional:
 Simple procedural programming
 Object-orientation
 Functional programming
Computer Programming for everybody :
 Portable: Different interpreters for many platforms: CPython, Jython, IronPython,
PyPy.
 Open source, so anyone can contribute to its development
 Code that is as understandable as plain English
 Suitability for everyday tasks, allowing for short development times
 Extensible: Reusable code using modules and packages
 Easy to write new modules in ‘C’.

8.
Comparison with other languages
 Python code is typically 3-5 times shorter than equivalent Java code, it
is often 5-10 times shorter than equivalent C++ code!
 Anecdotal evidence suggests that one Python programmer can finish in two
months what two C++ programmers can't complete in a year.
 Python shines as a glue language, used to combine components written in
C++.
So, Python can increase productivity
Reference: https://www.python.org/doc/essays/comparisons/

9.
Points to be noted
 “Python is a scripting language"
False. Python has been used as a scripting language, but it is also used
to develop large stand-alone applications.
Python is interpreted, thus slower than running native code
 True, But not always
 Python can be used to `glue' together native modules.
 Libraries (Numpy,Scipy etc.)are often very efficient.
 Dynamic typing is unsafe.
 Python is strongly typed and well behaved.
 It can deal with type errors at runtime.

10.
Use Cases/Applications
 Application Development
 Web Development
 Scripting
 Scientific Computing
Success Stories: https://www.python.org/about/success/

11.
Use Cases/Applications
 Google – Many components of search engine were written in Python
 Yahoo - maps were developed using Python
 RHEL – Installer developed using Python
 NASA – Uses Python as the main scripting language

12.
The RedMonk Programming Language
Rankings: 2015

13.
The RedMonk Programming Language
Rankings..(Cont’d)

14.
Python in Big Data & Data Science
http://www.kdnuggets.com/2015/05/r-vs-python-data-
science.html

15.
Python- Pros and Cons
 Pro: IPython Notebook or Jupyter
The IPython Notebook makes it easier to work with Python and data. You can
easily share notebooks with colleagues, without having them to install anything.
This drastically reduces the overhead of organizing code, output and notes
files. This will allow you to spend more time doing real work.
 A general purpose language
Python is a general purpose language that is easy and intuitive. This gives it a
relatively flat learning curve, and it increases the speed at which you can write
a program. In short, you need less time to code and you have more time to play
around with it! Furthermore, the Python testing framework is a built-in, low-
barrier-to-entry testing framework that encourages good test coverage. This
guarantees your code is reusable and dependable.

16.
Pros & Cons (Cont’d)…..
 Pro :A multi purpose language
Python brings people with different backgrounds together. As a common,
easy to understand language that is known by programmers and that can
easily be learnt by statisticians, you can build a single tool that integrates
with every part of your workflow.
 Pro/Con: Visualizations
Visualizations are an important criteria when choosing data analysis software.
Although Python has some nice visualization libraries, such as Seaborn,
Bokeh and Pygal, Matplotlib etc.
 Con: Python is a challenger
Python is a challenger to R. It does not offer an alternative to the hundreds of
essential R packages, Although it‟s catching up.

17.
Versions
Python2
 Python2 – Very Stable (Python-2.7) – All may not support
Python3
 Current Release – 3.5.1 (Released on 21-12-2015)
 Some major changes and clean-ups
 Not backward compatible (cannot execute 2.x code)
 V3.6 - Ongoing development