Lec-1-Introduction to Python.pdf gla University

Python for Data Science 2
Popular tools used in data science
 Data pre-processing and analysis
◦ Python, R, Microsoft Excel, SAS, SPSS
 Data exploration and visualization
◦ Tableau, Qlikview, Microsoft Excel
 Parallel and distributed computing incase of big data
◦ Apache Spark,Apache Hadoop

Evolution of Python
 Python was developed by Guido van Rossum in the late
eighties at the ‘National Research Institute for Mathematics
and Computer Science’ at Netherlands
 Python Editions
◦ Python 1.0
◦ Python 2.0
◦ Python 3.0

Python as a programming language
 Supports multiple programming paradigm
◦ Functional, Structural, OOPs, etc.
 Dynamic typing
◦ Runtime type safety checks
 Reference counts
◦ Deallocates objects which are not used for long
 Late binding
◦ Methods are looked up by name during runtime
 Python’s design is guided by 20 aphorisms as described in Zen of
Python by Tim Peters

 Standard CPython interpreter is managed by “Python Software
Foundation”
 There are other interpreters namely JPython (Java), Iron Python
(C#), Stackless Python (C, used for parallelism), PyPy (Python
itself JIT compilation)
 Standard libraries are written in python itself
 High standards of readability

 Cross-platform (Windows, Linux, Mac)
 Highly supported by a large community group
 Better error handle

 Comparison to Java
 Python vs Java
◦ Java is statically typed i.e. type safety is checked during compilation
(static compilation)
◦ Thus in Java the time required to develop the code is more
◦ Python which is dynamically typed compensates for huge
compilation time when compared to Java
◦ Codes which are dynamically typed tend to be less verbose
therefore offering more readability

Advantages of using python
 Python has several features that make it well suited for data
science
 Open source and community development
◦ Developed under Open Source Initiative license making it free to use
and distribute even commercially
 Syntax used is simple to understand and code
 Libraries designed for specific data science tasks
 Combines well with majority of the cloud platform service
providers

Coding environment
 A software program can be written using a terminal, a
command prompt (cmd), a text editor or through an Integrated
Development Environment (IDE)
 The program needs to be saved in a file with an appropriate
extension (.py for python, .mat for matlab, etc...) and can be
executed in corresponding environment (Python, Matlab, etc…)
 Integrated Development Environment (IDE) is a software
product solely developed to support software development in
various or specific programming language(s)

Coding environment
 Python 2.x support will be available till 2020
 Python 3.x is an enhanced version of 2.x and will only be maintained
from 3.6.x post 2020
 Install basic python version or use the online python console as in
https://www.python.org/
 Execute following commands and view the outputs in terminal or
command prompt
• Basic print statement
• Naming conventions for variables and functions, operators
• Conditional operations, looping statements (nested)
• Function declaration and calling
• Installing modules

Integrated development environment (IDE)
 Software application consisting of a cohesive unit of tools
required for development
 Designed to simplify software development
 Utilities provided by IDEs include tools for managing, compiling,
deploying and debugging software

Coding environment- IDE
 An IDE usually comprises of
◦ Source code editor
◦ Compiler
◦ Debugger
◦ Additional features include syntax and error highlighting,
code completion
 Offers supports in building and executing the program along
with debugging the code from within the environment

Coding environment- IDE
 Best IDEs provide version control features
 Eclipse+PyDev, SublimeText,Atom, GNU Emacs,Vi/Vim,Visual
Studio,Visual Studio Code are general IDEs with python
support
 Apart from these some of the python specific editors include
Pycharm, Jupyter, Spyder,Thonny

Spyder
 Supported across Linux, Mac OS X and Windows platforms
 Available as open source version
 Can be installed separately or through Anaconda distribution
 Developed for Python and specifically data science
 Features include
◦ Code editor with robust syntax and error highlighting
◦ Code completion and navigation
◦ Debugger
◦ Integrated document
 Interface similar to MATLAB and RStudio

Spyder

PyCharm
 Available as community (free open source) and professional (paid) version
 Supports only Python
 Can be installed separately or through Anaconda distribution
 Features include
◦ Code editor provides syntax and error highlighting
◦ Code completion and navigation
◦ Unit testing
◦ Debugger
◦ Version control

PyCharm

Jupyter Notebook
 Web application that allows creation and manipulation of
documents called ‘notebook’
 Available as open source version

Jupyter Notebook
Source-https://jupyter.org/

Jupyter Notebook
 Bundled with Anaconda
distribution or can be installed
separately
 Supports Julia, Python, R and
Scala
 Consists of ordered collection of
input and output cells that contain
code, text, plots etc.

Jupyter Notebook
 Allows sharing of code and
narrative text through output
formats like PDF, HTML etc.
◦ Education and presentation
tool
 Lacks most of the features of
a good IDE

How to choose the best IDE?
 Requirements
 Working with different IDEs helps us understand our own
requirement

Lec-1-Introduction to Python.pdf gla University

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