Python In Academic Research

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Objectives of the Course

1. To make students understand the use of Python in Research.
2. To teach the usage of python and its modules like
   • NumPy,
   • Pandas
   • Matplotlib,
   • Seaborn
   • SymPy

Course Syllabus:

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1. Python Programming

• Introduction to Google Colab
• Python version and pip package manager
• Python Program
• Python Arithmatic Operators
• Using Python as calculators
• IEEE 754 standard for floating point arithmetic
• How to define a variable name and Variable Naming convention
• Operator Precedence
• Changing and updating variable values in Python
• Data types in Python
• Number data type: int, float, complex
  • Number data type with conditionals
  • Anatomy of conditionals: if ... else statements
  • Indentation
  • Expression and Comparison operators
  • Nesting and chaining(if... elif... else) of conditionals
  • Logical Operators
• String data type in Python
  • Single line strings and multi-line strings
  • Indexing and slicing: How to access characters in a string?
  • range() method
  • for loop in python with range() method
  • continue vs break vs pass statements
  • characters vs substrings
  • string methods: .replace(), .lower(), .upper(), .lstrip(), .rstrip(), .strip(), .split()
• Sequence data type: List
  • Indexing, slicing, for loop with and without range() , while loop, for loop vs while loop
  • Calculating mean of list using loops
  • Negative Indexing
  • Membership operators: in , not in
  • Mutable vs Immutable data type with exmaple
  • List methods: .insert(), .append(), .remove(), .pop(), .sort()
  • List comprehension

• Sequence data type: Tuple

  • List vs tuple
  • Typecasting data types
  • loop in tuple
  • Unpacking of tuples

• Sets: unordered, unindexed

  • .remove() , .add() in sets
  • Type conversion
  • Set operation in Python : union, intersection, difference

• Mapping data type Dictionary

  • Accessing dictionary items and add key value pair
  • keys() and values() method in dictionary
  • Updating dictionary: The update() method
  • `pop()
  • Looping in dictionary
  • Nested Dictionary

• NoneType data type in Python

  • Identity Operators

• Python Functions

  • def keyword and function arguments
  • return statement
  • Default arguments and non default arguments
  • Handling multiple return values
  • Recursion and its advantage

• Object Oriented Programming in Python (OOP)

  • Characterstics of OOP
  • Class and Object --defining class and creating object
  • . operator
  • Instance attribute vs class attribute
  • What is this def __init__(self) ?
  • What is self parameter?
  • __new__() and __init__()
  • Object methods or user defined methods inside user defined class
  • Inheritance in Python
  • super() method
  • Polymorphism and operator overloading
  • Abstraction and Encapsulation
  • limiting behaviour of variables : private, public and protected

2. Numpy

• Install and check version of the numpy

• How to import numpy?

• Vectors, the 1D Arrays

  • What is array and Creating Numpy array: How do you know the shape and size of an array?
  • What’s the difference between a Python list and a NumPy array?
  • Array creation routines: .zeros(), .ones() and .empty()
  • Array initilization using Monotonic sequence : `.arange() , .linspace()
  • Creating random array: np.random.randint(), np.random.rand(), np.random.uniform(), np.random.randn(), np.random.normal()
  • Indexing (fancy indexing) and slicing 1D numpy array
  • Logic Functions: Truth value testing : np.any() vs np.all()
  • Adding, concatenate, and sorting array elements np.append() , np.sort(), np.concatenate()
  • Vector operations i.e. elementwise operations in 1D numpy array
  • Broadcasting and its application in Image Processing
  • Array Operation: np.floor(), np.ceil(), np.round()
  • Statistics using numpy: .max(), .min(), .argmax(), .argmin(), .sum(), .mean(), .std(), .var()

• Matrices, the 2D Arrays, and 3D arrays + Introduction to Computer vision

  • Creation of 2D numpy array using: list of list and 1D array, .ones(), .zeros(), .full(), .eye(), .reshape()
  • Indexing, slicing and modifying values in 2D array
  • Creating random matrix: np.random.randint(), np.random.rand(), np.random.uniform(), np.random.randn(), np.random.normal()
  • Matrix multiplication: Dot product
  • Cross Product
  • Inverse, Transpose and determinant of matrix using numpy
  • The axis argument in numpy: 2D: axis = 0 vs axis = 1
  • Matrix statistics: .min(), .min(axis = 1), .min(axis = 0), .argmin(), .argmin(axis = 1), .argmin(axis = 0), np.unravel_index(),
  • How morden day images are created? with Example of opencv library.

Links to the Notebooks

Day1: Intro to python programming

Day2: Number dtype with conditionals

Day3: String type

Day4: List and Tuple type

Day5 Part1: Dictionary in python

Day5 Part 2: Set and NoneType

Day6 Part1: Python Function

Day6 Part 2: OOP in Python