Python-Workshop

Official Python Website:

    

   

   

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

This course starts from July 18, 2023 and the first working days are dedicated to learning basics of Programming using Python.

Course:

1. Python Introduction

   • Python Syntax
   • Data Types
   • Operators
   • Lists
   • Control statements
   • loops
   • functions
   • OOP: Python Class
   • Modules: NumPy, Matplotlib, Pandas

2. NumPy

   • Data as lists, arrays, and tuples.
   • Find the average, stddev, quartiles, mode, etc. of the data.

3. Matplotlib

   • Plotting Data from lists in NumPy.
   • Plot curves of polynomial functions, trigonometriv functions, etc.
   • Plotting subplots
   • Plotting scatter plots, bar charts, histograms, pie charts, etc.

4. Pandas

   • Read CSV
   • Dataframes
   • Analyzing Data
   • Pandas Correlations
   • Plotting Data

5. SymPy

   • Defining Functions
   • Derivatives
   • Integrals
   • Solving differential equations

Course Syllabus:

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
• 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(), .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

3. Matplotlib

• Install and check version of matplotlib
• how to import matplotlib
• 2D plotting
  • Line plot
  • Scatter plot
  • Bar plot
  • Histogram
  • Pie chart
  • Box plot
  • Density plot
  • Meshgrid
  • Contourplot
• Subplots
• Customizing plots
  • Title, Axis labels, Legend, Figure size,
  • Spines, Ticks, Grid, Color, Linewidth,
  • Marker, Markerfacecolor, Markeredgecolor, Markeredgewidth
  • Adding legends, labels to the plot
• Tight Layouting Images/ Padding the images, Saving the images
• Other plotting libraries like seaborn and plotly

4. Pandas

• Install and check version of pandas

• How to import pandas?

• Series:

  • Creating Series
  • Accessing elements,
  • Indexing, Slicing,
  • Operations,
  • Missing values,
  • Sorting,
  • Statistics,
  • Applying functions,
  • Concatenating,
  • Filtering,
  • Grouping,
  • Merging,
  • Joining,
  • Reshaping,
  • Time series,
  • Plotting

• DataFrames:

  • Creating DataFrame,
  • Accessing elements,
  • Indexing,
  • Slicing,
  • Operations,
  • Missing values,
  • Sorting, Statistics,
  • Applying functions,
  • Concatenating,
  • Filtering,
  • Grouping,
  • Merging,
  • Joining,
  • Reshaping

• Reading csv files, creating csv files from DataFrames

• Groupby In Pandas:

  • Plotting in Pandas,
  • Missing values in Pandas,
  • Merging, Joining, Concatenating, and Reshaping DataFrames,
  • Time Series in Pandas,
  • Handling Missing values in Pandas,
  • Reading and Writing Files in Pandas

• Joins in Pandas: types of database join

• Loc and iLoc in Pandas:

  • Accessing elements in DataFrame,
  • Pivot Tables in Pandas,
  • Grouping and aggregating data

5. Sympy

• Introduction to Sympy
• importing Sympy
• Representing mathematical expressions
• Minor calculations using Sympy
• Plotting the equations and the solutions
• Derivatives In Sympy
  • Expressing in Sympy
  • Differentiation
  • Integration
  • Series expansion
  • Limit
  • Solving equations
    • Solving differential equations
    • Solving Initial Value Problems
    • Solving Higher Order Derivatives
    • Solving Partial Derivatives
• Integrals in Sympy
• Expressing the solution in Sympy
• Solving the integrals
• Solving Multiple integrals