Python Basics

Course Duration: 40 hrs
Trainer: Prathima
Training Date: Nov 2nd – Nov 6th (8 hrs each day)

Contents

INTRODUCTION TO PYTHON
Course Outline

1. Course Introduction
   1.1. Course Curriculum Overview
   1.2. Why Python?
2. Python Setup
   2.1. Command Line Basics
   2.2. Installing Python (Step by Step)
   2.3. Running Python Code.
   2.4. Getting the Notebooks [Anaconda, spyder]
3. Python Object and Data Structure Basics
   3.1. Introduction to Python Data Types
   3.2. Python Numbers
   3.3. Numbers: Simple Arithmetic
   3.4. Variable Assignments
   3.5. Introduction to Strings
   3.6. Indexing and Slicing with Strings
   3.7. String Indexing
   3.8. String Slicing
   3.9. String Properties and Methods
   3.10. Print Formatting with Strings
   3.11. Lists in Python
   3.12. Dictionaries in Python
   3.13. Tuples with Python
   3.14. Sets in Python
   3.15. Booleans in Python
   3.16. I/O with Basic Files in Python
   3.17. List Comprehension
4. Python Comparison Operators
   4.1. Comparison Operators in Python
   4.2. Chaining Comparison Operators in Python with Logical Operators
5. If, Elif and Else Statements in Python
   5.1. For Loops in Python
   5.2. While Loops in Python
   5.3. Useful Operators in Python
   5.4. List Comprehensions in Python
6. Methods and functions
   6.1. Methods and the Python Documentation
   6.2. Introduction to Functions
   –def Keyword
   6.3. Types of Python Functions
   6.4. Logic with Python Functions
   6.5. Tuple Unpacking with Python Functions
   6.6. Interactions between Python Functions
   6.7. Function arguments and parameters
   6.8. Default parameter values
   6.9. Functions returning values
   6.10 . Lambda functions in Python
7. Object-Oriented Programming
   7.1. Object-Oriented Programming – Introduction
   7.2. Object-Oriented Programming – Attributes and Class Keyword
   7.3. Object-Oriented Programming – Class Object Attributes and Methods
   7.4. Object-Oriented Programming – Inheritance and Polymorphism
   7.5. Object-Oriented Programming – Special (Magic/Dunder) Methods
8. Modules and Packages
   8.1. Pip Install and PyPi
   8.2. Modules and Packages
   8.3. __name__ and “__main__”
   8.4. Creating user-defined Modules
9. Errors and Exception Handling
   9.1. Errors and Exceptions Homework
   9.2. Errors and Exception Homework – Solutions
   9.3. Update for Pylint Users
   9.4. Pylint Overview
   9.5. Running tests with the Unittest Library
10. Python Decorators
    10.1. Decorators with Python Overview
    10.2. How to use Decorators with Python 3
11. Python Generators
    11.1. Generators with Python
12. Advanced Python Modules
    12.1. Introduction to Advanced Python Modules
    12.2. Python Collections Module
    12.3. Opening and Reading Files and Folders (Python OS Module)
    12.4. Python Datetime Module
    12.5. Python Math and Random Modules
    12.6. Python Debugger
    12.7. Python Regular Expressions Part One
    12.8. Python Regular Expressions Part Three
    12.9. Zipping and Unzipping files with Python
    12.10. Advanced Python Module Puzzle – Overview
13. Web Scraping with Python
    13.1. Introduction to Web Scraping
    13.2. How to perform Web Scraping with Python, BeautifulSoup, and Requests libraries.
    13.3. Setting Up Web Scraping Libraries
    13.4. Python Web Scraping – Grabbing a Title
    13.5. Python Web Scraping – Grabbing a Class
    13.6. Python Web Scraping – Grabbing an Image
    13.7. Python Web Scraping – Book Examples
14. Working with Images using Python
    14.1. Introduction to Images with Python
    14.2. How to use Python to work with image data
15. Emails with Python
    15.1. Introduction to Emails with Python
    15.2. Sending Emails with Python
    15.3. Receiving Emails with Python
16. Graphical User Interfaces with Tkinter
    16.1. Introduction to Tkinter
    16.2. Setting up a GUI with Widgets
    16.3. Connecting GUI Widgets with Callback Functions
    16.4. Create a Multi-widget GUI (Practice)
17. Python NumPy
    17.1. Intro to NumPy(Numerical Python)
    17.2. Creating NumPy Arrays
    17.3. NumPy Array Indexing
    17.4. Array Slicing
    17.5. Data Types in NumPy
    17.6. NumPy Copy vs View
    17.7. NumPy Array Shape
    17.8. NumPy Array Reshape
    17.9. NumPy Array Iterating
    17.10. NumPy Array Join
    17.11. NumPy Array Split
    17.12. NumPy Array Search
    17.13. NumPy Array Sort
    17.14. NumPy Array Filter
    17.15. NumPy Random
    17.16. NumPy ufunc
18. Python Pandas
    18.1. Python Pandas – Introduction
    18.2. Python Pandas – Environment Setup
    18.3. Introduction to Data Structures
    18.4. Python Pandas – Series
    18.5. Python Pandas – DataFrame
    18.6. Python Pandas – Panel
    18.7. Python Pandas – Basic Functionality
    18.8. Python Pandas – Reindexing
    18.9. Python Pandas – Iteration
    18.10. Python Pandas – Sorting
    18.11. Working with Text Data
    18.12. Indexing & Selecting Data
    18.13. Python Pandas – Window Functions
    18.14. Python Pandas – Aggregations
    18.15. Python Pandas – Missing Data
    18.16. Python Pandas – GroupBy
    18.17. Python Pandas – Merging/Joining
    18.18. Python Pandas – Concatenation
    18.19. Date Functionality
    18.20. Timedelta
    18.21. Categorical Data
    18.22. Visualization
    18.23. IO Tools
    18.24. Sparse Data

Trainer Summary:
Prathima is a Data Science and Java Trainer with over 18 years of experience with a demonstrated history of working in the information technology and services industry. Skilled in Python (Programming Language), HTML5, CSS3, JavaScript, Bootstrap, AngularJS, Java/J2EE, C/C++, OpenGL, PHP, MySQL, etc. Strong engineering professional with a MTech focused on Data Science with Python, Machine Learning with Python and Java Technologies.