Python Course Lecture Notes

Introduction

• Instructor: Mosh Hamedani
• Overview of Python course: learn core concepts and build projects
• Python popularity and use cases: automation, AI, web applications (e.g., Instagram, Dropbox)

Course Structure

• Learning the core concepts of Python
• Building three projects:
  1. Website for an imaginary grocery store (using Django)
  2. Machine learning project predicting music preferences
  3. Automating tasks with Python

Installing Python

1. Go to python.org
2. Click on "Downloads" and download the latest version (e.g., Python 3.7.2)
3. Install Python, making sure to check the box to add Python to PATH (Windows)

Installing a Code Editor

• Recommended code editor: PyCharm (IDE)

1. Download from jetbrains.com/pycharm
2. Choose Community Edition (free)
3. Install and create a new project in PyCharm

Writing Your First Python Program

• Create a project in PyCharm, add a Python file (app.py)
• Write a simple program: print("Your Name")
• Run the program using the run menu or shortcut (Ctrl/Option + R)

Core Concepts

1. Variables

   • Temporary data storage in memory (e.g., price = 10)
   • Types: integers, floats, strings, booleans

2. User Input

   • Use input() for getting input from users
   • Example:

3. Control Flow

   • If Statements
     • Example:
     if condition: # do something elif condition: # do something else else: # do another thing

4. Loops

   • For Loops: Iterate over collections (e.g., lists, strings)
   • While Loops: Repeat until a condition is false

5. Functions

   • Reusable blocks of code with parameters and return values
   • Define a function: def function_name(parameter): and optionally return value

6. Classes and Objects

   • Classes: Blueprints for creating objects (e.g., class Dog:)
   • Objects: Instances of classes with attributes and methods
   • Introduction to Inheritance: Use existing classes to create new classes with additional features
   • Example: class Mammal:

Modules and Packages

• Modules: Python files containing functions and classes
• Packages: Directories containing multiple modules (e.g., ecommerce)
• Importing modules using import or from ... import ...

Built-in Functions and Libraries

• Use built-in functions to perform common tasks (e.g., len(), print())
• Libraries like math, random, datetime, etc.

Pandas and DataFrames

• Using pandas for data analysis and manipulation
• Importing CSV files with pd.read_csv
• Basic operations: filtering, sorting, aggregating data

Machine Learning Overview

• Steps:
  1. Data importation
  2. Data cleaning
  3. Model creation and training (e.g., using scikit-learn)
  4. Predictions and evaluation

Error Handling

• Use try and except to handle exceptions gracefully
• Example: try: # some code except ValueError: print("Invalid value")

Conclusion

• Encouragement to explore and build projects with Python
• Call to action: What will you automate? Share ideas!