Mastering Python Data Types

Chapter 1: Understanding Python Variables and Data Types

1.1. Variables in Python

• Naming conventions
• Variable assignment
• Data type inference

1.2. Numeric Data Types

• Integers (int)
• Floating-Point Numbers (float)
• Complex Numbers (complex)

1.3. Strings (str)

• String creation and manipulation
• String formatting
• Common string methods

Chapter 2: Python Lists

2.1. Introduction to Lists

• Creating lists
• List operations
• List indexing and slicing

2.2. List Methods

• Adding and removing elements
• Modifying lists
• List comprehension

2.3. Common List Patterns and Techniques

• Sorting lists
• Reversing lists
• Nested lists

Chapter 3: Python Tuples

3.1. Understanding Tuples

• Creating tuples
• Tuple packing and unpacking
• Immutable nature of tuples

3.2. Tuple Methods and Operations

• Tuple concatenation and repetition
• Tuple indexing and slicing
• Converting between tuples and lists

Chapter 4: Python Sets

4.1. Introduction to Sets

• Creating sets
• Set operations
• Set membership testing

4.2. Set Methods and Common Use Cases

• Adding and removing elements
• Set operations (union, intersection, difference)
• Frozen sets

Chapter 5: Python Dictionaries

5.1. Dictionary Basics

• Creating dictionaries
• Accessing and modifying dictionary items
• Dictionary comprehension

5.2. Dictionary Methods and Techniques

• Iterating over dictionaries
• Merging dictionaries
• Nested dictionaries

Chapter 6: Advanced Data Types

6.1. Named Tuples

• Creating and using named tuples
• Named tuple methods

6.2. Deque

• Using deque for efficient queue and stack operations
• Deque methods

6.3. Counter

• Counting elements with Counter
• Common use cases

Chapter 7: Type Conversion and Type Checking

7.1. Type Conversion Functions

• int(), float(), str(), etc.
• Custom type conversions

7.2. Type Checking and Casting

• isinstance()
• Type casting

Chapter 8: Handling Missing Data with None and NaN

8.1. None: The Python Null Value

• Using None for missing or undefined data
• None as a placeholder

8.2. NaN: Missing Numerical Data

• Understanding NaN in Python
• Handling NaN in numerical operations

Chapter 9: Best Practices and Tips

9.1. Choosing the Right Data Type

• Guidelines for selecting data types
• Trade-offs between data types

9.2. Performance Considerations

• Performance implications of data type choices
• Optimization tips

Chapter 10: Case Studies and Projects

10.1. Real-world Examples

• Case studies using Python data types
• Solving practical problems

10.2. Projects for Practice

• Hands-on projects to reinforce learning

Conclusion

11.1. Recap and Key Takeaways 11.2. Continuing Your Python Journey 11.3. Acknowledgments 11.4. About the Author