Python Data Types to Know for Intro to Python Programming

Understanding Python data types is key to programming effectively. These types, like integers, floats, strings, and lists, help you manage and manipulate data in your code, making it easier to perform calculations, store information, and control program flow.

1. Integers (int)

   • Whole numbers without a decimal point, e.g., -3, 0, 42.
   • Can be positive, negative, or zero.
   • Supports basic arithmetic operations like addition, subtraction, multiplication, and division.
   • Useful for counting, indexing, and performing mathematical calculations.

2. Floating-point numbers (float)

   • Numbers that contain a decimal point, e.g., 3.14, -0.001, 2.0.
   • Used for representing real numbers and performing precise calculations.
   • Can lead to rounding errors due to their binary representation.
   • Essential for scientific calculations, financial applications, and any scenario requiring fractional values.

3. Strings (str)

   • A sequence of characters enclosed in quotes, e.g., "Hello, World!".
   • Can include letters, numbers, symbols, and whitespace.
   • Supports various operations like concatenation, slicing, and formatting.
   • Commonly used for text manipulation, user input, and displaying information.

4. Booleans (bool)

   • Represents one of two values: True or False.
   • Used for conditional statements and control flow in programs.
   • Essential for logical operations and comparisons (e.g., ==, !=, >, <).
   • Helps in decision-making processes within code.

5. Lists

   • An ordered collection of items that can be of different data types, e.g., [1, "apple", 3.14].
   • Mutable, meaning items can be added, removed, or changed.
   • Supports indexing and slicing for accessing elements.
   • Useful for storing multiple values in a single variable and iterating through collections.

6. Tuples

   • An ordered collection of items similar to lists but immutable, e.g., (1, "apple", 3.14).
   • Cannot be changed after creation, making them useful for fixed data.
   • Supports indexing and slicing like lists.
   • Often used to group related data together.

7. Dictionaries

   • A collection of key-value pairs, e.g., {"name": "Alice", "age": 25}.
   • Keys must be unique and immutable, while values can be of any data type.
   • Allows for fast retrieval of values based on their keys.
   • Ideal for representing structured data and performing lookups.

8. Sets

   • An unordered collection of unique items, e.g., {1, 2, 3}.
   • Automatically removes duplicate values and does not maintain order.
   • Supports mathematical operations like union, intersection, and difference.
   • Useful for membership testing and eliminating duplicates from a collection.