Introduction to Numbers

• Integers (int): These represent whole numbers, positive, negative, or zero. Examples include 42, -100, and 0. They are great for counting objects or representing whole units.
  
  
• Floats (float): These represent decimal numbers. Examples include 3.14159 (pi), 1.234, and -98.7. They are useful for measurements, calculations involving decimals, or any situation where precise fractional values are needed.
  
  
• Complex numbers (complex): These are a combination of a real number and an imaginary unit (represented by the letter "j"). They are less commonly used in basic programming but are essential in advanced scientific computing. An example is 3 + 4j (where 3 is the real part and 4j is the imaginary part).
  
  

• Operations: Different data types have different allowed operations. For example, you can add two integers, but you can't directly add an integer and a string.
• Memory Efficiency: Python allocates memory differently for each data type. Using the appropriate data type helps optimize memory usage.
• Accuracy: Floats can lose precision for certain operations due to limitations in how computers store decimals. Integers, on the other hand, provide exact calculations for whole numbers.

age = 30  # Integer (whole number of years)
price = 9.99  # Float (decimal price)

# You can perform arithmetic operations on numbers of the same type
total_cost = price * 2  # Multiplies two floats

# Mixing data types might require conversion
# age_in_seconds = age * 365  # This would result in an error (integer * float)
age_in_seconds = age * 365.25  # We convert age to float for accurate calculation