2 Variables, Data Types, and Expressions

Names, Values, and Literals

Programming begins with three connected ideas: values hold information, names make that information reusable, and operations transform it. A Variable gives a value a name, while a Literal is a value written directly in source code.

For example, a program might associate student_name with the string "Ada" and score with the integer 95. A variable can then be reused in another expression, such as final_score = score + bonus.

Naming guidelines

• Use letters, digits, and underscores, but usually do not begin a name with a digit.

• Do not use spaces, punctuation, or reserved words such as if, class, or return as variable names.

• Remember that names are often case-sensitive: score and Score may refer to different names.

• In Python, prefer lowercase words separated by underscores, such as total_price.

• Prefer descriptive names such as temperature, total_cost, and number_of_items over names such as x or thing.

Takeaway: Give each stored value a clear name so later expressions communicate its purpose.

Data Types and Appropriate Operations

A Data Type describes what a value represents and which operations make sense for it. Common types include:

• Integer: a whole number, such as -3, 0, or 42.

• Floating-point number: a number with a fractional part, such as 2.5 or -0.01.

• Boolean: one of two logical values, True or False.

• String: a sequence of characters, such as "hello" or "2026".

• Null or none value: the absence of a meaningful value, represented by None in Python.

• Collection: a group of values, such as [10, 20, 30].

Numbers support arithmetic, but floating-point values can have small rounding differences because some decimal fractions cannot be represented exactly in binary. For currency, decimal or fixed-point facilities may be more appropriate.

A string that looks like a number is still text: "42" is different from 42. Convert it before using it in a numeric calculation. Boolean values commonly result from comparisons, such as age >= 18.

Takeaway: Always consider both what a value contains and what its type allows you to do with it.

Expressions and Operators

An Expression produces a value. It can combine literals, variables, operators, function calls, and nested expressions. For example, price * quantity produces a total, while age >= 18 produces a Boolean result.

Arithmetic operators

Common Python arithmetic operators include:

• Addition: $7 + 2 = 9$

• Subtraction: $7 - 2 = 5$

• Multiplication: $7 \mathbin{*} 2 = 14$

• Division: $7 / 2 = 3.5$

• Floor division: $7 \mathbin{//} 2 = 3$

• Remainder: $7 \mathbin{\%} 2 = 1$

• Exponentiation: $2^3 = 8$, written as 2 ** 3 in Python.

Comparison operators produce Boolean values: == tests equality, != tests inequality, and <, <=, >, and >= compare order. Logical operators such as and, or, and not combine or modify Boolean expressions.

Do not confuse = with ==: the first performs assignment, while the second tests equality.

Precedence

Multiplication is normally evaluated before addition, so the expression 2 + 3 * 4 produces $14$. Parentheses change the order: (2 + 3) * 4 produces $20$. Use parentheses when they make the intended calculation clearer.

Takeaway: Expressions calculate values; operators determine how values are combined and compared.

Assignment and Constants

Assignment stores a computed value in a variable. In Python, an assignment uses =. In the statement count = count + 1, the current value of count is read, one is added, and the result replaces the previous value. This is a programming operation rather than a mathematical claim that a quantity equals itself plus one.

Python also provides shorthand assignment: count += 1 has the same effect as count = count + 1, and price *= 1.10 multiplies the current price by $1.10$ before storing the result. Multiple values can also be assigned together, as in width, height = 800, 600.

A Constant is a value that a program intends not to change after it is defined. Python does not enforce constants through a special declaration, so uppercase names communicate the intention. Examples include TAX_RATE, MAX_ATTEMPTS, and MAX_SCORE.

Replacing a repeated unexplained literal with a named constant improves readability and makes future changes safer. For example, if a score is compared with a passing threshold, a name such as PASSING_SCORE explains the meaning of that threshold.

Takeaway: Use assignment to update state and meaningful constants to document stable concepts.

Input, Conversion, and Output

Input enters a program, and Output communicates a result. A reliable input-processing-output pattern has four stages:

1. Read the required input.

2. Convert it to appropriate data types.

3. Compute a result with expressions.

4. Display or store the result.

In Python, input() returns text. If a user enters a quantity, convert it with int(...); if the user enters a price, convert it with float(...). Type Conversion must be valid for the target type: converting "20" to an integer succeeds, but converting "hello" with int(...) raises an error. A robust program anticipates invalid input and responds appropriately.

For a minutes-to-hours calculation, define TOTAL_MINUTES as 60. Given an integer minutes, complete hours can be calculated with $minutes \mathbin{//} 60$, and the remaining minutes with $minutes \mathbin{\%} 60$. Thus, 125 minutes gives $125 \mathbin{//} 60 = 2$ complete hours and $125 \mathbin{\%} 60 = 5$ remaining minutes.

Formatted output can combine text and computed values, for example by using an f-string such as f"{hours} hour(s) and {remaining_minutes} minute(s)". Explicit conversions and clear formatting help prevent errors when handling user input, money, dates, or measurements.

Takeaway: Treat incoming text as text until it has been deliberately validated and converted, then use expressions to produce clear output.