Data Types in Python

Consider the values 5 and "5". They look closely related, but Python does not treat them as the same kind of value. The first is a number; the second is a string containing a character. That distinction affects which operations make sense. A number can participate in arithmetic, while a string supports text-based operations such as combining text or searching for text within a larger string. Data types give Python the information it needs to handle these values predictably.

When you assign a value to a variable, Python stores the value in memory and binds the variable name to that location. The variable is a label, not the value itself. The stored value belongs to a type, and Python remembers that classification so it can determine which operations are appropriate later.

The diagram shows a useful mental model: score is only a label connected to a memory location. The location contains a value, and that value has a classification such as integer. Python uses that classification when deciding how an operation should treat the value. The diagram does not mean that a variable is itself a container holding a value; the source distinction is important: the variable is a label bound to the location.

Python's two most fundamental beginner-level categories are numbers and strings. Numbers represent quantities. The basic numeric types named in this material are integers and floating-point numbers. Numbers support mathematical operations such as addition and multiplication. Strings represent text: sequences of characters. Strings support text-based operations such as concatenation and searching for substrings.

An operator does not have one universal meaning independent of its operands. Python considers the types of the values involved and applies an operation that makes sense for those types. With numeric operands, an addition operation is mathematical. With string operands, the corresponding text operation is concatenation: joining text together. The key prediction skill is therefore to inspect the operands' types before predicting the behavior of an operation.

Numbers and strings are immutable types. They cannot be changed in place. If a variable is reassigned, Python creates a new value and rebinds the variable to that new value; the original value remains unchanged. This is another reason to separate the variable name from the value itself. The name can be redirected even though the original number or string cannot be edited in place.

In the before-and-after view, the value 5 has not been edited into 10. Instead, the name score is first bound to 5 and later rebound to a new value, 10. The original value is left unchanged. Thinking in terms of rebinding prevents the common mistake of imagining that an immutable number or string is modified inside its memory location.