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Collections & Data Structures · Lesson 31

Create and Inspect Lists

A list is an ordered, mutable Python sequence that can contain objects of mixed types.

ConceptWorked examplePracticeKnowledge check
Textbook walkthrough

Create and Inspect Lists

A list is an ordered, mutable Python sequence that can contain objects of mixed types. Creating a list establishes element order; inspecting len, indexing and iteration reveals its structure before mutation.

Learning goal: explain why Create and Inspect Lists behaves this way, apply it to a small example, and verify the result independently. Begin by being able to justify this first step: Create a list with square brackets or from another iterable when ordered, mutable storage is appropriate.

Deeper walkthrough

Read Create and Inspect Lists as a mechanism, not a recipe

Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: Create a list with square brackets or from another iterable when ordered, mutable storage is appropriate. Stage 2: Inspect length, element types and representative values before applying downstream operations. Stage 3: Access elements by zero-based index or slices while checking boundaries. Final checkpoint: Use a copy when independent mutation is required and verify the before/after state.

Mechanism

Follow the transformation

Create a list with square brackets or from another iterable when ordered, mutable storage is appropriate.

Inspect length, element types and representative values before applying downstream operations.

Access elements by zero-based index or slices while checking boundaries.

Evidence

Know what would convince you

  • Trace a tiny input by hand and compare the runtime result.
  • Inspect type, value/shape and any mutation/side effect explicitly.
Useful distinctionInput: Objects/values supplied to the operation.
Click a stage to inspect what happens, what changes, and what should be checked before moving on.
Stage 1

Create a list with square brackets…

Create a list with square brackets or from another iterable when ordered, mutable storage is appropriate.

State focus: identify exactly what changed at this stage and what observable evidence confirms that change.
How it works

Trace the mechanism step by step

  1. Create a list with square brackets or from another iterable when ordered, mutable storage is appropriate.
  2. Inspect length, element types and representative values before applying downstream operations.
  3. Access elements by zero-based index or slices while checking boundaries.
  4. Remember that assigning one list variable to another normally shares the same mutable object.
  5. Use a copy when independent mutation is required and verify the before/after state.
Worked demonstration

Create and Inspect Lists

# Step 1 — Compute the right-hand expression and store its result in `items` for the next step.
items = ["A", "B", "C"]
# Step 2 — Display the current value explicitly so the result/state can be inspected during execution.
print(len(items))
# Step 3 — Display the current value explicitly so the result/state can be inspected during execution.
print(items[0], items[-1])
# Step 4 — Display the current value explicitly so the result/state can be inspected during execution.
print(type(items).__name__)
Expected / illustrative result
The list has length 3; indexing is zero-based and negative indices count from the end.
Interpret the result.

For Create and Inspect Lists, trace the specific input through the mechanism above and independently verify one returned value, state change or side effect.

Distinctions & related ideas

Place the concept correctly

InputObjects/values supplied to the operation.
StateNames or mutable objects that may change during execution.
OutputReturned value, side effect, file, plot or exception to inspect.
Use deliberately

When it is appropriate

Use Create and Inspect Lists when it answers a defined question in Collections & Data Structures and its inputs/assumptions match the current data or program state.

Boundary conditions

When to stop or reconsider

Reconsider Create and Inspect Lists when the required information is unavailable, the operation would violate a validation/data boundary, or a simpler operation answers the question more transparently.

Common mistakes

Failure modes to recognise

  • Running the operation on the wrong object/type or in the wrong environment.
  • Inferring correctness from “no exception” without checking the produced value/state.
  • Hiding a boundary case instead of making its behaviour explicit.
Verification

How to check the result

  • Trace a tiny input by hand and compare the runtime result.
  • Inspect type, value/shape and any mutation/side effect explicitly.
  • Run an edge or invalid case and confirm the exception/behaviour is deliberate.
Hands-on practice

Demonstrate understanding

Try this:

Construct a tiny example of Create and Inspect Lists. First create a list with square brackets or from another iterable when ordered, mutable storage is appropriate. Then inspect length, element types and representative values before applying downstream operations. Predict the result before execution and explain one boundary or failure case.

Use the smallest values that expose the language rule. Write the expected value and type first, then compare the actual state/output with that prediction.
Knowledge check

Check reasoning, not memorisation

Which approach best demonstrates understanding of Create and Inspect Lists?

Quick reference

Remember the logic

Step 1Create a list with square brackets or from another iterable when ordered, mutable storage is appropriate.
Step 2Inspect length, element types and representative values before applying downstream operations.
Step 3Access elements by zero-based index or slices while checking boundaries.
Step 4Remember that assigning one list variable to another normally shares the same mutable object.
Lesson summary

What to remember

  • A list is an ordered, mutable Python sequence that can contain objects of mixed types. Creating a list establishes element order; inspecting len, indexing and iteration reveals its structure before mutation.
  • Identify the Python objects and types involved.
  • Running the operation on the wrong object/type or in the wrong environment.
  • Trace a tiny input by hand and compare the runtime result.