DATA1002 Chap.6 Functions, Modules, CSV and Pandas

Functions, Modules, CSV and Pandas

Define Python function

The course material gives this chapter a concrete anchor: The official Week 6 sequence introduces csv, pandas, modules and functions as the project shifts from exercises to reusable data work.

That Python function anchor controls how module is explained and how DataFrame is tested in changed practice.

Functions, Modules, CSV and Pandas turns Python function, module and DataFrame into executable reasoning.

The chapter's practical target is to organise ingestion and transformation into reusable functions with explicit inputs and outputs, so every explanation should connect syntax to program state, control flow and observable output.

Treat Python function as a precise program object, not a loose label.

Identify the value or responsibility of Python function before execution, then trace what can read it, change it or depend on it. This makes state changes visible before they become debugging guesses.

Use module to explain the program's next move. Work through one representative module input by hand and name the branch, iteration or call that follows.

If the module trace cannot be stated, the code may run by accident rather than by understood design.

Trace module

Bring in DataFrame as the test of structure.

Compare normal, boundary and invalid inputs for DataFrame; state the expected behaviour first; then use the mismatch between expectation and result to localise the defect.

For the application — organise ingestion and transformation into reusable functions with explicit inputs and outputs — write the smallest complete example that exposes the rule.

Explain why the DataFrame result works, what would break it and how the program should signal or recover from that failure.

Before running an Python function example, make a trace table with the important state before and after each operation. Include the value associated with Python function, the control decision governed by module and the output or object affected by DataFrame.

The Python function table turns an unexplained result into a sequence that can be tested one transition at a time.

Test three inputs: an ordinary case, a boundary case and an invalid case. State the expected DataFrame result for each before execution, then compare it with what the program actually does.

A useful test of module isolates one rule; changing several conditions at once cannot reveal which condition caused the failure.

Test with DataFrame

Practise explaining the solution without reading the code.

For DATA1002, name the data representation, the control flow, the responsibility of each function or class and the reason the chosen design supports organise ingestion and transformation into reusable functions with explicit inputs and outputs.

This DataFrame rehearsal matters when a written test or interview asks why the program works rather than whether it produces one correct output.

A complete response should make the task visible before the detail: identify what must be decided, define the relevant terms, connect the evidence to module, and use DataFrame to test the result.

The final sentence about DataFrame should answer the question actually asked rather than merely repeat the topic.

The controlling limit is specific: Modularity improves testability but does not validate the semantics of a column, transformation or external data source.

Keep that DataFrame limit beside the worked example, because it separates a careful DATA1002 answer from one that sounds confident but claims more than the task or evidence supports.

For revision, retrieve Python function, module and DataFrame without notes, explain their relationship aloud, then complete a changed version of the application: organise ingestion and transformation into reusable functions with explicit inputs and outputs.

Record the first failed module reasoning move and repair it before attempting another case.

What this chapter covers

• 01

  Python function

• 02

  module

• 03

  DataFrame

• 04

  Applying Python function

• 05

  Limits of module and DataFrame

AskSia practice: apply Functions, Modules, CSV and Pandas

Key terms

Python function

A named reusable block accepting inputs, performing operations and optionally returning a result. Use this definition when the task is to organise ingestion and transformation into reusable functions with explicit inputs and outputs.

module

A reusable Python file or library namespace containing functions, classes and data definitions. Use this definition when the task is to organise ingestion and transformation into reusable functions with explicit inputs and outputs.

DataFrame

A labelled two-dimensional tabular structure whose columns may contain different data types. Use this definition when the task is to organise ingestion and transformation into reusable functions with explicit inputs and outputs.

Functions, Modules, CSV and Pandas FAQ

Exam move

Reconstruct the relationship among Python function, module and DataFrame; complete the chapter application without notes; then test the result against this limit: Modularity improves testability but does not validate the semantics of a column, transformation or external data source.

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