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Basic Python (AI-powered) · Lesson 3 of 3

Python Functions, Files, and Reusable Analysis

Write small functions and load data from files with explicit paths and encodings.

Lesson

1. Introduction

A notebook becomes easier to rerun when repeated logic lives in functions and input/output boundaries are explicit. Functions also make calculations testable without needing to repeat an entire manual notebook workflow.

2. Understand the concept

A function receives arguments and returns a result. A CSV reader maps columns to values; JSON maps structured objects to Python dictionaries and lists. File paths depend on the process working directory, and text files should use an explicit encoding such as UTF-8.

3. Syntax and interpretation

from pathlib import Path
import csv

def read_rows(path: Path) -> list[dict[str, str]]:
    with path.open(newline='', encoding='utf-8') as file:
        return list(csv.DictReader(file))

The with statement closes the file even if reading fails. DictReader uses the header row as field names and keeps CSV values as strings until converted.

4. Apply it to an analysis

Write small functions and load data from files with explicit paths and encodings. Start with a small, known example and state what each row or value represents. After the transformation, check row counts, types, keys, and units against the original data. If an assumption fails, stop and investigate instead of presenting the output as a valid result.

5. Common mistakes

  • Using a mutable list as a default function argument.
  • Assuming relative paths are relative to the script rather than the current working directory.

6. Practice task

Write a function that accepts a list of values and returns the largest value, or None for an empty list.

7. One possible solution

def largest(values): return max(values) if values else None. Add type hints when the expected value type is known.

8. Summary and next step

Write small functions and load data from files with explicit paths and encodings. Continue to the next course section and apply this foundation to a different analytics tool or workflow.

Visual guide

Working through Python Functions, Files, and Reusable Analysis

  1. Step 01 Define

    State the question, data grain, and expected output.

  2. Step 02 Apply

    Use the method and check its assumptions on a small example.

  3. Step 03 Verify

    Reconcile the result and communicate limitations.

Code examples

Calculate a safe mean
PYTHON 
def mean(values: list[float]) -> float | None:
    return sum(values) / len(values) if values else None

print(mean([2, 4, 6]))
Expected output
4.0
Load JSON explicitly
PYTHON 
import json
from pathlib import Path

payload = json.loads(Path('settings.json').read_text(encoding='utf-8'))
print(payload.get('region', 'Unknown'))
Expected output
The region value, or Unknown when that key is absent.

Key points

  • Write small functions and load data from files with explicit paths and encodings.
  • Check the result against the stated data definition and a representative example.
  • Record assumptions and report data-quality limitations rather than hiding them.

Check your understanding

Practice

Which approach best applies the ideas in Python Functions, Files, and Reusable Analysis?

Your learning progress

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