Weather Utilities (Python)

This repository contains a small, dependency-free Python module for parsing simple daily weather records from CSV and producing human-readable summaries. It’s designed to be easy to reason about, testable end-to-end, and straightforward to extend.

Why it matters

• Focuses on composable, pure functions to simplify reasoning and testing.
• Clear separation between data access/formatting and core logic.
• Zero third-party dependencies for portability and reliability.

Repository Structure

• weather.py: Core, stateless functions for parsing, conversions, and summary generation.
• print_weather.py: Minimal example script for rendering results from the core module.
• tests: Unit tests, sample inputs under tests/data, and golden outputs under tests/expected_output.

Public API (core functions)

• convert_f_to_c(value_f): Fahrenheit to Celsius conversion.
• calculate_mean(values): Numeric mean for an iterable of numbers.
• find_min(values), find_max(values): Helpers used by summaries to identify extrema.
• convert_date(iso_string): Converts an ISO-like date string into a human-readable form.
• load_data_from_csv(path): Loads daily weather records from CSV into in-memory structures.
• generate_summary(data): Produces an overall summary string across the dataset.
• generate_daily_summary(data): Produces a per-day summary string across the dataset.

These functions are intentionally small, deterministic, and composable. Business formatting (e.g., user-facing strings) is centralized in the summary functions, while numeric operations and parsing are kept isolated and reusable.

Data Contract

• Inputs: Simple CSV files with daily readings (see examples in tests/data).
• Outputs: Human-readable summary strings (see expected results in tests/expected_output).
• Error handling: Non-numeric or malformed rows are safely skipped or normalized during load; downstream functions operate on already-sanitised inputs.

Design Notes

• Purity and isolation: Most functions are pure (no I/O), enabling targeted unit tests and easy reuse.
• Cohesion: Numeric helpers (mean, min, max, conversions) are decoupled from formatting.
• Extensibility: New metrics (e.g., median, range), units, or output formats can be added without touching existing logic.
• Testability: Golden-file style tests document the contract and prevent regressions.

Complexity and Performance

• Parsing is linear in the number of rows, O(n).
• Summary generation is linear in the number of daily records, O(n).
• Memory footprint is proportional to the number of rows parsed.

Getting Started

1. Ensure Python 3.10+ is available on your PATH.
2. Run the test suite:

python -m unittest tests/*.py

3. Explore the example inputs and expected outputs:

• Inputs: tests/data
• Expected outputs: tests/expected_output

4. Optional: Inspect or adapt the minimal runner in print_weather.py.

Extension Ideas

• Add aggregation windows (weekly/monthly) or additional statistics (median, stdev).
• Support alternate input formats (JSON, NDJSON) behind a parsing interface.
• Provide multiple renderers (plain text, Markdown) via strategy pattern.
• Add a lightweight CLI with subcommands for “summary” and “daily-summary”.

Notes for Reviewers

• The tests act as executable documentation of the expected behavior and string formatting.
• The API surface is intentionally small and focused on clarity over cleverness.