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Python Data Processing Projects

Two small, self-contained projects illustrating foundational Python programming and data-processing skills.

These examples were newly implemented and tested for portfolio use, inspired by programming concepts I practised in a 2023 University of Melbourne Introduction to Programming course. They are not original submitted assignments, nor copies of assignment prompts or university-supplied datasets. The portfolio adaptation and tests were prepared with AI assistance.

Projects

1. Movie review analysis

  • Clean review text and validate ratings
  • Deduplicate records using review IDs
  • Score sentiment with a small, custom demonstration lexicon
  • Compute a transparent, weighted favorability score
  • Produce a personalized reply based on a film rating

2. GPS distance processing

  • Read and write CSV files with Python's standard library
  • Validate coordinate pairs
  • Calculate distances between successive GPS points using the Haversine formula
  • Save a transformed CSV with a segment-distance column

Quick start

Requires Python 3.10+. No external packages or API keys.

python movie_review_analysis/main.py
python gps_distance_processing/main.py gps_distance_processing/data/sample_track.csv gps_distance_processing/data/output_track.csv
python -m unittest discover -s tests -v

The GPS output is written to gps_distance_processing/data/output_track.csv (ignored by Git).

Structure

movie_review_analysis/
  main.py                 Review analysis logic and demonstration
  data/sample_reviews.csv Fictional reviews
gps_distance_processing/
  main.py                 CSV transformation and distance logic
  data/sample_track.csv   Fictional GPS points
tests/
  test_movie_reviews.py
  test_gps_distance.py
PROJECT_NOTES.md

Limitations

  • Sentiment analysis is rule-based and uses a deliberately tiny demonstration lexicon. It is not machine learning, and negations or nuanced language may be misclassified.
  • Favorability is an illustrative blend of ratings and lexicon scores, not a validated metric.
  • GPS distances are between successive points, not road-routing distances; distances are in kilometres.
  • Sample records were generated specifically for this portfolio and do not represent actual customers or GPS traces.

Academic integrity and provenance

The project is a post-course educational adaptation; it does not redistribute examination materials, university assignment questions, supplied data or lecture slides. Course-related public sharing rules should be confirmed before switching the repository from Private to Public.

Additional portfolio exercises

  • movie_review_analysis/summary.py: per-movie ratings and basic lexicon-sentiment summaries.
  • gps_distance_processing/track_summary.py: total distance and longest consecutive segment in a CSV trajectory.
  • tests/test_summaries.py: additional synthetic-data and empty-input tests.

These are later, independently authored AI-assisted extensions, not original assessed submissions.