Python EDA — The Good Place (IMDb + Episode Data)

Coursework Project: Python for Data Analytics
International Business School (IBS), Budapest

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1. Project Overview

This project performs exploratory data analysis (EDA) in Python using episode-level data for The Good Place, combining:

• IMDb-derived information (ratings, votes, titles, descriptions, air dates)
• Episode metadata (writers, directors, season/episode structure, US viewership)

The notebook demonstrates a complete EDA workflow: data loading, cleaning, merging, descriptive statistics, visualization, and interpretation.

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2. Key Questions Explored

The analysis focuses on questions such as:

• How do IMDb ratings vary across seasons and episodes?
• Which episodes are top-rated and which have the most votes?
• How does US viewership change over time?
• Are ratings correlated with votes, viewership, or description length?

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3. Repository Structure

python-data-analytics-imdb-eda/
├─ notebooks/
│  └─ eda_the_good_place.ipynb
├─ data/
│  ├─ raw/                # raw datasets (included)
│  └─ processed/          # optional cleaned outputs
├─ outputs/
│  ├─ figures/            # saved charts (optional)
│  └─ tables/             # exported tables (optional)
├─ report/
│  └─ eda_summary.pdf
└─ docs/
   ├─ AI_USAGE.md
   └─ ACADEMIC_INTEGRITY.md

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4. Data

4.1 Included datasets

The raw datasets used in this project are included in the repository under data/raw/:

• data/raw/the_good_place_imdb.csv
• data/raw/the_good_place_episodes.csv

The datasets originate from Kaggle and are released under a CC0 (Public Domain) license, which permits redistribution and reuse without restriction.

Source reference:

• Kaggle notebook: The Good Place Episode Data Analysis https://www.kaggle.com/code/bcruise/the-good-place-episode-data-analysis/notebook

Additional details on data sources and structure are provided in:

• data/README_data.md

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5. How to run

Option A — pip (recommended)

py -m pip install -r requirements.txt

Option B — conda (reproducible environment)

conda env create -f environment.yml
conda activate python-data-analytics-eda

Then launch Jupyter and open:

• notebooks/eda_the_good_place.ipynb

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6. Deliverables

• Jupyter notebook: notebooks/eda_the_good_place.ipynb
• PDF export: report/eda_summary.pdf (generated from the final notebook)

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7. Notes on AI Usage and Academic Integrity

This project was completed as coursework and follows the course’s AI policy (Permitted Level: Level 4).

Full disclosure and integrity statements are available in:

• docs/AI_USAGE.md
• docs/ACADEMIC_INTEGRITY.md

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Author

Individual MSc coursework project International Business School (IBS), Budapest