{"id":27624361,"url":"https://github.com/scarblase/spotify_analysis","last_synced_at":"2026-05-02T17:31:06.909Z","repository":{"id":289338400,"uuid":"970915973","full_name":"scarblase/spotify_analysis","owner":"scarblase","description":"Analyzing Spotify's top 1000 tracks using Python, DuckDB, and Spotify-themed visualizations to uncover trends and insights.","archived":false,"fork":false,"pushed_at":"2025-04-22T18:37:24.000Z","size":463,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"origin","last_synced_at":"2025-04-22T19:46:39.775Z","etag":null,"topics":["dbt","duckdb","matplotlib","pandas","python3","sns","sql"],"latest_commit_sha":null,"homepage":"https://www.kaggle.com/datasets/kunalgp/top-1000-most-played-spotify-songs-of-all-time","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/scarblase.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-22T18:20:15.000Z","updated_at":"2025-04-22T18:38:57.000Z","dependencies_parsed_at":"2025-04-22T19:46:41.118Z","dependency_job_id":"cde2c178-ff30-4985-8475-44ba4682c078","html_url":"https://github.com/scarblase/spotify_analysis","commit_stats":null,"previous_names":["scarblase/spotify_analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scarblase%2Fspotify_analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scarblase%2Fspotify_analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scarblase%2Fspotify_analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scarblase%2Fspotify_analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/scarblase","download_url":"https://codeload.github.com/scarblase/spotify_analysis/tar.gz/refs/heads/origin","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250426403,"owners_count":21428735,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["dbt","duckdb","matplotlib","pandas","python3","sns","sql"],"created_at":"2025-04-23T11:34:52.843Z","updated_at":"2026-05-02T17:31:06.881Z","avatar_url":"https://github.com/scarblase.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Spotify Analysis 🎵\n\nWelcome to the **Spotify Analysis** repository! This project dives deep into the top 1000 most-played Spotify tracks of all time, exploring trends, patterns, and insights through data analysis and visualization.\n\n## 🌟 Project Highlights\n\n- Analyze and visualize Spotify's **Top 1000 Tracks** dataset.\n- Use **DuckDB** for SQL-style queries on the dataset. (Yes, we tried DuckDB just to see how it works! 😉)\n- Beautiful, Spotify-themed visualizations.\n- Explore trends in artists, albums, track durations, and popularity.\n\n---\n\n## 📊 Dataset Overview\n\nThe dataset contains information on 1000 tracks, with the following attributes:\n\n| Column         | Description                                      |\n|----------------|--------------------------------------------------|\n| **track_name** | Name of the track.                              |\n| **artist**     | Name of the artist.                             |\n| **album**      | Album associated with the track.                |\n| **release_date** | Track's release date.                         |\n| **popularity** | Popularity score (0-100).                       |\n| **spotify_url** | Link to the track on Spotify.                  |\n| **id**         | Unique Spotify track ID.                        |\n| **duration_min** | Track duration in minutes.                    |\n| **release_year** | Year of release (derived).                    |\n| **release_month** | Month of release (derived).                  |\n\n---\n\n## 🧰 Tools and Technologies\n\nThis project leverages the following tools and libraries:\n\n- **DuckDB**: SQL-style queries on DataFrames (because why not try it? 😄).\n- **Pandas**: Data wrangling and exploratory analysis.\n- **Matplotlib** \u0026 **Seaborn**: Stunning visualizations.\n- **BeautifulSoup**: Web scraping (initial attempts to fetch data).\n- **Python**: The powerhouse behind everything.\n\n---\n\n## 📈 Key Insights and Visualizations\n\n### 1. **Top Artists and Albums**\n- Bar charts showing the **Top 10 Artists** and **Top 5 Albums** based on the number of tracks.\n- Styled with Spotify's signature **black, green, and white** theme for a clean and modern look.\n\n### 2. **Popularity Trends**\n- **Line Plots**:\n  - Popularity trends over the years.\n  - Monthly popularity patterns.\n- **Scatter Plot**:\n  - Relationship between track duration and popularity.\n\n### 3. **Distribution Analysis**\n- **KDE Plots** to showcase the distribution of:\n  - Track popularity.\n  - Track durations.\n\n### 4. **Popular Tracks by Artist**\n- Extracted the **most popular track** for each artist with a popularity score ≥ 90 using **DuckDB**.\n\n---\n\n## 🎨 Spotify-Themed Styling\n\n- **Color Palette**:\n  - Spotify Black: `#121212`\n  - Spotify Green: `#1DB954`\n  - Spotify Light Grey: `#B3B3B3`\n  - Spotify White: `#FFFFFF`\n- Fonts and gridlines styled to match Spotify's sleek UI.\n\n---\n\n## 🚀 Getting Started\n\nFollow these steps to run the project locally:\n\n### 1. Clone the Repository\n```bash\ngit clone https://github.com/scarblase/spotify_analysis.git\ncd spotify_analysis\n```\n\n### 2. Install Dependencies\nMake sure you have Python installed, then run:\n```bash\npip install pandas duckdb matplotlib seaborn requests beautifulsoup4\n```\n\n### 3. Load the Dataset\nEnsure the file `spotify_top_1000_tracks.csv` is in the repository's root directory.\n\n### 4. Run the Jupyter Notebook\nLaunch Jupyter Notebook to explore the analysis:\n```bash\njupyter notebook\n```\n\n---\n\n## 🌐 References\n\n- **Dataset Source**: [Kaggle - Top 1000 Most Played Spotify Songs of All Time](https://www.kaggle.com/datasets/kunalgp/top-1000-most-played-spotify-songs-of-all-time)\n- **Spotify**: [Spotify](https://www.spotify.com)\n\n---\n\n✨ Dive into the dataset and enjoy uncovering the magic of Spotify's top tracks!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscarblase%2Fspotify_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fscarblase%2Fspotify_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscarblase%2Fspotify_analysis/lists"}