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The original source data was a .txt file containing a list of movies/tv shows, the order they were watched that year, and a rating out of 10\n\n2. The .txt file was parsed in `create_silver_jpmdb.py`, including parsing the ratings, seasons, watch order, year specifiers and other metadata\n\n3. Downloaded imdb data from [IMDb Datasets](https://datasets.imdbws.com/) and converted the .gz files into `silver/imdb/title_basics` and `silver/imdb/title_ratings` using `create_silver_imdb.py`\n\n4. The jpmdb and imdb datasets were initially joined using standard string cleaning and fuzzy matching approaches into `stg_jpmdb_combined` using `create_silver_stg_jpmdb_combined.py`\n\n5. Entries were manually reviewed a small CLI tool `review_combined_jpmdb.py`, giving an opportunity to correct fuzzy matching errors and manually add missing entries\n\n6. After all entries were validated, the data was moved to the gold table `gold/jpmdb` in `create_gold_jpmdb.py`\n\n\n## Dashboard\n\nThe dashboard is built using Dash and Plotly. It currently includes 4 visualizations:\n\n1. A virtualized table of all entries in the database\n2. A scatter plot of ratings over the watched order to show ratings over time\n3. A scatter plot comparing ratings to IMDb ratings\n4. A box plot showing distribution of ratings per IMDb genre\n\n\n## TODOs\n\n- [] incorporate scraped poster images into the dashboard\n- [] cross visualization filtering by genre\n- [] short summary of top 10 titles per year","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjeffbrennan%2Fjpmdb","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjeffbrennan%2Fjpmdb","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjeffbrennan%2Fjpmdb/lists"}