https://github.com/antrikshy/personalmovieanalysis
Finds interesting patterns in an IMDb ratings export; written as a Jupyter notebook, viz using Seaborn
https://github.com/antrikshy/personalmovieanalysis
data-visualization imdb jupyter-notebook movie-ratings pandas python seaborn
Last synced: about 2 months ago
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Finds interesting patterns in an IMDb ratings export; written as a Jupyter notebook, viz using Seaborn
- Host: GitHub
- URL: https://github.com/antrikshy/personalmovieanalysis
- Owner: Antrikshy
- Created: 2022-05-28T21:06:42.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-07-31T22:13:52.000Z (almost 4 years ago)
- Last Synced: 2025-03-23T21:47:05.656Z (over 1 year ago)
- Topics: data-visualization, imdb, jupyter-notebook, movie-ratings, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 3.91 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
PersonalMovieAnalysis
=====================
I have a habit of rating every single movie I watch on IMDb. This repo helps me analyze my IMDb ratings exports while cross-referencing [IMDb's datasets](http://imdb.com/interfaces). You can run it too!
See my detailed write up, with more beginner-friendly instructions for running it on your own data at [film.Antrikshy](http://antrikshy.com/film/analyzing-decades-worth-my-imdb-movie-ratings).
## Dependencies
### Packages
At this time, running this notebook requires:
1. Jupyter
2. Pandas
3. Seaborn
(and all transitive dependencies)
### Others
In addition to packages, the notebook assumes the presence of:
1. An export of a user's ratings.csv, as [generated](https://help.imdb.com/article/imdb/track-movies-tv/ratings-faq/G67Y87TFYYP6TWAV) by IMDb.
2. rackfocus_out.db, as generated by [Rackfocus](https://github.com/Antrikshy/Rackfocus) using [IMDb datasets](http://imdb.com/interfaces).
Both files should be placed in the top-level directory as cloned from this repo.
## Executing
My favorite way to run Jupyter notebooks is using VS Code's [built-in notebook support](https://code.visualstudio.com/docs/datascience/jupyter-notebooks).
Currently, this repo contains one notebook - Analysis.ipynb. In the presence of dependencies listed above, it can be run top-to-bottom as is.