You can access the datasets from /data in csv format.
Alternatively you can generate your own using the procedures given below.
- Ensure you have Python3.10+ installed.
-
Clone
git clone https://github.com/Simatwa/movies-dataset.git cd movies-dataset -
Install requirements
pip install -r requirements.txt
Running python -m data_hunter will retrieve data from the two categories available (Hollywood, Bollywood) across all genres available i.e (Action, Adventure, Animation, Biography, Comedy, Crime, Documentary, Drama, Family, Fantasy, Film-Noir, History, Horror, Music, Musical, Mystery, Romance, Sci-Fi, Sport, Thriller, War, Western)
You can pass other options such as limit, dir etc so as to meet your specific needs.
For more usage info you can run $ python -m data_hunter --help
usage: data-hunter [-h]
[-g [[Action|Adventure|Animation|Biography|Comedy|Crime|Documentary|Drama|Family|Fantasy|Film-Noir|History|Horror|Music|Musical|Mystery|Romance|Sci-Fi|Sport|Thriller|War|Western] ...]]
[-c [[Bollywood|Bollywood|_] ...]] [-l LIMIT]
[-d DIR] [-p PREFIX] [-q] [-w] [-t] [-v]
A collection of movies dataset for your ML project or any other
task.
options:
-h, --help show this help message and exit
-g, --genres [[Action|Adventure|Animation|Biography|Comedy|Crime|Documentary|Drama|Family|Fantasy|Film-Noir|History|Horror|Music|Musical|Mystery|Romance|Sci-Fi|Sport|Thriller|War|Western] ...]
Movie genres - ['_']
-c, --categories [[Bollywood|Bollywood|_] ...]
Movie category - ['_']
-l, --limit LIMIT Total movies per genre(multiple of 20) -
1000000
-d, --dir DIR Parent directory to save the datasets to -
/home/smartwa/git/smartwa/movies-dataset
-p, --prefix PREFIX Datasets filename prefix -
-q, --quiet Do not stdout any informative texts - False
-w, --overwrite Clear all $prefix*.csv file in the $dir -
False
-t, --trace Maintain trace of the hunting progress -
False
-v, --version show program's version number and exit
There's no gurantee that the data generated are correct.
| No. | Genre | Link |
|---|---|---|
| 1 | Action | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/action.csv |
| 2 | Adventure | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/adventure.csv |
| 3 | Animation | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/animation.csv |
| 4 | Biography | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/biography.csv |
| 5 | Comedy | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/comedy.csv |
| 6 | Crime | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/crime.csv |
| 7 | Documentary | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/documentary.csv |
| 8 | Drama | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/drama.csv |
| 9 | Family | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/family.csv |
| 10 | Fantasy | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/fantasy.csv |
| 11 | Film-Noir | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/film-noir.csv |
| 12 | History | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/history.csv |
| 13 | Horror | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/horror.csv |
| 14 | Music | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/music.csv |
| 15 | Musical | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/musical.csv |
| 16 | Mystery | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/mystery.csv |
| 17 | Romance | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/romance.csv |
| 18 | Sci-Fi | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/sci-fi.csv |
| 19 | Sport | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/sport.csv |
| 20 | Thriller | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/thriller.csv |
| 21 | War | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/war.csv |
| 22 | Western | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/western.csv |
| No. | Identity | Link |
|---|---|---|
| 1 | Combined csv |
https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/combined.csv |
| 2 | Sqlite3 Database | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/combined.db |
| 2 | Sqlite3 Database (Relational) | https://raw.githubusercontent.com/Simatwa/movies-dataset/main/data/combined-relational.db |
In order to make work easier, there's cli that comes handy in manipulating the data. This is just but not limited to converting them to various formats and even piling them into one sqlite3 database.