AlloCiné is a company which provides information on French cinema and provide ratings from the press and from their users for a large number of movies. In this repo we analyse AlloCiné movies ratings and provide the full dataset we use.
We had web scrape (e.g
🎉🎉🎉 Update : a module to retrieve the data from allociné is available here
We provide the dataset in two version :
- A one csv files format (brut and clean versions) : allocine_dataset.zip
- A multiple csv files format (clean version only): allocine_rel-dataset.zip
The brut file contains 59 966 movies, but only 10 424 movies have both press and users ratings. If you decide to use the clean version, you directly start with the 10 424 movies and if you decide to use the multiple csv files, you don't have to use ast library (see Getting Started).
movie_title: the movies title (in french)release_date: the original release datere_release_date: the re-release dateduration: the movies lengthgenre: the movies types (as an array, up to three different types)directors: movies directors (as an array)actors: main movie characters (as an array)nationality: nationality of the movies (as an array)press_rating: press ratings (from 0 to 5 stars)nber_press_vote: number of press votesuser_rating: AlloCiné users ratings (from 0 to 5 stars)nber_user_vote: number of users votes
We save the Pandas DataFrame as a csv file and several columns store Arrays, but the csv file keep it as a string. So, we need to call ast.literal_eval() function on these columns.
Example :
import pandas as pd
import ast
allocine = pd.read_csv("allocine.csv")
allocine["actors"] = allocine["actors"].apply(ast.literal_eval)
allocine["directors"] = allocine["directors"].apply(ast.literal_eval)
allocine["genre"] = allocine["genre"].apply(ast.literal_eval)
- Olivier Maillot - Initial work - Allociné Project - Blog Post
This project is free. Have fun.