Metadata-Version: 2.1
Name: Oscarscrapper
Version: 0.0.7
Summary: ///
Home-page: https://github.com/omasamo/Final-Project-JEM207
Author: Yann Aubineau, Samuel Bozon
Author-email: yann.aubineau@gmail.com, samuel.bozon@outlook.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.txt

# Final-Project-JEM207
Final project for JEM207 Data Processing with Python
We scrape from oscars.org site historical datasets of every nominees for the following 4 awards:
>

> Best Picture (1927-2021)
> Best Director (1927-2021)
> Best Actor (1927-2021)
> Best Actress (1927-2021)

Each dataset would be indexed by year and category.

The sources used for the following datas are: 
oscars.org and tmdb.com's API

Firstly we scrape the data for nominees and winners for each selected year for all the selected categories. 
Then, we will use these datas to aks tmdb's API for more informations about the movie. However, given that there may be multiple films and persons with the same name, we are selecting from the API's response only relevant informations. 

These are afterwards processed and added to our dataframe, which is later stored for the future use. 

User can through easy GUI select categories and years in which he is interested and for each of his selection, new dataset stored in csv file is created, permitting easy creation of multiple datasets with different searches. 





Notes from WIP:

string familiarity library python

json save the progress + raise Error pour Scrapping

focus to the end :

full documentation !!
executable project: one script executable 
jupyter is good for exploratory + presentation, but "professional" = script executable
MAKE IT INSTALLABLE : PYTHON PACKAGE

File that is an example of the use of the project
documentation information in lecture 9


