Scrapping of 6 paintings websites with python
Budget: $250 – $750 USD
We are seeking the development of six simple Python scripts, designed for data collection from various art websites. These scripts will be tasked with aggregating extensive information on paintings from multiple renowned online art repositories, with the ultimate goal of storing this data into an SQLite database.
The scripts must be equipped to handle and gracefully skip over any HTML parsing errors or encounters with 404 pages, logging such problems.
Data to Collect: For each painting, the following information must be meticulously gathered and stored:
Title
Artist
Description
Imageurl (of the largest size)
Date
Style
Genre
Media/Medium
Location/Current location/Museum
Size/Dimensions
Data Extraction and Parsing:
Each script should proficiently extract detailed information on paintings from the following sources:
https://en.wikipedia.org/wiki/Category:Paintings_by_artist
https://www.metmuseum.org/art/collection/search?showOnly=withImage&material=Paintings
https://search.getty.edu/gateway/search?q&cat=type&rows=10&srt=a&dir=s&dsp=0&img=0&f=%22Paintings%22&types=%22Paintings%22&pg=1
https://www.rijksmuseum.nl/en/search?f=1&p=2&ps=12&type=painting&st=Objects&ii=0
(on the artwork follow the link "More details" or "Entry" to get all informations)
https://www.nga.gov/collection-search-result.html?sortOrder=DEFAULT&artobj_classification=painting&pageSize=30&pageNumber=144&lastFacet=artobj_classification
https://artsandculture.google.com/partner
(After partner, follow "View All")
Objective:
Our aim is to create a centralized, easily accessible database of painting information, aiding in research and appreciation of these artworks. This project is an excellent opportunity for Python developers with an interest in art and data management to contribute to a valuable cultural resource.
Project Deliverables:
Six Python scripts, each corresponding to a specific data source.
A comprehensive SQLite database schema for storing the collected data.
Documentation outlining the usage and features of each script, including error-handling mechanisms.
Ideal Candidate:
Proficient in Python, with experience in web scraping and data parsing.
Familiarity with SQLite and database management.
Strong problem-solving skills, particularly in the context of web data extraction.
An eye for detail and a commitment to accuracy in data collection.
The scripts must be equipped to handle and gracefully skip over any HTML parsing errors or encounters with 404 pages, logging such problems.
Data to Collect: For each painting, the following information must be meticulously gathered and stored:
Title
Artist
Description
Imageurl (of the largest size)
Date
Style
Genre
Media/Medium
Location/Current location/Museum
Size/Dimensions
Data Extraction and Parsing:
Each script should proficiently extract detailed information on paintings from the following sources:
https://en.wikipedia.org/wiki/Category:Paintings_by_artist
https://www.metmuseum.org/art/collection/search?showOnly=withImage&material=Paintings
https://search.getty.edu/gateway/search?q&cat=type&rows=10&srt=a&dir=s&dsp=0&img=0&f=%22Paintings%22&types=%22Paintings%22&pg=1
https://www.rijksmuseum.nl/en/search?f=1&p=2&ps=12&type=painting&st=Objects&ii=0
(on the artwork follow the link "More details" or "Entry" to get all informations)
https://www.nga.gov/collection-search-result.html?sortOrder=DEFAULT&artobj_classification=painting&pageSize=30&pageNumber=144&lastFacet=artobj_classification
https://artsandculture.google.com/partner
(After partner, follow "View All")
Objective:
Our aim is to create a centralized, easily accessible database of painting information, aiding in research and appreciation of these artworks. This project is an excellent opportunity for Python developers with an interest in art and data management to contribute to a valuable cultural resource.
Project Deliverables:
Six Python scripts, each corresponding to a specific data source.
A comprehensive SQLite database schema for storing the collected data.
Documentation outlining the usage and features of each script, including error-handling mechanisms.
Ideal Candidate:
Proficient in Python, with experience in web scraping and data parsing.
Familiarity with SQLite and database management.
Strong problem-solving skills, particularly in the context of web data extraction.
An eye for detail and a commitment to accuracy in data collection.