Lubimyczytac Scraper to Goodreads Pipeline

A Selenium-based data pipeline that reads a public Lubimyczytac profile library, enriches book records with per-book metadata, and exports a Goodreads-compatible CSV.

Scope

• Source: public Lubimyczytac profile library pages
• Output: normalized local CSV files, including Goodreads import format

Stack

• Python 3
• Selenium + ChromeDriver
• uv (environment and dependency management)

Project Layout

.
|-- scraper/
|   |-- profile_scraper.py   # phase 1: list scraping from profile pages
|   |-- enrichment.py        # phase 2: per-book enrichment orchestration
|   |-- book_details.py      # phase 2: ISBN/original title extraction
|   `-- __init__.py
|-- models/
|   |-- book.py              # Book dataclass and CSV schema
|   `-- __init__.py
|-- data_io/
|   |-- csv_utils.py         # CSV read/write and Goodreads export mapping
|   `-- __init__.py
|-- dane/
|   |-- books.csv            # phase 1 output
|   |-- books_enriched.csv   # phase 2 output
|   `-- goodreads.csv        # phase 3 output
|-- tests/
|-- main.py                  # pipeline entry point
|-- config.example.ini
|-- pyproject.toml
`-- LICENSE

Setup (uv)

1. Install uv (if missing):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

1. Sync dependencies:

uv sync

1. Ensure Google Chrome and a compatible ChromeDriver are available in PATH.

Configuration & Running

Create your local config.ini from the config.example.ini:

Then edit config.ini:

[settings]
profile_url = https://lubimyczytac.pl/profil/YOUR_PROFILE_ID/YOUR_PROFILE_NAME

Run the pipeline entry point:

uv run python main.py

Phase Artifacts Summary

• dane/books.csv: raw list scrape from profile pages (phase 1)
• dane/books_enriched.csv: per-book ISBN and original title enrichment (phase 2)
• dane/goodreads.csv: Goodreads import-ready export (phase 3)

Pipeline Phases

Phase 1: Profile Scraping

• Module: scraper/profile_scraper.py
• Entry function: scrape_books(profile_url)
• Input:
• profile_url from config.ini (expanded in main.py with list query parameters)
• Processing:
• Opens profile library pages in Selenium
• Iterates pagination
• Extracts row-level metadata (title, author, ratings, shelves, link, etc.)
• Produces Book objects (domain model) before CSV serialization
• Output file:
• dane/books.csv via save_books_to_csv(...)
• Shelf fields in this phase:
• Na półkach Główne: primary state shelf (e.g. Przeczytane, Teraz czytam, Chcę przeczytać)
• Na półkach Pozostałe: custom user shelves/tags (optional)

Phase 2: Record Enrichment

• Modules: scraper/enrichment.py, scraper/book_details.py
• Entry function: fill_isbn_and_original_titles(books)
• Input file:
• dane/books.csv loaded by load_books_from_csv(...)
• Processing:
• Visits each book URL from column Link
• Extracts ISBN and original title from the book detail page
• Fills missing original title fallback with the Polish title
• Output file:
• dane/books_enriched.csv via save_books_to_csv(...)

Phase 3: Goodreads Conversion

• Module: data_io/csv_utils.py
• Entry function: convert_books_to_goodreads(input_file, output_file)
• Input file:
• dane/books_enriched.csv
• Processing:
• Maps Lubimyczytac columns to Goodreads import schema
• Na półkach Główne -> Goodreads Shelves
• Na półkach Pozostałe -> Goodreads Bookshelves
• Writes Goodreads-required headers and transformed rows
• Output file:
• dane/goodreads.csv

Educational Purpose

This project is intended for educational use only. It is designed to demonstrate web scraping workflow design, CSV data processing, and multi-phase data transformation in Python.

License

This project is licensed under the MIT License. See LICENSE for details.