pythonfix
scholarly 1.7.11
Simple access to Google Scholar authors and citations
Simple access to Google Scholar authors and citations
Stars: 1786, Watchers: 1786, Forks: 344, Open Issues: 59The scholarly-python-package/scholarly repo was created 11 years ago and the last code push was 9 months ago.
The project is very popular with an impressive 1786 github stars!
How to Install scholarly
You can install scholarly using pip
pip install scholarly
or add it to a project with poetry
poetry add scholarly
Package Details
- Author
- Steven A. Cholewiak, Panos Ipeirotis, Victor Silva, Arun Kannawadi
- License
- Unlicense
- Homepage
- https://github.com/scholarly-python-package/scholarly
- PyPi:
- https://pypi.org/project/scholarly/
- GitHub Repo:
- https://github.com/scholarly-python-package/scholarly
Classifiers
- Software Development/Libraries/Python Modules
Related Packages
Errors
A list of common scholarly errors.
Code Examples
Here are some scholarly code examples and snippets.
Related Packages & Articles
pytest-sbase 4.46.5
A complete web automation framework for end-to-end testing.
selenium-base 4.46.5
A complete web automation framework for end-to-end testing.
seleniumbase 4.46.5
A complete web automation framework for end-to-end testing.
igneous-pipeline 4.34.0
Manage, Downsample, Mesh, Skeletonize, and Transform Neuroglancer Datasets
ILAMB 2.7.3
The International Land Model Benchmarking Package
pymobiledevice3 7.7.1
Pure python3 implementation for working with iDevices (iPhone, etc…)
cf_clearance 0.31.0
Purpose To make a cloudflare v2 challenge pass successfully, Can be use cf_clearance bypassed by cloudflare, However, with the cf_clearance, make sure you use the same IP and UA as when you got it.
redis-py-cluster 2.1.3
Library for communicating with Redis Clusters. Built on top of redis-py lib
detectors 0.1.11
This is a comprehensive library for generalized Out-of-Distribution (OOD) detection research. It provides over 20 detection methods, evaluation pipelines, OOD datasets, and model architectures integrated with timm. Additionally, it offers fast OOD evaluation metrics, multi-layer detection methods, and pipelines for open set recognition and covariate drift detection.