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A resume parser used for extracting information from resumes
Built with ❤︎ and ☕ by Kumar Rajwani and Brian Njoroge
- Extract name
- Extract email
- Extract mobile numbers
- Extract skills
- Extract total experience
- Extract college name
- Extract degree
- Extract designation
- Extract company names
- You can install this package using
pip install resume-parser- Dependency of spacy
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
pip install importlib-metadata==3.2.0- For NLP operations we use spacy and nltk. Install them using below commands:
# spaCy
python -m spacy download en_core_web_sm
# nltk
python -m nltk.downloader stopwords
python -m nltk.downloader punkt
python -m nltk.downloader averaged_perceptron_tagger
python -m nltk.downloader universal_tagset
python -m nltk.downloader wordnet
python -m nltk.downloader brown
python -m nltk.downloader maxent_ne_chunker- PDF and DOCx and TXT files are supported on all Operating Systems
- Import it in your Python project
from resume_parser import resumeparse
data = resumeparse.read_file('/path/to/resume/file')For first time it will take around a minute so please keep patience.
The module would return a dictionary with result as follows:
{'degree': ['BSc','MSc'],
'designition': [
'content writer',
'data scientist',
'systems administrator',
],
'email': 'maunarokguy@gmail.com',
'name': 'Brian Njoroge',
'phone': '+918511593595',
'skills': [
'Python',
' C++',
'Power BI',
'Tensorflow',
'Keras',
'Pytorch',
'Scikit-Learn',
'Pandas',
'NLTK',
'OpenCv',
'Numpy',
'Matplotlib',
'Seaborn',
'Django',
'Linux',
'Docker'],
'total_exp': 3,
'university': ['gujarat university', 'wuhan university', 'egerton university']}