resume-parser 0.8.4

A resume parser used for extracting information from resumes

Resume parser

Premium resume parsing services have been moved to [Resume-Parser](https://www.resume-parser.com/application/resumes). Please try the demo for free and give us your [feedback](https://www.resume-parser.com)

Premium resume parsing services have been moved to Resume-Parser. Please try the demo for free and give us your feedback

A resume parser used for extracting information from resumes

Built with ❤︎ and :coffee: by Kumar Rajwani and Brian Njoroge

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Features

• Extract name

• Extract email

• Extract mobile numbers

• Extract skills

• Extract total experience

• Extract college name

• Extract degree

• Extract designation

• Extract company names

Installation

• You can install this package using

pip install resume-parser

• 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

Supported File Formats

• PDF and DOCx and TXT files are supported on all Operating Systems

Usage

• Import it in your Python project

from resume_parser import resumeparse

data = resumeparse.read_file('/path/to/resume/file')

Result

The module would return a dictionary with result as follows:

{'degree': ['BSc','MSc'],
     'designition': [
         'content writer',
         'data scientist',
         'systems administrator',
     ],
     'email': '[e-mail]',
     'name': 'Brian Njoroge',
     'phone': '[phone]',
     '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']}