pyresparser 1.0.1

A simple resume parser used for extracting information from resumes

pyresparser

A simple resume parser used for extracting information from resumes

Features

• Extract name

• Extract email

• Extract mobile numbers

• Extract skills

• Extract total experience

• Extract education (not very accurate)

• Extract experience (not very accurate)

Installation

• You can install this package using

pip install pyresparser

• 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 words

• For extracting other supporting dependencies, execute:

# If you want to parse .docx and .pdf files (all OS supported)
pip install -r resume_parser/requirements.txt

# If you want to parse .docx, .doc and .pdf files (Ubuntu and OSX supported)
pip install -r resume_parser/requirements_with_textract.txt

• Modify resume_parser/resume_parser/skills.csv as per your requirements

• Modify Education Degrees as per you requirements in resume_parser/resume_parser/constants.py

• Place all the resumes that you want to parse in resume_parser/resumes/ directory

• Run python resume_parser/cli.py -f <resume_file_path>

CLI

For running the resume extractor you can also use the cli provided

usage: cli.py [-h] [-f FILE] [-d DIRECTORY]

optional arguments:
  -h, --help                              show this help message and exit
  -f FILE, --file FILE                    resume file to be extracted
  -d DIRECTORY, --directory DIRECTORY     directory containing all the resumes to be extracted
  -r REMOTEFILE, --remotefile REMOTEFILE  remote path for resume file to be extracted

For extracting data from a single resume file, use

python resume_parser/cli.py -f <resume_file_path>

For extracting data from several resumes, place them in a directory and then execute

python resume_parser/cli.py -d <resume_directory_path>

For extracting data from remote resumes, execute

python resume_parser/cli.py -r <path_to_remote_resume_file>

Notes:

• If you are running the app on windows, then you can only extract .docs and .pdf files

Result

The module would return a list of dictionary objects with result as follows:

[{'education': [('BE', '2014')],
  'email': '[e-mail]',
  'experience': [' Schlumberger DATA ENGINEER Pune'],
  'mobile_number': '8087996634',
  'name': 'Omkar Pathak',
  'no_of_pages': 3,
  'skills': ['Python',
             'C',
             'Technical',
             'Linux',
             'Machine learning',
             'System',
             'Html',
             'C++',
             'Security',
             'Testing',
             'Content',
             'Apis',
             'Engineering',
             'Payments',
             'Django',
             'Excel',
             'Admissions',
             'Mysql',
             'Windows',
             'Automation',
             'Opencv',
             'Website',
             'Css',
             'Js',
             'Algorithms',
             'Flask',
             'Programming',
             'Writing',
             'Training',
             'Php',
             'Reports',
             'Photography',
             'Open source',
             'Github',
             'Analytics',
             'Api'],
  'total_experience': 0.58}]

References that helped me get here

• https://www.kaggle.com/nirant/hitchhiker-s-guide-to-nlp-in-spacy

• https://www.analyticsvidhya.com/blog/2017/04/natural-language-processing-made-easy-using-spacy-%E2%80%8Bin-python/

• [https://medium.com/@divalicious.priya/information-extraction-from-cv-acec216c3f48](https://medium.com/@divalicious.priya/information-extraction-from-cv-acec216c3f48)

Built with ♥ and :coffee: by `Omkar Pathak <http://www.omkarpathak.in/>`__

Donation

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