A simple resume parser used for extracting information from resumes
Project description
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': 'omkarpathak27@gmail.com', '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
Built with ♥ and :coffee: by `Omkar Pathak <http://www.omkarpathak.in/>`__
Donation
If you have found my softwares to be of any use to you, do consider helping me pay my internet bills. This would encourage me to create many such softwares :)
PayPal |
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₹ (INR) |
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