Python is often the choice for developers who need to apply data analysis in their work or mainly data scientists/data engineers whose tasks are more related deriving insight from the data.
One of Python’s greatest assets is its extensive set of libraries. Recently, I was working on very popular Data Mining algorithms (i.e: FP-Growth and Custom A-Priori). There was a situation I wanted to get comprehensive analysis report on results generated by these algorithms.
As a support lib for Data Science work introducing “doc-dff — Generate the diff data between two files”
doc-diff supports the following features:
Generate the following comparison reports
- common_in_doc1-and-doc2-%Y-%m-%d.csv
- common_key_with_diff_values-%Y-%m-%d.csv
- exclusive_in_doc1-%Y-%m-%d.csv
- exclusive_in_doc2-%Y-%m-%d.csv
Compare two files and return following ‘dicts(prodCode, recommendation)’
- common_in_doc1_and_doc2_list = dicts()
- common_key_with_diff_values_list = dicts()
- exclusive_in_doc1_list = dicts()
- exclusive_in_doc2_list = dicts()
Install
$ pip install doc-diff
Implementation
from doc_diff import Difffrom doc_diff import gen_comp_report
if __name__ == '__main__':
_\# Data file location_ a\_priori\_csv\_location = **"./data/a-priori.csv"** pfp\_csv\_location = **"./data/pfp.csv"**
_# Process a-priori.csv data file_ a\_priori\_diff = Diff(a\_priori\_csv\_location)
a\_priori\_diff.process\_file()
_# Process pfp.csv data file_ pfp\_diff = Diff(pfp\_csv\_location)
pfp\_diff.process\_file()
gen\_comp\_report(a\_priori\_diff, pfp\_diff)
I’m looking forward to open source all my supportive lib for Data Science/Data Engineering work. Let me know what you think about ‘doc-diff’ below in the comments and share your thoughts. If you want to share any new features/issues, feel free to open an issue in the GitHub repository.