Open kenuiuc opened 1 year ago
The package includes all the following forms of documentation:
pyproject.toml
file or elsewhere.Readme file requirements The package meets the readme requirements below:
The README should include, from top to bottom:
NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)
Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:
Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.
The package contains a paper.md
matching JOSS's requirements with:
Estimated hours spent reviewing: 1
num_imputer
. Consider adding a small toy dataframe to the function examples.date_imputer
do not have examples in the function documentationPlease check off boxes as applicable, and elaborate in the comments below. Your review is not limited to these topics, as described in the reviewer guide
The package includes all the following forms of documentation:
pyproject.toml
file or elsewhere.Readme file requirements The package meets the readme requirements below:
The README should include, from top to bottom:
NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)
Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:
Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.
The package contains a paper.md
matching JOSS's requirements with:
Estimated hours spent reviewing: 1 hour
Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide
The package includes all the following forms of documentation:
pyproject.toml
file or elsewhere.Readme file requirements The package meets the readme requirements below:
The README should include, from top to bottom:
NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)
Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:
Estimated hours spent reviewing: 2 hours
dummy.ipynb
file added in the src folder, I don't understand the point of it. Maybe it is commited by mistake?
Submitting Author: Ken Wang @kenuiuc All current maintainers: @kenuiuc, @LisaSeq, @renee-kwon, @Althrun-sun Package Name: simpute_py One-Line Description of Package: A simple data imputation tool. Repository Link: https://github.com/UBC-MDS/simpute-py Version submitted: v0.1.0 Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD Date accepted (month/day/year): TBD
Description
Our python package for simple data imputation will allow you to quickly and seamlessly impute any missing data (be numeric, categorical, date/time or boolean values) using any large datasets.
Scope
For all submissions, explain how the and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):
Beginner level data analysts and data engineers who have basic
Python
skills.We do have other packages you can use such as AutoImpute and MIDASpy. However our package aims to provide functionalities not provided in either package (such as imputing
Date
type data) and provide an easier to use solution to the beginner users.@tag
the editor you contacted:NA
Technical checks
For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:
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