Submission: eazieda (Python)

Submitting Author:

Vignesh Lakshmi Rajakumar (@vigneshrajakumar)
Dustin Andrews (@dbandrews)
Arash Shamseddini (@arashshams)
Yuyan Guo (@yuyanguo)

Package Name: eazieda
One-Line Description of Package: eazieda makes data wrangling and exploratory data analysis (EDA) quite simple and fast Repository Link: eazieda Version submitted: 0.1.11 Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD

Description

Include a brief paragraph describing what your package does:

Almost every data analysis project involves the process of doing some exploratory data analysis (EDA) and data preprocessing. Usually they serve as a very crucial and inevitable step in a data analysis workflow. Typically these steps are followed by some preprocessing like imputation and dealing with outliers. All of these steps together may require lots of coding effort and can be repeated for several projects. To solve this issue, Python package eazieda is designed so that it wraps all of those lines of code into four convenient functions that will allow you to quickly and easily carry out EDA along with some simple preprocessing using just a few lines of code!

Scope

Please indicate which category or categories this package falls under:
- [ ] Data retrieval
- [ ] Data extraction
- [x] Data munging
- [ ] Data deposition
- [ ] Reproducibility
- [ ] Geospatial
- [ ] Education
- [x] Data visualization*

* Please fill out a pre-submission inquiry before submitting a data visualization package. For more info, see notes on categories of our guidebook.

Explain how the and why the package falls under these categories (briefly, 1-2 sentences):

eazieda has the functionality to produce interactive plots (e.g. histograms and correlation plots) to graphically demonstrate the distribution and correlation of features inside a given dataset. Another functionality of eazieda is data wrangling since at its core it is designed to deal with missing data and outliers.

Who is the target audience and what are scientific applications of this package?

The target audience would be those who are interested to get an interactive visualization of the dataset at hand and also people who wish to do a quick data munging especially if their dataset contains missing values and outliers.

Are there other Python packages that accomplish the same thing? If so, how does yours differ?

There are similar Python packages such as "pandasprofiling" or "sweetviz", but eazieda's functionality is to address the most-wanted EDA and Data wrangling jobs quickly and conveniently. Another difference is that eazieda is quite light weighted.

If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

[x] does not violate the Terms of Service of any service it interacts with.
[x] has an OSI approved license.
[x] contains a README with instructions for installing the development version.
[x] includes documentation with examples for all functions.
[x] contains a vignette with examples of its essential functions and uses.
[x] has a test suite.
[x] has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.

Publication options

[ ] Do you wish to automatically submit to the Journal of Open Source Software? If so:

JOSS Checks

- [ ] The package has an **obvious research application** according to JOSS's definition in their [submission requirements][JossSubmissionRequirements]. Be aware that completing the pyOpenSci review process **does not** guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS. - [ ] The package is not a "minor utility" as defined by JOSS's [submission requirements][JossSubmissionRequirements]: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria. - [ ] The package contains a `paper.md` matching [JOSS's requirements][JossPaperRequirements] with a high-level description in the package root or in `inst/`. - [ ] The package is deposited in a long-term repository with the DOI: *Note: Do not submit your package separately to JOSS*

Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?

This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.

[x] Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

Code of conduct

[x] I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

P.S. *Have feedback/comments about our review process? Leave a comment here

Editor and Review Templates

Editor and review templates can be found here

Package Review

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

[x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

[x] A statement of need clearly stating problems the software is designed to solve and its target audience in README
[x] Installation instructions: for the development version of package and any non-standard dependencies in README
[x] Vignette(s) demonstrating major functionality that runs successfully locally
[x] Function Documentation: for all user-facing functions
[x] Examples for all user-facing functions
[x] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
[x] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a setup.py file or elsewhere.

Readme requirements The package meets the readme requirements below:

[x] Package has a README.md file in the root directory.

The README should include, from top to bottom:

[x] The package name
[x] Badges for continuous integration and test coverage, a repostatus.org badge, and any other badges. 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 badge for pyOpenSci peer-review will be provided upon acceptance.)
[x] Short description of goals of package, with descriptive links to all vignettes (rendered, i.e. readable, cf the documentation website section) unless the package is small and there’s only one vignette repeating the README.
[x] Installation instructions
[ ] Any additional setup required (authentication tokens, etc)
[ ] Brief demonstration usage
- Missing demonstration in the readme
[x] Direction to more detailed documentation (e.g. your documentation files or website).
[x] If applicable, how the package compares to other similar packages and/or how it relates to other packages
[x] Citation information

Usability

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:

[x] The documentation is easy to find and understand
[x] The need for the package is clear
[x] All functions have documentation and associated examples for use

Functionality

[x] Installation: Installation succeeds as documented.
[ ] Functionality: Any functional claims of the software been confirmed.
- outliers_detect not working as intended when using default method. See below for more information
[x] Performance: Any performance claims of the software been confirmed.
[x] Automated tests: Tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
[x] Continuous Integration: Has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.
[x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.

For packages co-submitting to JOSS

[ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

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:

[ ] A short summary describing the high-level functionality of the software
[ ] Authors: A list of authors with their affiliations
[ ] A statement of need clearly stating problems the software is designed to solve and its target audience.
[ ] References: with DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

[ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 1 hour

Review Comments

Hello Eazieda Team,

Good job on completing the package! I am very impressed that you guys wrote 6 functions even though you only have 4 people! Good job on writing the documentations in the vignette as they are very easy to follow and understand. Now onto areas where I think this package could improve on:

I think it will be better if we could have a small demonstration of the package functions in the Readme.
Regarding the missing_impute function, I think we can incorporate most_frequent method to numerical data as well. Think of it as finding the mode of a numerical Series.
Regarding the the outliers_detect function, the default method 'zscore' doesn't seem to detect outlier if I remove one item from the example you provided. See below for additional information.
I think we can add an exception handling to handle an empty dataframe.
Maybe just nitpicking here, but I kind of think the default height/width for histograms are too small. Perhaps changing them to 250 by 250 ?

Many thanks @jachang0628 for your review. We will be taking your comments in consideration to improve our package.

Thanks @jachang0628 for the useful comments and taking the time for the thorough review!

You raise an interesting point about the outlier function. The default method uses a z-score threshold of 3 to detect outliers. In the example you used, the 'outlier' has a z-score of less than 3. So it is working as intended, since that's just how z-scores work; they aren't ideal for finding outliers in small samples. Having said that, you have a point that this could be misleading. It might be better if we used iqr as the default method instead. I'll check this with the other contributors and implement it

These comments are super helpful and we'll put them in our backlog! (Especially the exception handling one, that's a good catch!)

Package Review

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

[x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).