Submission Group 7: pyeasyeda (Python)

Submitting Author: Sukhleen Kaur (@sukhleen999)
Package Name: pyeasyeda One-Line Description of Package: A Python package that simplifies preliminary exploratory data analysis Repository Link: https://github.com/UBC-MDS/pyeasyeda Version submitted: 1.2.0 Editor: TBD
Reviewer 1: Manju Neervaram Abhinandana Kumar
Reviewer 2: Steven Lio Reviewer 3: Wenxin Xiang Reviewer 4: Nikita Shymberg Archive: TBD
Version accepted: TBD

Description

Exploratory Data Analysis is a crucial step for any analysis. However, it can be repetitive for the most part. pyeasyeda helps simplify data wrangling and data visualization to easily conduct the preliminary EDA of any project.

Scope

Please indicate which category or categories this package falls under:
- [ ] Data retrieval
- [ ] Data extraction
- [x] Data munging
- [ ] Data deposition
- [x] 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):

This package is designed to offer simple functions that make the initial steps of exploratory data analysis easier by performing data wrangling and data visualization.

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

Any individual who faces the need to perform data analysis of any complexity can leverage this package to kick-start their work.

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

One of the packages that offer similar functionality is QuickDA. However, the functions in reasyeda enable the users to customize them as appropriate.

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
[ ] 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.
[ ] 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)
[ x] Brief demonstration usage
[ x] Direction to more detailed documentation (e.g. your documentation files or website).
[ ] 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.
[ x] Functionality: Any functional claims of the software been confirmed.
[ ] Performance: Any performance claims of the software been confirmed.
[ ] 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.

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:

--- 30 minutes

Review Comments

Excellent package idea for robust EDA process in Python. Functions were simple to use and worked *almost like it is shown in the example. Great potential for EDA packages like this one, more analysis like missing data, imputation can be added in the future to enrich the package.

Comment regarding the clean_up function: It is not clear to the user if the original df is cleaned (overwrite) or a new object needs to be created. (output of the function is not clear)
for all the other plotting functions, it is dangerous to not let the user to choose how many variables they want to see. Since if the dataframe has 1000 numerical / categorical variables, then 1000 plot will automatically filled up the screen.
The example can be more descriptive on what the output is going to be.
The example is only runnable in Jupyter and not native Python environment - user should be advised.
import matplotlib.pyplot as plt library should be included for plot to generate successfully. It is called within the function but in user's global process that should also be called for user to manipulate the result plots.

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
[X] Any additional setup required (authentication tokens, etc)
[X] Brief demonstration usage
[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
[ ] Citation information

Usability

[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.
[X] Functionality: Any functional claims of the software been confirmed.
[ ] 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:

45 Minutes

Review Comments

Great function documentation! Very detailed and clear.
It would be better if the code example in the README was formatted as "python" rather than just a code block.
I would prefer that your example dfs were defined in the example code in the docstrings.
Personally, I'm not a fan of the funciton names birds_eye_view and close_up. I wish they were more descriptive.
Great code coverage! 98% is very good.
I like the comments throughout your code. At times I almost felt like there were too many comments, but I definitely think too many is better than too few!

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
[x] Any additional setup required (authentication tokens, etc)
[x] Brief demonstration usage
[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

[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.
[x] Functionality: Any functional claims of the software been confirmed.
[ ] 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

I really like the function documentation, and the package includes functions that are very useful in preliminary EDA. It's very impressive that the test coverage for the package is 98%.

For the clean_up function, it's better to show how much Nas in each columns to help users decide whether they need to drop the entire row. For example, if one of the column has too many Nas, it's not a good idea to delete all those rows. Maybe it's better to remove that column.
It would be better if the examples for user facing function were included in the readme file.
The guidelines for contributing was well documented in the CONTRIBUTING.md, however, the contact email of code maintainer was not provided. You may want to include it as people want to contribute would like to communicate with the authors prior to contributing.
When I run python in terminal, and try to use the birds_eye_view(df), there's no plot popping out. It would have been better if you can include solution for this in the readme file.
For the function names, I think the function close_up is not quite intuitive to understand the what to expect out of the function.
For the test of birds_eye_view_error(), I personally suggest it would be better if the input error could provide more details like "the input type should be a list" rather than raising TypeError. It would be more informative for users.

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
[x] Any additional setup required (authentication tokens, etc)
[x] Brief demonstration usage
[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

[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.
[x] Functionality: Any functional claims of the software been confirmed.
[ ] 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

Good job team! pyeasyeda is an excellent and useful package for performing EDA. A few things that I felt that could be improved are:

The usage in the README could be similar to what is given in the readthedocs, brief description of function and the code could be included.
The dependencies the package uses or imports could be included in the README.md
Installed the package using pip install and couldn't reproduce the example as there was no .csv file saved. After cloning the package I was able to execute the example from terminal. However, couldn't view the plots as it didn't pop up or get saved.
I was able to reproduce the example in jupyter notebook and view the charts.
The tests passed and all functions work as expected.

UBC-MDS / software-review-2022