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: 0.1.10
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!
* 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.
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*
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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.
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arashshams
commented
3 years ago
Submitting Author:
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: 0.1.10 Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD
Description
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 fill out a pre-submission inquiry before submitting a data visualization package. For more info, see notes on categories of our guidebook.
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.
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.
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.
@tagthe 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:
Publication options
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.
Code of conduct
P.S. *Have feedback/comments about our review process? Leave a comment here
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