Open vincentho32 opened 1 year ago
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
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Estimated hours spent reviewing: 1 hour
Congratulations on creating such an interesting package. Your package is well-developed and fun to use.
I have the following suggestions for your reference:
from socceranalysis.rankingplayers import * data = pd.read_excel('soccer_data.xlsx') rankingplayers(data, "Goals_total", "Assists_Total")
The ModuleNotFoundError appears: `--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) Cell In[9], line 1 ----> 1 from socceranalysis.rankingplayers import * 2 data = pd.read_excel('soccer_data.xlsx') 3 rankingplayers(data, "Goals_total", "Assists_Total")
ModuleNotFoundError: No module named 'socceranalysis.rankingplayers' `
FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
You may take a look at it for the benefit of future users.Great job! It is a pleasure reviewing your package.
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:
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
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Estimated hours spent reviewing:
seaborn
to reduce the chance of having issues related to incompatibility with altair..py
file for simplicity.Well done! I wish you all the best in your journey of software development :)
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:
1
Really neat package. Works exactly as described with no issues. I've noted a few of my thoughts below on future improvements / quality of life fixes but none are serious.
1) I got an error trying to use the direct installation as specified in your "installation" section, but pip install socceranalysis
works as expected.
2) Would be nice to not have to download your dataset manually
3) Could add more badges - they make it look professional!
4) Can I have more information about the dataset itself? What's the source? What years does it cover / how recent is it? Any copyright or usage issues with the data I should be concerned about? etc.
5) If would be useful to list somewhere (either in the documentation or as a function) which stats are available as function inputs. You functions assume we know 1. what stats are available and 2. how the column headers are formatted
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:
Submitting Author: Flora Ouedraogo (@florawendy19), Gaoxiang Wang (@louiewang820), Manvir Kohli (@manvirsingh96) , Vincent Ho (@vincentho32) All current maintainers: (@florawendy19, @louiewang820, @manvirsingh96, @vincentho32) Package Name: socceranalysis One-Line Description of Package: With this package, you can quickly obtain summary statistics for a particular team, identify outliers based on market value, rank players by goals per game and display different plots with soccer data. Repository Link: https://github.com/UBC-MDS/socceranalysis_python Version submitted: v1.0.0 Editor: Flora Ouedraogo (@florawendy19), Gaoxiang Wang (@louiewang820), Manvir Kohli (@manvirsingh96) , Vincent Ho (@vincentho32)
Reviewer 1: Morris Chan Reviewer 2: Daniel Cairns Reviewer 3: Vikram Grewal Reviewer 4: Yaou Hu Archive: TBD
Version accepted: TBD Date accepted (month/day/year): TBD
Description
socceranalysis is a powerful Python package designed to make it easy to analyze and understand soccer statistics. With its set of functions, you can quickly obtain summary statistics for a particular team, identify outliers based on market value, rank players by goals per game and display different plots.
The package is built in a way that allows user to easily customize the functions to their own interests, giving them the flexibility to analyze the data in a way that is most meaningful to them. Whether you're a coach, a sports journalist or an analyst, socceranalysis will help you unlock the insights hidden in your soccer data and make more informed decisions.
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):
Who is the target audience and what are scientific applications of this package?
The target audience is anyone who is interested in analyzing soccer data. It can be coaches, sports journalists, analysts or fans.
Are there other Python packages that accomplish the same thing? If so, how does yours differ? There is a package on PyPi named pyrankingfifa to get the current ranking of the FIFA team. We also have a similar ranking function and three other functions that can help users to get the summary statistics, outliers and visualizations of numerical features.
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