football-stats | Tracking of football players and analysis | Analytics library

by dev-labs-bg Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | football-stats Summary

football-stats is a Python library typically used in Analytics applications. football-stats has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Football stats is a system which has the purpose of helping football match analyses. The final goal of the project is to have the capability of ball and players' position analysis, creating heatmaps and statistics of different actions or situations. Note: Readme in BG | Прочети.ме на български.

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Quality

Security

License

Reuse

Support

football-stats has a low active ecosystem.

It has 76 star(s) with 29 fork(s). There are 27 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 4 have been closed. On average issues are closed in 17 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of football-stats is current.

Quality

football-stats has 0 bugs and 0 code smells.

Security

football-stats has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

football-stats code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

football-stats is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

football-stats releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed football-stats and discovered the below as its top functions. This is intended to give you an instant insight into football-stats implemented functionality, and help decide if they suit your requirements.

Computes the relative coordinates of the original point
Transform a window to a rectangular field
Calculate the absolute coordinates of a given position
Calculates the average coordinates of the running history

Get all kandi verified functions for this library.

football-stats Key Features

No Key Features are available at this moment for football-stats.

football-stats Examples and Code Snippets

No Code Snippets are available at this moment for football-stats.

Community Discussions

Trending Discussions on football-stats

Why are columns and values data lost in pd.concat?

QUESTION

Why are columns and values data lost in pd.concat?

Asked 2020-Nov-30 at 19:45

User Experience
I am recent engineering (Not C.S.) graduate with basic proficiency in MATLAB. I have no prior experience with Python/Jupyter. I have scoured SO and google for help but cannot find a similar issue. The code for this project is based on the following article: https://medium.com/@shahrezanjum/using-python-to-automate-fantasy-football-stats-in-madden-ff9020fc2d2d

Motivation
Madden is a NFL video game. In franchise mode, players can cooperatively play as different teams in the same league. Madden has the ability to output player statistics for this league as CSV files. CSV files are separate, and are organized in folders by week and by team. As such, this output format requires modification in order to perform data analysis. See Madden output structure here

Problem Statement
The objective is to concatenate these CSVs into a single CSV file to facilitate data analysis. Madden CSV column orders are not identical. The code I have so far has two issues: 1)The values for the first column "defCatchAllowed" is missing ONLY for the first data frame. 2)The values for the column "fullName" is missing values for every data frame after the first.

Code Strategy
Unlike the code in the link, I see 3 objectives for the code:

Find all CSV files for a given week.
Fill in blank cells with a value of zero.
Concatenate CSV files. (Concat can sort columns so different col orders for df's is ok.)

Here is the code that I have so far:

-Create DFs from CSV (starting with just 3 df, will add all teams when code works)

...

ANSWER

Answered 2020-Nov-30 at 02:09

The core issue is having a disjoint set of columns across [df1,df2, df3]... and these need to be wrangled to a normalized set of columns? If this is not the problem, stop here.

Recommend one should define the norm set of columns for downstream analysis. Choices are:

drop unneccessary columns per df
rename N diff columns into 1 normalized column name & format
normalize all to common format
categorize all similiar columns to unified identifiers... e.g. name + fullname -> playerID

Beyond this, one has to see specifics. Wrangling is messy.

Source https://stackoverflow.com/questions/65066964

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install football-stats

You can download it from GitHub.
You can use football-stats like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

We accept all kind of contributions that you guys make and we'll love you for them! <3. If you find any problems, have any suggestions or want to discuss something you can either open an issue here or make a pull request with code changes instead. If you want to contribute, but you're not sure where to start you can always take a look at the issues here we have open and pick up with some of them. Try to follow our conventions for naming issues, branches and existing code structure.

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