fantasy-football | Choosing a fantasy football team using spark hive

by hougs Java Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | fantasy-football Summary

fantasy-football is a Java library. fantasy-football has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Choosing a fantasy football team using spark, hive, python, and really just about anything.

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Security

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fantasy-football has a low active ecosystem.

It has 18 star(s) with 7 fork(s). There are 11 watchers for this library.

It had no major release in the last 6 months.

fantasy-football has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of fantasy-football is current.

Quality

fantasy-football has 0 bugs and 0 code smells.

Security

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

fantasy-football code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

fantasy-football does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

fantasy-football 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.

fantasy-football saves you 877 person hours of effort in developing the same functionality from scratch.

It has 2006 lines of code, 297 functions and 17 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Setter for field .
Retrieve information about a field .
Sets the year value .
Set the value of this field .
Gets the value of the wr2 property .
Gets the value of the totalGamePlayplayed property .
Gets the value of the playerId property .
Creates a builder builder for a PlayerYearly .
Gets the value of the mean property .
Sets the mean value .

Get all kandi verified functions for this library.

fantasy-football Key Features

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

fantasy-football Examples and Code Snippets

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

Community Discussions

Trending Discussions on fantasy-football

Python Webscraping Approach for Comparing Football Players' college alma maters with total NFL Fantasy Football output

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

QUESTION

Python Webscraping Approach for Comparing Football Players' college alma maters with total NFL Fantasy Football output

Asked 2020-Dec-16 at 13:20

I am looking to a data science project where I will be able to sum up the fantasy football points by the college the players went to (e.g. Alabama has 56 active players in the NFL so I will go through a database and add up all of their fantasy points to compare with other schools).

I was looking at the website: https://fantasydata.com/nfl/fantasy-football-leaders?season=2020&seasontype=1&scope=1&subscope=1&aggregatescope=1&range=3

and I was going to use Beautiful Soup to scrape the rows of players and statistics and ultimately, fantasy football points.

However, I am having trouble figuring out how to extract the players' college alma mater. To do so, I would have to:

Click each "players" name
Scrape each and every profile of the hundreds of NFL players for one line "College"
Place all of this information into its own column.

Any suggestions here?

...

ANSWER

Answered 2020-Dec-16 at 11:03

There's no need for Selenium, or other headless, automated browsers. That's overkill.

If you take a look at your browser's network traffic, you'll notice that your browser makes a POST request to this REST API endpoint: https://fantasydata.com/NFL_FantasyStats/FantasyStats_Read

If the POST request is well-formed, the API responds with JSON, containing information about every single player. Normally, this information would be used to populate the DOM asynchronously using JavaScript. There's quite a lot of information there, but unfortunately, the college information isn't part of the JSON response. However, there is a field PlayerUrlString, which is a relative-URL to a given player's profile page, which does contain the college name. So:

Make a POST request to the API to get information about all players

For each player in the response JSON:

Visit that player's profile
Use BeautifulSoup to extract the college name from the current player's profile

Code:

Source https://stackoverflow.com/questions/65320791

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 fantasy-football

You can download it from GitHub.
You can use fantasy-football like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the fantasy-football component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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