Transformations

A brief set of python functions for transforming en ef el data for optimization

Purpose

When trying to run analysis for my fantasy team, I always ended up with a bunch of odd csv/excel sheets. This is an example of a series of functions I use to clean, transform, and combine data sources.

Requires

• Python 3.x
• petl
• numpy
• pandas

Setup

• Download salary csv from Draft Kings dot com.
• Download play by play data from nflsavant.com

Use

1. The files DKSalaries.csv and pbp-2018.csv should be in your working directory.
2. Import ftbl_etl and call football_etl with a list of injured players.
3. returns a Pandas dataframe that can be used for analysis

import ftbl_etl as fetl

injured_players = ['j.flacco', 'a.dalton']

fbl_df = fetl.football_etl(injured_players)

Todo

1. Better handling for files (ie. names and location)
2. Speed optimization
3. clean up code

Use

See license.