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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.