new_contents: # Written by Jonathan Saewitz, released August 17th, 2016 for Statisti.ca
  # Released under the MIT License (https://opensource.org/licenses/MIT)
  
  import csv, plotly.plotly as plotly, plotly.graph_objs as go
  from collections import Counter
  
  LEAGUE_SIZE_TO_ANALYZE=10 #the size of the league (number of teams) to analyze (e.g. leagues with 10 teams)
  
  #initialize counters
  c=Counter()
  number_teams_c=Counter()
  
  with open('ff_draft_picks.csv', 'r') as f:
  	reader=csv.reader(f)
  	reader.next() #skip the header row
  	for row in reader: #loop through every row
  		try: #try to get the number of teams in each league
  			number_teams=int(row[2]) #row[2] is the number of teams
  			number_teams_c[number_teams]+=1 #increment the number of teams in each league counter
  		except ValueError:
  			continue
  		if number_teams==LEAGUE_SIZE_TO_ANALYZE:
  			pick=int(row[1]) #row[1] is the pick
  			if not pick>number_teams: #only include the pick if it was done in the first round
  									  #(i.e., winning team didn't trade first round pick for a higher round)
  				c[pick]+=1 #increment the counter for the pick number
  
  #create the graph
  trace = go.Bar(
      x = c.keys(),
      y = c.values()
  )
  
  layout=go.Layout(
  	title="Fantasy Football Wins By Pick #",
  	xaxis=dict(
  		title="Pick #",
  	),
  	yaxis=dict(
  		title="# Wins",
  	)
  )
  
  data=[trace]
  fig=dict(data=data, layout=layout)
  # plotly.plot(fig)
  
  trace = go.Bar(
      x = c.keys(),
      y = c.values()
  )
  
  layout=go.Layout(
  	title="Fantasy Football Wins By Pick #",
  	xaxis=dict(
  		title="Pick #",
  	),
  	yaxis=dict(
  		title="# Wins",
  	)
  )
  
  data=[trace]
  fig=dict(data=data, layout=layout)
  plotly.plot(fig)
  
  #number of teams in each league:
  fig = {
  	'data':
  			[{'labels': number_teams_c.keys(),
  			  'values': number_teams_c.values(),
  			  'type': 'pie'}],
  	'layout': {'title': "Number of Teams in Fantasy Football Leagues"}
  }
  
  plotly.plot(fig)
  
subject: Create league_analysis.py