Receiving Empty Dataframe For Solved Pulp Optimization Problem

I am dabbling with PuLP (and Python, in general) for the first time to run an optimization problem for a fantasy football game. My code below runs successfully, but it outputs an e

Solution 1:

First thing, vars is a builtin function, don't use that as a variable.

Secondly, you can simplify the one-hot encoding you do for the binary positions by simply using panda's .get_dummies().

Lastly, it's not assigning the 1.0 value to your "is_drafted" column. Try .loc instead of .iloc. I'd also use the column name as opposed to the index position for "is_drafted" too, but that just my preference.

Give this a try. I commented where I made changes. I couldn't really test it with out your specific data. So if it doesn't work, you may need to share your csv file so I could debug:

import pandas as pd
import pulp

print('--- (1/4) Defining the problem ---')

# Read csv
raw_data = pd.read_csv('./csv/fantasypros.csv')

# create new columns that has binary numbers if player == a specific position
encoded = pd.get_dummies(raw_data['Pos. Parent']) #<-- One-Hote Encoding 
raw_data = raw_data.join(encoded) #<-- joining it to the raw_data table

raw_data["salary"] = raw_data["Point Cost"].astype(float)

model = pulp.LpProblem("NFTdraft", pulp.LpMaximize)

total_points = {}
cost = {}
qb = {}
rb = {}
wr = {}
te = {}
k = {}
dst = {}
dk = {}
num_players = {}

vars_list = []

# i = row index, player = player attributes
for i, player in raw_data.iterrows():
    var_name = 'x' + str(i)  # Create variable name
    decision_var = pulp.LpVariable(var_name, cat='Binary')  # Initialize Variables

    total_points[decision_var] = player["FPTS"]  # Create FPTS Dictionary
    cost[decision_var] = player["salary"]  # Create Cost Dictionary# Create Dictionary for Player Types
    qb[decision_var] = player["QB"]
    rb[decision_var] = player["RB"]
    wr[decision_var] = player["WR"]
    te[decision_var] = player["TE"]
    k[decision_var] = player["K"]
    dst[decision_var] = player["DST"]
    dk[decision_var] = player["DK"]
    num_players[decision_var] = 1.0

objective_function = pulp.LpAffineExpression(total_points)
model += objective_function

total_cost = pulp.LpAffineExpression(cost)
model += (total_cost <= 135)

print('--- (2/4) Defining the constraints ---')
QB_constraint = pulp.LpAffineExpression(qb)
RB_constraint = pulp.LpAffineExpression(rb)
WR_constraint = pulp.LpAffineExpression(wr)
TE_constraint = pulp.LpAffineExpression(te)
K_constraint = pulp.LpAffineExpression(k)
DST_constraint = pulp.LpAffineExpression(dst)
DK_constraint = pulp.LpAffineExpression(dk)
total_players = pulp.LpAffineExpression(num_players)

model += (QB_constraint >= 1)
model += (QB_constraint <= 2)
model += (RB_constraint <= 8)
model += (WR_constraint <= 8)
model += (TE_constraint <= 8)
model += (K_constraint <= 1)
model += (DST_constraint <= 1)
model += (DK_constraint <= 2)
model += (total_players == 10)

print('--- (3/4) Solving the problem ---')
model.solve()

print('--- (4/4) Formatting the results ---')
raw_data["is_drafted"] = 0.0

for var in model.variables():
    raw_data.loc[int(var.name[1:]), 'is_drafted'] = var.varValue     # <--- CHANGED HERE
    
my_team = raw_data[raw_data["is_drafted"] == 1.0]
my_team = my_team[["Asset Name", "Player", "Pos. Parent", "Rarity", "Point Cost", "FPTS"]]

print(my_team)
print("Total used amount of salary cap: {}".format(my_team["Point Cost"].sum()))
print("Projected points: {}".format(my_team["FPTS"].sum().round(1)))
print('--- Completed ---')