import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

qb = pd.read_csv("fantasy football - QB.csv")

wr = pd.read_csv("fantasy football - WR.csv")

te = pd.read_csv("fantasy football - TE.csv")

rb = pd.read_csv("fantasy football - RB.csv")

all_pos = [qb,wr,te,rb]

all_pos_fpts = []

names = {0:"QB",1:"WR", 2: "TE",3:"RB"}

every_ten_mean = {"QB": [],"WR": [],"TE": [],"RB": []}

for i in all_pos:

fpts = np.asfarray(list(map(lambda y: float(y),list(filter(lambda x:x!="--" ,list(i['FPTS'])))))) #removes "--"

all_pos_fpts.append(fpts)

print(f"Length: {fpts.shape[0]}")

print(f"Mean: {np.mean(fpts)}")

print(f"Standard Deviation: {np.std(fpts)}")

pos_counter = 0

for pos in all_pos_fpts:

counter = 1

a = []

for j in range(pos.shape[0]):

a.append(pos[j])

if counter %10 == 0:

every_ten_mean[names[pos_counter]].append(a)

a = []

counter = 1

else:

counter += 1

pos_counter+=1

pos_counter = 0

for v in every_ten_mean.values():

lag_10_means = list(map(lambda x: sum(x)/len(x),v))

print(f"{names[pos_counter]}: {lag_10_means}")

plt.plot(range(len(lag_10_means)),lag_10_means)

plt.title(f"{names[pos_counter]} means")

plt.show()

pos_counter+=1