### MLB ALL Create a list of the 10 best players to pick for a position

## Packages/funcitions

import csv

import numpy as np

from math import sqrt

import pandas as pd

from pandas import DataFrame

# Extracting PCA vectors

SecondB_PCA = pd.read_csv("export/SecondB_pca.csv")

# Extracting Player Names for Positions

SecondB_Info = pd.read_csv("export/SecondB_Names.csv", names = ['Players','Team'])

#print(FirstB_Names)

#FirstB_PCA.drop(['pc1', 'pc2'], axis=1)

#FirstB_max = pd.DataFrame.max(FirstB_PCA['pc1'])

#print(FirstB_max)

# God Player

God = [200,60] # God[0] = -150 and God[1] = 35

# Combining Datasets

SecondB = SecondB_PCA.join(SecondB_Info)

#print(FirstB['pc1'][0])

# create list of the best Players

Closiest_to_God = np.empty([len(SecondB),1])

count = 0

while count < len(SecondB):

Closiest_to_God[count][0] = sqrt(((SecondB['pc1'][count] - God[0])**2) + ((SecondB['pc2'][count] - God[1])**2))

#Closiest_to_God[count][1] = FirstB_Names['Players'][count+1]

#print(Closiest_to_God[count][0])

# print(count)

count += 1

if count >= len(SecondB):

break

print(len(Closiest_to_God))

print(len(SecondB_Info))

Closiest_to_God = np.hstack((Closiest_to_God, SecondB_Info[1:]))

# Attaches the names of each player to their Euclidean distance to God

Closiest_to_God = Closiest_to_God[Closiest_to_God[:,0].argsort()]

print(Closiest_to_God)

##Export to CSV

dataset = DataFrame(Closiest_to_God)

df= dataset.to_csv (r'export/SecondB_List.csv', index = None, header=True)

print(dataset)