import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

diamonds = pd.read_csv('data/diamonds.csv')
new_diamonds = pd.read_csv('data/new-diamonds.csv')

diamonds

new_diamonds

def calculate_diamond_price(carat, cut, clarity):
    diamond_price = -5269 + (8413 * carat) + (158.1 * cut) + (454 * clarity)
    return diamond_price

def format_currency(amount):
    return '${:,.2f}'.format(amount)

diamond_one_carat = calculate_diamond_price(1,1,1)
diamond_two_carats = calculate_diamond_price(2,1,1)

price_difference = diamond_two_carats -  diamond_one_carat

print("Diamond 1 carat price:\t ",diamond_one_carat,"$\nDiamond 2 carats price:\t",diamond_two_carats,
      "$\nPrice difference:\t ",price_difference,"$")

diamond_one_half_carat = calculate_diamond_price(1.5,3,5)

print("Diamond Carat: 1.5  Cut: Very Good  Clarity: VS2 price:\t ",diamond_one_half_carat,"$")

x = diamonds['carat']
y = diamonds['price']
plt.figure(figsize=(14,8))
plt.ylabel('Price')
plt.xlabel('Carat')
plt.title('Price vs Carat')
plt.scatter(x, y, alpha=0.4, color='green')

new_diamonds["predicted_price"] = calculate_diamond_price(new_diamonds['carat'],new_diamonds['cut_ord'], new_diamonds['clarity_ord'])

new_diamonds

plt.style.use('seaborn')
plt.figure(figsize=(14,8))
x_new = new_diamonds['carat']
y_new = new_diamonds['predicted_price']
plt.ylabel('Predicted Price')
plt.xlabel('Carat')
plt.title('Predicted Price vs Carat')
plt.scatter(x_new, y_new, alpha=0.4, color = 'blue')

plt.figure(figsize=(14,8))
plt.scatter(x, y, alpha=0.3, color='green')
plt.scatter(x_new, y_new, alpha=0.3, color = 'blue')
plt.ylabel('Price', fontsize=15)
plt.xlabel('Carat', fontsize=15)
plt.title('Price vs Carat', fontsize=15)
plt.legend(loc='upper right', frameon=False, fontsize=15)
plt.show

total_diamonds_cost = np.sum(new_diamonds['predicted_price'])
print("Total diamonds cost estimate: ",format_currency(total_diamonds_cost))

bid_value = (total_diamonds_cost * 70) / 100

print("Total bid value recommendation: ",format_currency(bid_value))