# Step 1

import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns

# Step 2
netflix_stocks = pd.read_csv("NFLX.csv")
#print(netflix_stocks.head())
dowjones_stocks = pd.read_csv("DJI.csv")
#print(dowjones_stocks.head())
netflix_stocks_quarterly = pd.read_csv("NFLX_daily_by_quarter.csv")
#print(netflix_stocks_quarterly.head())

#Step 3
# Some questions to answer

#Step 4
netflix_stocks.rename(columns = {
	"Adj Close": "Price"
	}, inplace = True)
print(netflix_stocks.head())

dowjones_stocks.rename(columns = {
	"Adj Close": "Price"
	}, inplace = True)
print(dowjones_stocks.head())

netflix_stocks_quarterly.rename(columns = {
	"Adj Close": "Price"
	}, inplace = True)
print(netflix_stocks_quarterly.head())

# Step 5

ax = sns.violinplot(
	data = netflix_stocks_quarterly,
	x = "Quarter",
	y = "Price")
ax.set_title("Distribution of 2017 Netflix Stock Prices by Quarter")
ax.set_ylabel("Closing Stock Prices")
ax.set_xlabel("Business Quarters in 2017")
plt.show()
plt.savefig("Netflix Price per Quarter")

# Step 6
x_positions = [1, 2, 3, 4]
chart_labels = ["1Q2017","2Q2017","3Q2017","4Q2017"]
earnings_actual =[.4, .15,.29,.41]
earnings_estimate = [.37,.15,.32,.41 ]
plt.scatter(x_positions, earnings_actual, color = 'red', alpha = 0.5)
plt.scatter(x_positions, earnings_estimate, color = 'blue', alpha =0.5)
plt.legend(["Actual", "Estimate"])
plt.xticks(x_positions, chart_labels)
plt.xlabel("Quarter of the Year")
plt.ylabel("Money")
plt.title("Earning per share in cent")
plt.show()
plt.savefig("Eaning per share")
# Step 7

# The metrics below are in billions of dollars
revenue_by_quarter = [2.79, 2.98,3.29,3.7]
earnings_by_quarter = [.0656,.12959,.18552,.29012]
quarter_labels = ["2Q2017","3Q2017","4Q2017", "1Q2018"]

# Revenue
n = 1  # This is our first dataset (out of 2)
t = 2 # Number of dataset
d = 4 # Number of sets of bars
w = 0.5 # Width of each bar
bars1_x = [t*element + w*n for element
             in range(d)]
plt.bar(bars1_x,revenue_by_quarter)
# Earnings
n = 2  # This is our second dataset (out of 2)
t = 2 # Number of dataset
d = 4 # Number of sets of bars
w = 0.5 # Width of each bar
bars2_x = [t*element + w*n for element
             in range(d)]
plt.bar(bars2_x,earnings_by_quarter)
middle_x = [ (a + b) / 2.0 for a, b in zip(bars1_x, bars2_x)]
plt.xticks(middle_x, quarter_labels)
labels = ["Revenue", "Earnings"]
plt.legend(labels)
plt.title ("Earnings and Revenue per Quarter in Netflix")
plt.xlabel("Quarter of the Year")
plt.ylabel ("Money (in billion dollars)")
plt.show()
plt.savefig("Earning and Revenue")
# Step 8
date = netflix_stocks.Date
netflix_price = netflix_stocks.Price
dowjones_price = dowjones_stocks.Price
ax1 = plt.subplot(1,2,1)
plt.plot(date, netflix_price,color = 'red' , marker = 'o')
plt.title("Netflix")
plt.xlabel("Date")
plt.xticks(rotation = 75)
plt.ylabel("Stock Price")
ax2 = plt.subplot(1,2,2)
plt.plot(date, dowjones_price, color= 'green' , marker = 'o')
plt.title("Dow Jones")
plt.xlabel("Date")
plt.xticks(rotation = 75)
plt.ylabel("Stock Price")
plt.subplots_adjust(wspace=.5)
plt.show()
plt.savefig("Netflix vs Dow Jones")