1
2# Import Libraries
3import numpy as np 
4import pandas as pd
5import matplotlib.pyplot as plt
6import seaborn as sns
7import datetime as dt
8from fbprophet import Prophet
9# Statsmodels widely known for forecasting than Prophet
10import statsmodels.api as sm
11from scipy import stats
12from pandas.core import datetools
13from plotly import tools
14import plotly.plotly as py
15import plotly.figure_factory as ff
16import plotly.tools as tls
17import plotly.graph_objs as go
18from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
19init_notebook_mode(connected=True)
20import warnings
21warnings.filterwarnings("ignore")
22
23# plt.style.available
24plt.style.use("seaborn-whitegrid")
25
26# Simple plotting of Amazon Stock Price
27# First Subplot
28f, (ax1, ax2) = plt.subplots(1, 2, figsize=(14,5))
29ax1.plot(amzn_df["date"], amzn_df["close"])
30ax1.set_xlabel("Date", fontsize=12)
31ax1.set_ylabel("Stock Price")
32ax1.set_title("Amazon Close Price History")
33
34# Second Subplot
35ax1.plot(amzn_df["date"], amzn_df["high"], color="green")
36ax1.set_xlabel("Date", fontsize=12)
37ax1.set_ylabel("Stock Price")
38ax1.set_title("Amazon High Price History")
39
40# Third Subplot
41ax1.plot(amzn_df["date"], amzn_df["low"], color="red")
42ax1.set_xlabel("Date", fontsize=12)
43ax1.set_ylabel("Stock Price")
44ax1.set_title("Amazon Low Price History")
45
46# Fourth Subplot
47ax2.plot(amzn_df["date"], amzn_df["volume"], color="orange")
48ax2.set_xlabel("Date", fontsize=12)
49ax2.set_ylabel("Stock Price")
50ax2.set_title("Amazon's Volume History")
51