Extracting and Visualizing Stock Data

Description

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

Table of Contents

• Define a Function that Makes a Graph
• Question 1: Use yfinance to Extract Stock Data
• Question 2: Use Webscraping to Extract Tesla Revenue Data
• Question 3: Use yfinance to Extract Stock Data
• Question 4: Use Webscraping to Extract GME Revenue Data
• Question 5: Plot Tesla Stock Graph
• Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min

---

In [29]:

!pip install yfinance==0.1.67
#!pip install pandas==1.3.3
#!pip install requests==2.26.0
!mamba install bs4==4.10.0 -y
#!pip install plotly==5.3.1

!pip install yfinance==0.1.67 #!pip install pandas==1.3.3 #!pip install requests==2.26.0 !mamba install bs4==4.10.0 -y #!pip install plotly==5.3.1

Requirement already satisfied: yfinance==0.1.67 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (0.1.67)
Requirement already satisfied: lxml>=4.5.1 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from yfinance==0.1.67) (4.7.1)
Requirement already satisfied: pandas>=0.24 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from yfinance==0.1.67) (1.3.4)
Requirement already satisfied: multitasking>=0.0.7 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from yfinance==0.1.67) (0.0.10)
Requirement already satisfied: numpy>=1.15 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from yfinance==0.1.67) (1.20.3)
Requirement already satisfied: requests>=2.20 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from yfinance==0.1.67) (2.26.0)
Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from pandas>=0.24->yfinance==0.1.67) (2.8.2)
Requirement already satisfied: pytz>=2017.3 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from pandas>=0.24->yfinance==0.1.67) (2021.3)
Requirement already satisfied: six>=1.5 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance==0.1.67) (1.15.0)
Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from requests>=2.20->yfinance==0.1.67) (3.3)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from requests>=2.20->yfinance==0.1.67) (1.26.7)
Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from requests>=2.20->yfinance==0.1.67) (2.0.4)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from requests>=2.20->yfinance==0.1.67) (2022.5.18.1)
/usr/bin/sh: mamba: command not found

In [30]:

import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

import yfinance as yf import pandas as pd import requests from bs4 import BeautifulSoup import plotly.graph_objects as go from plotly.subplots import make_subplots

Define Graphing Function¶

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

In [31]:

def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()

def make_graph(stock_data, revenue_data, stock): fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3) stock_data_specific = stock_data[stock_data.Date <= '2021--06-14'] revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30'] fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1) fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1) fig.update_xaxes(title_text="Date", row=1, col=1) fig.update_xaxes(title_text="Date", row=2, col=1) fig.update_yaxes(title_text="Price ($US)", row=1, col=1) fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1) fig.update_layout(showlegend=False, height=900, title=stock, xaxis_rangeslider_visible=True) fig.show()

Question 1: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

In [32]:

tsla = yf.Ticker("TSLA")
tsla_info = tsla.info
tsla_info

tsla = yf.Ticker("TSLA") tsla_info = tsla.info tsla_info

Out[32]:

{'zip': '78725',
 'sector': 'Consumer Cyclical',
 'fullTimeEmployees': 99290,
 'longBusinessSummary': 'Tesla, Inc. designs, develops, manufactures, leases, and sells electric vehicles, and energy generation and storage systems in the United States, China, and internationally. The company operates in two segments, Automotive, and Energy Generation and Storage. The Automotive segment offers electric vehicles, as well as sells automotive regulatory credits. It provides sedans and sport utility vehicles through direct and used vehicle sales, a network of Tesla Superchargers, and in-app upgrades; and purchase financing and leasing services. This segment is also involved in the provision of non-warranty after-sales vehicle services, sale of used vehicles, retail merchandise, and vehicle insurance, as well as sale of products to third party customers; services for electric vehicles through its company-owned service locations, and Tesla mobile service technicians; and vehicle limited warranties and extended service plans. The Energy Generation and Storage segment engages in the design, manufacture, installation, sale, and leasing of solar energy generation and energy storage products, and related services to residential, commercial, and industrial customers and utilities through its website, stores, and galleries, as well as through a network of channel partners. This segment also offers service and repairs to its energy product customers, including under warranty; and various financing options to its solar customers. The company was formerly known as Tesla Motors, Inc. and changed its name to Tesla, Inc. in February 2017. Tesla, Inc. was incorporated in 2003 and is headquartered in Austin, Texas.',
 'city': 'Austin',
 'phone': '(512) 516-8177',
 'state': 'TX',
 'country': 'United States',
 'companyOfficers': [],
 'website': 'https://www.tesla.com',
 'maxAge': 1,
 'address1': '13101 Tesla Road',
 'industry': 'Auto Manufacturers',
 'ebitdaMargins': 0.20424,
 'profitMargins': 0.13505,
 'grossMargins': 0.27096,
 'operatingCashflow': 13850999808,
 'revenueGrowth': 0.805,
 'operatingMargins': 0.1549,
 'ebitda': 12702000128,
 'targetLowPrice': 67,
 'recommendationKey': 'buy',
 'grossProfits': 13606000000,
 'freeCashflow': 7054624768,
 'targetMedianPrice': 972,
 'currentPrice': 785.785,
 'earningsGrowth': 6.315,
 'currentRatio': 1.354,
 'returnOnAssets': 0.10118,
 'numberOfAnalystOpinions': 40,
 'targetMeanPrice': 922.22,
 'debtToEquity': 19.841,
 'returnOnEquity': 0.28261,
 'targetHighPrice': 1620,
 'totalCash': 18012999680,
 'totalDebt': 7024999936,
 'totalRevenue': 62189998080,
 'totalCashPerShare': 17.387,
 'financialCurrency': 'USD',
 'revenuePerShare': 61.927,
 'quickRatio': 0.953,
 'recommendationMean': 2.5,
 'exchange': 'NMS',
 'shortName': 'Tesla, Inc.',
 'longName': 'Tesla, Inc.',
 'exchangeTimezoneName': 'America/New_York',
 'exchangeTimezoneShortName': 'EDT',
 'isEsgPopulated': False,
 'gmtOffSetMilliseconds': '-14400000',
 'quoteType': 'EQUITY',
 'symbol': 'TSLA',
 'messageBoardId': 'finmb_27444752',
 'market': 'us_market',
 'annualHoldingsTurnover': None,
 'enterpriseToRevenue': 12.476,
 'beta3Year': None,
 'enterpriseToEbitda': 61.085,
 '52WeekChange': 0.29245508,
 'morningStarRiskRating': None,
 'forwardEps': 15.87,
 'revenueQuarterlyGrowth': None,
 'sharesOutstanding': 1036009984,
 'fundInceptionDate': None,
 'annualReportExpenseRatio': None,
 'totalAssets': None,
 'bookValue': 32.901,
 'sharesShort': 25651653,
 'sharesPercentSharesOut': 0.0248,
 'fundFamily': None,
 'lastFiscalYearEnd': 1640908800,
 'heldPercentInstitutions': 0.43117002,
 'netIncomeToCommon': 8403999744,
 'trailingEps': 7.4,
 'lastDividendValue': None,
 'SandP52WeekChange': -0.02185148,
 'priceToBook': 23.883316,
 'heldPercentInsiders': 0.17263001,
 'nextFiscalYearEnd': 1703980800,
 'yield': None,
 'mostRecentQuarter': 1648684800,
 'shortRatio': 0.92,
 'sharesShortPreviousMonthDate': 1649894400,
 'floatShares': 856790568,
 'beta': 2.11875,
 'enterpriseValue': 775897874432,
 'priceHint': 2,
 'threeYearAverageReturn': None,
 'lastSplitDate': 1598832000,
 'lastSplitFactor': '5:1',
 'legalType': None,
 'lastDividendDate': None,
 'morningStarOverallRating': None,
 'earningsQuarterlyGrowth': 6.575,
 'priceToSalesTrailing12Months': 13.090224,
 'dateShortInterest': 1652400000,
 'pegRatio': 1.54,
 'ytdReturn': None,
 'forwardPE': 49.513863,
 'lastCapGain': None,
 'shortPercentOfFloat': 0.030299999,
 'sharesShortPriorMonth': 24552598,
 'impliedSharesOutstanding': 0,
 'category': None,
 'fiveYearAverageReturn': None,
 'previousClose': 740.37,
 'regularMarketOpen': 732.47,
 'twoHundredDayAverage': 913.21027,
 'trailingAnnualDividendYield': 0,
 'payoutRatio': 0,
 'volume24Hr': None,
 'regularMarketDayHigh': 792.63,
 'navPrice': None,
 'averageDailyVolume10Day': 32235210,
 'regularMarketPreviousClose': 740.37,
 'fiftyDayAverage': 902.9568,
 'trailingAnnualDividendRate': 0,
 'open': 732.47,
 'toCurrency': None,
 'averageVolume10days': 32235210,
 'expireDate': None,
 'algorithm': None,
 'dividendRate': None,
 'exDividendDate': None,
 'circulatingSupply': None,
 'startDate': None,
 'regularMarketDayLow': 726.41,
 'currency': 'USD',
 'trailingPE': 106.18716,
 'regularMarketVolume': 25023663,
 'lastMarket': None,
 'maxSupply': None,
 'openInterest': None,
 'marketCap': 814081048576,
 'volumeAllCurrencies': None,
 'strikePrice': None,
 'averageVolume': 27117311,
 'dayLow': 726.41,
 'ask': 782.06,
 'askSize': 2200,
 'volume': 25023663,
 'fiftyTwoWeekHigh': 1243.49,
 'fromCurrency': None,
 'fiveYearAvgDividendYield': None,
 'fiftyTwoWeekLow': 571.22,
 'bid': 781.79,
 'tradeable': False,
 'dividendYield': None,
 'bidSize': 1400,
 'dayHigh': 792.63,
 'regularMarketPrice': 785.785,
 'preMarketPrice': 732.765,
 'logo_url': 'https://logo.clearbit.com/tesla.com'}

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to max so we get information for the maximum amount of time.

In [33]:

tesla_data = tsla.history(period='max')
tesla_data.head()

tesla_data = tsla.history(period='max') tesla_data.head()

Out[33]:

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2010-06-29	3.800	5.000	3.508	4.778	93831500	0	0.0
2010-06-30	5.158	6.084	4.660	4.766	85935500	0	0.0
2010-07-01	5.000	5.184	4.054	4.392	41094000	0	0.0
2010-07-02	4.600	4.620	3.742	3.840	25699000	0	0.0
2010-07-06	4.000	4.000	3.166	3.222	34334500	0	0.0

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

In [34]:

tesla_data.reset_index(inplace=True)
tesla_data.head()

tesla_data.reset_index(inplace=True) tesla_data.head()

Out[34]:

	Date	Open	High	Low	Close	Volume	Dividends	Stock Splits
0	2010-06-29	3.800	5.000	3.508	4.778	93831500	0	0.0
1	2010-06-30	5.158	6.084	4.660	4.766	85935500	0	0.0
2	2010-07-01	5.000	5.184	4.054	4.392	41094000	0	0.0
3	2010-07-02	4.600	4.620	3.742	3.840	25699000	0	0.0
4	2010-07-06	4.000	4.000	3.166	3.222	34334500	0	0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data¶

Use the requests library to download the webpage https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue. Save the text of the response as a variable named html_data.

In [ ]:

url = 'https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue'
html_data = requests.get(url).text
# html_data

url = 'https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue' html_data = requests.get(url).text # html_data

Running cells with 'Python 3.8.8 ('myenv')' requires ipykernel package.

Run the following command to install 'ipykernel' into the Python environment. 

Command: 'conda install -n myenv ipykernel --update-deps --force-reinstall'

Parse the html data using beautiful_soup.

In [39]:

soup = BeautifulSoup(html_data, 'html5lib')
# soup

soup = BeautifulSoup(html_data, 'html5lib') # soup

Using BeautifulSoup or the read_html function extract the table with Tesla Quarterly Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.

Click here if you need help locating the table

    
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
    
soup.find_all("tbody")[1]
    
If you want to use the read_html function the table is located at index 1

In [65]:

tesla_revenue = pd.DataFrame(columns=["Date", "Revenue"])

for row in (soup.find_all("tbody")[1]).find_all("tr") : # take the 2nd tbody in the revenue table in html
    col = row.find_all("td")
    date = col[0].text
    revenue = col[1].text
    tesla_revenue = tesla_revenue.append({"Date": date, "Revenue": revenue}, ignore_index=True)
    
tesla_revenue.head()  

tesla_revenue = pd.DataFrame(columns=["Date", "Revenue"]) for row in (soup.find_all("tbody")[1]).find_all("tr") : # take the 2nd tbody in the revenue table in html col = row.find_all("td") date = col[0].text revenue = col[1].text tesla_revenue = tesla_revenue.append({"Date": date, "Revenue": revenue}, ignore_index=True) tesla_revenue.head()

Out[65]:

	Date	Revenue
0	2022-03-31	$18,756
1	2021-12-31	$17,719
2	2021-09-30	$13,757
3	2021-06-30	$11,958
4	2021-03-31	$10,389

In [66]:

# tesla_revenue_read_html = pd.read_html(url)
# # tesla_revenue = pd.read_html(str(soup))

# tesla_revenue = tesla_revenue_read_html[1]
# tesla_revenue

# tesla_revenue_read_html = pd.read_html(url) # # tesla_revenue = pd.read_html(str(soup)) # tesla_revenue = tesla_revenue_read_html[1] # tesla_revenue

Execute the following line to remove the comma and dollar sign from the Revenue column.

In [68]:

tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")

tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")

/tmp/wsuser/ipykernel_164/349343550.py:1: FutureWarning: The default value of regex will change from True to False in a future version.
  tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")

Execute the following lines to remove an null or empty strings in the Revenue column.

In [71]:

tesla_revenue.dropna(inplace=True)

tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

tesla_revenue.dropna(inplace=True) tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

In [72]:

tesla_revenue.tail()

tesla_revenue.tail()

Out[72]:

	Date	Revenue
46	2010-09-30	31
47	2010-06-30	28
48	2010-03-31	21
50	2009-09-30	46
51	2009-06-30	27

Question 3: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

In [88]:

gme = yf.Ticker("GME")
gme_info = gme.info
gme_info

gme = yf.Ticker("GME") gme_info = gme.info gme_info

Out[88]:

{'zip': '76051',
 'sector': 'Consumer Cyclical',
 'fullTimeEmployees': 12000,
 'longBusinessSummary': 'GameStop Corp., a specialty retailer, provides games and entertainment products through its e-commerce properties and various stores in the United States, Canada, Australia, and Europe. The company sells new and pre-owned gaming platforms; accessories, such as controllers, gaming headsets, virtual reality products, and memory cards; new and pre-owned gaming software; and in-game digital currency, digital downloadable content, and full-game downloads. It also sells collectibles comprising licensed merchandise primarily related to the gaming, television, and movie industries, as well as pop culture themes. As of January 29, 2022, the company operated 4,573 stores and ecommerce sites under the GameStop, EB Games, and Micromania brands; and 50 pop culture themed stores that sell collectibles, apparel, gadgets, electronics, toys, and other retail products under the Zing Pop Culture brand, as well as offers Game Informer, a print and digital video game publication featuring reviews of new releases, previews of the big titles on the horizon, and coverage of the latest developments in the gaming industry. The company was formerly known as GSC Holdings Corp. GameStop Corp. was founded in 1996 and is headquartered in Grapevine, Texas.',
 'city': 'Grapevine',
 'phone': '817 424 2000',
 'state': 'TX',
 'country': 'United States',
 'companyOfficers': [],
 'website': 'https://www.gamestop.com',
 'maxAge': 1,
 'address1': '625 Westport Parkway',
 'industry': 'Specialty Retail',
 'ebitdaMargins': -0.04359,
 'profitMargins': -0.063439995,
 'grossMargins': 0.22423,
 'operatingCashflow': -434300000,
 'revenueGrowth': 0.062,
 'operatingMargins': -0.05643,
 'ebitda': -262000000,
 'targetLowPrice': 23,
 'recommendationKey': 'underperform',
 'grossProfits': 1347800000,
 'freeCashflow': -296512512,
 'targetMedianPrice': 30,
 'currentPrice': 134.5,
 'earningsGrowth': None,
 'currentRatio': 1.918,
 'returnOnAssets': -0.071,
 'numberOfAnalystOpinions': 3,
 'targetMeanPrice': 47.67,
 'debtToEquity': 40.499,
 'returnOnEquity': -0.37397,
 'targetHighPrice': 90,
 'totalCash': 1271399936,
 'totalDebt': 649000000,
 'totalRevenue': 6010699776,
 'totalCashPerShare': 16.74,
 'financialCurrency': 'USD',
 'revenuePerShare': 82.792,
 'quickRatio': 1.167,
 'recommendationMean': 4,
 'exchange': 'NYQ',
 'shortName': 'GameStop Corporation',
 'longName': 'GameStop Corp.',
 'exchangeTimezoneName': 'America/New_York',
 'exchangeTimezoneShortName': 'EDT',
 'isEsgPopulated': False,
 'gmtOffSetMilliseconds': '-14400000',
 'quoteType': 'EQUITY',
 'symbol': 'GME',
 'messageBoardId': 'finmb_1342560',
 'market': 'us_market',
 'annualHoldingsTurnover': None,
 'enterpriseToRevenue': 1.43,
 'beta3Year': None,
 'enterpriseToEbitda': -32.817,
 '52WeekChange': -0.5297854,
 'morningStarRiskRating': None,
 'forwardEps': -4.2,
 'revenueQuarterlyGrowth': None,
 'sharesOutstanding': 76339000,
 'fundInceptionDate': None,
 'annualReportExpenseRatio': None,
 'totalAssets': None,
 'bookValue': 21.222,
 'sharesShort': 15123772,
 'sharesPercentSharesOut': 0.1981,
 'fundFamily': None,
 'lastFiscalYearEnd': 1643414400,
 'heldPercentInstitutions': 0.28224,
 'netIncomeToCommon': -381300000,
 'trailingEps': -5.252,
 'lastDividendValue': 0.38,
 'SandP52WeekChange': -0.02185148,
 'priceToBook': 6.337763,
 'heldPercentInsiders': 0.15913999,
 'nextFiscalYearEnd': 1706486400,
 'yield': None,
 'mostRecentQuarter': 1643414400,
 'shortRatio': 5.7,
 'sharesShortPreviousMonthDate': 1649894400,
 'floatShares': 63032081,
 'beta': -1.008035,
 'enterpriseValue': 8597991424,
 'priceHint': 2,
 'threeYearAverageReturn': None,
 'lastSplitDate': 1174262400,
 'lastSplitFactor': '2:1',
 'legalType': None,
 'lastDividendDate': 1552521600,
 'morningStarOverallRating': None,
 'earningsQuarterlyGrowth': None,
 'priceToSalesTrailing12Months': 1.7082196,
 'dateShortInterest': 1652400000,
 'pegRatio': 0.45,
 'ytdReturn': None,
 'forwardPE': -32.02381,
 'lastCapGain': None,
 'shortPercentOfFloat': 0.22540002,
 'sharesShortPriorMonth': 14128064,
 'impliedSharesOutstanding': 0,
 'category': None,
 'fiveYearAverageReturn': None,
 'previousClose': 121.4,
 'regularMarketOpen': 119,
 'twoHundredDayAverage': 151.89874,
 'trailingAnnualDividendYield': 0,
 'payoutRatio': 0,
 'volume24Hr': None,
 'regularMarketDayHigh': 136.5,
 'navPrice': None,
 'averageDailyVolume10Day': 5302810,
 'regularMarketPreviousClose': 121.4,
 'fiftyDayAverage': 130.4082,
 'trailingAnnualDividendRate': 0,
 'open': 119,
 'toCurrency': None,
 'averageVolume10days': 5302810,
 'expireDate': None,
 'algorithm': None,
 'dividendRate': None,
 'exDividendDate': 1552521600,
 'circulatingSupply': None,
 'startDate': None,
 'regularMarketDayLow': 115.56,
 'currency': 'USD',
 'regularMarketVolume': 6392450,
 'lastMarket': None,
 'maxSupply': None,
 'openInterest': None,
 'marketCap': 10267595776,
 'volumeAllCurrencies': None,
 'strikePrice': None,
 'averageVolume': 4926874,
 'dayLow': 115.56,
 'ask': 134.97,
 'askSize': 800,
 'volume': 6392450,
 'fiftyTwoWeekHigh': 344.66,
 'fromCurrency': None,
 'fiveYearAvgDividendYield': None,
 'fiftyTwoWeekLow': 77.58,
 'bid': 134.48,
 'tradeable': False,
 'dividendYield': None,
 'bidSize': 1100,
 'dayHigh': 136.5,
 'regularMarketPrice': 134.5,
 'preMarketPrice': 119,
 'logo_url': 'https://logo.clearbit.com/gamestop.com'}

Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to max so we get information for the maximum amount of time.

In [89]:

gme_data = gme.history(period='max')

gme_data = gme.history(period='max')

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

In [90]:

gme_data.reset_index(inplace=True)
gme_data.head()
# gme_data.tail()

gme_data.reset_index(inplace=True) gme_data.head() # gme_data.tail()

Out[90]:

	Date	Open	High	Low	Close	Volume	Dividends	Stock Splits
0	2002-02-13	6.480514	6.773400	6.413183	6.766666	19054000	0.0	0.0
1	2002-02-14	6.850827	6.864293	6.682502	6.733000	2755400	0.0	0.0
2	2002-02-15	6.733002	6.749834	6.632007	6.699337	2097400	0.0	0.0
3	2002-02-19	6.665672	6.665672	6.312190	6.430017	1852600	0.0	0.0
4	2002-02-20	6.463681	6.648839	6.413183	6.648839	1723200	0.0	0.0

Question 4: Use Webscraping to Extract GME Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data.

In [91]:

url = ' https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html'
html_data = requests.get(url).text
html_data

url = ' https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html' html_data = requests.get(url).text html_data

Out[91]:

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Profit</a></li>\n\n\t\t\t\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t<li><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/total-assets">Assets &amp; Liabilities</a></li>\n\n\t\t\t\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t<li><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/profit-margins">Margins</a></li>\n\n\t\t\t\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t<li><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/pe-ratio">Price Ratios</a></li>\n\n\t\t\t\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t<li><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/current-ratio">Other Ratios</a></li>\n\n\t\t\t\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t<li><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/dividend-yield-history">Other Metrics</a></li>\n\n\t\t\t\n\t\t\n\t</ul>\n\n\n\t<ul class="nav nav-tabs" id="myTabs" style="font-size:15px;">\n\n\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t<li class="active"><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue">Revenue</a></li>\n\n\n\t\t\t\t\t\t\n\t\t\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<li><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/gross-profit">Gross Profit</a></li>\t\t\t\n\n\t\t\t\t\t\t\n\t\t\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<li><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/operating-income">Operating Income</a></li>\t\t\t\n\n\t\t\t\t\t\t\n\t\t\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<li><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/ebitda">EBITDA</a></li>\t\t\t\n\n\t\t\t\t\t\t\n\t\t\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<li><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/net-income">Net Income</a></li>\t\t\t\n\n\t\t\t\t\t\t\n\t\t\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<li><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/eps-earnings-per-share-diluted">EPS</a></li>\t\t\t\n\n\t\t\t\t\t\t\n\t\t\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<li><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/shares-outstanding">Shares Outstanding</a></li>\t\t\t\n\n\t\t\t\t\t\t\n\t\t\n\n\t</ul>\n\t\n\t\t\t\n</div>\n\t\t\t\n\n\t\t\n\n\t\t\t<div style="background-color:#fff; 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Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services.  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{\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\twindow.location = "https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/" + ticker + "/" + slug + "/revenue";\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t                        }\n                    }\n                });\n            });\n        </script>\n        <input id="jqxInput" role="textbox" aria-autocomplete="both" aria-disabled="false" aria-readonly="false" aria-multiline="false" class="jqx-widget-content jqx-widget-content-bootstrap jqx-input jqx-input-bootstrap jqx-widget jqx-widget-bootstrap jqx-rc-all jqx-rc-all-bootstrap" placeholder="Search for ticker or company name..." style="width: 100%; height: 22px;">\n\n\t\t</div>\n\t\t\t\t\n\t\t<div style="width:280px; margin-top: -32px; margin-left:80px; border-bottom:none; float:right;">\n\t\t\n\t\t\n<button id="compareStocks" style="margin-right:15px;" class="chart_buttons btn btn-success btn-sm"><span class="glyphicon glyphicon-stats"></span>&nbsp;&nbsp;<strong>Compare GME With Other Stocks</strong></button>&nbsp;&nbsp;\n\n</div>\t\n\t\t\n\t\t</div>\n\t\t\n\t\t<div style="height:690px; background-color:#fff; border:1px solid #dfdfdf;">\n\t\t\n<iframe id="chart_iframe" title="Interactive chart: GameStop Revenue 2006-2020 | GME" valign="middle" margin="0px" width="800" height="680" marginheight="0" marginwidth="0" frameborder="0" vspace="0" hspace="0" scrolling="NO" src="./GameStop Revenue 2006-2020 _ GME _ MacroTrends_files/fundamental_iframe.html"></iframe>\t\n\t\n\t\t\n\t\t</div>\n\n\n\t\t\n\t\t\t\t\n\t\t<div style="background-color:#fff; margin: 30px 0px; padding:10px 30px; border:1px solid #dfdfdf;">\n\t\t\n\t\t\t<iframe id="dianomi_below_chart" valign="middle" width="100%" height="300" marginheight="0" marginwidth="0" frameborder="0" vspace="0" hspace="0" scrolling="NO" src="./GameStop Revenue 2006-2020 _ GME _ MacroTrends_files/smartads(1).html"></iframe>\t\n\t\t\n\t\t</div>\n\t\t\t\n\t\t\n\n\t\t\t\n \n\t\t\t\n\t\t\t<div id="style-1" style="background-color:#fff; height:510px; overflow:auto; margin: 30px 0px 30px 0px; padding:0px 30px 20px 0px; border:1px solid #dfdfdf;">\n\t\t\t\n\t\t\t  \t<div class="col-xs-6">\n\t\t\t\n\t\t\t\t<table class="historical_data_table table">\n\t\t\t\t<thead>\n\t\t\t\t  <tr>\n\t\t\t\t\t<th colspan="2" style="text-align:center">GameStop Annual Revenue<br><span style="font-size:14px;">(Millions of US $)</span></th>\n\t\t\t\t  </tr>\n\t\t\t\t</thead>\t\t\t\t\t\n\t\t\t\t<tbody>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2020</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$6,466</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2019</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$8,285</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2018</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$8,547</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2017</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$7,965</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2016</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$9,364</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2015</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$9,296</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2014</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$9,040</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2013</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$8,887</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2012</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$9,551</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2011</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$9,474</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2010</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$9,078</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2009</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$8,806</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2008</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$7,094</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2007</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$5,319</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2006</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$3,092</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2005</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<td style="text-align:center">$1,843</td>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t</tr>\n\t\t\t\t  \n\t\t\t\t  \n\t\t\t\t</tbody>\n\t\t\t  </table>\n\t\t\t  \n\t\t\t  </div>\n\n\t\t\t  <div class="col-xs-6">\n\t\t\t\n\t\t\t\t<table class="historical_data_table table">\n\t\t\t\t<thead>\n\t\t\t\t  <tr>\n\t\t\t\t\t<th colspan="2" style="text-align:center">GameStop Quarterly Revenue<br><span style="font-size:14px;">(Millions of US $)</span></th>\n\t\t\t\t  </tr>\n\t\t\t\t</thead>\t\t\t\t\n\t\t\t\t<tbody>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2020-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,021</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2020-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,194</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2019-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,439</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2019-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,286</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2019-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,548</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2019-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$3,063</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2018-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,935</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2018-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,501</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2018-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,786</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2018-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,825</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2017-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,989</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2017-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,688</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2017-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,046</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2017-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,403</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2016-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,959</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2016-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,632</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2016-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,972</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2016-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$3,525</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2015-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,016</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2015-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,762</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2015-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,061</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2015-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$3,476</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2014-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,092</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2014-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,731</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2014-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,996</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2014-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$3,684</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2013-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,107</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2013-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,384</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2013-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,865</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2013-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$3,562</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2012-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,773</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2012-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,550</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2012-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,002</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2012-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$3,579</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2011-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,947</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2011-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,744</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2011-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,281</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2011-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$3,693</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2010-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,899</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2010-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,799</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2010-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,083</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2010-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$3,524</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2009-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,835</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2009-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,739</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2009-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,981</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2009-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$3,492</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2008-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,696</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2008-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,804</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2008-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,814</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2008-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,866</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2007-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,611</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2007-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,338</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2007-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,279</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2007-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$2,304</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2006-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,012</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2006-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$963</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2006-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,040</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2006-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$1,667</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2005-10-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$534</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2005-07-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$416</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2005-04-30</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$475</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:center">2005-01-31</td>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t<td style="text-align:center">$709</td>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t  </tr>\n\t\t\t\t  \n\t\t\t\t  \n\t\t\t\t</tbody>\n\t\t\t  </table>\n\t\t\t  \n\t\t\t  </div>\n\t\t\t  \n\n\t\t\t\n</div>\t\t\t\n\t\t\t\n\t\t\t\n\t\t\n\t\t<div style="background-color:#fff; margin: 0px 0px 20px 0px; padding:5px 50px 5px 10px; border:1px solid #dfdfdf;">\n\n\t\t\t  <table class="historical_data_table table">\n\t\t\t\t<thead>\n\t\t\t\t  <tr>\n\t\t\t\t\t<th style="text-align:center">Sector</th>\n\t\t\t\t\t<th style="text-align:center">Industry</th>\n\t\t\t\t\t<th style="text-align:center">Market Cap</th>\n\t\t\t\t\t<th style="text-align:center">Revenue</th>\n\t\t\t\t  </tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody>\n\t\t\t\t  <tr>\n\t\t\t\t\t<td style="text-align:center"><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/sector/3/retail-wholesale">Retail/Wholesale</a></td>\n\t\t\t\t\t<td style="text-align:center"><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/industry/156/">Retail - Consumer Electronics</a></td>\n\t\t\t\t\t<td style="text-align:center">$0.293B</td>\n\t\t\t\t\t<td style="text-align:center">$6.466B</td>\n\t\t\t\t  </tr>\n\t\t\t\t  <tr>\n\t\t\t\t\t  <td colspan="4" style="padding:15px;">\n\t\t\t\t\t\t<span>GameStop Corp. is the world\'s largest video game and entertainment software retailer. The company operates 4,816 retail stores across the United States and in fifteen countries worldwide. The company also operates two e-commerce sites, GameStop.com and EBgames.com, and publishes Game Informer? magazine, a leading multi-platform video game publication. GameStop Corp. sells new and used video game software, hardware and accessories for next generation video game systems from Sony, Nintendo, and Microsoft. In addition, the company sells PC entertainment software, related accessories and other merchandise.</span>\n\t\t\t\t\t  </td>\n\t\t\t\t  </tr>\n\t\t\t\t</tbody>\n\t\t\t  </table>\t\t\t\n\t\t\t</div>\t\n\t\n\t\t\n\t\t<div style="background-color:#fff; margin: 20px 0px 30px 0px; padding:5px 50px 5px 10px; border:1px solid #dfdfdf;">\n\t\t\t\n\n\t\t\t\t<table class="historical_data_table table">\n\t\t\t\t<thead>\n\t\t\t\t  <tr>\n\t\t\t\t\t<th style="text-align:center; width:40%;">Stock Name</th>\n\t\t\t\t\t<th style="text-align:center; width:20%;">Country</th>\n\t\t\t\t\t<th style="text-align:center; width:20%;">Market Cap</th>\n\t\t\t\t\t<th style="text-align:center; width:20%;">PE Ratio</th>\n\t\t\t\t  </tr>\n\t\t\t\t</thead>\n\t\t\t\t<tbody>\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:left"><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/BBY/best-buy/revenue">Best Buy (BBY)</a></td>\n\t\t\t\t\t<td style="text-align:center">United States</td>\n\t\t\t\t\t<td style="text-align:center">$27.033B</td>\n\t\t\t\t\t<td style="text-align:center">18.16</td>\n\t\t\t\t </tr>\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:left"><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/AAN/aarons,-/revenue">Aaron\'s,  (AAN)</a></td>\n\t\t\t\t\t<td style="text-align:center">United States</td>\n\t\t\t\t\t<td style="text-align:center">$3.975B</td>\n\t\t\t\t\t<td style="text-align:center">15.14</td>\n\t\t\t\t </tr>\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:left"><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GMELY/gome-retail-holdings/revenue">GOME Retail Holdings (GMELY)</a></td>\n\t\t\t\t\t<td style="text-align:center">China</td>\n\t\t\t\t\t<td style="text-align:center">$1.684B</td>\n\t\t\t\t\t<td style="text-align:center">0.00</td>\n\t\t\t\t </tr>\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:left"><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/SYX/systemax/revenue">Systemax (SYX)</a></td>\n\t\t\t\t\t<td style="text-align:center">United States</td>\n\t\t\t\t\t<td style="text-align:center">$0.873B</td>\n\t\t\t\t\t<td style="text-align:center">18.34</td>\n\t\t\t\t </tr>\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:left"><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/CONN/conns/revenue">Conn\'s (CONN)</a></td>\n\t\t\t\t\t<td style="text-align:center">United States</td>\n\t\t\t\t\t<td style="text-align:center">$0.325B</td>\n\t\t\t\t\t<td style="text-align:center">0.00</td>\n\t\t\t\t </tr>\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<tr>\n\t\t\t\t\t<td style="text-align:left"><a href="https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/TAIT/taitron-components/revenue">Taitron Components (TAIT)</a></td>\n\t\t\t\t\t<td style="text-align:center">United States</td>\n\t\t\t\t\t<td style="text-align:center">$0.016B</td>\n\t\t\t\t\t<td style="text-align:center">10.50</td>\n\t\t\t\t </tr>\n\n\t\t\t\t\t\t\n\t\t\t\t</tbody>\n\t\t\t  </table>\t\t\t\n\t\t\t\n\t\t\t</div>\n\t\t\t\n\t\t\t\n\n\t\t\t</div>\n\t\t\t\n\n\t\t\t<div id="right_sidebar" "="">\n\n\t\t\t\t<!--<a href="/stocks/stock-screener" style="text-decoration:none; 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Parse the html data using beautiful_soup.

In [92]:

soup = BeautifulSoup(html_data, 'html5lib')

soup = BeautifulSoup(html_data, 'html5lib')

Using BeautifulSoup or the read_html function extract the table with GameStop Quarterly Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column using a method similar to what you did in Question 2.

Click here if you need help locating the table

    
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
    
soup.find_all("tbody")[1]
    
If you want to use the read_html function the table is located at index 1

In [93]:

gme_revenue = pd.DataFrame(columns=["Date", "Revenue"])

for row in (soup.find_all("tbody")[1]).find_all("tr") : # take the 2nd tbody in the revenue table in html
    col = row.find_all("td")
    date = col[0].text
    revenue = col[1].text
    gme_revenue = gme_revenue.append({"Date": date, "Revenue": revenue}, ignore_index=True)
    

gme_revenue = pd.DataFrame(columns=["Date", "Revenue"]) for row in (soup.find_all("tbody")[1]).find_all("tr") : # take the 2nd tbody in the revenue table in html col = row.find_all("td") date = col[0].text revenue = col[1].text gme_revenue = gme_revenue.append({"Date": date, "Revenue": revenue}, ignore_index=True)

In [94]:

gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"")
gme_revenue.dropna(inplace=True)

gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]

gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"") gme_revenue.dropna(inplace=True) gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]

/tmp/wsuser/ipykernel_164/2914211459.py:1: FutureWarning: The default value of regex will change from True to False in a future version.
  gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"")

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

In [95]:

gme_revenue.tail()

gme_revenue.tail()

Out[95]:

	Date	Revenue
57	2006-01-31	1667
58	2005-10-31	534
59	2005-07-31	416
60	2005-04-30	475
61	2005-01-31	709

Question 5: Plot Tesla Stock Graph¶

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(tesla_data, tesla_revenue, 'Tesla'). Note the graph will only show data upto June 2021.

In [96]:

make_graph(tesla_data, tesla_revenue, 'Tesla')

make_graph(tesla_data, tesla_revenue, 'Tesla')

Question 6: Plot GameStop Stock Graph¶

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.

In [97]:

make_graph(gme_data, gme_revenue, 'GameStop')

make_graph(gme_data, gme_revenue, 'GameStop')

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Azim Hirjani

Change Log¶

Date (YYYY-MM-DD)	Version	Changed By	Change Description
2022-02-28	1.2	Lakshmi Holla	Changed the URL of GameStop
2020-11-10	1.1	Malika Singla	Deleted the Optional part
2020-08-27	1.0	Malika Singla	Added lab to GitLab

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