Solution of Peer-graded Assignment: Analyzing Historical Stock/Revenue Data and Building a Dashboard

Get Solution of Peer-graded Assignment: Analyzing Historical Stock/Revenue Data and Building a Dashboard

At this point please ensure you have completed the two previous yfinance and web scraping labs. In this assignment you will upload screenshots of your code and results. You will also be reviewing the submission for one of your peers and grading their work.

As a data scientist working for an investment firm, you will extract the revenue data for Tesla and GameStop and build a dashboard to compare the price of the stock vs the revenue.

Review criteria:

Full Points: Working code that yields correct results

You will be graded on the dashboards displaying the specified data and the screenshots you took during the final project lab questions. There are 12 possible points for this assignment. Here is the breakdown:

Question 1 – Extracting Tesla Stock Data Using yfinance – 2 Points Question 2 – Extracting Tesla Revenue Data Using Webscraping – 1 Points Question 3 – Extracting GameStop Stock Data Using yfinance – 2 Points Question 4 – Extracting GameStop Revenue Data Using Webscraping – 1 Points Question 5 – Tesla Stock and Revenue Dashboard – 2 Points Question 6 – GameStop Stock and Revenue Dashboard- 2 Points Question 7 – Sharing your Assignment Notebook – 2 Points

For each problem points will be awarded as follows:

Full Points: Working code that yields correct results Partial Points: Partially correct code or results No Points: Did not attempt the problem or did not upload any solution

Example Submissions

Here are some examples of a submission clearly showing both the Code and its Results/Output when executed from a Jupyter Notebook.

Solution of Peer-graded Assignment: Analyzing Historical Stock/Revenue Data and Building a Dashboard

!pip install yfinance
#!pip install pandas
#!pip install requests
!pip install bs4
#!pip install plotly

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
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)
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data.Date, infer_datetime_format=True), y=stock_data.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data.Date, infer_datetime_format=True), y=revenue_data.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

1. 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.
2. 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.
3. 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.

#1
tesla = yf.Ticker("TSLA")

#2
tesla_data = tesla.history(period="max")

#3
tesla_data.reset_index(inplace=True)
tesla_data.head()

Question 2: Use Webscraping to Extract Tesla Revenue Data

1. 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.
2. Parse the html data using beautiful_soup.
3. Using beautiful soup extract the table with Tesla Quarterly Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column.
4. Remove the rows in the dataframe that are empty strings or are NaN in the Revenue column. Print the entire tesla_revenue DataFrame to see if you have any.
5. Display the last 5 row of the tesla_revenue dataframe using the tail function.

#1
tesla_url = "https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue"
tesla_html_data = requests.get(tesla_url).text

#2
tesla_soup = BeautifulSoup(tesla_html_data, "html5lib")

#3
tesla_tables = tesla_soup.find_all('table')

for index,table in enumerate(tesla_tables):
    if ("Tesla Quarterly Revenue" in str(table)):
        tesla_table_index = index

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

for row in tesla_tables[tesla_table_index].tbody.find_all("tr"):
    col = row.find_all("td")
    if (col !=[]):
        date = col[0].text
        revenue = col[1].text.replace("$", "").replace(",", "")
        tesla_revenue = tesla_revenue.append({"Date" : date, "Revenue" : revenue}, ignore_index=True)

#4
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
tesla_revenue

#5
tesla_revenue.tail()

Question 3: Use yfinance to Extract Stock Data

1. 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.
2. 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.
3. 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.

#1
gamestop = yf.Ticker("GME")

#2
gme_data = gamestop.history(period="max")

#3
gme_data.reset_index(inplace=True)
gme_data.head()

Question 4: Use Webscraping to Extract GME Revenue Data

1. Use the requests library to download the webpage https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue. Save the text of the response as a variable named html_data.
2. Parse the html data using beautiful_soup.
3. Using beautiful soup 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.
4. Display the last five rows of the gme_revenue dataframe using the tail function.

#1
gme_url = "https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue"
gme_html_data = requests.get(gme_url).text

#2
gme_soup = BeautifulSoup(gme_html_data, "html5lib")

#3
gme_tables = gme_soup.find_all('table')

for index,table in enumerate(gme_tables):
    if ("GameStop Quarterly Revenue" in str(table)):
        gme_table_index = index

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

for row in gme_tables[gme_table_index].tbody.find_all("tr"):
    col = row.find_all("td")
    if (col !=[]):
        date = col[0].text
        revenue = col[1].text.replace("$", "").replace(",", "")
        gme_revenue = gme_revenue.append({"Date" : date, "Revenue" : revenue}, ignore_index=True)

#4
gme_revenue.tail()

Question 5: Plot Stock Graphs

1. Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph.
2. Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph.

#1
make_graph(tesla_data, tesla_revenue, 'Tesla')

#2
make_graph(gme_data, gme_revenue, 'GameStop')

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