#Arman Rezaian Final Project Submission
!mamba install bs4==4.10.0 -y
!mamba install html5lib==1.1 -y
!pip install lxml==4.6.4
!pip install yfinance==0.1.67
!pip install nbformat==4.2.0
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mamba (0.15.3) supported by @QuantStack
GitHub: https://github.com/mamba-org/mamba
Twitter: https://twitter.com/QuantStack
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- bs4==4.10.0
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Package Version Build Channel Size
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- beautifulsoup4 4.11.1 pyha770c72_0 installed
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mamba (0.15.3) supported by @QuantStack
GitHub: https://github.com/mamba-org/mamba
Twitter: https://twitter.com/QuantStack
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Looking for: ['html5lib==1.1']
pkgs/main/linux-64 Using cache
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- python 3.7.*
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Prefix: /home/jupyterlab/conda/envs/python
Updating specs:
- html5lib==1.1
- ca-certificates
- certifi
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+ webencodings 0.5.1 py37_1 pkgs/main/linux-64 19 KB
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Collecting lxml==4.6.4
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import pandas as pd
import requests
from bs4 import BeautifulSoup
import yfinance as yf
import plotly.graph_objects as go
from plotly.subplots import make_subplots
Question 1 - Extracting Tesla Stock Data Using yfinance - 2 Points Reset the index, save, and display the first five rows of the tesla_data dataframe using the head function. Upload a screenshot of the results and code from the beginning of Question 1 to the results below.
tesla = yf.Ticker("TSLA")
tesla_data = tesla.info
tesla_data = tesla.history(period="max")
tesla_data.reset_index(inplace=True)
tesla_data.head()
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2010-06-29 | 1.266667 | 1.666667 | 1.169333 | 1.592667 | 281494500 | 0 | 0.0 |
| 1 | 2010-06-30 | 1.719333 | 2.028000 | 1.553333 | 1.588667 | 257806500 | 0 | 0.0 |
| 2 | 2010-07-01 | 1.666667 | 1.728000 | 1.351333 | 1.464000 | 123282000 | 0 | 0.0 |
| 3 | 2010-07-02 | 1.533333 | 1.540000 | 1.247333 | 1.280000 | 77097000 | 0 | 0.0 |
| 4 | 2010-07-06 | 1.333333 | 1.333333 | 1.055333 | 1.074000 | 103003500 | 0 | 0.0 |
Question 2 - Extracting Tesla Revenue Data Using Webscraping - 1 Points Display the last five rows of the tesla_revenue dataframe using the tail function. Upload a screenshot of the results.
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
html_data = requests.get(url).text
soup = BeautifulSoup(html_data, 'html5lib')
tesla_revenue = pd.DataFrame(columns=["Date", "Revenue"])
for row in soup.find_all("tbody")[1].find_all('tr'):
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["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")
tesla_revenue.dropna(inplace=True)
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
tesla_revenue.tail(5)
/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:14: FutureWarning: The default value of regex will change from True to False in a future version.
| Date | Revenue | |
|---|---|---|
| 48 | 2010-09-30 | 31 |
| 49 | 2010-06-30 | 28 |
| 50 | 2010-03-31 | 21 |
| 52 | 2009-09-30 | 46 |
| 53 | 2009-06-30 | 27 |
Question 3 - Extracting GameStop Stock Data Using yfinance - 2 Points Reset the index, save, and display the first five rows of the gme_data dataframe using the head function. Upload a screenshot of the results and code from the beginning of Question 1 to the results below.
gamestop = yf.Ticker("GME")
gme_data = gamestop.history(period="max")
gme_data.reset_index(inplace=True)
gme_data.head()
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2002-02-13 | 1.620129 | 1.693350 | 1.603296 | 1.691667 | 76216000 | 0.0 | 0.0 |
| 1 | 2002-02-14 | 1.712707 | 1.716074 | 1.670626 | 1.683250 | 11021600 | 0.0 | 0.0 |
| 2 | 2002-02-15 | 1.683251 | 1.687459 | 1.658002 | 1.674834 | 8389600 | 0.0 | 0.0 |
| 3 | 2002-02-19 | 1.666418 | 1.666418 | 1.578047 | 1.607504 | 7410400 | 0.0 | 0.0 |
| 4 | 2002-02-20 | 1.615921 | 1.662210 | 1.603296 | 1.662210 | 6892800 | 0.0 | 0.0 |
Question 4 - Extracting GameStop Revenue Data Using Webscraping - 1 Points Display the last five rows of the gme_revenue dataframe using the tail function. Upload a screenshot of the results.
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
soup = BeautifulSoup(html_data, 'html5lib')
gme_revenue = pd.DataFrame(columns=["Date", "Revenue"])
for row in soup.find_all("tbody")[1].find_all('tr'):
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["Revenue"] = gamestop_revenue['Revenue'].str.replace(',|\$',"")
gme_revenue.dropna(inplace=True)
gme_revenue.tail()
/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:14: FutureWarning: The default value of regex will change from True to False in a future version.
| 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 - Tesla Stock and Revenue Dashboard - 2 Point Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph.
Upload a screenshot of your results.
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()
make_graph(tesla_data, tesla_revenue, 'Tesla')
Question 6 - GameStop Stock and Revenue Dashboard- 2 Points Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph.
Upload a screenshot of your results.
make_graph(gme_data, gme_revenue, 'GameStop')
Question 7 - Sharing your Assignment Notebook - 2 Points
tesla_data = pd.read_html(url)
for tesla_data in tesla_data:
col = row.find_all("td")
date = col[0].tx
print(tesla_data.tail(5))