This project was completed as the final assignment for the IBM Python Project for Data Science. It represents a deliberate shift from theoretical mathematics to practical data engineering, focusing on how raw financial data can be harvested, transformed, and translated into insight.
Rather than relying on static CSV files, this project builds an automated data pipeline that pulls live stock prices via an API and quarterly revenue data via web scraping. The relationship between market valuation and company fundamentals is explored using two contrasting case studies:
- Tesla (TSLA) – a growth-driven technology company
- GameStop (GME) – a volatility-driven market anomaly
The result is a clean, reproducible workflow that extracts, cleans, integrates, and visualizes financial data end-to-end.
- API Extraction:
yfinance– real-time and historical stock data - Web Scraping:
requests(HTML retrieval),BeautifulSoup(DOM parsing) - Data Manipulation:
pandas– ETL (Extract, Transform, Load) - Visualization:
matplotlib– custom financial dashboards
Quarterly revenue data was embedded in unstructured HTML tables, not available via API.
Solution:
HTML was fetched using requests, parsed with BeautifulSoup, and the second <tbody> element was explicitly targeted to isolate quarterly revenue, excluding annual summaries.
Stock prices (API) and revenue figures (scraped HTML) came from different sources with different formats.
Solution:
A structured ETL pipeline was applied:
- Regex-based cleaning to remove
$and, - Removal of missing and empty values
- Index resetting and date alignment
This produced synchronized datasets suitable for comparative visualization.
!pip install -r requirements.txtimport yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import matplotlib.pyplot as pltdef make_graph(stock_data, revenue_data, stock):
stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
fig, axes = plt.subplots(2, 1, figsize=(12, 8), sharex=True)
# Stock price plot
axes[0].plot(
pd.to_datetime(stock_data_specific.Date),
stock_data_specific.Close.astype("float")
)
axes[0].set_ylabel("Price ($US)")
axes[0].set_title(f"{stock} - Historical Share Price")
# Revenue plot
axes[1].plot(
pd.to_datetime(revenue_data_specific.Date),
revenue_data_specific.Revenue.astype("float")
)
axes[1].set_ylabel("Revenue ($US Millions)")
axes[1].set_xlabel("Date")
axes[1].set_title(f"{stock} - Historical Revenue")
plt.tight_layout()
plt.show()# Extract Tesla stock data
tesla = yf.Ticker("TSLA")
tesla_data = tesla.history(period="max")
tesla_data.reset_index(inplace=True)
# Scrape Tesla revenue data
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
beautiful_soup = BeautifulSoup(html_data, "html.parser")
tesla_revenue = pd.DataFrame(columns=["Date", "Revenue"])
table_body = beautiful_soup.find_all("tbody")[1]
for row in table_body.find_all("tr"):
col = row.find_all("td")
date, revenue = col[0].text, col[1].text
tesla_revenue = pd.concat(
[tesla_revenue, pd.DataFrame({"Date": [date], "Revenue": [revenue]})],
ignore_index=True
)
# Data cleaning
tesla_revenue["Revenue"] = tesla_revenue["Revenue"].str.replace(r"[,\$]", "", regex=True)
tesla_revenue.dropna(inplace=True)
tesla_revenue = tesla_revenue[tesla_revenue["Revenue"] != ""]
# Visualization
# make_graph(tesla_data, tesla_revenue, "Tesla")# Extract GameStop stock data
gme = yf.Ticker("GME")
gme_data = gme.history(period="max")
gme_data.reset_index(inplace=True)
# Scrape GameStop revenue data
url_gme = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
html_data_2 = requests.get(url_gme).text
beautiful_soup = BeautifulSoup(html_data_2, "html.parser")
gme_revenue = pd.DataFrame(columns=["Date", "Revenue"])
table_body_gme = beautiful_soup.find_all("tbody")[1]
for row in table_body_gme.find_all("tr"):
col = row.find_all("td")
date, revenue = col[0].text, col[1].text
gme_revenue = pd.concat(
[gme_revenue, pd.DataFrame({"Date": [date], "Revenue": [revenue]})],
ignore_index=True
)
# Data cleaning
gme_revenue["Revenue"] = gme_revenue["Revenue"].str.replace(r"[,\$]", "", regex=True)
gme_revenue.dropna(inplace=True)
gme_revenue = gme_revenue[gme_revenue["Revenue"] != ""]
# Visualization
# make_graph(gme_data, gme_revenue, "GameStop")tesla_revenue.tail()| 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 |
make_graph(tesla_data, tesla_revenue, "Tesla")
The dashboard shows a strong coupling between revenue growth and stock appreciation, particularly during Tesla’s late-2020 expansion phase. The market appears to price in scaling production and future earnings.
gme_revenue.tail()| Date | Revenue | |
|---|---|---|
| 57 | 2006-01-31 | 1667.0 |
| 58 | 2005-10-31 | 534.0 |
| 59 | 2005-07-31 | 416.0 |
| 60 | 2005-04-30 | 475.0 |
| 61 | 2005-01-31 | 709.0 |
make_graph(gme_data, gme_revenue, "GameStop")
In the Dashboard above, a clear decoupling emerges in early 2021. Revenue remains relatively flat while the stock price experiences an extreme, sentiment-driven surge—an illustration of market psychology overpowering fundamentals.
I am a 3MTT Fellow and ForbesBLK Member with a background in Industrial Mathematics (FUTA). This project reflects my commitment to moving beyond theory into applied data analytics and engineering, where data is not just collected—but made to speak.
Data is most powerful when it serves as a clear, honest bridge between raw numbers and strategic growth.
Connect with Me:
LinkedIn: https://linkedin.com/in/emycodes
GitHub Portfolio: https://github.com/EmyCodes/Data-Analytics-Portfolio
©️ EmyCodes | 2026