📌 Extracting Tesla & GameStop Stock and Revenue Data
📖 Overview
This project involves extracting stock and revenue data for Tesla (TSLA) and GameStop (GME) using Python. We utilize yfinance for stock data and web scraping (BeautifulSoup & pandas) for revenue data from Macrotrends.
🚀 Requirements
Ensure you have the required dependencies installed:
pip install yfinance pandas requests beautifulsoup4 matplotlib
📌 Questions & Solutions
✅ Question 1: Extracting Tesla Stock Data Using yfinance
Fetch Tesla (TSLA) historical and real-time stock data:
import yfinance as yf import pandas as pd import matplotlib.pyplot as plt
tesla_stock = yf.download("TSLA", start="2023-08-01", end="2024-02-16") print(tesla_stock.head())
📌 Real-time Stock Price:
tesla = yf.Ticker("TSLA") print("Current Price:", tesla.history(period="1d")["Close"].iloc[-1])
📌 Save Data:
tesla_stock.to_csv("tesla_stock_data.csv")
📌 Plot Tesla Stock Price:
plt.plot(tesla_stock.index, tesla_stock["Close"], label="TSLA Closing Price", color="blue") plt.xlabel("Date") plt.ylabel("Price (USD)") plt.title("Tesla Stock Price Over Time") plt.legend() plt.grid() plt.show()
✅ Question 2: Extracting Tesla Revenue Data Using Web Scraping
We scrape Tesla's revenue data from Macrotrends:
import requests import pandas as pd from bs4 import BeautifulSoup
url = "https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue" headers = {"User-Agent": "Mozilla/5.0"} response = requests.get(url, headers=headers) soup = BeautifulSoup(response.text, "html.parser")
tables = pd.read_html(response.text) for table in tables: if "Tesla Quarterly Revenue" in str(table): tesla_revenue = table break
tesla_revenue.columns = ["Date", "Revenue"] tesla_revenue["Revenue"] = tesla_revenue["Revenue"].str.replace(",", "").str.replace("$", "") tesla_revenue["Revenue"] = pd.to_numeric(tesla_revenue["Revenue"], errors="coerce") print(tesla_revenue.head())
📌 Save Data:
tesla_revenue.to_csv("tesla_revenue_data.csv", index=False)
✅ Question 3: Extracting GameStop Stock Data Using yfinance
Fetch GameStop (GME) historical and real-time stock data:
gamestop_stock = yf.download("GME", start="2023-08-01", end="2024-02-16") print(gamestop_stock.head())
📌 Real-time Stock Price:
gamestop = yf.Ticker("GME") print("Current Price:", gamestop.history(period="1d")["Close"].iloc[-1])
📌 Save Data:
gamestop_stock.to_csv("gamestop_stock_data.csv")
📌 Plot GameStop Stock Price:
plt.plot(gamestop_stock.index, gamestop_stock["Close"], label="GME Closing Price", color="red") plt.xlabel("Date") plt.ylabel("Price (USD)") plt.title("GameStop Stock Price Over Time") plt.legend() plt.grid() plt.show()
✅ Question 4: Extracting GameStop Revenue Data Using Web Scraping
We scrape GameStop's revenue data from Macrotrends:
url = "https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue" response = requests.get(url, headers=headers) soup = BeautifulSoup(response.text, "html.parser")
tables = pd.read_html(response.text) for table in tables: if "GameStop Quarterly Revenue" in str(table): gamestop_revenue = table break
gamestop_revenue.columns = ["Date", "Revenue"] gamestop_revenue["Revenue"] = gamestop_revenue["Revenue"].str.replace(",", "").str.replace("$", "") gamestop_revenue["Revenue"] = pd.to_numeric(gamestop_revenue["Revenue"], errors="coerce") print(gamestop_revenue.head())
📌 Save Data:
gamestop_revenue.to_csv("gamestop_revenue_data.csv", index=False)
📌 Additional Notes
yfinance is used to extract real-time and historical stock data.
BeautifulSoup and pandas are used for web scraping revenue data.
Macrotrends is the source for revenue data.
Data is saved as CSV files for further analysis.
📌 How to Run the Code
Clone the repository or download the script.
Install dependencies using pip install -r requirements.txt.
Run the Python scripts in order.
Analyze the extracted data in CSV files or visualize it using Matplotlib.
📌 License
This project is for educational purposes only. The data is extracted from publicly available sources.
🚀 Now you can analyze Tesla & GameStop stock and revenue data easily! 🚀