Intro to Data Visualization
  • Introduction
  • Getting started
    • Introduction to Pandas
    • Accessing Files on Colab
    • Reviewing Data
      • Understanding type(data) in Pandas
    • Data Types
      • Categorical Data
      • Numeric Data
      • Temporal Data
      • Geographic Data
    • How to Check Data Type
    • Slicing and Subsetting DataFrames
    • Aggregating Data
  • Visualization Types
    • Exploratory Process
    • Explanatory Process
  • data exploration
    • Exploration Overview
    • Exploration with Plotly
      • Exploring Distributions
      • Exploring Relationships
      • Exploring with Regression Plots
      • Exploring Correlations
      • Exploring Categories
      • Exploring Time Series
      • Exploring Stocks with Candlestick
      • Exploring with Facets
      • Exploring with Subplots
    • Exploring with AI
  • Data Explanation
    • Data Explanation with Plotly
      • Using Text
      • Using Annotations
      • Using Color
      • Using Shape
      • Accessibility
      • Using Animations
    • Use Cases
  • Exercises and examples
    • Stock Market
      • Loading Yahoo! Finance Data
      • Use Cases for YF
      • Exploring YF Data
      • Understanding Boeing Data Over Time
      • Polishing the visualization
      • Analyzing with AI
      • Comparisons
    • The Gapminder Dataset
      • Loading the Gapminder Data
      • Use Cases
      • Exploring the Data
      • Exporting a Static Image
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  1. Exercises and examples

Stock Market

Yahoo! Finance Data and Its API

Yahoo! Finance is a widely used platform that provides a wealth of financial information, including stock prices, market indices, exchange rates, company profiles, and historical data. It is a valuable resource for investors, analysts, and researchers seeking insights into financial markets.

While Yahoo! Finance does not officially offer a public API, several open-source Python libraries, such as yfinance, allow users to interact with Yahoo! Finance data programmatically. These libraries scrape or leverage endpoints to provide access to a range of data, including historical stock prices, dividend records, and market analytics.

Key Features of Yahoo! Finance Data

  1. Historical Data: Provides historical stock prices, including daily open, high, low, and close (OHLC) values, as well as adjusted close prices.

  2. Real-Time Data: Offers real-time or near real-time stock prices for tracking market movements.

  3. Financial Statements: Access to company financials such as balance sheets, income statements, and cash flow reports.

  4. Market Insights: Includes sector performance, trending tickers, and global market indices.

  5. Dividends and Splits: Contains records of dividend payments and stock splits for specific securities.

Using the Yahoo! Finance API with yfinance

yfinance is a Python library that simplifies interaction with Yahoo! Finance data. It is easy to use and supports fetching data for individual stocks, indices, ETFs, and more.

Example: Fetching Data with yfinance

import yfinance as yf

# Fetch historical data for a stock (e.g., Boeing)
ticker = 'BA'  # Boeing's ticker symbol
data = yf.download(ticker, start='2023-01-01', end='2023-12-31')

# Display the first few rows
print(data.head())

Key Data Returned:

  • Date: Trading day.

  • Open/High/Low/Close: Prices at the start, highest point, lowest point, and end of the trading day.

  • Volume: The number of shares traded during the day.

  • Adjusted Close: Adjusted for splits and dividends.

Advantages of Using Yahoo! Finance Data

  1. Comprehensive Coverage: Data spans global markets and a variety of financial instruments.

  2. Free Access: Available without the need for paid subscriptions or API keys.

  3. Ease of Use: With libraries like yfinance, fetching and analyzing data is straightforward.

  4. Wide Adoption: Data is well-structured, making it easy to integrate with tools like Pandas for analysis.

Limitations

  1. Unofficial API: Since Yahoo! Finance doesn’t provide an official API, reliability depends on third-party tools like yfinance, which may face periodic disruptions.

  2. Rate Limits: Excessive queries might lead to temporary blocks due to usage restrictions.

  3. Data Granularity: Minute-level or tick data may not always be available.

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Last updated 3 months ago