Reading and Writing Data

Real-world financial analysis starts with loading data from files. Pandas makes reading and writing data incredibly simple.

Reading CSV Files

CSV (Comma-Separated Values) is the most common format for financial data:

Reading with Options

Working with Date Indexes

Creating Sample Data for Practice

Since we can't read files in the browser, let's create realistic sample data:

Calculating Returns from Price Data

Writing Data (Conceptual)

Practice: Process Price Data

Practice: Calculate Returns

Key Takeaways

• Read CSV with pd.read_csv('filename.csv')
• Use index_col to set index column
• Use parse_dates=True for date columns
• Calculate returns with .pct_change()
• Write CSV with df.to_csv('filename.csv')
• Write Excel with df.to_excel('filename.xlsx')

This completes Module 2! You now have the essential NumPy and Pandas skills for financial data analysis.