Introduction to Pandas

If NumPy is a powerful calculator, Pandas is a complete spreadsheet with programming capabilities. It's built on top of NumPy and provides labeled, structured data - exactly what we deal with in finance.

Pandas Series: Labeled Data

A Series is like a NumPy array but with labels (an index):

Adding Meaningful Labels

Pandas DataFrame: Your Financial Spreadsheet

A DataFrame is a table with rows and columns - the most important data structure for financial analysis:

Viewing Data

Accessing Columns

Adding New Columns

Practice: Create a Portfolio DataFrame

Practice: Add Weight Column

Key Takeaways

• Pandas Series = NumPy array + labels
• Pandas DataFrame = table with rows and columns
• Access columns with df['ColumnName']
• Add columns with df['NewColumn'] = calculation
• Use .head(), .tail(), .shape to explore data
• DataFrames make financial calculations intuitive

Next, we'll learn about filtering, sorting, and more DataFrame operations!