Updating and Modifying Data

After loading a DataFrame, you’ll often need to make adjustments — such as correcting errors, updating values, or adding new columns.

Pandas makes this process simple and efficient.

Real-world data is rarely clean or consistent. You may need to:

• Fix typos or incorrect values in cells
• Standardize formats, such as capitalizing city names
• Add new columns, like a calculated discount or score
• Update values conditionally, for example, flagging all users under 18

These adjustments are often essential before analysis or visualization.

In the notebook, you'll learn how to:

• Change a specific cell value using .loc[]
• Modify multiple rows based on conditions
• Create new columns from existing ones
• Apply functions to update entire columns