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Practice

Updating and Modifying Data

Once you’ve loaded a DataFrame, you’ll often need to tweak it — maybe correct a mistake, change a value, or add a new column. Pandas makes this process intuitive.


Why Modify Your Data?​

Real-world data is rarely perfect. You might need to:

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

These small edits are essential before analysis or visualization.


What You’ll Learn​

In the notebook, you’ll see 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

These techniques form the core of real-world data cleaning.


What’s Next?​

Run the Jupyter notebook to practice each method. You’ll update individual cells, modify rows based on rules, and add new columns to bring your DataFrame to life.