Updating and Modifying Data
Once you've loaded a DataFrame, you'll often need to make changes, such as correcting mistakes, updating values, or adding new columns.
Pandas makes this process straightforward.
Why Modify Your Data?
Real-world data is rarely perfect. You might 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.
What You'll Learn
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
Want to learn more?
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