Understand the idea
A condition creates a True/False flag for each row. df[mask] returns the whole rows whose flags are True.
A small example
For ages [2, 4, 6], age > 4 gives [False, False, True]; filtering keeps only the row with age 6.
Follow the code
Apply the idea to the supplied table. Read from top to bottom; the final line displays the result.
df[df["price"] > 2.1]What each part does
df["price"] > 2.1- Test each price; the result is one True or False per row.
df[mask]- keep rows where mask is True
Other choices for later exercises
==- compare values; = assigns a value
Your inputs
The editable setup on the right creates df. Run executes the setup and your work from top to bottom.
| candy | flavour | price | rating | shelf |
|---|---|---|---|---|
| Gummy Bear | fruity | 1.2 | 4.1 | A |
| Choco Pop | chocolate | 2.1 | 4.6 | B |
| Mint Bite | mint | 1.5 | 3.8 | A |
| Berry Loop | fruity | 2.8 | 4.4 | B |
| Cocoa Cube | chocolate | 3.4 | 4.9 | A |
| Lemon Drop | fruity | 1.8 | 4 | B |
Your task · Follow
Using df, keep every row where price is greater than 2.1.
Hint
The comparison makes a mask; df[mask] selects the rows where it is True.
Reveal solution
One way to do it. Keep any supplied setup in the editor and use this in the Your work section.
df[df["price"] > 2.1]