Filter unwanted rows

A safe working copy · W05 · 8 MIN

Keep observations using a clear rule.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

Filter rows with a rule tied to the report. Assign the selection back to df when the task asks to update the working table.

Illustration · not the exercise output

A small example

For [2, 3, 6, 7], the inclusive condition 3 <= value <= 6 keeps 3 and 6.

Follow the code

Apply the idea to the supplied table. Read from top to bottom; the final line displays the result.

df = df[df["flavour"] == "fruity"]
df

What each part does

df["flavour"] == "fruity": Produce a True/False test for every row; == compares rather than assigns.
df[mask]: Keep only rows whose test is True.
df =: Store those matching rows as the new df.

Your inputs

The editable setup on the right creates df. Run executes the setup and your work from top to bottom.

Candy shop · 6 synthetic rows
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

For a report limited to flavour equal to "fruity", keep just those rows in df.
Keep the changes in df and display it.

Hint

Build a row mask and assign the selected table back to df.

Reveal solution

One way to do it. Keep any supplied setup in the editor and use this in the Your work section.

df = df[df["flavour"] == "fruity"]
df

Your task · Follow

For a report limited to flavour equal to "fruity", keep just those rows in df.
Keep the changes in df and display it.

YOUR PYTHON

Tab: indent · Shift+Tab: outdent · Esc, then Tab: leave editor

# Supplied setup — editable
import pandas as pd

data = {
    "candy": [
        "Gummy Bear",
        "Choco Pop",
        "Mint Bite",
        "Berry Loop",
        "Cocoa Cube",
        "Lemon Drop",
    ],
    "flavour": ["fruity", "chocolate", "mint", "fruity", "chocolate", "fruity"],
    "price": [1.2, 2.1, 1.5, 2.8, 3.4, 1.8],
    "rating": [4.1, 4.6, 3.8, 4.4, 4.9, 4],
    "shelf": ["A", "B", "A", "B", "A", "B"],
}

df = pd.DataFrame(data)

# Your work
df = ____
df

Each run executes all editor code in a fresh Python session. Display a value by leaving it on the final line.

Python starts when you open a lesson.

Output

Run your code to see what Python returns.