Take the first look

Getting oriented · I02 · 8 MIN

Look at a few rows before making assumptions.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

A preview returns selected rows with all their columns. It leaves the original table unchanged.

Illustration · not the exercise output

A small example

For rows A, B, C, D: head(2) shows A, B; tail(2) shows C, D.

Follow the code

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

df.head(2)

What each part does

df: our table
.: access a tool belonging to the table
head: the tool for first rows
(2): call it and ask for two rows

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

Using df, return the first 2 rows of the Candy shop table.
Use: head().

Hint

head starts at the beginning; the number in parentheses limits the rows.

Reveal solution

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

df.head(2)

Optional stretch

Try df.tail(2), df.sample(2, random_state=1), and df.head() separately. A sample is not necessarily representative.

Your task · Follow

Using df, return the first 2 rows of the Candy shop table.
Use: head().

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.____(____)

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.