Rows by position

Selecting data · I08 · 8 MIN

Select rows using their zero-based positions.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

iloc selects by numbered position, starting at zero. A slice includes its start but stops before its end.

Illustration · not the exercise output

A small example

For rows A, B, C, D: iloc[1:3] selects B and C. Position 1 means the second row.

Follow the code

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

df.iloc[:2, :2]

What each part does

df.iloc[:2, :2]: Select a rectangular part of df by numbered position.
first :2: Take row positions 0 and 1; stop before position 2.
comma: Separate the row selection from the column selection.
second :2: Take column positions 0 and 1.

Other choices for later exercises

df.iloc[0]: A single row position returns a Series instead of a table.

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, call iloc to return the first 2 rows and first two columns.

Hint

A positional slice excludes the stop. A second slice after the comma selects columns.

Reveal solution

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

df.iloc[:2, :2]

Your task · Follow

Using df, call iloc to return the first 2 rows and first two columns.

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
# Build the requested result from the given table
result = df.iloc[____, ____]
result

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.