Stack datasets

Combining & preparing · W25 · 8 MIN

Append compatible observations vertically.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt the workflow
TransferChoose and combine

Understand the idea

concat appends tables in the order listed. It matches column names, rather than looking for matching records.

Illustration · not the exercise output

A small example

Batches with indices [0, 1] and [0, 1] produce [0, 1, 0, 1], or [0, 1, 2, 3] with ignore_index=True.

Follow the code

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

pd.concat([first, second], ignore_index=True)

What each part does

pd.concat([first, second]): stack tables in list order
ignore_index=True: number new rows from zero

Your inputs

The editable setup on the right creates df and the additional inputs shown below. 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

first · first three 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

second · remaining rows
candy	flavour	price	rating	shelf
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

Stack the supplied first and second tables, in that order, with a fresh index.
Use: concat().

Hint

concat takes a list of tables; ignore_index makes fresh row labels.

Reveal solution

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

pd.concat([first, second], ignore_index=True)

Your task · Follow

Stack the supplied first and second tables, in that order, with a fresh index.
Use: concat().

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)

first = df.iloc[:3].copy()
second = df.iloc[3:].copy()

# Your work
# Build the requested result from the given table
result = ____
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.