Groupwise transformation

Groups & reshaping · W20 · 8 MIN

Add a group statistic beside each original row.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

transform returns a group statistic at every original row. This lets each observation be compared with its own group.

Illustration · not the exercise output

A small example

For A: 2, A: 6, B: 9, transform("mean") returns 4, 4, 9; subtracting gives −2, 2, 0.

Follow the code

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

df["group_mean"] = df.groupby("flavour")["price"].transform("mean")
df

What each part does

df.groupby("flavour"): Make one group for each flavour.
["price"]: Select the price values to summarise inside each group.
.transform("mean"): Calculate each group mean and repeat it at every original row in that group.
df["group_mean"] =: Store those aligned means in a new column beside the original 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, add group_mean containing the mean price for each row’s flavour category.
Preserve all rows.
Keep the changes in df and display it.
Use: transform().

Hint

transform keeps one result per original row; agg would reduce each group.

Reveal solution

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

df["group_mean"] = df.groupby("flavour")["price"].transform("mean")
df

Your task · Follow

Using df, add group_mean containing the mean price for each row’s flavour category.
Preserve all rows.
Keep the changes in df and display it.
Use: transform().

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["group_mean"] = ____
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.