Review · distribution detail

Optional · distribution detail · VR4 · 12 MIN

Review · distribution detail

Task 1 · Retrieve and combine

Exercises within this concept

Task 1Retrieve and combineCurrent exercise
Task 2Retrieve and combine
Task 3Retrieve and combine
Task 4Retrieve and combine

Task 1 · Board games

For df games lasting at least 20 minutes, inspect the distribution of minutes using a KDE with cut=0 and bw_adjust=1.

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

Your requirements

Treat its shape as exploratory.
Chart: title "Board games"; x "minutes"; y "Density".

Read the result: Would these few observations justify a claim about the population’s modes?

Your inputs

Board games · 6 synthetic rows
game	genre	minutes	rating	players
Orbit	Strategy	20	4.1	Two
Tiles	Puzzle	15	3.8	Solo
Quest	Adventure	60	4.7	Two
Grove	Strategy	35	4.4	Solo
Castle	Adventure	45	4.5	Two
Cards	Puzzle	10	3.6	Solo

Revisit the concept lesson →

Remember the idea

A kernel density estimate (KDE) smooths observations into a curve. Its height is density, not a count or a probability at one exact value.

A small example

Increasing bw_adjust from 1 to 2 makes bumps wider and the combined curve smoother; it does not add observations.

What each choice does
Code or choice	Meaning
`bw_adjust=1`	Use the default smoothing bandwidth. Larger values smooth more; smaller values reveal more bumps.
`cut=0`	Stop drawing at the observed minimum and maximum.
`sns.kdeplot(..., x="value", ax=ax)`	Draw a smoothed view of a numeric column.

Hint

Choose the observations first, then map the requested measurements to the chart.

Reveal solution

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

import matplotlib.pyplot as plt
import seaborn as sns

fig, ax = plt.subplots(figsize=(6, 4))
selected = df[df["minutes"] >= 20]
sns.kdeplot(data=selected, x="minutes", cut=0, bw_adjust=1, ax=ax)
ax.set(title="Board games", xlabel="minutes", ylabel="Density")
fig.tight_layout()
plt.show()

Your task · Task 1

For df games lasting at least 20 minutes, inspect the distribution of minutes using a KDE with cut=0 and bw_adjust=1.
Treat its shape as exploratory.
Chart: title "Board games"; x "minutes"; y "Density".

YOUR PYTHON

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

# Supplied setup — editable
import pandas as pd

data = {
    "game": ["Orbit", "Tiles", "Quest", "Grove", "Castle", "Cards"],
    "genre": ["Strategy", "Puzzle", "Adventure", "Strategy", "Adventure", "Puzzle"],
    "minutes": [20, 15, 60, 35, 45, 10],
    "rating": [4.1, 3.8, 4.7, 4.4, 4.5, 3.6],
    "players": ["Two", "Solo", "Two", "Solo", "Two", "Solo"],
}

df = pd.DataFrame(data)

import matplotlib.pyplot as plt

import seaborn as sns

# Your work
# Retrieve the earlier skill

Each run executes all editor code in a fresh Python session. Display charts with plt.show().

Python starts when you open a lesson.

Output

Run your code to see what Python returns.