Categorical scatter with jitter in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of jittered scatter plot x-coordinates

What is the range of x-coordinates generated by the jitter in this categorical scatter plot code?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

categories = ['A', 'B', 'C']
values = [5, 7, 6]

x = np.arange(len(categories))
jitter = (np.random.rand(len(categories)) - 0.5) * 0.2
x_jittered = x + jitter

print(x_jittered)

A Values between -0.1 and 2.1

B Values between 0 and 2

C Values between -0.5 and 2.5

D Values between 0.5 and 2.5

💡 Hint

Consider how jitter is calculated and added to the integer positions.

The jitter is calculated as a random number between -0.1 and 0.1 (since (rand - 0.5) * 0.2 ranges from -0.1 to 0.1). Adding this to x values 0,1,2 results in values between -0.1 and 2.1.

❓ data_output

intermediate

Number of points plotted with jitter

Given this code, how many points will be plotted on the scatter plot?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

categories = ['X', 'Y']
values = [3, 4]

x = np.arange(len(categories))
jitter = (np.random.rand(len(categories)*3) - 0.5) * 0.3
x_jittered = np.repeat(x, 3) + jitter

plt.scatter(x_jittered, np.tile(values, 3))
plt.show()

print(len(x_jittered))

A 9

B 3

C 2

D 6

💡 Hint

Check how many times each category is repeated and how jitter is applied.

Each of the 2 categories is repeated 3 times, so total points = 2 * 3 = 6. But jitter array length is 6, matching repeated x. So 6 points plotted.

❓ visualization

advanced

Effect of jitter on categorical scatter plot

Which option shows the correct scatter plot with jitter applied to categories 'Cat1', 'Cat2', 'Cat3' with values [10, 15, 12]?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

categories = ['Cat1', 'Cat2', 'Cat3']
values = [10, 15, 12]

x = np.arange(len(categories))
jitter = (np.random.rand(len(categories)) - 0.5) * 0.4
x_jittered = x + jitter

plt.scatter(x_jittered, values)
plt.xticks(x, categories)
plt.show()

A Scatter points spread horizontally around 0,1,2 with small random offsets

B Scatter points randomly scattered with no relation to categories

C Scatter points spread vertically with no horizontal jitter

D Scatter points aligned exactly at 0,1,2 on x-axis with no horizontal spread

💡 Hint

Jitter adds small horizontal noise to x positions.

Jitter adds small random horizontal offsets to the categorical positions, so points appear spread horizontally around each category.

🔧 Debug

advanced

Identify error in jittered scatter plot code

What error will this code produce when run?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

categories = ['A', 'B', 'C']
values = [1, 2, 3]

x = np.arange(len(categories))
jitter = np.random.rand(len(categories)) * 0.2 - 0.1
x_jittered = x + jitter

plt.scatter(x_jittered, values)
plt.xticks(x, categories)
plt.show()

A TypeError due to adding list and numpy array

B ValueError due to mismatched array lengths in scatter

C No error, plot displays correctly

D IndexError from plt.xticks

💡 Hint

Check array lengths and types used in scatter and xticks.

x_jittered and values both have length 3, so scatter works fine. plt.xticks uses x and categories of same length, so no error.

🚀 Application

expert

Calculate mean y-value per category after jittered scatter

Given this jittered scatter plot data, what is the mean y-value for category 'B'?

Matplotlib

import numpy as np

categories = ['A', 'B', 'C']
values = [5, 10, 15]

x = np.repeat(np.arange(len(categories)), 4)
jitter = (np.random.rand(len(x)) - 0.5) * 0.3
x_jittered = x + jitter

# y-values repeated 4 times per category
y = np.tile(values, 4)

# Select y-values where x corresponds to category 'B' (index 1)
y_b = y[x == 1]
mean_y_b = np.mean(y_b)

print(mean_y_b)

A 7.5

B 10.0

C 12.5

D Cannot determine due to jitter

💡 Hint

Jitter does not affect y-values or category indexing here.

The y-values for category 'B' are repeated 4 times as 10, so mean is 10.0. Jitter affects x_jittered but selection uses x without jitter.