Why categorical visualization matters in Matplotlib - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this bar chart code?

Look at the code below that creates a bar chart for fruit counts. What will the heights of the bars be?

Matplotlib

import matplotlib.pyplot as plt
fruits = ['Apple', 'Banana', 'Cherry']
counts = [10, 15, 7]
plt.bar(fruits, counts)
plt.show()

A Bars with heights 10, 7, and 15 for Apple, Banana, and Cherry respectively

B Bars with heights 7, 10, and 15 for Apple, Banana, and Cherry respectively

C Bars with heights 15, 10, and 7 for Apple, Banana, and Cherry respectively

D Bars with heights 10, 15, and 7 for Apple, Banana, and Cherry respectively

💡 Hint

Check the order of the counts list and how it matches the fruits list.

The plt.bar function pairs each fruit with the count at the same index. So Apple gets 10, Banana 15, and Cherry 7.

❓ data_output

intermediate

How many categories are shown in this pie chart?

Given this pie chart code, how many slices (categories) will appear?

Matplotlib

import matplotlib.pyplot as plt
labels = ['Red', 'Blue', 'Green', 'Yellow']
sizes = [20, 30, 25, 25]
plt.pie(sizes, labels=labels)
plt.show()

A 2

B 4

C 5

D 3

💡 Hint

Count the number of labels provided.

Each label corresponds to one slice in the pie chart, so there are 4 slices.

❓ visualization

advanced

Which option produces a correct stacked bar chart?

You want to show sales of two products over three months stacked in one bar per month. Which code produces the correct stacked bar chart?

Matplotlib

import matplotlib.pyplot as plt
months = ['Jan', 'Feb', 'Mar']
sales_A = [5, 7, 6]
sales_B = [3, 4, 5]

A

plt.bar(months, sales_A)
plt.bar(months, sales_B)
plt.show()

B

plt.bar(months, sales_A + sales_B)
plt.show()

C

plt.bar(months, sales_A)
plt.bar(months, sales_B, bottom=sales_A)
plt.show()

D

plt.bar(months, sales_B, bottom=sales_A)
plt.bar(months, sales_A)
plt.show()

💡 Hint

Stacked bars need the bottom parameter to stack the second bar on top of the first.

Option C stacks sales_B on top of sales_A using the bottom parameter. Option C tries to add lists which causes an error. Option C overlays bars without stacking. Option C stacks in wrong order causing incorrect visualization.

🧠 Conceptual

advanced

Why is categorical visualization important in data science?

Which option best explains why visualizing categorical data helps in data science?

A It helps to quickly compare groups and see patterns or differences between categories.

B It is only useful for numerical data and does not apply to categories.

C It replaces the need for any statistical analysis.

D It makes data look colorful but does not add understanding.

💡 Hint

Think about how charts help us understand group differences.

Categorical visualization helps us see how different groups compare, making patterns easier to spot.

🔧 Debug

expert

What error does this code raise?

This code tries to plot a bar chart but fails. What error will it raise?

Matplotlib

import matplotlib.pyplot as plt
categories = ['A', 'B', 'C']
values = [10, 20]
plt.bar(categories, values)
plt.show()

A ValueError: x and height must have the same size

B TypeError: unsupported operand type(s) for +: 'list' and 'int'

C SyntaxError: invalid syntax

D IndexError: list index out of range

💡 Hint

Check if the categories and values lists have the same length.

The categories list has 3 items but values has only 2, so matplotlib raises a ValueError about size mismatch.