Basic pie chart with plt.pie in Matplotlib - Time & Space Complexity

Time Complexity: Basic pie chart with plt.pie

O(n)

Understanding Time Complexity

We want to understand how the time to draw a pie chart changes as the data size grows.

How does the number of slices affect the work matplotlib does?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import matplotlib.pyplot as plt

sizes = [10, 20, 30, 40]
plt.pie(sizes)
plt.show()

This code creates a pie chart with 4 slices representing the sizes given.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Drawing each slice of the pie chart.
• How many times: Once for each slice in the sizes list.

How Execution Grows With Input

As the number of slices increases, matplotlib draws more pieces, so the work grows with the number of slices.

Input Size (n)	Approx. Operations
10	10 drawing steps
100	100 drawing steps
1000	1000 drawing steps

Pattern observation: The work grows directly with the number of slices.

Final Time Complexity

Time Complexity: O(n)

This means the time to draw the pie chart grows linearly with the number of slices.

Common Mistake

[X] Wrong: "Drawing a pie chart always takes the same time no matter how many slices there are."

[OK] Correct: Each slice requires separate drawing work, so more slices mean more work and more time.

Interview Connect

Understanding how drawing steps grow with data size helps you reason about performance in data visualization tasks.

Self-Check

"What if we added labels and exploded slices? How would the time complexity change?"

Practice

1. What does the plt.pie() function in matplotlib do?

easy

A. It creates a line chart to show trends over time.

B. It creates a pie chart to show parts of a whole.

C. It creates a bar chart to compare categories.

D. It creates a scatter plot to show data points.

Solution

1. Step 1: Understand the purpose of plt.pie()

   The function plt.pie() is designed to create pie charts, which visually represent parts of a whole as slices of a circle.

2. Step 2: Compare with other chart types

   Line charts, bar charts, and scatter plots are created with different functions like plt.plot(), plt.bar(), and plt.scatter(), not plt.pie().

3. Final Answer:

   It creates a pie chart to show parts of a whole. -> Option B

4. Quick Check:

   Pie chart = plt.pie() [OK]

Hint: Pie chart = parts of whole, use plt.pie() [OK]

Common Mistakes:

• Confusing pie chart with bar or line charts
• Thinking plt.pie() creates scatter plots
• Mixing up chart functions in matplotlib

2. Which of the following is the correct syntax to create a basic pie chart with labels using matplotlib?

easy

A. plt.pie(sizes, labels=labels)

B. plt.pie(labels, sizes=sizes)

C. plt.pie(labels=sizes, sizes=labels)

D. plt.pie(sizes, labels)

Solution

1. Step 1: Check the order of arguments in plt.pie()

   The first argument to plt.pie() is the sizes (values) of slices, and labels are passed as a keyword argument labels=.

2. Step 2: Validate correct keyword usage

   plt.pie(sizes, labels=labels) correctly uses plt.pie(sizes, labels=labels). Options B and C misuse argument order or keywords, and D misses the keyword for labels.

3. Final Answer:

   plt.pie(sizes, labels=labels) -> Option A

4. Quick Check:

   Sizes first, labels keyword [OK]

Hint: Sizes first, labels with labels= keyword [OK]

Common Mistakes:

• Passing labels as first argument instead of sizes
• Omitting labels= keyword
• Swapping sizes and labels arguments

3. What will be the output of this code?

import matplotlib.pyplot as plt
sizes = [30, 20, 50]
labels = ['A', 'B', 'C']
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.show()

medium

A. A pie chart with three slices labeled A, B, C showing percentages 30.0%, 20.0%, 50.0%

B. A bar chart with bars labeled A, B, C

C. A pie chart with slices but no labels or percentages

D. Syntax error due to autopct usage

Solution

1. Step 1: Understand plt.pie() parameters

   The sizes list defines slice sizes, labels assign names, and autopct='%1.1f%%' adds percentage text on slices.

2. Step 2: Predict the visual output

   The code will produce a pie chart with three slices labeled A, B, C, each showing their percentage values 30.0%, 20.0%, and 50.0% respectively.

3. Final Answer:

   A pie chart with three slices labeled A, B, C showing percentages 30.0%, 20.0%, 50.0% -> Option A

4. Quick Check:

   autopct adds percentages [OK]

Hint: autopct='%1.1f%%' shows slice percentages [OK]

Common Mistakes:

• Thinking autopct causes errors
• Expecting bar chart instead of pie chart
• Ignoring labels or percentages on chart

4. Identify the error in this code snippet that tries to create a pie chart:

import matplotlib.pyplot as plt
sizes = [40, 30, 30]
labels = ['X', 'Y', 'Z']
plt.pie(labels, sizes=sizes)
plt.show()

medium

A. The sizes list must be strings, not numbers.

B. The labels list is missing a keyword argument.

C. plt.show() is missing parentheses.

D. The sizes argument should be the first positional argument, not labels.

Solution

1. Step 1: Check argument order in plt.pie()

   The first argument must be sizes (numbers), but the code passes labels first, which is incorrect.

2. Step 2: Correct argument usage

   Sizes should be the first positional argument, and labels should be passed with labels=labels keyword.

3. Final Answer:

   The sizes argument should be the first positional argument, not labels. -> Option D

4. Quick Check:

   Sizes first, labels keyword [OK]

Hint: Sizes first, labels with labels= keyword [OK]

Common Mistakes:

• Passing labels as first argument
• Forgetting labels= keyword
• Confusing argument order

5. You want to create a pie chart showing sales distribution for 4 products with sizes [15, 25, 35, 25] and labels ['P1', 'P2', 'P3', 'P4']. You also want to highlight the largest slice by "exploding" it out. Which code correctly does this?

hard

A. plt.pie([15,25,35,25], labels=['P1','P2','P3','P4'], explode=[0,0,0,0.1], autopct='%1.1f%%')

B. plt.pie([15,25,35,25], labels=['P1','P2','P3','P4'], explode=[0.1,0,0,0], autopct='%1.1f%%')

C. plt.pie([15,25,35,25], labels=['P1','P2','P3','P4'], explode=[0,0,0.1,0], autopct='%1.1f%%')

D. plt.pie([15,25,35,25], labels=['P1','P2','P3','P4'], explode=[0,0.1,0,0], autopct='%1.1f%%')

Solution

1. Step 1: Identify the largest slice

   The sizes are [15, 25, 35, 25]. The largest value is 35 at index 2 (third element).

2. Step 2: Set explode to highlight largest slice

   The explode list should have 0.1 at index 2 and 0 elsewhere: [0, 0, 0.1, 0]. plt.pie([15,25,35,25], labels=['P1','P2','P3','P4'], explode=[0,0,0.1,0], autopct='%1.1f%%') matches this.

3. Final Answer:

   plt.pie([15,25,35,25], labels=['P1','P2','P3','P4'], explode=[0,0,0.1,0], autopct='%1.1f%%') -> Option C

4. Quick Check:

   Explode largest slice index 2 [OK]

Hint: Explode list: 0.1 at largest slice index [OK]

Common Mistakes:

• Exploding wrong slice index
• Using explode without matching list length
• Forgetting autopct for percentages