Figure creation with plt.figure in Matplotlib - Time & Space Complexity

Time Complexity: Figure creation with plt.figure

O(n)

Understanding Time Complexity

When we create a figure using plt.figure(), it takes some time to set up the drawing space.

We want to know how this setup time changes as we create more figures.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import matplotlib.pyplot as plt

for i in range(n):
    fig = plt.figure()
    plt.close(fig)

This code creates and closes n figures one after another.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Creating a figure with plt.figure() inside a loop.
• How many times: Exactly n times, once per loop iteration.

How Execution Grows With Input

Each figure creation takes roughly the same time, so total time grows directly with n.

Input Size (n)	Approx. Operations
10	10 figure creations
100	100 figure creations
1000	1000 figure creations

Pattern observation: Doubling n doubles the total work.

Final Time Complexity

Time Complexity: O(n)

This means the time to create n figures grows in a straight line as n increases.

Common Mistake

[X] Wrong: "Creating multiple figures at once is instant and does not add up."

[OK] Correct: Each figure requires setup, so time adds up linearly with the number of figures.

Interview Connect

Understanding how repeated figure creation scales helps you reason about performance in data visualization tasks.

Self-Check

What if we reused the same figure instead of creating a new one each time? How would the time complexity change?

Practice

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

easy

A. Plots a line graph directly

B. Creates a new blank canvas for plotting graphs

C. Saves the current plot to a file

D. Displays the plot on the screen

Solution

1. Step 1: Understand the role of plt.figure()

   The plt.figure() function creates a new empty figure, which is like a blank canvas for drawing plots.

2. Step 2: Differentiate from other functions

   Functions like plt.plot() draw on the figure, plt.show() displays it, and plt.savefig() saves it. Only plt.figure() creates the blank space.

3. Final Answer:

   Creates a new blank canvas for plotting graphs -> Option B

4. Quick Check:

   plt.figure() = new blank canvas [OK]

Hint: Remember: figure = blank canvas for plots [OK]

Common Mistakes:

• Confusing plt.figure() with plt.plot()
• Thinking plt.figure() displays the plot
• Assuming plt.figure() saves the plot

2. Which of the following is the correct way to create a figure with size 8 inches wide and 6 inches tall using plt.figure()?

easy

A. plt.figure(size=(8, 6))

B. plt.figure(dimensions=(8, 6))

C. plt.figure(size=[8, 6])

D. plt.figure(figsize=(8, 6))

Solution

1. Step 1: Recall the correct parameter name

   The parameter to set figure size is figsize, which takes a tuple of (width, height) in inches.

2. Step 2: Check the syntax of options

   Only plt.figure(figsize=(8, 6)) uses figsize=(8, 6) correctly. Others use wrong parameter names or data types.

3. Final Answer:

   plt.figure(figsize=(8, 6)) -> Option D

4. Quick Check:

   figsize=(width, height) = plt.figure(figsize=(8, 6)) [OK]

Hint: Use figsize=(width, height) tuple inside plt.figure() [OK]

Common Mistakes:

• Using 'size' instead of 'figsize'
• Passing list instead of tuple
• Using wrong parameter names like 'dimensions'

3. What will be the output of this code?

import matplotlib.pyplot as plt
fig = plt.figure(figsize=(4, 3), dpi=100)
print(fig.get_size_inches())
print(fig.dpi)

medium

A. [4 3]
100.0

B. [400 300]
100.0

C. [4. 3.]
100.0

D. [4.0 3.0]
10

Solution

1. Step 1: Understand figsize and dpi

   The figsize is in inches as a float array, so fig.get_size_inches() returns [4. 3.]. The dpi is dots per inch, set to 100.0.

2. Step 2: Check print outputs

   Printing fig.get_size_inches() shows floats with decimal points. Printing fig.dpi shows 100.0 as set.

3. Final Answer:

   [4. 3.] 100.0 -> Option C

4. Quick Check:

   figsize floats and dpi number = [4. 3.] and 100.0 [OK]

Hint: fig.get_size_inches() returns floats, dpi is float [OK]

Common Mistakes:

• Expecting integers instead of floats for size
• Confusing dpi value with size
• Misreading output format

4. Identify the error in this code snippet:

import matplotlib.pyplot as plt
fig = plt.figure(size=(5, 4))
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()

medium

A. The parameter 'size' is invalid; should be 'figsize'

B. plt.plot() cannot be used after plt.figure()

C. plt.show() is missing parentheses

D. The lists in plt.plot() have different lengths

Solution

1. Step 1: Check parameter names in plt.figure()

   The correct parameter to set figure size is figsize, not size. Using size causes an error.

2. Step 2: Verify other parts of the code

   Using plt.plot() after plt.figure() is valid. plt.show() has parentheses. Lists have same length.

3. Final Answer:

   The parameter 'size' is invalid; should be 'figsize' -> Option A

4. Quick Check:

   Use figsize= not size= in plt.figure() [OK]

Hint: Use figsize=, not size=, to set figure size [OK]

Common Mistakes:

• Using 'size' instead of 'figsize'
• Thinking plt.plot() can't follow plt.figure()
• Forgetting parentheses in plt.show()

5. You want to create two separate plots in two separate windows. Which code correctly creates two figures and plots a simple line in each?

hard

A. fig1 = plt.figure(figsize=(4,3))
plt.plot([1,2,3],[4,5,6])
fig2 = plt.figure(figsize=(4,3))
plt.plot([1,2,3],[6,5,4])
plt.show()

B. plt.figure(figsize=(8,3))
plt.subplot(1,2,1)
plt.plot([1,2,3],[4,5,6])
plt.subplot(1,2,2)
plt.plot([1,2,3],[6,5,4])
plt.show()

C. fig = plt.figure(figsize=(8,3))
plt.plot([1,2,3],[4,5,6])
plt.plot([1,2,3],[6,5,4])
plt.show()

D. plt.figure(figsize=(4,3))
plt.plot([1,2,3],[4,5,6])
plt.figure(figsize=(4,3))
plt.plot([1,2,3],[6,5,4])

Solution

1. Step 1: Understand figure creation for multiple plots

   Creating two separate figures requires calling plt.figure() twice, each time creating a new blank canvas.

2. Step 2: Check code correctness

   fig1 = plt.figure(figsize=(4,3)) plt.plot([1,2,3],[4,5,6]) fig2 = plt.figure(figsize=(4,3)) plt.plot([1,2,3],[6,5,4]) plt.show() creates two figures with specified sizes and plots on each before showing. plt.figure(figsize=(8,3)) plt.subplot(1,2,1) plt.plot([1,2,3],[4,5,6]) plt.subplot(1,2,2) plt.plot([1,2,3],[6,5,4]) plt.show() uses subplots in one figure, not two figures. fig = plt.figure(figsize=(8,3)) plt.plot([1,2,3],[4,5,6]) plt.plot([1,2,3],[6,5,4]) plt.show() plots both lines on one figure. plt.figure(figsize=(4,3)) plt.plot([1,2,3],[4,5,6]) plt.figure(figsize=(4,3)) plt.plot([1,2,3],[6,5,4]) misses plt.show().

3. Final Answer:

   fig1 = plt.figure(figsize=(4,3)) plt.plot([1,2,3],[4,5,6]) fig2 = plt.figure(figsize=(4,3)) plt.plot([1,2,3],[6,5,4]) plt.show() -> Option A

4. Quick Check:

   Two plt.figure() calls = two separate plots [OK]

Hint: Call plt.figure() twice for separate plot windows [OK]

Common Mistakes:

• Using subplots instead of separate figures
• Plotting multiple lines on one figure expecting separate windows
• Forgetting plt.show() to display plots