Font properties customization in Matplotlib - Time & Space Complexity
When customizing font properties in matplotlib, it's important to understand how the time to render text changes as we add more text elements or customize more properties.
We want to know how the execution time grows when we change font settings for multiple text items.
Analyze the time complexity of the following code snippet.
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
fig, ax = plt.subplots()
font = FontProperties(family='serif', style='italic', size=12)
n = 10
for i in range(n):
ax.text(0.5, i*0.1, f'Text {i}', fontproperties=font)
plt.show()
This code creates a plot and adds n text labels with customized font properties.
Identify the loops, recursion, array traversals that repeat.
- Primary operation: Adding text labels with font customization inside a loop.
- How many times: The loop runs
ntimes, once for each text label.
Each new text label requires setting font properties and rendering, so the total work grows as we add more labels.
| Input Size (n) | Approx. Operations |
|---|---|
| 10 | 10 font setups and text renders |
| 100 | 100 font setups and text renders |
| 1000 | 1000 font setups and text renders |
Pattern observation: The execution time grows roughly in direct proportion to the number of text items.
Time Complexity: O(n)
This means the time to customize and render fonts grows linearly with the number of text elements.
[X] Wrong: "Customizing font properties once will speed up adding all text labels."
[OK] Correct: Each text label needs its own font setup and rendering, so the work adds up with more labels.
Understanding how rendering time grows with more customized text helps you explain performance in data visualization tasks clearly and confidently.
What if we reused the same font properties object without creating a new one inside the loop? How would the time complexity change?