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Which matplotlib method is best suited for efficiently plotting large datasets without excessive memory use?

easy📝 Syntax Q3 of 15
Matplotlib - Performance and Large Data
Which matplotlib method is best suited for efficiently plotting large datasets without excessive memory use?
AUsing <code>plt.plot(x, y, marker='o')</code> to mark every data point
BUsing <code>plt.scatter(x, y, s=10)</code> with large marker size
CUsing <code>plt.plot(x, y, linewidth=0.5)</code> to reduce line thickness
DUsing <code>plt.plot(x, y)</code> with default settings
Step-by-Step Solution
Solution:
  1. Step 1: Understand plotting methods

    Line plots with thinner lines use less memory and render faster than scatter plots with large markers.
  2. Step 2: Analyze options

    Using plt.plot(x, y, linewidth=0.5) to reduce line thickness reduces line thickness, improving performance. Using plt.scatter(x, y, s=10) with large marker size uses large markers, which slows rendering. Using plt.plot(x, y, marker='o') to mark every data point plots markers for every point, increasing overhead. Using plt.plot(x, y) with default settings uses defaults, which may be inefficient for large data.
  3. Final Answer:

    Using plt.plot(x, y, linewidth=0.5) -> Option C
  4. Quick Check:

    Thinner lines reduce rendering load [OK]
Quick Trick: Thinner lines speed up large dataset plots [OK]
Common Mistakes:
  • Using large markers for every point
  • Plotting with default line width on huge data
  • Confusing scatter and line plot performance

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