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You have a very large dataset with 1 million points. You want to plot it using matplotlib but keep the plot responsive and clear. Which downsampling strategy should you choose and how?

hard📝 Application Q15 of 15
Matplotlib - Performance and Large Data

You have a very large dataset with 1 million points. You want to plot it using matplotlib but keep the plot responsive and clear. Which downsampling strategy should you choose and how?

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 100, 1_000_000)
y = np.sin(x) + np.random.normal(0, 0.1, 1_000_000)

fig, ax = plt.subplots()
line, = ax.plot(x, y)
# What next?
AUse line.set_downsample(False) to disable downsampling
BUse line.set_downsample(True, method='max') to show only max points
CUse line.set_downsample(True, method='mean') to average points in bins
DUse line.set_downsample(True, method='min') to show only min points
Step-by-Step Solution
Solution:
  1. Step 1: Understand large data plotting needs

    With 1 million points, plotting all slows down and clutters the plot.
  2. Step 2: Choose downsampling method for clarity and smoothness

    Using 'mean' averages points in bins, giving a smooth, clear line.
  3. Step 3: Apply correct method call

    line.set_downsample(True, method='mean') enables downsampling with averaging.
  4. Final Answer:

    Use line.set_downsample(True, method='mean') to average points in bins -> Option C
  5. Quick Check:

    Large data + mean downsampling = smooth plot [OK]
Quick Trick: Mean downsampling smooths large data plots best [OK]
Common Mistakes:
  • Disabling downsampling on large data
  • Using min or max which may lose trend info
  • Not enabling downsampling at all

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