如何创建年龄vs看电视时长的NumPy随机数组并绘制散点图？

Got it, let's adjust that code to fit your exact requirements! The original code uses standard normal distribution values (via randn), which don't align with your 18-50 age range and 0-24 hour TV time range. Here's a modified version that generates valid data and produces a clear scatter plot:

Step-by-Step Breakdown #

• Import the right libraries: We'll use numpy for controlled random data generation and matplotlib.pyplot for plotting (more explicit than relying on pylab).
• Generate age data: Use np.random.uniform() to create values uniformly distributed between 18 and 50. We'll cast to integers since ages are typically whole numbers, but you can skip that if you want decimal values.
• Generate TV time data: Similarly, use uniform() to generate values between 0 and 24 hours.
• Plot with context: Add axis labels and a title to make the plot meaningful, unlike the bare-bones original.

Full Working Code #

import numpy as np
import matplotlib.pyplot as plt

# Generate 500 random age values (18-50, integer)
ages = np.random.uniform(low=18, high=51, size=500).astype(int)
# Note: Using high=51 instead of 50 because uniform is exclusive of the upper bound

# Generate 500 random daily TV time values (0-24 hours, can be decimal)
tv_hours = np.random.uniform(low=0, high=24, size=500)

# Create the scatter plot
plt.scatter(ages, tv_hours, alpha=0.6)  # Alpha adds slight transparency for overlapping points
plt.title("Relationship Between Age and Daily TV Watching Time")
plt.xlabel("Age (18-50)")
plt.ylabel("Daily TV Time (Hours)")
plt.xlim(17, 51)  # Add small padding to x-axis for better visibility
plt.ylim(-0.5, 24.5)  # Same for y-axis
plt.grid(True, linestyle="--", alpha=0.3)  # Add subtle grid for readability

# Show the plot
plt.show()

Key Notes #

• Why uniform() instead of randn()?: randn() produces values centered around 0 with a standard deviation of 1, which doesn't fit your fixed ranges. uniform() generates values evenly spread across your desired interval, which is perfect for this use case.
• Integer ages: The astype(int) converts the float output of uniform() to whole numbers, which makes sense for age data. If you prefer decimal ages (e.g., 25.5), just remove that part.
• Transparency (alpha): This helps when points overlap, so you can see density in busy areas of the plot.

内容的提问来源于stack exchange，提问作者user6882757