User Preference distribution
Description
The data visualized in the plot represents a three-dimensional distribution of user preferences based on age and geographic region. X-axis (Age): The horizontal axis represents age, ranging from 20 to 70 years. Age values are evenly distributed within this range. Y-axis (Geographic Region): The vertical axis represents geographic regions, with values ranging from 1 to 10. Geographic regions are evenly distributed within this range. Z-axis (User Satisfaction): The depth axis represents user satisfaction, indicating the level of satisfaction associated with specific combinations of age and geographic region. User satisfaction values are calculated based on a user satisfaction function, which incorporates both age and geographic region as parameters. Plot: The plot depicts a surface that represents the distribution of user satisfaction across different age groups and geographic regions. The surface is generated using the plot_surface function from the mpl_toolkits.mplot3d module. Color Map: The color of the surface varies to indicate the magnitude of user satisfaction. A color map (cmap) called 'viridis' is applied to the surface, where different colors correspond to different levels of user satisfaction. Labels: The plot includes labels for each axis: 'Age' for the x-axis, 'Geographic Region' for the y-axis, and 'User Satisfaction' for the z-axis. Title: The title of the plot is "User Preference Distribution", providing an overview of what the plot represents. Overall, this visualization allows for the exploration of how user satisfaction varies across different age groups and geographic regions, providing insights into potential patterns or trends in user preferences.
Files
Steps to reproduce
from mpl_toolkits.mplot3d import Axes3D # Age and location grid age_range = np.linspace(20, 70, 50) geographic_region = np.linspace(1, 10, 10) age, region = np.meshgrid(age_range, geographic_region) # User satisfaction function def user_satisfaction(age, region): return np.random.rand() * (age + region) # User preference distribution preference_distribution = user_satisfaction(age, region) # Plot fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.plot_surface(age, region, preference_distribution, cmap='viridis') ax.set_xlabel('Age') ax.set_ylabel('Geographic Region') ax.set_zlabel('User Satisfaction') ax.set_title('User Preference Distribution') plt.show()