Matplotlib: How to adjust marker size dynamically based on data values in a scatter plot?
I'm trying to implement Hey everyone, I'm running into an issue that's driving me crazy. Could someone explain I'm currently trying to create a scatter plot using Matplotlib 3.6 where the marker sizes should reflect the values of a specific dataset... However, I'm running into issues with the scaling of the marker sizes. I want larger values to have larger markers and vice versa, but the result is not visually intuitive. Here's the code snippet I've been working with: ```python import matplotlib.pyplot as plt import numpy as np # Sample data x = np.random.rand(100) y = np.random.rand(100) values = np.random.rand(100) * 100 # Values to scale marker sizes plt.scatter(x, y, s=values, alpha=0.5) plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.title('Dynamic Marker Sizes Based on Data Values') plt.show() ``` I've set the marker sizes using the `s` parameter directly to the `values` array, but sometimes the markers are too large on the plot or overlap significantly, obscuring the data points. I've tried normalizing the `values` array using `MinMaxScaler()` from `sklearn.preprocessing`, but it didn't seem to help much. Could anyone offer insights on dynamically adjusting marker sizes more effectively? Is there a recommended approach to ensure that marker sizes remain proportional but also visually distinct? Any suggestions would be appreciated! What would be the recommended way to handle this? Am I missing something obvious? For reference, this is a production application. I've been using Python for about a year now.