I'm plotting three datasets using matplotlib.pyplot.scatter. Each dataset is loaded into a pandas DataFrame and contains Time and Delay_ms columns. I normalize the Time column to start from 0 by subtracting the minimum, so all datasets should start at 0 and end near their respective durations (545s, 542s, 577s respectively). However, on the plot, the x-axis limits appear inconsistent between datasets, even though their durations are similar. Key points: Datasets although with similar duration have different row densities (with respect to time) df0 has ~3.2 million rows df1 has ~3.5 million rows df2 has ~88k rows All durations are roughly in the same range (meaning min to max Time) x (Time) values have very small diffrences: Time Sched_delay_ms 0 0.0000000000 0.4190000000 1 0.0013510000 0.0000000000 2 0.0014590000 1.5880000000 3 0.0015320000 0.0000000000 4 0.0029610000 0.0000000000 This issue also appears when plotting each dataset individually Could scatter be dropping or skipping points silently? Here's the relevant code: plt.figure(figsize=(15, 8), dpi=300) for (df, label) in datasets: df['Time'] = df['Time'] - df['Time'].min() # Normalize time df.sort_values(by='Time', inplace=True) avg_delay = df['Delay_ms'].mean() scatter = plt.scatter(df['Time'], df['Delay_ms'], label=label, alpha=0.6, s=4) plt.axhline(y=avg_delay, linestyle='--', alpha=0.8, color=scatter.get_facecolor()[0], label=f'{label} avg ({avg_delay:.2f} ms)') plt.show() Continue reading...
