Summary
The goal is to create a broken line plot with tiered axes using matplotlib or seaborn. The brokenaxes package can break the axes, but it does not provide a tiered effect. We need to find an elegant method to achieve this type of plot.
Root Cause
The root cause of the issue is that the brokenaxes package only breaks the axes, but does not provide a way to create a tiered effect. The main causes are:
- Lack of built-in support for tiered axes in matplotlib and seaborn
- Limited functionality of the brokenaxes package
Why This Happens in Real Systems
This issue occurs in real systems because:
- Data visualization often requires creative and customized solutions
- matplotlib and seaborn are powerful libraries, but they may not have built-in support for every type of plot
- The brokenaxes package is a useful tool, but it has its limitations
Real-World Impact
The impact of not being able to create a tiered broken line plot is:
- Ineffective communication of data insights
- Limited ability to visualize complex data
- Increased time spent trying to find workarounds or alternative solutions
Example or Code
import matplotlib.pyplot as plt
import numpy as np
# Create a broken line plot with tiered axes
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), gridspec_kw={'width_ratios': [1, 1]})
x1 = np.linspace(0, 10, 100)
x2 = np.linspace(195, 200, 100)
ax1.plot(x1, np.sin(x1), label='Part 1')
ax2.plot(x2, np.cos(x2), label='Part 2')
ax1.set_xlim(0, 10)
ax2.set_xlim(195, 200)
ax1.set_ylim(-1.1, 1.1)
ax2.set_ylim(-1.1, 1.1)
ax1.legend()
ax2.legend()
plt.show()How Senior Engineers Fix It
Senior engineers fix this issue by:
- Understanding the limitations of the brokenaxes package and matplotlib and seaborn libraries
- Using creative workarounds, such as creating multiple subplots and adjusting the axes limits
- Leveraging the power of matplotlib and seaborn to create customized and effective data visualizations
Why Juniors Miss It
Juniors may miss this solution because:
- Lack of experience with matplotlib and seaborn libraries
- Limited understanding of data visualization principles and best practices
- Insufficient knowledge of creative workarounds and troubleshooting techniques