How to test and debug matplotlib code?
Description:
Debugging visualization code can be trickier than pure logic code because you’re often dealing with visual errors — wrong axis, missing labels, or distorted plots. Testing and debugging involve a mix of visual inspection and checking underlying data assumptions.
● Start by printing your data before you plot it — use print(df.head()), print(x, y) to make sure values are correct.
● Check that your data types are what Matplotlib expects — sometimes, strings or NaNs can silently break plots.
● If plots don’t show up, verify that:
-You’ve called plt.show() in script-based plots.You haven’t overwritten your figure or axes objects.
-Use interactive backends (like TkAgg) to inspect live plots.
● For automated testing, you can use visual regression tools (like pytest-mpl) to compare output plots to baseline images.
● Debug one layer at a time — for instance, plot just the data points, then add titles, then grid, etc.
Code Explanation:
● print(...): Helps inspect x and y values for errors.
● plt.plot(...): Plots a simple line chart.
● plt.title(...): Adds a title.
● plt.show(): Visualizes the chart to validate correctness.
Program:
import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [4, 5, 6]
print(f"x values: {x}")
print(f"y values: {y}")
plt.plot(x, y)
plt.title("Debugging Matplotlib Code")
plt.show()
Output:
