mBlock STEM Robotics Starter Program for Busan Students

1. Why Visualize?

Tables and summary stats alone hide patterns. Charts make trends and anomalies easier to see and to share with stakeholders. Matplotlib is Python’s core plotting library for that work.

2. Figure vs Axes

• Figure (fig) — the full canvas/window; can hold one or many plots
• Axes (ax) — the plot region inside the figure (title, x/y labels, data)

fig, ax = plt.subplots() creates both at once.

3. Stateful + Object-Oriented Style

Matplotlib is stateful (commands update the active plot). Prefer the OO interface—call methods on fig and ax—for clearer, reusable code, especially with multiple subplots.

4. Practical Workflow

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.set_title("Blank Figure Example")
ax.set_xlabel("x-axis label")
ax.set_ylabel("y-axis label")
fig.savefig("blank_plot.png", dpi=100)
plt.show()

• set_title / set_xlabel / set_ylabel — annotate the chart
• fig.savefig(..., dpi=100) — export resolution for reports/print
• plt.show() — display on screen

With no data, axes default to 0–1. When data is plotted, scales adjust to the data range.