Matplotlib · Styling

My styling checklist from today:

1. Axis labels with units ("Temperature (°C)").
2. A title that states the finding, not just the variable names.
3. Remove chart junk: hide the top and right spines, and use light grid lines.
4. Annotate the one point that matters with ax.annotate.
5. Use perceptually uniform colormaps like viridis for continuous data. The Matplotlib docs explain why jet misleads.
6. Save at a sensible DPI and call bbox_inches="tight".

rcParams and style sheets (plt.style.use(...)) change the defaults everywhere at once.

import os
import numpy as np
import matplotlib.pyplot as plt

plt.rcParams.update({"font.size": 11, "axes.spines.top": False, "axes.spines.right": False})

months = np.arange(1, 13)
temp = np.array([20.1, 22.8, 27.9, 32.4, 35.0, 33.6, 30.2, 29.1, 29.6, 28.4, 25.2, 21.6])  # illustrative values
fig, ax = plt.subplots(figsize=(7, 3.6), dpi=150)
ax.plot(months, temp, marker="o", color="#c2410c")
ax.grid(axis="y", alpha=0.3)
ax.set_xticks(months, ["J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D"])
ax.set_xlabel("Month")
ax.set_ylabel("Mean temperature (°C)")
ax.set_title("Illustrative data: the warmest month stands out")
peak = temp.argmax()
ax.annotate(f"peak {temp[peak]} °C", xy=(months[peak], temp[peak]),
            xytext=(months[peak] + 1.5, temp[peak] - 1), arrowprops={"arrowstyle": "->"})
fig.savefig(os.environ["SNIPPET_FIG"], bbox_inches="tight")

print("available styles include ggplot:", "ggplot" in plt.style.available)
print("peak month index:", int(months[peak]))

Output:

available styles include ggplot: True
peak month index: 5

The temperatures here are made-up practice numbers, not real weather data.