Python Fundamentals — Data Visualization with Matplotlib II

Before you begin, take a few minutes to check what you already know and how confident you feel. You will see the same questions again at the end of the module — this helps both you and us measure what you have learned. Click an option for every question and confidence rating, then click Next to continue.

In Part I you set up figures and drew the core plot types: line, scatter, bar, and histogram. A plot that is correct, though, is not yet a plot that is clear. It still needs labels and a legend a reader can follow, it often needs to sit alongside related panels, and it usually needs to be saved to a file you can drop into a report or slide.

This second part is about turning those plots into finished, presentation-ready figures:

• Customizing plots — labels, titles, colors, line styles, markers, and the legend that ties it all together.
• Subplots and multiple figures — putting two, four, or more panels in the same figure with plt.subplots.
• Saving figures — writing your finished plot to a PNG or PDF you can drop into a slide, an email, or a paper.

Try every snippet in the Python Scratchpad on the right. By the end of this part you will be able to label and style a figure, lay out a multi-panel figure with plt.subplots, and save a publication-ready image to disk.

A plot without context is just a line. Adding labels, titles, and legends makes the difference between a figure that confuses and one that convinces.

Here is a practical guide to formatting, structuring, and exporting your plots so they are always ready for reports or presentations.

Get into the habit of adding these foundational elements to every figure, even quick throwaway ones:

• plt.xlabel() and plt.ylabel() – Always include the units in parentheses (e.g., "Weight (kg)").
• plt.title() – Sits at the top of the figure to summarize the data.
• plt.grid(True) – Adds a faint background grid to make reading values easier.
• plt.legend() – Creates the legend box. Crucial detail: You must pass label="Your Name" inside your plt.plot() calls first, otherwise the legend will be completely empty.

import matplotlib.pyplot as plt

months = [0, 3, 6, 12]
weight_kg = [82, 79, 75, 73]

plt.plot(months, weight_kg)
plt.xlabel("Months since baseline")
plt.ylabel("Weight (kg)")
plt.title("Patient weight during 12-month follow-up")
plt.grid(True)
plt.show()

To customize the look of your lines, use the color (names like steelblue or hex codes), linestyle (-, --, :), and marker (o, s, ^) arguments.

import matplotlib.pyplot as plt

months = [0, 3, 6, 12]
patient_a = [82, 79, 75, 73]
patient_b = [78, 76, 75, 74]

plt.plot(months, patient_a, color="steelblue", linestyle="-", marker="o", label="Patient A")
plt.plot(months, patient_b, color="darkorange", linestyle="--", marker="s", label="Patient B")
plt.xlabel("Months since baseline")
plt.ylabel("Weight (kg)")
plt.title("Two patients, twelve months")
plt.legend()
plt.show()

Often, you need to highlight a specific clinical cutoff or zoom in on a region of interest.

• Threshold lines – Use plt.axhline(y=...) to draw a horizontal line across the entire plot, or plt.axvline(x=...) for a vertical one. Both accept color and linestyle arguments.
• Axis limits – Override the default zoom using plt.xlim(lo, hi) and plt.ylim(lo, hi).
• Custom ticks – plt.xticks(positions, labels) allows you to replace standard numbers with named categories (e.g., mapping [0, 1] to ["Baseline", "Week 12"]).

Try this snippet to see how plt.axhline() draws a horizontal threshold line across a plot

import matplotlib.pyplot as plt

months = [0, 3, 6, 12]
hba1c = [7.2, 8.1, 6.5, 5.9]
plt.plot(months, hba1c, marker="o")
plt.axhline(y=7.0, color="red", linestyle="--", label="Threshold")
plt.xlabel("Month")
plt.ylabel("HbA1c (%)")
plt.legend()
plt.show()

Try this snippet to see how to customize the x- and y-axis limits and ticks.

import matplotlib.pyplot as plt

x = [0, 1, 2, 3, 4]
y = [10, 25, 30, 35, 40]
plt.plot(x, y, marker="o")
plt.xlim(-0.5, 4.5)
plt.ylim(0, 50)
plt.xticks([0, 1, 2, 3, 4], ["Baseline", "Week 1", "Week 2", "Week 4", "Week 12"])
plt.show()

When you need to compare datasets, placing them in a single figure is almost always cleaner than generating separate images. The reader's eye can compare panels directly without flipping pages.

fig, axes = plt.subplots(nrows, ncols) creates a grid. It returns the overall figure object and an array of individual axes.

When working with subplots, you must switch to object-oriented syntax:

• Use ax instead of plt – Inside a multi-panel figure, standard plt.something calls only apply to the most recently created axis, which gets confusing fast. Instead, call methods directly on the specific axis object you want to change: ax.plot(), ax.set_title(), and ax.set_xlabel().
• figsize=(width, height) – Sets the overall figure size in inches. Matplotlib's default is quite small. For slides or papers, bump it up to 8 by 5 or 10 by 6. Match your aspect ratio to your grid—use a wide figure for side-by-side panels, and a tall one for stacked panels.
• plt.tight_layout() – Always call this once at the very end. It automatically adjusts the spacing between subplots so that titles and axis labels do not collide and overlap.

import matplotlib.pyplot as plt

ages = [54, 61, 47, 72, 38, 65, 49, 58, 71, 44]
bmi  = [27.3, 24.1, 31.2, 22.8, 29.5, 26.0, 30.1, 25.4, 23.7, 28.2]

fig, axes = plt.subplots(1, 2, figsize=(10, 4))

axes[0].hist(ages, bins=8)
axes[0].set_title("Age distribution")
axes[0].set_xlabel("Age (years)")

axes[1].hist(bmi, bins=5)
axes[1].set_title("BMI distribution")
axes[1].set_xlabel("BMI")

plt.tight_layout()
plt.show()

When you create a 2D grid (like 2 by 2), axes becomes a 2D array. You index it using axes[row, col]:

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 2, figsize=(8, 6))

axes[0, 0].plot([1, 2, 3], [1, 4, 9])
axes[0, 0].set_title("Top left")

axes[0, 1].scatter([1, 2, 3], [3, 1, 2])
axes[0, 1].set_title("Top right")

axes[1, 0].bar(["A", "B", "C"], [3, 5, 2])
axes[1, 0].set_title("Bottom left")

axes[1, 1].hist([1, 2, 2, 3, 3, 3, 4, 4, 5], bins=5)
axes[1, 1].set_title("Bottom right")

plt.tight_layout()
plt.show()

A figure that lives only in a popup window is gone the moment you close it. Most of the time you want a saved file you can drop into a slide, attach to an email, or include in a paper. Use plt.savefig("filename.ext") to export a clean, high-quality image.

• File Formats – Use .png for slides and quick sharing. Use .pdf or .svg for journal submissions because they are vector formats that stay perfectly sharp at any zoom level. Never use .jpg for plots. It causes blurry text and artifacting.
• Quality – Pass dpi=300 to guarantee print-quality resolution.
• Cropping – Pass bbox_inches="tight" to automatically trim off excess white borders.

Two critical rules for saving:

• Order matters – Always call plt.savefig() before plt.show(). If you call show() first, matplotlib often clears the canvas, resulting in a blank saved file.
• Prevent memory leaks – If you are saving figures inside a loop (e.g., one plot per patient), add plt.close(fig) at the end of each loop iteration to free up your computer's memory.

import matplotlib.pyplot as plt

patients = [
    ("P001", [82, 80, 78, 76]),
    ("P002", [91, 90, 88, 85]),
    ("P003", [70, 71, 70, 69]),
]

# One figure per patient -- save and close inside the loop.
for pid, weights in patients:
    fig, ax = plt.subplots()
    ax.plot([0, 3, 6, 12], weights, marker="o")
    ax.set_title(f"Weight trajectory -- {pid}")
    fig.savefig(f"{pid}_weight.png", dpi=300, bbox_inches="tight")
    plt.close(fig) # frees memory before the next iteration

Note: This will appear under the files tab in the Python Scratchpad. You can download the graphs to view.

The tools across both parts of this module are usually used together. A typical short visualization workflow picks the right plot type from Part I, then labels and styles it, arranges it next to related panels, and saves the result. That is exactly what the worked example below does on a small patient dataset.

import matplotlib.pyplot as plt

ages = [54, 61, 47, 72, 38, 65, 49, 58, 71, 44]
bmi  = [27.3, 24.1, 31.2, 22.8, 29.5, 26.0, 30.1, 25.4, 23.7, 28.2]

fig, axes = plt.subplots(1, 2, figsize=(10, 4))

axes[0].hist(ages, bins=5, color="steelblue")
axes[0].set_xlabel("Age (years)")
axes[0].set_ylabel("Number of patients")
axes[0].set_title("Age distribution")

axes[1].scatter(ages, bmi, color="darkorange")
axes[1].set_xlabel("Age (years)")
axes[1].set_ylabel("BMI")
axes[1].set_title("BMI vs age")

plt.tight_layout()
plt.savefig("patient_overview.pdf", dpi=300, bbox_inches="tight")
plt.show()

You have reached the end of the module. Try the same questions again — your answers here, paired with your pre-test answers, are how we measure what the module taught you. Answer every question and confidence rating, then click Submit and see results to view your score.

Here is how your post-test answers compare with your pre-test answers. The pre/post pairing is the most reliable way to see what this module actually taught you.