Data Visualization Introduction

A table of 1,000 numbers hides its story. A chart shows it in two seconds: the trend going up, the one month that crashed, the outlier nobody noticed. Data visualization is the skill of turning numbers into pictures that answer a question.

In Python, the two libraries you’ll use are:

• Matplotlib: the original plotting library. Full control over every line and label.
• Seaborn: built on Matplotlib. Beautiful statistical charts from a DataFrame in one line.

pip install matplotlib seaborn

Pick the chart from the question#

The most common mistake is choosing a chart because it looks nice. Start from your question instead:

Your question	Chart
How does something change over time?	Line chart
How do categories compare?	Bar chart
How are values spread out?	Histogram, box plot
Are two numbers related?	Scatter plot
What share does each part make of the whole?	Stacked bar (or a pie, for 2–4 parts only)
How do many numbers relate to each other?	Heatmap

Your first chart#

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
visitors = [1200, 1350, 1100, 1800, 2400, 2900]

plt.figure(figsize=(7, 4))
plt.plot(months, visitors, marker="o")
plt.title("Website visitors are growing fast since April")
plt.xlabel("Month")
plt.ylabel("Visitors")
plt.show()

plt.show() displays the chart. In Jupyter or Colab, charts appear right under the cell.

The parts of a Matplotlib chart#

Every chart has the same anatomy, and knowing the names helps when you search for “how do I change…”:

• Figure: the whole picture, like the paper. figsize=(7, 4) is its width and height in inches.
• Axes: one chart area inside the figure, with its own x-axis and y-axis. A figure can hold several axes (subplots).
• Title, labels, ticks, legend: the text that tells readers what they’re looking at.

There are two ways to write Matplotlib code. The quick plt. style above, and the object style, which is clearer when you have more than one chart:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(["Jan", "Feb", "Mar"], [10, 14, 9], marker="o")
ax.set_title("Orders per month")
ax.set_ylabel("Orders")
plt.show()

fig is the figure, ax is the axes. Methods on ax start with set_ for labels and titles.

Saving charts#

plt.savefig("visitors.png", dpi=150, bbox_inches="tight")

Call savefig before plt.show(). bbox_inches="tight" trims empty margins, and dpi=150 gives a sharp image for reports and slides.

Quick check

You want to show how a student’s marks changed across 5 exams. Which chart? A line chart, because the x-axis is time (exam 1, 2, 3…).

Summary#

• Choose the chart from the question: time → line, categories → bar, spread → histogram, relationship → scatter.
• A Matplotlib figure holds one or more axes (charts).
• Always add a title and axis labels; save with plt.savefig.