Day 1: Matplotlib Fundamentals: Your First Charts | Data Visualization

What You'll Build

Four production-ready charts: a line chart, bar chart, scatter plot, and histogram — all with proper labels, clean styling, and saved as high-res image files.

Section 1 · 8 min

Setup and the Figure/Axes Model

Install matplotlib and seaborn:

bash

$ pip install matplotlib seaborn pandas numpy

The most important thing to understand in matplotlib is the object hierarchy: Figure is the entire canvas. Axes is one individual plot on that canvas. A Figure can contain multiple Axes.

pythonfundamentals.py

import matplotlib.pyplot as plt
import numpy as np

# Create a Figure and one Axes object
fig, ax = plt.subplots(figsize=(10, 6))

# Data
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
revenue = [42000, 51000, 48000, 63000, 71000, 68000]

# Plot
ax.plot(months, revenue, color="#1e3a5f", linewidth=2, marker="o")

# Labels — always include these
ax.set_title("Monthly Revenue H1 2025", fontsize=16, pad=20)
ax.set_xlabel("Month")
ax.set_ylabel("Revenue ($)")

plt.tight_layout()
plt.savefig("revenue_chart.png", dpi=150, bbox_inches="tight")
plt.show()

Section 2 · 12 min

Bar Charts, Scatter Plots, and Histograms

The three other chart types you'll use constantly:

pythoncharts.py

import matplotlib.pyplot as plt
import numpy as np

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# Bar Chart
categories = ["Product A", "Product B", "Product C"]
values = [340, 280, 195]
axes[0].bar(categories, values, color=["#1e3a5f", "#c4873e", "#4a7fa5"])
axes[0].set_title("Product Sales")

# Scatter Plot
x = np.random.normal(50, 15, 100)
y = x * 0.8 + np.random.normal(0, 8, 100)
axes[1].scatter(x, y, alpha=0.6, color="#1e3a5f")
axes[1].set_title("Correlation Plot")

# Histogram
data = np.random.normal(75, 12, 500)
axes[2].hist(data, bins=20, color="#1e3a5f", edgecolor="white")
axes[2].set_title("Score Distribution")

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

Section 3 · 25 min

Styling and Annotations

Default matplotlib charts look dated. Two changes make them look professional: a clean style and annotations that tell the story.

pythonstyled_chart.py

import matplotlib.pyplot as plt
import matplotlib.style as style

# Clean built-in style
style.use("seaborn-v0_8-whitegrid")

fig, ax = plt.subplots(figsize=(10, 6))

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
revenue = [42000, 51000, 48000, 63000, 71000, 68000]

ax.plot(months, revenue, color="#1e3a5f", linewidth=2.5, marker="o", markersize=8)

# Annotate the peak
peak_idx = revenue.index(max(revenue))
ax.annotate(
    f"Peak: ${max(revenue):,}",
    xy=(peak_idx, max(revenue)),
    xytext=(peak_idx - 1, max(revenue) + 3000),
    arrowprops={"arrowstyle": "->", "color": "#c4873e"},
    fontsize=11, color="#c4873e", fontweight="bold"
)
ax.set_title("Monthly Revenue H1 2025", fontsize=16, pad=16)
plt.tight_layout()
plt.savefig("styled.png", dpi=150, bbox_inches="tight")

What You Learned Today

The Figure/Axes object model — always use fig, ax = plt.subplots() pattern
Four core chart types: line, bar, scatter, histogram
How to use plt.subplots() to create multi-chart layouts
How to annotate charts to tell the story, not just display data

Your Challenge

Go Further on Your Own

Build a 2x2 grid of charts from a dataset of your choice: line, bar, scatter, and histogram in one figure
Add a second line to your line chart showing a benchmark or prior year comparison, with a legend
Modify the bar chart to use horizontal bars (barh) — which orientation works better for your data?

Day 1 Complete

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Course Overview Day 2 →

Course Progress

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