Matplotlib Basics | Complete Guide to Data Visualization in Python

Introduction

“Turn data into pictures”

Matplotlib is Python’s standard library for data visualization.

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1. Matplotlib basics

Installation

pip install matplotlib

First plot

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# Plot
plt.plot(x, y)
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Line plot')
plt.show()

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2. Line plots

Basic line plot

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.plot(x, y1, label='sin(x)', color='blue', linestyle='-')
plt.plot(x, y2, label='cos(x)', color='red', linestyle='--')

plt.xlabel('x')
plt.ylabel('y')
plt.title('Trigonometric functions')
plt.legend()
plt.grid(True)
plt.show()

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3. Bar charts

Vertical bars

categories = ['A', 'B', 'C', 'D']
values = [25, 40, 30, 55]

plt.bar(categories, values, color='skyblue')
plt.xlabel('Category')
plt.ylabel('Value')
plt.title('Bar chart')
plt.show()

Horizontal bars

plt.barh(categories, values, color='lightgreen')
plt.xlabel('Value')
plt.ylabel('Category')
plt.title('Horizontal bar chart')
plt.show()

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4. Histogram

# Normal-ish sample
data = np.random.randn(1000)

plt.hist(data, bins=30, color='purple', alpha=0.7, edgecolor='black')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Histogram')
plt.show()

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5. Scatter plot

x = np.random.rand(50)
y = np.random.rand(50)
colors = np.random.rand(50)
sizes = 1000 * np.random.rand(50)

plt.scatter(x, y, c=colors, s=sizes, alpha=0.5, cmap='viridis')
plt.colorbar()
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Scatter plot')
plt.show()

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6. Multiple subplots

fig, axes = plt.subplots(2, 2, figsize=(10, 8))

# Top-left
axes[0, 0].plot([1, 2, 3], [1, 4, 9])
axes[0, 0].set_title('Line')

# Top-right
axes[0, 1].bar(['A', 'B', 'C'], [3, 7, 5])
axes[0, 1].set_title('Bar')

# Bottom-left
axes[1, 0].hist(np.random.randn(100), bins=20)
axes[1, 0].set_title('Histogram')

# Bottom-right
axes[1, 1].scatter(np.random.rand(50), np.random.rand(50))
axes[1, 1].set_title('Scatter')

plt.tight_layout()
plt.show()

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7. Practical example

Sales visualization

import matplotlib.pyplot as plt
import pandas as pd

# Data
sales_data = pd.DataFrame({
    'month': ['1월', '2월', '3월', '4월', '5월', '6월'],
    'sales': [150, 180, 165, 220, 250, 240],
    'profit': [30, 45, 35, 60, 75, 70]
})

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# Revenue trend
ax1.plot(sales_data['month'], sales_data['sales'],
         marker='o', linewidth=2, markersize=8)
ax1.set_title('월별 매출 추이', fontsize=14, fontweight='bold')
ax1.set_xlabel('월')
ax1.set_ylabel('매출 (만원)')
ax1.grid(True, alpha=0.3)

# Profit bars
ax2.bar(sales_data['month'], sales_data['profit'],
        color='green', alpha=0.7)
ax2.set_title('월별 수익', fontsize=14, fontweight='bold')
ax2.set_xlabel('월')
ax2.set_ylabel('수익 (만원)')

plt.tight_layout()
plt.savefig('sales_report.png', dpi=300)
plt.show()

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Practical tips

Styling

# Built-in style
plt.style.use('seaborn-v0_8')

# Font for CJK labels (OS-specific; example: Windows)
plt.rcParams['font.family'] = 'Malgun Gothic'
plt.rcParams['axes.unicode_minus'] = False

# Figure size
plt.figure(figsize=(10, 6))

# Color palette
colors = ['#FF6B6B', '#4ECDC4', '#45B7D1']

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Going deeper

Scatter with regression line and residual histogram (runnable)

Synthetic data with numpy, linear fit via polyfit, and a residual histogram—includes savefig for reports.

import numpy as np
import matplotlib.pyplot as plt

rng = np.random.default_rng(42)
x = np.linspace(0, 10, 80)
y = 2.5 * x + 1.0 + rng.normal(0, 1.8, size=x.shape)

coef = np.polyfit(x, y, 1)
y_hat = np.poly1d(coef)(x)
residuals = y - y_hat

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4), constrained_layout=True)
ax1.scatter(x, y, alpha=0.7, label="Observed")
ax1.plot(x, y_hat, color="crimson", linewidth=2, label="Linear fit")
ax1.set_title("Scatter with regression line")
ax1.legend()
ax1.grid(True, alpha=0.3)

ax2.hist(residuals, bins=18, color="steelblue", edgecolor="black", alpha=0.85)
ax2.set_title("잔차 분포")
ax2.grid(True, alpha=0.3)

fig.savefig("regression_residuals.png", dpi=200)
plt.show()

Common mistakes

• Calling savefig after show() in some backends yields empty files—order matters.
• Missing fonts for non-Latin labels—configure per OS.
• Mixing OO API and pyplot state so artists land on the wrong axes.

Caveats

• Journals often prefer vector formats (PDF/SVG); for raster, set dpi explicitly.
• Consider colorblind-friendly palettes (cividis, etc.).

In production

• Share styles via matplotlibrc or plt.style.context.
• For batch reports, call plt.close(fig) to free memory.

Alternatives

Tool	Best for
Matplotlib	Fine control, papers, non-interactive backends
Seaborn	Quick statistical plots
Plotly	Interactive web charts

Further reading

• Matplotlib documentation
• Matplotlib gallery

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Summary

Key takeaways

1. Matplotlib: core Python plotting library
2. pyplot: convenient stateful API
3. Chart types: line, bar, histogram, scatter
4. Subplots: grid of axes
5. Style: colors, fonts, layout

Next steps

• Data preprocessing
• Hands-on data analysis

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Related posts

• Python environment setup