Python · Data Visualization · Matplotlib

Matplotlib
Cheatsheet

A quick reference guide for data visualization in Python using Matplotlib — covering line plots, bar charts, histograms, scatter plots, subplots, and figure customization.

Library: matplotlib ≥ 3.7

Language: Python 3

Level: Beginner → Intermediate

Topics: 10 sections

📖 View Official Docs 📄 Official Cheatsheets

📦 1. Importing & Setup

Always import matplotlib.pyplot as plt. Pair with NumPy for data generation.

import matplotlib.pyplot as plt
import numpy as np

# Jupyter inline display
%matplotlib inline

# Set default figure size globally
plt.rcParams['figure.figsize'] = (10, 6)
plt.rcParams['font.size']        = 12

# Check version
import matplotlib
print(matplotlib.__version__)

📈 2. Basic Line Plot

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.figure(figsize=(10, 4))
plt.plot(x, y)
plt.title("Sine Wave")
plt.xlabel("x")
plt.ylabel("sin(x)")
plt.grid(True)
plt.tight_layout()
plt.show()

🎨 3. Line Styles & Colors

Line style options

plt.plot(x, y, linestyle='--')  # dashed
plt.plot(x, y, linestyle=':')   # dotted
plt.plot(x, y, linestyle='-.')  # dash-dot
plt.plot(x, y, linestyle='-')   # solid
plt.plot(x, y, linewidth=2.5)   # thickness
plt.plot(x, y, marker='o')      # circle markers

Color options

plt.plot(x, y, color='red')
plt.plot(x, y, color='#1f77b4')  # hex
plt.plot(x, y, color='C0')        # cycle

# Shorthand: color + linestyle + marker
plt.plot(x, y, 'r--o')   # red dashed with dots
plt.plot(x, y, 'b-')     # blue solid
plt.plot(x, y, 'g^')     # green triangles

🏷️ 4. Titles & Labels

plt.title("My Chart", fontsize=16, fontweight='bold')
plt.xlabel("X Axis", fontsize=12)
plt.ylabel("Y Axis", fontsize=12)

# Axis limits
plt.xlim(0, 10)
plt.ylim(-1.5, 1.5)

# Tick customization
plt.xticks([0, 2, 4, 6, 8, 10])
plt.yticks([-1, 0, 1])
plt.xticks(rotation=45)

# Grid
plt.grid(True, linestyle='--', alpha=0.5)

📌 5. Legends

# Add label in plot(), then call legend()
plt.plot(x, np.sin(x), label="sin(x)")
plt.plot(x, np.cos(x), label="cos(x)")
plt.legend()

# Location options
plt.legend(loc='upper right')   # best, upper left/right, lower left/right
plt.legend(loc='best')           # auto-placed

# Style
plt.legend(fontsize=10, framealpha=0.5, shadow=True)

🔵 6. Scatter Plot

x = np.random.rand(100)
y = np.random.rand(100)
sizes  = np.random.rand(100) * 200
colors = np.random.rand(100)

plt.scatter(x, y, s=sizes, c=colors, cmap='viridis',
           alpha=0.7, edgecolors='black', linewidths=0.5)

plt.colorbar(label="Value")   # show colour scale
plt.title("Scatter Plot")
plt.show()

📊 7. Bar Chart

Vertical bar

cats   = ['A', 'B', 'C', 'D']
values = [23, 45, 12, 67]

plt.bar(cats, values, color='steelblue',
       edgecolor='black')
plt.bar(cats, values, width=0.5)  # bar width
plt.show()

Horizontal bar

plt.barh(cats, values, color='salmon')

# Grouped bars
x = np.arange(len(cats))
plt.bar(x - 0.2, vals1, 0.4, label='G1')
plt.bar(x + 0.2, vals2, 0.4, label='G2')
plt.xticks(x, cats)

📉 8. Histograms

data = np.random.randn(1000)     # normal distribution

plt.hist(data, bins=30, color='steelblue',
        edgecolor='black', alpha=0.7)

# Density (normalized)
plt.hist(data, bins=30, density=True)

# Stacked / multiple
plt.hist([data1, data2], bins=20, label=['A', 'B'],
        color=['blue', 'orange'], alpha=0.7)
plt.legend()
plt.show()

🔲 9. Subplots

# Create 2×2 grid of subplots
fig, axes = plt.subplots(2, 2, figsize=(12, 8))

axes[0, 0].plot(x, np.sin(x))
axes[0, 0].set_title("Sine")

axes[0, 1].plot(x, np.cos(x), color='orange')
axes[0, 1].set_title("Cosine")

axes[1, 0].bar(cats, values)
axes[1, 0].set_title("Bar")

axes[1, 1].hist(data, bins=20)
axes[1, 1].set_title("Histogram")

plt.tight_layout()   # prevent overlap
plt.show()

💾 10. Saving Figures

# Save before plt.show()
plt.savefig("chart.png")
plt.savefig("chart.pdf")
plt.savefig("chart.svg")

# With options
plt.savefig("chart.png",
           dpi=300,             # resolution
           bbox_inches='tight', # no clipping
           transparent=True)    # transparent bg

# Using figure object
fig, ax = plt.subplots()
ax.plot(x, y)
fig.savefig("output.png", dpi=150)