Matplotlib Cheatsheet

Matplotlib Cheatsheet 2025: From Beginner to Advanced

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In the world of data science and machine learning, visualization is the bridge between raw numbers and meaningful insights. Among Python’s visualization tools, Matplotlib stands as the most widely used and versatile library. From quick exploratory data analysis plots to highly customized publication-ready graphs, Matplotlib provides the flexibility to do it all.

This Matplotlib Cheatsheet 2025 is designed as a beginner-to-advanced quick reference. Whether you’re just starting with your first line plot or working on advanced 3D visualizations and animations, this guide will give you all the essential functions, methods and examples you need in one place.

1. Setup

import matplotlib.pyplot as plt
import numpy as np

2. Basic Plots

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

plt.plot(x, y)   # Line
plt.scatter(x, y) # Scatter
plt.bar([1,2,3],[4,5,6]) # Bar
plt.hist(np.random.randn(1000)) # Histogram

3. Extra Plot Types

Stem Plot

plt.stem(x, np.cos(x))

Step Plot

plt.step(x, y)

Error Bars

y_err = 0.2
plt.errorbar(x, y, yerr=y_err, fmt="o")

Heatmap (with imshow)

data = np.random.rand(10,10)
plt.imshow(data, cmap="viridis", interpolation="nearest")
plt.colorbar()

Contour Plot

X, Y = np.meshgrid(x, x)
Z = np.sin(X) * np.cos(Y)
plt.contour(X, Y, Z, cmap="plasma")
plt.contourf(X, Y, Z, cmap="plasma")

Quiver Plot (arrows)

X, Y = np.meshgrid(np.arange(-5,5), np.arange(-5,5))
U, V = -1*X, Y
plt.quiver(X, Y, U, V)

Polar Plot

theta = np.linspace(0, 2*np.pi, 100)
r = np.abs(np.sin(5*theta))
plt.polar(theta, r)

4. Customization

Titles, Labels, Legends

plt.title("Sine Wave", fontsize=14, fontweight="bold")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.legend(["sin(x)"])

Axis Ticks & Labels

plt.xticks([0, 2, 4, 6, 8, 10], ["zero","two","four","six","eight","ten"])
plt.yticks(np.arange(-1, 1.5, 0.5))

Axis Formatting (Dates)

import matplotlib.dates as mdates
dates = np.arange("2025-01-01","2025-01-10",dtype="datetime64[D]")
values = np.random.rand(len(dates))

plt.plot(dates, values)
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter("%b %d"))
plt.gcf().autofmt_xdate()

Axis Scales

plt.xscale("log")
plt.yscale("symlog")

5. Multiple Plots

Subplots

fig, axs = plt.subplots(2, 2, figsize=(8,6))
axs[0,0].plot(x, y)
axs[0,1].scatter(x, y)
axs[1,0].bar([1,2,3],[3,5,7])
axs[1,1].hist(np.random.randn(1000))
plt.tight_layout()

Twin Axes

fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot(x, y, "g-")
ax2.plot(x, np.cos(x), "b-")

Shared Axes

fig, axs = plt.subplots(2, sharex=True)
axs[0].plot(x, np.sin(x))
axs[1].plot(x, np.cos(x))

6. Advanced

Fill Between

plt.plot(x, y)
plt.fill_between(x, y, alpha=0.3)

Annotations

plt.annotate("Peak", xy=(np.pi/2,1), xytext=(2,1.5),
             arrowprops=dict(facecolor="black", arrowstyle="->"))

Colormaps

plt.scatter(x, y, c=y, cmap="viridis")
plt.colorbar()

3D Plot

from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
z = np.cos(x)
ax.plot(x, y, z)

Animation

import matplotlib.animation as animation
fig, ax = plt.subplots()
line, = ax.plot([], [])

def update(frame):
    line.set_data(x[:frame], y[:frame])
    return line,

ani = animation.FuncAnimation(fig, update, frames=len(x), interval=50)

7. Styling

Built-in Styles

plt.style.use("ggplot")
plt.plot(x, y)

Available Styles

print(plt.style.available)

8. Save Plots

plt.savefig("plot.png", dpi=300, bbox_inches="tight")
plt.savefig("plot.pdf")

Conclusion

Matplotlib may feel overwhelming at first due to its vast functionality, but once you understand its building blocks, it becomes an incredibly powerful tool. With this cheatsheet, you now have a complete reference covering everything from basic plots, customization, and subplots to heatmaps, animations, 3D graphics and advanced styling.

Use it as your go-to guide for creating clear, impactful, and professional visualizations in Python. Keep practicing with real datasets, and soon you’ll be able to bring any data story to life with Matplotlib.

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