Matplotlib cheatsheet

Matplotlib is a comprehensive library for creating static, animated, and interactive plots in Python.
This cheat sheet provides a quick reference from basic to advanced usage, covering essential features for data science, machine learning, and scientific computing.

#Getting Started

#Importing

import matplotlib.pyplot as plt  # Core plotting library
import numpy as np               # For numerical operations

#Basic Plot

x = np.linspace(0, 10, 100)      # 100 points between 0 and 10
y = np.sin(x)                    # Sine function values
plt.plot(x, y)                   # Create a line plot
plt.show()                       # Display the plot

#Plot Types

#Line Plot

plt.plot(x, y)                   # Line plot of y vs x
plt.title("Sine Wave")          # Set title
plt.xlabel("x-axis")            # Label x-axis
plt.ylabel("y-axis")            # Label y-axis
plt.grid(True)                  # Show gridlines
plt.show()

#Scatter Plot

plt.scatter(x, y)               # Scatter plot
plt.title("Scatter Plot")
plt.show()

#Bar Plot

categories = ['A', 'B', 'C']
values = [10, 20, 15]
plt.bar(categories, values)     # Vertical bar chart
plt.title("Bar Chart")
plt.show()

#Horizontal Bar Plot

plt.barh(categories, values)    # Horizontal bar chart
plt.title("Horizontal Bar Chart")
plt.show()

#Histogram

data = np.random.randn(1000)    # Random normal distribution
plt.hist(data, bins=30)         # Histogram with 30 bins
plt.title("Histogram")
plt.show()

#Pie Chart

sizes = [25, 35, 20, 20]
labels = ['A', 'B', 'C', 'D']
plt.pie(sizes, labels=labels, autopct='%1.1f%%')  # Pie chart with % labels
plt.title("Pie Chart")
plt.show()

#Customization

Feature	Code Example	Description
Title	`plt.title("Title")`	Set the title of the plot
X/Y Labels	`plt.xlabel("X")`, `plt.ylabel("Y")`	Label the axes
Grid	`plt.grid(True)`	Show grid
Legend	`plt.legend(["line1"])`	Add legend
Line Style	`plt.plot(x, y, linestyle='--')`	Dashed line
Color	`plt.plot(x, y, color='green')`	Set line color
Marker	`plt.plot(x, y, marker='o')`	Show markers on points
Axis Limits	`plt.xlim(0, 10)`, `plt.ylim(-1, 1)`	Set axis range
Tick Labels	`plt.xticks([...])`, `plt.yticks([...])`	Customize tick positions
Text Annotation	`plt.text(5, 0, "Midpoint")`	Add text at a specific coordinate
Arrow	`plt.annotate("Peak", xy=(7, 1), xytext=(6, 1.5), arrowprops=dict(arrowstyle="->"))`	Add annotation arrow
Style Sheets	`plt.style.use('ggplot')`	Use predefined styles like `seaborn`, `bmh`

#Subplots & Layouts

#Multiple Subplots

fig, axs = plt.subplots(2, 2)        # Create 2x2 grid of subplots
axs[0, 0].plot(x, y)                 # Top-left subplot
axs[0, 1].scatter(x, y)              # Top-right subplot
axs[1, 0].bar(categories, values)    # Bottom-left subplot
axs[1, 1].hist(data)                 # Bottom-right subplot
plt.tight_layout()                   # Adjust spacing to prevent overlap
plt.show()

#Figure Size

plt.figure(figsize=(10, 5))         # Set figure size (width, height in inches)

#Advanced Visualizations

#Heatmap

data = np.random.rand(10, 10)       # Random 10x10 matrix
plt.imshow(data, cmap='hot', interpolation='nearest')  # Display as image
plt.colorbar()                      # Show color scale
plt.title("Heatmap")
plt.show()

#Contour Plot

X, Y = np.meshgrid(x, x)
Z = np.sin(X) * np.cos(Y)
plt.contour(X, Y, Z)                # Contour lines
plt.title("Contour Plot")
plt.show()

#3D Plot

from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')  # 3D subplot
ax.plot3D(x, y, np.cos(x))                  # 3D line
plt.title("3D Plot")
plt.show()

#Working with Images

import matplotlib.image as mpimg
img = mpimg.imread('image.jpg')     # Load image
plt.imshow(img)                     # Display image
plt.axis('off')                     # Hide axes
plt.title("Image Display")
plt.show()

#For Machine Learning

#Plotting Loss vs Epoch

epochs = range(1, 11)
loss = [0.9, 0.7, 0.5, 0.4, 0.3, 0.25, 0.2, 0.18, 0.15, 0.13]
plt.plot(epochs, loss)
plt.title("Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.show()

#Confusion Matrix (Heatmap)

from sklearn.metrics import confusion_matrix
import seaborn as sns

y_true = [0, 1, 2, 2, 0]
y_pred = [0, 0, 2, 2, 1]
cm = confusion_matrix(y_true, y_pred)    # Create confusion matrix
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')  # Heatmap visualization
plt.title("Confusion Matrix")
plt.show()

#ROC Curve

from sklearn.metrics import roc_curve, auc

fpr, tpr, _ = roc_curve([0, 0, 1, 1], [0.1, 0.4, 0.35, 0.8])  # Compute ROC
roc_auc = auc(fpr, tpr)                 # Area under curve
plt.plot(fpr, tpr, label=f'AUC = {roc_auc:.2f}')
plt.plot([0, 1], [0, 1], 'k--')         # Diagonal line
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("ROC Curve")
plt.legend()
plt.show()

#Saving Plots

plt.savefig("figure.png", dpi=300, bbox_inches='tight')  # Save plot to file

#Show & Clear

plt.show()     # Show plot window
plt.clf()      # Clear current figure (useful when plotting in loops)
plt.close()    # Close figure window (useful in scripts or GUI apps)

#More Useful Functions

Function	Use Case
`plt.fill_between(x, y1, y2)`	Fill area between curves
`plt.axhline(y=value)`	Draw horizontal line at y
`plt.axvline(x=value)`	Draw vertical line at x
`plt.errorbar(x, y, yerr=...)`	Plot with error bars
`plt.twinx()`	Create secondary y-axis
`plt.subplots_adjust(...)`	Manually adjust subplot spacing
`plt.gca()`	Get current axes
`plt.gcf()`	Get current figure