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Matplotlib CHEAT SHEET

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.

data visualizationmatplotlibplotting
10
Sections
16
Cards

#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

FeatureCode ExampleDescription
Titleplt.title("Title")Set the title of the plot
X/Y Labelsplt.xlabel("X"), plt.ylabel("Y")Label the axes
Gridplt.grid(True)Show grid
Legendplt.legend(["line1"])Add legend
Line Styleplt.plot(x, y, linestyle='--')Dashed line
Colorplt.plot(x, y, color='green')Set line color
Markerplt.plot(x, y, marker='o')Show markers on points
Axis Limitsplt.xlim(0, 10), plt.ylim(-1, 1)Set axis range
Tick Labelsplt.xticks([...]), plt.yticks([...])Customize tick positions
Text Annotationplt.text(5, 0, "Midpoint")Add text at a specific coordinate
Arrowplt.annotate("Peak", xy=(7, 1), xytext=(6, 1.5), arrowprops=dict(arrowstyle="->"))Add annotation arrow
Style Sheetsplt.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

FunctionUse 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