Matplotlib

Matplotlib#

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
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