Matplotlib Pyplot
Master the pyplot interface for easy plotting
🎨 Understanding Pyplot
Pyplot is matplotlib's state-based interface that makes plotting as easy as MATLAB. It provides a simple way to create plots with just a few lines of code.
import matplotlib.pyplot as plt
# Simple pyplot example
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.ylabel('Values')
plt.xlabel('Numbers')
plt.title('Simple Plot')
plt.show()
🎯 Pyplot Basics
State-Based
Keeps track of current figure and axes
plt.plot([1, 2, 3])
plt.title('Auto Current Figure')Easy Styling
Simple commands for colors and styles
plt.plot([1, 2, 3], 'ro-')
plt.grid(True)Quick Labels
Add titles and labels easily
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('My Plot')Save & Show
Display or save with one command
plt.show()
plt.savefig('plot.png')🔹 Basic Pyplot Commands
Essential pyplot functions for creating plots
import matplotlib.pyplot as plt
# Create data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
# Basic plot
plt.plot(x, y)
# Add labels and title
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('Basic Pyplot Example')
# Add grid
plt.grid(True)
# Show the plot
plt.show()
# Save the plot
plt.savefig('my_plot.png')🔹 Figure and Axes Management
Control figures and subplots with pyplot
# Create new figure
plt.figure(figsize=(10, 6))
# Plot on current figure
plt.plot([1, 2, 3], [1, 4, 9], label='Line 1')
plt.plot([1, 2, 3], [1, 2, 3], label='Line 2')
# Add legend
plt.legend()
# Create subplots
plt.figure(figsize=(12, 4))
# First subplot
plt.subplot(1, 3, 1)
plt.plot([1, 2, 3], [1, 4, 9])
plt.title('Plot 1')
# Second subplot
plt.subplot(1, 3, 2)
plt.plot([1, 2, 3], [1, 2, 3])
plt.title('Plot 2')
# Third subplot
plt.subplot(1, 3, 3)
plt.plot([1, 2, 3], [3, 2, 1])
plt.title('Plot 3')
plt.tight_layout()
plt.show()🔹 Pyplot vs Object-Oriented
Understanding the two matplotlib interfaces
🎨 Pyplot Interface (State-based)
- Simple and MATLAB-like
- Good for quick plots and scripts
- Automatically manages current figure
🔧 Object-Oriented Interface
- More control and flexibility
- Better for complex applications
- Explicit figure and axes objects
# Pyplot style (state-based)
plt.plot([1, 2, 3], [1, 4, 9])
plt.title('Pyplot Style')
plt.show()
# Object-oriented style
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9])
ax.set_title('Object-Oriented Style')
plt.show()
# Both produce the same result!🔹 Common Pyplot Functions
Most frequently used pyplot commands
import matplotlib.pyplot as plt
import numpy as np
# Sample data
x = np.linspace(0, 10, 100)
y = np.sin(x)
# Plotting functions
plt.plot(x, y) # Line plot
plt.scatter([1, 2, 3], [1, 4, 9]) # Scatter plot
plt.bar(['A', 'B', 'C'], [1, 2, 3]) # Bar chart
# Labeling functions
plt.title('My Plot') # Title
plt.xlabel('X Label') # X-axis label
plt.ylabel('Y Label') # Y-axis label
plt.legend(['Line 1']) # Legend
# Appearance functions
plt.grid(True) # Grid
plt.xlim(0, 10) # X-axis limits
plt.ylim(-1, 1) # Y-axis limits
# Output functions
plt.show() # Display plot
plt.savefig('plot.png') # Save plot
plt.close() # Close figure🔹 Interactive Features
Pyplot's interactive capabilities
# Interactive mode
plt.ion() # Turn on interactive mode
# Create plot
plt.plot([1, 2, 3], [1, 4, 9])
plt.draw() # Update plot
# Add more data
plt.plot([1, 2, 3], [1, 2, 3])
plt.draw()
# Turn off interactive mode
plt.ioff()
# Pause execution (useful in scripts)
plt.pause(2) # Pause for 2 seconds
# Wait for user input
plt.waitforbuttonpress() # Wait for key press or mouse click