Matplotlib Scatter Plots
Visualize relationships between two variables using scatter plots
🔍 Scatter Plot Visualization
Scatter plots are perfect for showing relationships between two numerical variables. Each point represents one observation with x and y coordinates.
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 5, 3, 8, 7]
plt.scatter(x, y)
plt.xlabel('X values')
plt.ylabel('Y values')
plt.show()
2D
Relationships
Color
Coding
Size
Variation
Scatter Plot Features
Customize your scatter plots with various visual elements:
Basic Points
Simple x,y coordinate plotting
plt.scatter()
Color Mapping
Color points by third variable
c parameter
colormap
Size Variation
Point size represents data values
s parameter
Transparency
Handle overlapping points
alpha
🔹 Basic Scatter Plot
Create simple scatter plots to show data relationships
import matplotlib.pyplot as plt
# Sample data
height = [150, 160, 165, 170, 175, 180, 185]
weight = [50, 60, 65, 70, 75, 80, 85]
# Basic scatter plot
plt.scatter(height, weight)
plt.xlabel('Height (cm)')
plt.ylabel('Weight (kg)')
plt.title('Height vs Weight')
plt.show()
# With custom styling
plt.scatter(height, weight,
color='red', # Point color
s=100, # Point size
alpha=0.7) # Transparency
plt.xlabel('Height (cm)')
plt.ylabel('Weight (kg)')
plt.title('Height vs Weight (Styled)')
plt.grid(True, alpha=0.3)
plt.show()🔹 Color-Coded Scatter Plots
Use colors to represent additional data dimensions
import matplotlib.pyplot as plt
import numpy as np
# Generate sample data
np.random.seed(42)
x = np.random.randn(100)
y = np.random.randn(100)
colors = np.random.randn(100) # Third variable for coloring
# Color-coded scatter plot
plt.scatter(x, y, c=colors, cmap='viridis', alpha=0.7)
plt.colorbar(label='Color Scale') # Add color bar
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('Color-Coded Scatter Plot')
plt.show()
# Using different colormaps
fig, axes = plt.subplots(1, 3, figsize=(15, 4))
colormaps = ['viridis', 'plasma', 'coolwarm']
titles = ['Viridis', 'Plasma', 'Coolwarm']
for i, (cmap, title) in enumerate(zip(colormaps, titles)):
scatter = axes[i].scatter(x, y, c=colors, cmap=cmap, alpha=0.7)
axes[i].set_title(title)
plt.colorbar(scatter, ax=axes[i])
plt.tight_layout()
plt.show()🔹 Variable Point Sizes
Use point size to represent data magnitude
import matplotlib.pyplot as plt
import numpy as np
# Sample data: cities
cities = ['NYC', 'LA', 'Chicago', 'Houston', 'Phoenix']
x_coord = [1, 4, 2, 3, 5] # Longitude (simplified)
y_coord = [4, 2, 5, 1, 3] # Latitude (simplified)
population = [8.4, 4.0, 2.7, 2.3, 1.7] # Population in millions
# Scale population for point sizes
sizes = [p * 100 for p in population] # Multiply for visibility
plt.scatter(x_coord, y_coord, s=sizes, alpha=0.6, color='blue')
# Add city labels
for i, city in enumerate(cities):
plt.annotate(city, (x_coord[i], y_coord[i]),
xytext=(5, 5), textcoords='offset points')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.title('City Populations (Size = Population)')
plt.show()
# Bubble chart with color and size
np.random.seed(42)
x = np.random.randn(50)
y = np.random.randn(50)
sizes = np.random.randint(20, 200, 50) # Random sizes
colors = np.random.randn(50)
plt.scatter(x, y, s=sizes, c=colors, cmap='rainbow', alpha=0.6)
plt.colorbar(label='Color Value')
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('Bubble Chart (Size and Color Vary)')
plt.show()🔹 Multiple Data Series
Compare different groups in the same scatter plot
import matplotlib.pyplot as plt
import numpy as np
# Generate sample data for two groups
np.random.seed(42)
group1_x = np.random.normal(2, 1, 50)
group1_y = np.random.normal(3, 1, 50)
group2_x = np.random.normal(5, 1, 50)
group2_y = np.random.normal(6, 1, 50)
# Plot both groups
plt.scatter(group1_x, group1_y,
color='red', alpha=0.6, label='Group A')
plt.scatter(group2_x, group2_y,
color='blue', alpha=0.6, label='Group B')
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('Two Groups Comparison')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()
# Different markers for groups
plt.scatter(group1_x, group1_y,
marker='o', color='red', alpha=0.6, label='Circles')
plt.scatter(group2_x, group2_y,
marker='s', color='blue', alpha=0.6, label='Squares')
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('Different Markers for Groups')
plt.legend()
plt.show()🔹 Advanced Scatter Plot
Combine multiple visual elements for rich data visualization
import matplotlib.pyplot as plt
import numpy as np
# Generate realistic sample data
np.random.seed(42)
n_points = 100
# Simulate student data
study_hours = np.random.normal(5, 2, n_points)
test_scores = study_hours * 10 + np.random.normal(0, 5, n_points)
class_year = np.random.choice([1, 2, 3, 4], n_points) # Freshman to Senior
# Create advanced scatter plot
fig, ax = plt.subplots(figsize=(10, 8))
# Create scatter plot with multiple dimensions
scatter = ax.scatter(study_hours, test_scores,
c=class_year, # Color by class year
s=study_hours*20, # Size by study hours
alpha=0.6,
cmap='viridis',
edgecolors='black', # Add edge to points
linewidth=0.5)
# Customize the plot
ax.set_xlabel('Study Hours per Week', fontsize=12)
ax.set_ylabel('Test Score', fontsize=12)
ax.set_title('Student Performance Analysis\n(Size=Study Hours, Color=Class Year)',
fontsize=14, fontweight='bold')
# Add colorbar
cbar = plt.colorbar(scatter)
cbar.set_label('Class Year', fontsize=12)
cbar.set_ticks([1, 2, 3, 4])
cbar.set_ticklabels(['Freshman', 'Sophomore', 'Junior', 'Senior'])
# Add grid
ax.grid(True, alpha=0.3)
# Set axis limits
ax.set_xlim(0, 10)
ax.set_ylim(0, 100)
plt.tight_layout()
plt.show()
# Add trend line
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(study_hours, test_scores)
line = slope * study_hours + intercept
plt.figure(figsize=(10, 6))
plt.scatter(study_hours, test_scores, alpha=0.6, color='blue')
plt.plot(study_hours, line, 'r-', linewidth=2,
label=f'Trend line (R² = {r_value**2:.3f})')
plt.xlabel('Study Hours per Week')
plt.ylabel('Test Score')
plt.title('Study Hours vs Test Scores with Trend Line')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()