Data Visualization with Matplotlib

Create compelling visualizations to communicate insights using Matplotlib for line plots, bar charts, scatter plots, and more.

32 minutes

Data Visualization with Matplotlib

Matplotlib is Python's foundational plotting library for creating static, animated, and interactive visualizations.

Why Visualize Data?

Discover patterns: See trends and relationships
Communicate insights: Share findings effectively
Identify outliers: Spot unusual data points
Compare data: Visualize differences and similarities

Basic Plot Types

Matplotlib supports many chart types:

Line plots: Show trends over time
Bar charts: Compare categories
Scatter plots: Show relationships between variables
Histograms: Display distributions
Pie charts: Show proportions
Box plots: Visualize statistical distributions

Creating Plots

Basic plotting workflow:

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.plot(x, y)
plt.title('My Plot')
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.show()

Customization

Make your plots professional:

Colors: Use color palettes and custom colors
Markers: Add shapes to data points
Line styles: Solid, dashed, dotted
Grid: Add gridlines for readability
Legends: Label multiple series
Annotations: Highlight important points

Multiple Plots

Create subplots for comparison:

fig, axes = plt.subplots(2, 2, figsize=(12, 10))
axes[0, 0].plot(x1, y1)
axes[0, 1].bar(categories, values)

Saving Figures

Export plots for reports:

plt.savefig('plot.png', dpi=300, bbox_inches='tight')

Best Practices

Choose the right chart type for your data
Use clear, descriptive labels
Keep designs simple and uncluttered
Use color purposefully
Consider your audience
Make plots accessible (color blind friendly)

Code Example

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Sample data
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [120, 135, 128, 145, 160, 155]
expenses = [80, 85, 82, 95, 105, 100]

# Create figure with subplots
fig, axes = plt.subplots(2, 2, figsize=(12, 10))

# 1. Line plot
axes[0, 0].plot(months, sales, marker='o', label='Sales', color='#2ecc71', linewidth=2)
axes[0, 0].plot(months, expenses, marker='s', label='Expenses', color='#e74c3c', linewidth=2)
axes[0, 0].set_title('Sales vs Expenses Over Time', fontsize=14, fontweight='bold')
axes[0, 0].set_xlabel('Month')
axes[0, 0].set_ylabel('Amount ($1000s)')
axes[0, 0].legend()
axes[0, 0].grid(True, alpha=0.3)

# 2. Bar chart
profit = [s - e for s, e in zip(sales, expenses)]
colors = ['#2ecc71' if p > 0 else '#e74c3c' for p in profit]
axes[0, 1].bar(months, profit, color=colors)
axes[0, 1].set_title('Monthly Profit', fontsize=14, fontweight='bold')
axes[0, 1].set_xlabel('Month')
axes[0, 1].set_ylabel('Profit ($1000s)')
axes[0, 1].axhline(y=0, color='black', linestyle='-', linewidth=0.5)
axes[0, 1].grid(True, alpha=0.3, axis='y')

# 3. Scatter plot
temperature = [65, 68, 72, 75, 80, 85]
ice_cream_sales = [200, 250, 300, 350, 450, 500]
axes[1, 0].scatter(temperature, ice_cream_sales, s=100, alpha=0.6, color='#3498db')
axes[1, 0].set_title('Ice Cream Sales vs Temperature', fontsize=14, fontweight='bold')
axes[1, 0].set_xlabel('Temperature (°F)')
axes[1, 0].set_ylabel('Sales')
axes[1, 0].grid(True, alpha=0.3)

# Add trend line
z = np.polyfit(temperature, ice_cream_sales, 1)
p = np.poly1d(z)
axes[1, 0].plot(temperature, p(temperature), "r--", alpha=0.8, label='Trend')
axes[1, 0].legend()

# 4. Pie chart
categories = ['Electronics', 'Clothing', 'Food', 'Books']
values = [35, 25, 20, 20]
colors_pie = ['#3498db', '#e74c3c', '#2ecc71', '#f39c12']
axes[1, 1].pie(values, labels=categories, autopct='%1.1f%%', colors=colors_pie, startangle=90)
axes[1, 1].set_title('Sales by Category', fontsize=14, fontweight='bold')

plt.tight_layout()
plt.savefig('data_visualizations.png', dpi=300, bbox_inches='tight')
plt.show()

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