Creating Visualizations

Creating Visualizations with Matplotlib

Visualizations help you understand your data, identify patterns, and communicate insights. Matplotlib is Python's primary plotting library, and mastering it is essential for data analysis and ML.

Good visualizations can reveal trends, outliers, and relationships that numbers alone cannot show. They're crucial for exploratory data analysis before building ML models.

Line Plots

Line plots are perfect for showing trends over time or relationships between variables:

line_plot.py
# Creating Line Plots
import matplotlib.pyplot as plt
import numpy as np

# Sample data
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [100, 120, 140, 130, 150, 180]

# Create line plot
plt.figure(figsize=(8, 5))
plt.plot(months, sales, marker='o', linewidth=2, color='#22d3ee')
plt.title('Monthly Sales Trend', fontsize=14, fontweight='bold')
plt.xlabel('Month')
plt.ylabel('Sales ($)')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

print("Line plot created showing sales trend over 6 months")

Bar Charts

Bar charts are ideal for comparing categories or discrete values:

bar_chart.py
# Creating Bar Charts
import matplotlib.pyplot as plt

# Category data
categories = ['Product A', 'Product B', 'Product C', 'Product D']
revenue = [45000, 52000, 38000, 61000]

# Create bar chart
plt.figure(figsize=(8, 5))
plt.bar(categories, revenue, color=['#22d3ee', '#06b6d4', '#a855f7', '#8b5cf6'])
plt.title('Revenue by Product', fontsize=14, fontweight='bold')
plt.xlabel('Product')
plt.ylabel('Revenue ($)')
plt.xticks(rotation=45)
plt.grid(axis='y', alpha=0.3)
plt.tight_layout()
plt.show()

print("Bar chart created comparing revenue across products")

Scatter Plots

Scatter plots show relationships between two continuous variables:

scatter_plot.py
# Creating Scatter Plots
import matplotlib.pyplot as plt
import numpy as np

# Generate sample data
np.random.seed(42)
hours_studied = np.random.randint(10, 50, 30)
test_scores = hours_studied * 2 + np.random.randint(-10, 10, 30)

# Create scatter plot
plt.figure(figsize=(8, 5))
plt.scatter(hours_studied, test_scores, alpha=0.6, s=100, color='#22d3ee')
plt.title('Study Hours vs Test Scores', fontsize=14, fontweight='bold')
plt.xlabel('Hours Studied')
plt.ylabel('Test Score')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

print("Scatter plot shows positive correlation between study hours and scores")

Histograms

Histograms show the distribution of a single variable:

histogram.py
# Creating Histograms
import matplotlib.pyplot as plt
import numpy as np

# Generate sample data (ages)
np.random.seed(42)
ages = np.random.normal(35, 10, 1000)  # Mean=35, Std=10, 1000 samples

# Create histogram
plt.figure(figsize=(8, 5))
plt.hist(ages, bins=30, color='#22d3ee', edgecolor='black', alpha=0.7)
plt.title('Age Distribution', fontsize=14, fontweight='bold')
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.grid(axis='y', alpha=0.3)
plt.tight_layout()
plt.show()

print("Histogram shows the distribution of ages in the dataset")

Multiple Subplots

You can create multiple plots in one figure using subplots:

subplots.py
# Creating Multiple Subplots
import matplotlib.pyplot as plt
import numpy as np

# Create figure with 2x2 subplots
fig, axes = plt.subplots(2, 2, figsize=(12, 10))

# Plot 1: Line plot
x = np.linspace(0, 10, 100)
axes[0, 0].plot(x, np.sin(x))
axes[0, 0].set_title('Sine Wave')

# Plot 2: Bar chart
categories = ['A', 'B', 'C']
values = [10, 20, 15]
axes[0, 1].bar(categories, values)
axes[0, 1].set_title('Bar Chart')

# Plot 3: Scatter plot
x_scatter = np.random.randn(50)
y_scatter = np.random.randn(50)
axes[1, 0].scatter(x_scatter, y_scatter)
axes[1, 0].set_title('Scatter Plot')

# Plot 4: Histogram
data = np.random.normal(0, 1, 1000)
axes[1, 1].hist(data, bins=30)
axes[1, 1].set_title('Histogram')

plt.tight_layout()
plt.show()

print("Created 4 different plots in one figure using subplots")

Exercise: Create Visualizations

Complete the exercise on the right side:

Task 1: Create a line plot showing temperature over 7 days
Task 2: Create a bar chart comparing sales for 4 products
Task 3: Create a scatter plot showing the relationship between two variables
Task 4: Add titles and labels to all plots

Write your code to create these visualizations! (Note: In this environment, plots will be described in text output)

💡 Learning Tip

Practice is essential. Try modifying the code examples, experiment with different parameters, and see how changes affect the results. Hands-on experience is the best teacher!

🎉

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