Data Visualization with Matplotlib and Seaborn

Why Visualize Data?

No matter how good your analysis is, if others can’t understand it, it loses impact. Visualization bridges that gap. Charts help you uncover patterns, detect outliers, and communicate findings clearly.

Whether you’re making dashboards, building reports, or debugging model results — you’ll need visual storytelling.

Meet the Visualization Tools

• Matplotlib: The foundational plotting library in Python. It’s like the low-level drawing tool.
• Seaborn: Built on top of Matplotlib — more elegant, concise, and statistically aware.

Start by installing them (if not already):

bashCopyEditpip install matplotlib seaborn

Then import:

pythonCopyEditimport matplotlib.pyplot as plt
import seaborn as sns

Basic Plots with Matplotlib

Line Plot (Good for trends over time):

pythonCopyEditplt.plot([1, 2, 3], [10, 20, 15])
plt.title("Sample Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Bar Chart (Great for comparisons):

pythonCopyEditnames = ['Anay', 'Sara', 'John']
scores = [80, 95, 90]

plt.bar(names, scores)
plt.title("Test Scores")
plt.show()

Histogram (Shows distribution of a variable):

pythonCopyEditages = [22, 23, 22, 25, 26, 27, 22, 24]
plt.hist(ages, bins=5)
plt.title("Age Distribution")
plt.show()

Seaborn for Cleaner, Richer Visuals

Seaborn is perfect for faster, more attractive plots.

pythonCopyEditimport seaborn as sns
import pandas as pd

df = pd.DataFrame({
    'Name': ['Anay', 'Sara', 'John', 'Anay', 'Sara', 'John'],
    'Subject': ['Math', 'Math', 'Math', 'Science', 'Science', 'Science'],
    'Score': [90, 85, 78, 88, 91, 74]
})

Bar Plot:

pythonCopyEditsns.barplot(x='Name', y='Score', data=df)
plt.title("Average Score by Student")
plt.show()

Box Plot (Shows median, quartiles, and outliers):

pythonCopyEditsns.boxplot(x='Subject', y='Score', data=df)
plt.title("Score Distribution by Subject")
plt.show()

Scatter Plot (Shows relationships between two numeric variables):

pythonCopyEdittips = sns.load_dataset('tips')
sns.scatterplot(x='total_bill', y='tip', data=tips)
plt.title("Bill vs Tip")
plt.show()

When to Use What

Make Your Plots Beautiful

• Use plt.title(), plt.xlabel(), plt.ylabel() for clarity.
• Use sns.set_style("whitegrid") to improve aesthetics.
• Save plots using plt.savefig('filename.png').

Visualization isn’t just about decoration — it’s about communication. Focus on clarity, not clutter.

Real-World Use Case

Imagine you’re analyzing customer satisfaction scores across regions. Instead of just printing means, you:

• Use a bar plot to compare regions
• Use a box plot to identify outliers
• Use a heatmap to see correlation between satisfaction, sales, and repeat visits

With just a few lines of code, your insight becomes obvious.

Final Thoughts

A good visual tells a story before you say a word. Data scientists don’t just analyze — they present. Tools like Matplotlib and Seaborn are essential for that storytelling.

Next Up: Article 7 – Data Cleaning and Preprocessing Techniques