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Data Visualization

Intermediate
18 min

What is Data Visualization?

Create charts and graphs using Python libraries like matplotlib and seaborn, making data insights clear.

Key Components:

  • Matplotlib for basic plots
  • Seaborn for statistical visuals
  • Highly customizable
  • Save as images

Why it matters

Data Exploration

Visualize patterns quickly

Presentation

Create professional charts

Analysis

Identify outliers and trends

Reporting

Include in reports and presentations

Key Concepts

Matplotlib

Base plotting library

Example: import matplotlib.pyplot as plt...

Line Plot

Show trends over time

Example: plt.plot(x, y)...

Bar Plot

Compare categories

Example: plt.bar(categories, values)...

Seaborn

Statistical visuals

Example: sns.barplot(data=df, x="Product", y="Sales")...

How to use

1

Import libraries

import matplotlib.pyplot as plt

2

Prepare data

Get x and y values

3

Create plot

plt.plot(x, y) or plt.bar()

4

Customize

Add title, labels, legend

5

Show/save

plt.show() or plt.savefig()

6

Close

plt.close() to free memory

Example

Goal: Line chart of monthly sales
months = ["Jan", "Feb", "Mar"]
sales = [100, 150, 120]
plt.plot(months, sales)
plt.title("Monthly Sales")
plt.show()
Result: Line chart showing sales trend

Pro Tips

  • •Use figsize: plt.figure(figsize=(10,6))
  • •Add grid: plt.grid(True)
  • •Save before show: plt.savefig() before plt.show()

Practice

Create a bar chart showing sales by product category with custom colors
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