Data Visualization

Visualization is how we communicate insights. We'll use Matplotlib (the foundation) and Seaborn (the beautifier).

1. Matplotlib Basics 📉

Matplotlib is the most popular plotting library in Python.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 25, 30, 50]

# 1. Create a figure
plt.figure(figsize=(8, 5))

# 2. Plot data
plt.plot(x, y, color='blue', marker='o', linestyle='--')

# 3. Add labels
plt.title("Growth Over Time")
plt.xlabel("Days")
plt.ylabel("Users")

# 4. Show plot
plt.show()

2. Common Plot Types 📊

| Plot Type | Use Case | Function | | :--- | :--- | :--- | | Line Plot | Trends over time | plt.plot() | | Bar Chart | Comparing categories | plt.bar() | | Scatter Plot | Relationship between 2 variables | plt.scatter() | | Histogram | Distribution of data | plt.hist() |

Scatter Plot Example

height = [160, 170, 180, 165, 175]
weight = [60, 70, 80, 65, 75]

plt.scatter(height, weight)
plt.title("Height vs Weight")
plt.xlabel("Height (cm)")
plt.ylabel("Weight (kg)")
plt.show()

3. Introduction to Seaborn 🌊

Seaborn is built on top of Matplotlib. It makes complex plots easier and prettier.

import seaborn as sns
import pandas as pd

# Load built-in dataset
tips = sns.load_dataset("tips")

# Create a boxplot
sns.boxplot(x="day", y="total_bill", data=tips)
plt.title("Bill Distribution by Day")
plt.show()

Heatmaps

Great for showing correlation matrices.

corr = tips.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.show()

Interactive Challenge: The Artist

Create a visualization for this sales data.

Quiz

Key Takeaways

✅ Matplotlib gives you full control over every pixel.
✅ Seaborn makes statistical plots (like heatmaps) easy.
✅ Choose the right plot: Line for time, Bar for categories, Scatter for relationships.

What's Next?

We have the tools. Now let's put it all together to analyze a dataset from start to finish.

Next Chapter: Exploratory Data Analysis (EDA).