Seaborn - Scatter Plots

📝 Overview

Scatter plots help visualize the relationship between two numerical variables. Seaborn provides various enhancements like color grouping (hue), size variation (size), and different marker styles (style).

📌 Loading Data

We’ll use Pandas to load a dataset.

import pandas as pd
import seaborn as sns
 
# Load dataset
df = pd.read_csv("dm_office_sales.csv")
 
# Display basic info
df.head()
df.info()

📈 Basic Scatter Plot

sns.scatterplot(x='salary', y='sales', data=df)

🔍 Matplotlib Integration

Even though Seaborn builds on Matplotlib, we can still modify figures using matplotlib.pyplot:

import matplotlib.pyplot as plt
 
plt.figure(figsize=(12,8))
sns.scatterplot(x='salary', y='sales', data=df)
plt.show()

🎨 Seaborn Parameters

1️⃣ hue (Color by Category)

Use hue to color points based on a categorical feature:

plt.figure(figsize=(12,8))
sns.scatterplot(x='salary', y='sales', data=df, hue='division')
 
plt.figure(figsize=(12,8))
sns.scatterplot(x='salary', y='sales', data=df, hue='work experience')

🎨 Custom Color Palette

You can specify a color palette using Matplotlib colormaps:

plt.figure(figsize=(12,8))
sns.scatterplot(x='salary', y='sales', data=df, hue='work experience', palette='viridis')

2️⃣ size (Varying Marker Size)

Adjust the size of markers based on another column:

plt.figure(figsize=(12,8))
sns.scatterplot(x='salary', y='sales', data=df, size='work experience')

🔹 Uniform marker size:
Use s= to apply a constant marker size:

plt.figure(figsize=(12,8))
sns.scatterplot(x='salary', y='sales', data=df, s=200)

🔹 Customize marker transparency & border:

plt.figure(figsize=(12,8))
sns.scatterplot(x='salary', y='sales', data=df, s=200, linewidth=0, alpha=0.2)

3️⃣ style (Different Marker Shapes)

Change marker styles based on a categorical feature:

plt.figure(figsize=(12,8))
sns.scatterplot(x='salary', y='sales', data=df, style='level of education')

🔹 Combine hue and style for better distinction:

plt.figure(figsize=(12,8))
sns.scatterplot(x='salary', y='sales', data=df, style='level of education', hue='level of education', s=100)

🔹 Customize markers manually:

plt.figure(figsize=(12,8))
sns.scatterplot(x='salary', y='sales', data=df, style='level of education', markers=['*', '+', 'o'])

📤 Exporting a Seaborn Figure

To save your scatter plot:

plt.figure(figsize=(12,8))
sns.scatterplot(x='salary', y='sales', data=df, style='level of education', hue='level of education', s=100)
 
# Save figure
plt.savefig('example_scatter.jpg')

🪴LYC