Introduction to Seaborn

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Global theme
sns.set_theme(style='whitegrid', palette='husl', font_scale=1.1)

# Built-in datasets
tips = sns.load_dataset('tips')
iris = sns.load_dataset('iris')
penguins = sns.load_dataset('penguins')

print(tips.head())
print(tips.dtypes)
print(tips['day'].value_counts())

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style='whitegrid')
tips = sns.load_dataset('tips')

fig, axes = plt.subplots(2, 3, figsize=(16, 10))

# 1: Bar plot (with CI)
sns.barplot(data=tips, x='day', y='total_bill', ax=axes[0,0],
             palette='Blues_d', errorbar='ci', capsize=0.1)
axes[0,0].set_title('Avg Bill by Day (with 95% CI)')

# 2: Count plot
sns.countplot(data=tips, x='day', hue='sex', ax=axes[0,1], palette='Set2')
axes[0,1].set_title('Customer Count by Day & Gender')

# 3: Box plot
sns.boxplot(data=tips, x='day', y='total_bill', hue='time',
             ax=axes[0,2], palette='Set3')
axes[0,2].set_title('Bill Distribution by Day & Time')

# 4: Violin plot
sns.violinplot(data=tips, x='day', y='total_bill', inner='quartile',
                ax=axes[1,0], palette='husl')
axes[1,0].set_title('Bill Distribution (Violin)')

# 5: Strip plot
sns.stripplot(data=tips, x='day', y='total_bill', jitter=True,
               alpha=0.5, ax=axes[1,1], palette='Set1')
axes[1,1].set_title('All Individual Bills (Jittered)')

# 6: Point plot
sns.pointplot(data=tips, x='day', y='total_bill', hue='sex',
               dodge=True, ax=axes[1,2], palette='deep')
axes[1,2].set_title('Mean Bill by Day & Gender')

for ax in axes.flatten():
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

plt.suptitle('Seaborn Categorical Plots', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig('seaborn_categorical.png', dpi=150)
plt.show()

# FacetGrid — create the same chart for each subset
g = sns.FacetGrid(tips, col='time', row='sex', height=4, aspect=1.2)
g.map(sns.scatterplot, 'total_bill', 'tip', alpha=0.6)
g.add_legend()
g.set_titles(col_template='{col_name}', row_template='{row_name}')
g.set_axis_labels('Total Bill ($)', 'Tip ($)')
g.figure.suptitle('Bill vs Tip by Time and Gender', y=1.02, fontsize=13, fontweight='bold')
plt.savefig('facetgrid.png', dpi=150, bbox_inches='tight')
plt.show()