Table of Contents

Overview

Seaborn is a powerful Python data visualization library built on top of matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It comes with several built-in themes, color palettes, and plot types designed to make complex visualizations more accessible and aesthetically pleasing.

Key Features

  • High-level interface for complex visualizations with minimal code
  • Built-in statistical estimation and plotting functions
  • Beautiful default themes and color palettes
  • Tight integration with pandas DataFrames
  • Support for multi-plot grids and complex layouts
  • Automatic statistical aggregation for categorical data

When to Use Seaborn

  • Creating statistical visualizations quickly
  • Exploring relationships in datasets
  • Visualizing distributions and categorical data
  • Building publication-quality graphics
  • Working with pandas DataFrames
  • Needing automatic statistical estimation

Installation

Basic Installation

pip install seaborn

Installation with Optional Dependencies

pip install seaborn[all]

Verify Installation

import seaborn as sns
print(sns.__version__)

Getting Started

Basic Imports

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

# Set default theme
sns.set_theme()

Loading Sample Datasets

Seaborn includes several built-in datasets for learning and testing:

# Available datasets
print(sns.get_dataset_names())

# Load a dataset
tips = sns.load_dataset('tips')
iris = sns.load_dataset('iris')
titanic = sns.load_dataset('titanic')
diamonds = sns.load_dataset('diamonds')

Basic Plotting Workflow

# Create a simple scatter plot
sns.scatterplot(data=tips, x='total_bill', y='tip', hue='time')
plt.title('Tips vs Total Bill')
plt.show()

Distribution Plots

Distribution plots help visualize the distribution of data values.

Histogram with KDE

# Basic histogram
sns.histplot(data=tips, x='total_bill')

# Histogram with KDE overlay
sns.histplot(data=tips, x='total_bill', kde=True)

# Multiple distributions
sns.histplot(data=tips, x='total_bill', hue='time', multiple='stack')

# With custom bins
sns.histplot(data=tips, x='total_bill', bins=20, kde=True)

KDE Plot

# Kernel Density Estimation plot
sns.kdeplot(data=tips, x='total_bill')

# Bivariate KDE
sns.kdeplot(data=tips, x='total_bill', y='tip')

# Multiple distributions
sns.kdeplot(data=tips, x='total_bill', hue='time', fill=True)

# Adjust bandwidth
sns.kdeplot(data=tips, x='total_bill', bw_adjust=0.5)

Distribution Plot (displot)

The displot() function is a figure-level function for distribution visualization:

# Basic distribution plot
sns.displot(data=tips, x='total_bill', kde=True)

# Multiple plots by category
sns.displot(data=tips, x='total_bill', col='time', row='sex')

# ECDF (Empirical Cumulative Distribution Function)
sns.displot(data=tips, x='total_bill', kind='ecdf')

# Rug plot addition
sns.displot(data=tips, x='total_bill', kde=True, rug=True)

Box Plot

# Basic box plot
sns.boxplot(data=tips, x='day', y='total_bill')

# Horizontal box plot
sns.boxplot(data=tips, x='total_bill', y='day')

# Multiple categories
sns.boxplot(data=tips, x='day', y='total_bill', hue='time')

# Custom width
sns.boxplot(data=tips, x='day', y='total_bill', width=0.5)

Violin Plot

# Basic violin plot
sns.violinplot(data=tips, x='day', y='total_bill')

# Split violin for comparison
sns.violinplot(data=tips, x='day', y='total_bill', hue='sex', split=True)

# Inner representation options
sns.violinplot(data=tips, x='day', y='total_bill', inner='box')
sns.violinplot(data=tips, x='day', y='total_bill', inner='quartile')
sns.violinplot(data=tips, x='day', y='total_bill', inner='point')

Box-Violin Combination

# Violin with box plot inside
fig, ax = plt.subplots(figsize=(10, 6))
sns.violinplot(data=tips, x='day', y='total_bill', ax=ax, color='lightgray')
sns.boxplot(data=tips, x='day', y='total_bill', ax=ax, width=0.3)
plt.show()

Categorical Plots

Categorical plots show relationships between categorical and numerical variables.

Bar Plot

# Basic bar plot (shows mean by default)
sns.barplot(data=tips, x='day', y='total_bill')

# Custom estimator
sns.barplot(data=tips, x='day', y='total_bill', estimator=sum)
sns.barplot(data=tips, x='day', y='total_bill', estimator=np.median)

# With error bars
sns.barplot(data=tips, x='day', y='total_bill', errorbar='sd')

# Multiple categories
sns.barplot(data=tips, x='day', y='total_bill', hue='sex')

Count Plot

# Count occurrences
sns.countplot(data=tips, x='day')

# Horizontal count plot
sns.countplot(data=tips, y='day')

# Multiple categories
sns.countplot(data=tips, x='day', hue='sex')

# Order categories
sns.countplot(data=tips, x='day', order=['Thur', 'Fri', 'Sat', 'Sun'])

Point Plot

# Point plot with error bars
sns.pointplot(data=tips, x='day', y='total_bill')

# Multiple categories
sns.pointplot(data=tips, x='day', y='total_bill', hue='sex')

# Custom markers and line styles
sns.pointplot(data=tips, x='day', y='total_bill', markers=['o', 's'], 
              linestyles=['-', '--'])

Strip Plot

# Show all points
sns.stripplot(data=tips, x='day', y='total_bill')

# Add jitter to avoid overlap
sns.stripplot(data=tips, x='day', y='total_bill', jitter=True)

# Combine with violin plot
sns.violinplot(data=tips, x='day', y='total_bill', color='lightgray')
sns.stripplot(data=tips, x='day', y='total_bill', color='black', alpha=0.3)

Swarm Plot

# Non-overlapping points
sns.swarmplot(data=tips, x='day', y='total_bill')

# Multiple categories
sns.swarmplot(data=tips, x='day', y='total_bill', hue='sex')

# Combine with box plot
sns.boxplot(data=tips, x='day', y='total_bill', color='lightgray')
sns.swarmplot(data=tips, x='day', y='total_bill', color='black', size=3)

Categorical Plot (catplot)

Figure-level function for categorical data:

# Basic categorical plot
sns.catplot(data=tips, x='day', y='total_bill', kind='box')

# Multiple plots
sns.catplot(data=tips, x='day', y='total_bill', col='time', kind='violin')

# Different plot types
sns.catplot(data=tips, x='day', y='total_bill', kind='bar')
sns.catplot(data=tips, x='day', y='total_bill', kind='strip')
sns.catplot(data=tips, x='day', y='total_bill', kind='swarm')

Regression Plots

Visualize relationships and trends between variables.

Scatter Plot with Regression

# Basic regression plot
sns.regplot(data=tips, x='total_bill', y='tip')

# Polynomial regression
sns.regplot(data=tips, x='total_bill', y='tip', order=2)

# Logistic regression
sns.regplot(data=tips, x='total_bill', y='tip', logistic=True)

# Robust regression
sns.regplot(data=tips, x='total_bill', y='tip', robust=True)

# Custom scatter and line properties
sns.regplot(data=tips, x='total_bill', y='tip', 
            scatter_kws={'alpha': 0.5, 's': 50},
            line_kws={'color': 'red', 'linewidth': 2})

LM Plot (Figure-level)

# Basic linear model plot
sns.lmplot(data=tips, x='total_bill', y='tip')

# Multiple regression lines
sns.lmplot(data=tips, x='total_bill', y='tip', hue='smoker')

# Separate plots
sns.lmplot(data=tips, x='total_bill', y='tip', col='time')

# Grid of plots
sns.lmplot(data=tips, x='total_bill', y='tip', col='time', row='sex')

# No confidence interval
sns.lmplot(data=tips, x='total_bill', y='tip', ci=None)

Residual Plot

# Check regression assumptions
sns.residplot(data=tips, x='total_bill', y='tip')

# Lowess smoothing
sns.residplot(data=tips, x='total_bill', y='tip', lowess=True)

Relational Plots

Show relationships between variables.

Scatter Plot

# Basic scatter plot
sns.scatterplot(data=tips, x='total_bill', y='tip')

# Multiple categories with color
sns.scatterplot(data=tips, x='total_bill', y='tip', hue='time')

# Size by variable
sns.scatterplot(data=tips, x='total_bill', y='tip', size='size')

# Style by variable
sns.scatterplot(data=tips, x='total_bill', y='tip', style='time')

# Combine all aesthetics
sns.scatterplot(data=tips, x='total_bill', y='tip', 
                hue='time', size='size', style='sex')

# Custom palette
sns.scatterplot(data=tips, x='total_bill', y='tip', 
                hue='time', palette='Set2')

Line Plot

# Basic line plot
data = pd.DataFrame({
    'time': range(100),
    'value': np.cumsum(np.random.randn(100))
})
sns.lineplot(data=data, x='time', y='value')

# Multiple lines
sns.lineplot(data=tips, x='size', y='total_bill', hue='time')

# Confidence interval
sns.lineplot(data=tips, x='size', y='total_bill', ci='sd')

# Multiple estimators
sns.lineplot(data=tips, x='size', y='total_bill', estimator=np.median)

Relational Plot (relplot)

# Figure-level scatter plot
sns.relplot(data=tips, x='total_bill', y='tip', hue='time')

# Multiple plots
sns.relplot(data=tips, x='total_bill', y='tip', col='time', row='sex')

# Line plot
sns.relplot(data=tips, x='size', y='total_bill', kind='line')

# Faceted plots
sns.relplot(data=tips, x='total_bill', y='tip', 
            col='time', hue='smoker', style='sex')

Matrix Plots

Visualize matrices and correlations.

Heatmap

# Correlation matrix
corr = tips.corr(numeric_only=True)
sns.heatmap(corr)

# With annotations
sns.heatmap(corr, annot=True, fmt='.2f')

# Custom color map
sns.heatmap(corr, cmap='coolwarm', center=0)

# Square cells
sns.heatmap(corr, square=True, linewidths=1)

# Pivot table heatmap
pivot = tips.pivot_table(values='tip', index='day', columns='time')
sns.heatmap(pivot, annot=True)

Cluster Map

# Hierarchical clustering
iris_data = iris.drop('species', axis=1)
sns.clustermap(iris_data.corr())

# Custom clustering
sns.clustermap(iris_data.T, method='average', metric='euclidean',
               cmap='viridis', standard_scale=1)

# No row/column dendrogram
sns.clustermap(iris_data.corr(), row_cluster=False, col_cluster=False)

Pair Plots

Visualize pairwise relationships in a dataset.

Basic Pair Plot

# All pairwise relationships
sns.pairplot(iris)

# Colored by category
sns.pairplot(iris, hue='species')

# Custom plot types
sns.pairplot(iris, hue='species', diag_kind='kde')

# Subset of variables
sns.pairplot(iris, vars=['sepal_length', 'sepal_width'], hue='species')

Pair Grid

More control over pair plots:

# Create grid
g = sns.PairGrid(iris, hue='species')

# Map different plots
g.map_upper(sns.scatterplot)
g.map_lower(sns.kdeplot)
g.map_diag(sns.histplot)
g.add_legend()

# Custom mapping
g = sns.PairGrid(iris)
g.map_diag(plt.hist)
g.map_offdiag(plt.scatter)

Joint Plots

Combine bivariate and univariate plots.

# Basic joint plot
sns.jointplot(data=tips, x='total_bill', y='tip')

# Hexbin
sns.jointplot(data=tips, x='total_bill', y='tip', kind='hex')

# KDE
sns.jointplot(data=tips, x='total_bill', y='tip', kind='kde')

# Regression
sns.jointplot(data=tips, x='total_bill', y='tip', kind='reg')

# Multiple categories
sns.jointplot(data=tips, x='total_bill', y='tip', hue='time')

Multi-Plot Grids

Create complex multi-plot layouts.

FacetGrid

# Create grid
g = sns.FacetGrid(tips, col='time', row='sex')
g.map(sns.scatterplot, 'total_bill', 'tip')

# With categories
g = sns.FacetGrid(tips, col='time', hue='smoker')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()

# Custom layout
g = sns.FacetGrid(tips, col='day', col_wrap=2, height=3)
g.map(sns.histplot, 'total_bill', kde=True)

# Multiple plots per facet
g = sns.FacetGrid(tips, col='time')
g.map(sns.scatterplot, 'total_bill', 'tip', alpha=0.5)
g.map(sns.regplot, 'total_bill', 'tip', scatter=False, color='red')

Styling and Aesthetics

Themes

Seaborn provides five preset themes:

# Available themes: darkgrid, whitegrid, dark, white, ticks
sns.set_theme(style='darkgrid')
sns.set_theme(style='whitegrid')
sns.set_theme(style='dark')
sns.set_theme(style='white')
sns.set_theme(style='ticks')

# Remove spines
sns.set_theme(style='white')
sns.despine()  # Remove top and right spines
sns.despine(left=True)  # Remove left spine too

Contexts

Adjust plot elements for different uses:

# Available contexts: paper, notebook, talk, poster
sns.set_context('paper')      # Smallest
sns.set_context('notebook')   # Default
sns.set_context('talk')       # Presentations
sns.set_context('poster')     # Largest

# Custom scaling
sns.set_context('notebook', font_scale=1.5)

# Fine-grained control
sns.set_context('notebook', rc={'lines.linewidth': 2.5})

Color Palettes

# Categorical palettes
sns.set_palette('deep')
sns.set_palette('muted')
sns.set_palette('pastel')
sns.set_palette('bright')
sns.set_palette('dark')
sns.set_palette('colorblind')

# Sequential palettes
sns.set_palette('Blues')
sns.set_palette('YlOrRd')

# Diverging palettes
sns.set_palette('RdBu')
sns.set_palette('coolwarm')

# View palette
sns.palplot(sns.color_palette('husl', 8))

# Custom palette
custom_palette = ['#FF6B6B', '#4ECDC4', '#45B7D1']
sns.set_palette(custom_palette)

# Create color palette
palette = sns.color_palette('viridis', n_colors=10)
palette = sns.light_palette('green', n_colors=8)
palette = sns.dark_palette('purple', n_colors=8)
palette = sns.diverging_palette(220, 20, n=7)

Palette Functions

# Cubehelix palette
sns.cubehelix_palette(start=0, rot=0.4, dark=0.3, light=0.8, n_colors=8)

# Categorical palette
sns.color_palette('Set2')

# Perceptually uniform
sns.color_palette('rocket')
sns.color_palette('mako')

Setting Palette in Context

# Temporary palette
with sns.color_palette('husl'):
    sns.boxplot(data=tips, x='day', y='total_bill')

Customization

Figure Size and DPI

# Set figure size
sns.set_theme(rc={'figure.figsize': (12, 8)})

# Set DPI
sns.set_theme(rc={'figure.dpi': 100})

# For specific plot
plt.figure(figsize=(10, 6))
sns.scatterplot(data=tips, x='total_bill', y='tip')

Font Properties

# Font size
sns.set_theme(font_scale=1.2)

# Font family
sns.set_theme(rc={'font.family': 'serif'})

# Title and labels
plt.figure(figsize=(10, 6))
sns.scatterplot(data=tips, x='total_bill', y='tip')
plt.title('Tips vs Total Bill', fontsize=16, fontweight='bold')
plt.xlabel('Total Bill ($)', fontsize=12)
plt.ylabel('Tip ($)', fontsize=12)

Axes Properties

# Grid
sns.set_theme(style='whitegrid')
g = sns.scatterplot(data=tips, x='total_bill', y='tip')
g.grid(True, alpha=0.3)

# Limits
plt.xlim(0, 60)
plt.ylim(0, 12)

# Ticks
plt.xticks(rotation=45)
plt.yticks(range(0, 13, 2))

Legend

# Move legend
plt.legend(loc='upper left')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

# Remove legend
plt.legend([],[], frameon=False)

# Custom legend
plt.legend(title='Time of Day', title_fontsize=12, fontsize=10)

Advanced Techniques

Statistical Annotations

from scipy import stats

# Add statistical test result
fig, ax = plt.subplots()
sns.boxplot(data=tips, x='time', y='total_bill', ax=ax)

# Perform t-test
lunch = tips[tips['time'] == 'Lunch']['total_bill']
dinner = tips[tips['time'] == 'Dinner']['total_bill']
t_stat, p_value = stats.ttest_ind(lunch, dinner)

ax.text(0.5, 45, f'p-value: {p_value:.4f}', ha='center')

Custom Color Mapping

# Create custom color mapping
color_map = {'Lunch': 'skyblue', 'Dinner': 'coral'}
sns.scatterplot(data=tips, x='total_bill', y='tip', 
                hue='time', palette=color_map)

Combining Multiple Plots

# Create subplots
fig, axes = plt.subplots(2, 2, figsize=(12, 10))

sns.histplot(data=tips, x='total_bill', ax=axes[0, 0])
sns.boxplot(data=tips, x='day', y='total_bill', ax=axes[0, 1])
sns.scatterplot(data=tips, x='total_bill', y='tip', ax=axes[1, 0])
sns.violinplot(data=tips, x='day', y='total_bill', ax=axes[1, 1])

plt.tight_layout()
plt.show()

Custom Aggregation

# Custom aggregation function
def percentile_75(x):
    return np.percentile(x, 75)

sns.barplot(data=tips, x='day', y='total_bill', estimator=percentile_75)

Adding Annotations

# Annotate specific points
fig, ax = plt.subplots()
sns.scatterplot(data=tips, x='total_bill', y='tip', ax=ax)

# Find and annotate max tip
max_tip_idx = tips['tip'].idxmax()
max_tip_row = tips.loc[max_tip_idx]
ax.annotate('Highest tip', 
            xy=(max_tip_row['total_bill'], max_tip_row['tip']),
            xytext=(10, 10), textcoords='offset points',
            arrowprops=dict(arrowstyle='->', color='red'))

Performance Optimization

Sampling Large Datasets

# Sample data for faster plotting
large_data = pd.DataFrame({
    'x': np.random.randn(100000),
    'y': np.random.randn(100000)
})

# Plot sample
sample = large_data.sample(n=10000)
sns.scatterplot(data=sample, x='x', y='y', alpha=0.3)

Using Binning

# Hexbin for large datasets
sns.jointplot(data=large_data, x='x', y='y', kind='hex')

# 2D histogram
sns.histplot(data=large_data, x='x', y='y', bins=50, cmap='Blues')

Common Patterns

Time Series Visualization

# Create time series data
dates = pd.date_range('2025-01-01', periods=100, freq='D')
values = np.cumsum(np.random.randn(100))
ts_data = pd.DataFrame({'date': dates, 'value': values})

# Plot time series
plt.figure(figsize=(12, 6))
sns.lineplot(data=ts_data, x='date', y='value')
plt.xticks(rotation=45)
plt.title('Time Series Plot')
plt.tight_layout()

Distribution Comparison

# Compare multiple distributions
fig, axes = plt.subplots(1, 2, figsize=(14, 6))

sns.violinplot(data=tips, x='day', y='total_bill', ax=axes[0])
axes[0].set_title('Violin Plot')

sns.boxplot(data=tips, x='day', y='total_bill', ax=axes[1])
axes[1].set_title('Box Plot')

plt.tight_layout()

Correlation Analysis

# Create correlation heatmap with significance
fig, ax = plt.subplots(figsize=(10, 8))
corr = tips.corr(numeric_only=True)
mask = np.triu(np.ones_like(corr, dtype=bool))
sns.heatmap(corr, mask=mask, annot=True, fmt='.2f', 
            cmap='coolwarm', center=0, square=True, ax=ax)
plt.title('Correlation Matrix')

Best Practices

Data Preparation

# Clean data before plotting
tips_clean = tips.dropna()
tips_clean = tips_clean[tips_clean['total_bill'] > 0]

# Create meaningful categories
tips_clean['tip_percent'] = tips_clean['tip'] / tips_clean['total_bill'] * 100

Choosing Plot Types

  • Distribution: histplot, kdeplot, boxplot, violinplot
  • Categorical: barplot, countplot, pointplot, stripplot
  • Relational: scatterplot, lineplot
  • Regression: regplot, lmplot
  • Matrix: heatmap, clustermap

Figure-level vs Axes-level Functions

Figure-level functions (displot, catplot, relplot, lmplot, jointplot, pairplot):

  • Return FacetGrid object
  • Create entire figure
  • Support faceting with col, row
  • Better for multi-plot layouts

Axes-level functions (histplot, boxplot, scatterplot, etc.):

  • Work with matplotlib axes
  • More flexible for custom layouts
  • Can be combined in subplots
  • Better integration with matplotlib
# Figure-level
g = sns.displot(data=tips, x='total_bill', col='time')

# Axes-level
fig, ax = plt.subplots()
sns.histplot(data=tips, x='total_bill', ax=ax)

Saving Plots

# Save figure-level plot
g = sns.relplot(data=tips, x='total_bill', y='tip')
g.savefig('plot.png', dpi=300, bbox_inches='tight')

# Save axes-level plot
plt.figure(figsize=(10, 6))
sns.scatterplot(data=tips, x='total_bill', y='tip')
plt.savefig('plot.pdf', bbox_inches='tight')

Troubleshooting

Common Issues

Issue: Overlapping labels

# Solution: Rotate labels
plt.xticks(rotation=45, ha='right')

# Or use smaller font
sns.set_context('paper')

Issue: Legend outside plot area

# Solution: Adjust layout
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()

Issue: Colors not distinguishable

# Solution: Use colorblind-friendly palette
sns.set_palette('colorblind')

Issue: Slow plotting with large datasets

# Solution: Sample or use appropriate plot type
sample_data = large_data.sample(n=10000)
sns.scatterplot(data=sample_data, x='x', y='y')

# Or use hexbin/2D histogram
sns.jointplot(data=large_data, x='x', y='y', kind='hex')

Integration with Other Libraries

Matplotlib Integration

# Combine seaborn and matplotlib
fig, axes = plt.subplots(1, 2, figsize=(12, 5))

sns.boxplot(data=tips, x='day', y='total_bill', ax=axes[0])
axes[0].axhline(y=tips['total_bill'].mean(), color='r', linestyle='--')
axes[0].set_title('With Mean Line')

sns.violinplot(data=tips, x='day', y='total_bill', ax=axes[1])
axes[1].set_ylabel('Total Bill ($)')

plt.tight_layout()

Pandas Integration

# Plot directly from pandas
tips.plot(kind='scatter', x='total_bill', y='tip', alpha=0.5)

# With seaborn styling
sns.set_theme()
tips.plot(kind='scatter', x='total_bill', y='tip')

SciPy Integration

from scipy import stats

# Statistical tests with visualization
fig, ax = plt.subplots()
sns.regplot(data=tips, x='total_bill', y='tip', ax=ax)

# Add correlation
r, p = stats.pearsonr(tips['total_bill'], tips['tip'])
ax.text(0.05, 0.95, f'r={r:.3f}, p={p:.4f}', 
        transform=ax.transAxes, verticalalignment='top')

Examples and Use Cases

Publication-Quality Figure

# Set publication style
sns.set_theme(style='white', context='paper', font_scale=1.2)

fig, ax = plt.subplots(figsize=(6, 4))
sns.scatterplot(data=tips, x='total_bill', y='tip', 
                hue='time', style='sex', s=100, alpha=0.7, ax=ax)

ax.set_xlabel('Total Bill ($)', fontweight='bold')
ax.set_ylabel('Tip ($)', fontweight='bold')
ax.set_title('Restaurant Tips Analysis', fontsize=14, fontweight='bold')
ax.legend(title='Category', title_fontsize=10)
sns.despine()

plt.tight_layout()
plt.savefig('publication_figure.pdf', dpi=300, bbox_inches='tight')

Dashboard Layout

# Create dashboard-style layout
fig = plt.figure(figsize=(16, 10))
gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3)

# Summary statistics
ax1 = fig.add_subplot(gs[0, :])
summary_data = tips.groupby('day')['total_bill'].agg(['mean', 'std', 'count'])
ax1.axis('tight')
ax1.axis('off')
ax1.table(cellText=summary_data.values, colLabels=summary_data.columns,
          rowLabels=summary_data.index, loc='center')

# Distribution plots
ax2 = fig.add_subplot(gs[1, 0])
sns.histplot(data=tips, x='total_bill', kde=True, ax=ax2)
ax2.set_title('Bill Distribution')

ax3 = fig.add_subplot(gs[1, 1])
sns.boxplot(data=tips, x='day', y='total_bill', ax=ax3)
ax3.set_title('Bills by Day')

ax4 = fig.add_subplot(gs[1, 2])
sns.violinplot(data=tips, x='time', y='total_bill', ax=ax4)
ax4.set_title('Bills by Time')

# Relationship plots
ax5 = fig.add_subplot(gs[2, :2])
sns.scatterplot(data=tips, x='total_bill', y='tip', hue='time', size='size', ax=ax5)
ax5.set_title('Tips vs Bills')

ax6 = fig.add_subplot(gs[2, 2])
corr = tips[['total_bill', 'tip', 'size']].corr()
sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm', ax=ax6)
ax6.set_title('Correlations')

plt.suptitle('Restaurant Tips Dashboard', fontsize=16, fontweight='bold')

Exploratory Data Analysis

# Comprehensive EDA
def explore_dataset(df, target=None):
    """Comprehensive visualization of dataset"""
    
    # Numerical columns
    num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
    cat_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()
    
    # Distribution of numerical variables
    if len(num_cols) > 0:
        fig, axes = plt.subplots(len(num_cols), 2, figsize=(12, 4*len(num_cols)))
        for i, col in enumerate(num_cols):
            if len(num_cols) == 1:
                ax1, ax2 = axes[0], axes[1]
            else:
                ax1, ax2 = axes[i, 0], axes[i, 1]
            
            sns.histplot(data=df, x=col, kde=True, ax=ax1)
            ax1.set_title(f'{col} Distribution')
            
            sns.boxplot(data=df, y=col, ax=ax2)
            ax2.set_title(f'{col} Box Plot')
        
        plt.tight_layout()
        plt.show()
    
    # Categorical distributions
    if len(cat_cols) > 0:
        fig, axes = plt.subplots(1, len(cat_cols), figsize=(5*len(cat_cols), 4))
        if len(cat_cols) == 1:
            axes = [axes]
        
        for i, col in enumerate(cat_cols):
            sns.countplot(data=df, x=col, ax=axes[i])
            axes[i].set_title(f'{col} Distribution')
            axes[i].tick_params(axis='x', rotation=45)
        
        plt.tight_layout()
        plt.show()
    
    # Correlation matrix
    if len(num_cols) > 1:
        plt.figure(figsize=(10, 8))
        sns.heatmap(df[num_cols].corr(), annot=True, fmt='.2f', 
                    cmap='coolwarm', center=0, square=True)
        plt.title('Correlation Matrix')
        plt.tight_layout()
        plt.show()
    
    # Pairplot if target specified
    if target and target in df.columns:
        sns.pairplot(df, hue=target, diag_kind='kde')
        plt.show()

# Use the function
explore_dataset(tips, target='time')

Resources and Further Learning

Official Documentation

Key Concepts

  • Statistical graphics
  • Data visualization
  • Exploratory data analysis
  • Publication-quality plots
  • Color theory and palettes
  • Figure composition
  • Matplotlib fundamentals
  • Pandas data manipulation
  • NumPy arrays
  • Statistical analysis
  • Data science workflow

See Also

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