Visualization

A collection of data visualization recipes using Matplotlib, Seaborn, and Plotly. Learn various visualization techniques from basic charts to interactive dashboards.

Library Comparison

Library	Features	When to Use
Matplotlib	Basic, fine-grained customization	Static charts for papers, reports
Seaborn	Specialized for statistical visualization, beautiful default styles	EDA, distribution/correlation analysis
Plotly	Interactive, web-based	Dashboards, presentations
Altair	Declarative grammar, concise	Quick exploratory analysis

Curriculum

1. Basic Charts

Beginner

Learn basic charts such as bar graphs, line charts, and scatter plots.

Bar Chart
Line Chart
Scatter Plot
Pie Chart
Histogram

Get Started with Basic Charts →

2. Heatmaps

Intermediate

Learn how to visualize correlations and time-based patterns using heatmaps.

Correlation Heatmap
Time x Day of Week Heatmap (Activity Patterns)
Pivot Table-based Heatmap
Clustermap

Get Started with Heatmaps →

3. Treemaps

Intermediate

Learn treemaps that represent hierarchical data using area.

Sales Share by Category
Hierarchical Structure Representation (Department > Team > Product)
Expressing Performance with Color (Growth Rate, etc.)
Using Plotly and Squarify

Get Started with Treemaps →

4. Sankey Diagrams

IntermediateAdvanced

Learn Sankey diagrams that visualize flow.

Marketing Funnel Visualization
Channel Conversion Flow
Customer Journey Analysis
Node and Link Configuration

Get Started with Sankey Diagrams →

5. Geospatial Visualization

IntermediateAdvanced

Learn map-based data visualization methods.

Interactive Maps with Folium
Choropleth Maps
Markers and Popups
Location-based Clustering

Get Started with Geospatial Visualization →

6. Animated Charts

Advanced

Learn how to express changes over time through animation.

Matplotlib Animation
Plotly Express Animation
Ranking Changes Over Time (Racing Bar Chart)
Exporting to GIF/MP4

Get Started with Animated Charts →

7. Interactive Dashboards

Advanced

Learn to build interactive dashboards using Plotly and Streamlit.

Plotly Dash Basics
Streamlit Dashboard
Filters and Callbacks
Real-time Data Integration

Get Started with Interactive Dashboards →

Chart Selection Guide

ℹ️

Which chart should you choose?

For Comparison:

Category comparison → Bar Chart
Changes over time → Line Chart
Ranking comparison → Horizontal Bar Chart

For Distribution:

Single variable distribution → Histogram, Box Plot
Relationship between two variables → Scatter Plot
Correlation matrix → Heatmap

For Composition:

Proportion of total → Pie Chart, Treemap
Sum of parts → Stacked Bar Chart
Flow/Conversion → Sankey Diagram

For Geography:

Regional values → Choropleth
Location markers → Marker Map
Density → Heatmap Map

Basic Setup Code


import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
 
# Matplotlib Korean font settings
plt.rcParams['font.family'] = 'AppleGothic'  # macOS
# plt.rcParams['font.family'] = 'Malgun Gothic'  # Windows
# plt.rcParams['font.family'] = 'NanumGothic'  # Linux
plt.rcParams['axes.unicode_minus'] = False
 
# Default style settings
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette('husl')
plt.rcParams['figure.figsize'] = (12, 6)
 
# Plotly default template
import plotly.io as pio
pio.templates.default = 'plotly_white'

Recommended Color Palettes

Purpose	Palette	Code
Categorical	tab10	`sns.color_palette('tab10')`
Sequential	Blues	`sns.color_palette('Blues')`
Diverging	RdYlGn	`sns.color_palette('RdYlGn')`
Emphasis	Set2	`sns.color_palette('Set2')`