What are you showing?
│
├── Comparison
│   ├── Few categories → Bar chart
│   ├── Many categories → Horizontal bar
│   └── Over time → Line chart
│
├── Distribution
│   ├── Single variable → Histogram
│   ├── Compare distributions → Box plot
│   └── Density → Violin plot
│
├── Relationship
│   ├── Two variables → Scatter plot
│   ├── With categories → Colored scatter
│   └── Many variables → Pair plot
│
├── Composition
│   ├── Parts of whole → Pie (sparingly) / Stacked bar
│   ├── Over time → Stacked area
│   └── Hierarchical → Treemap
│
└── Trend
    ├── Time series → Line chart
    ├── With uncertainty → Line + confidence band
    └── Multiple series → Multi-line / Small multiples

1. Matplotlib Essentials

{% if library == "matplotlib" %}

Basic Setup

import matplotlib.pyplot as plt
import numpy as np

# Set style
plt.style.use('seaborn-v0_8-whitegrid')

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

# DPI for crisp output
fig, ax = plt.subplots(figsize=(10, 6), dpi=100)

Line Charts

# Basic line chart
fig, ax = plt.subplots(figsize=(10, 6))

ax.plot(dates, values, label='Sales', color='#2563eb', linewidth=2)
ax.fill_between(dates, values, alpha=0.1, color='#2563eb')

ax.set_xlabel('Date')
ax.set_ylabel('Sales ($)')
ax.set_title('Monthly Sales Trend')
ax.legend()

# Rotate x-axis labels
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('sales_trend.png', bbox_inches='tight')

Bar Charts

# Grouped bar chart
categories = ['Q1', 'Q2', 'Q3', 'Q4']
product_a = [100, 120, 90, 150]
product_b = [80, 100, 110, 130]

x = np.arange(len(categories))
width = 0.35

fig, ax = plt.subplots(figsize=(10, 6))

bars1 = ax.bar(x - width/2, product_a, width, label='Product A', color='#2563eb')
bars2 = ax.bar(x + width/2, product_b, width, label='Product B', color='#16a34a')

ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.legend()

# Add value labels
for bar in bars1:
    height = bar.get_height()
    ax.annotate(f'{height}',
                xy=(bar.get_x() + bar.get_width()/2, height),
                ha='center', va='bottom')

Scatter Plots

fig, ax = plt.subplots(figsize=(10, 8))

# Scatter with size and color
scatter = ax.scatter(
    df['x'],
    df['y'],
    s=df['size'] * 10,          # Point size
    c=df['category'].astype('category').cat.codes,  # Color by category
    cmap='viridis',
    alpha=0.7
)

ax.set_xlabel('X Variable')
ax.set_ylabel('Y Variable')
plt.colorbar(scatter, label='Category')

Subplots and Layouts

# Grid of plots
fig, axes = plt.subplots(2, 3, figsize=(15, 10))

for i, (ax, col) in enumerate(zip(axes.flat, columns)):
    ax.hist(df[col], bins=30, edgecolor='white')
    ax.set_title(col)

plt.tight_layout()

# Different sized subplots
fig = plt.figure(figsize=(12, 8))
gs = fig.add_gridspec(2, 3)

ax1 = fig.add_subplot(gs[0, :])  # Top row, all columns
ax2 = fig.add_subplot(gs[1, 0])  # Bottom left
ax3 = fig.add_subplot(gs[1, 1:]) # Bottom right (2 columns)

{% endif %}

2. Plotly for Interactive Charts

{% if library == "plotly" %}

Basic Setup

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Set default template
import plotly.io as pio
pio.templates.default = "plotly_white"

Line Charts

# Simple line chart
fig = px.line(
    df,
    x='date',
    y='value',
    color='category',
    title='Sales Over Time'
)

fig.update_layout(
    xaxis_title='Date',
    yaxis_title='Sales ($)',
    hovermode='x unified'
)

fig.show()

# With confidence interval
fig = go.Figure()

fig.add_trace(go.Scatter(
    x=df['date'],
    y=df['upper'],
    mode='lines',
    line=dict(width=0),
    showlegend=False
))

fig.add_trace(go.Scatter(
    x=df['date'],
    y=df['lower'],
    mode='lines',
    line=dict(width=0),
    fill='tonexty',
    fillcolor='rgba(68, 68, 68, 0.2)',
    showlegend=False
))

fig.add_trace(go.Scatter(
    x=df['date'],
    y=df['value'],
    mode='lines',
    name='Value'
))

Interactive Bar Charts

fig = px.bar(
    df,
    x='category',
    y='value',
    color='segment',
    barmode='group',
    text='value',
    title='Sales by Category'
)

fig.update_traces(textposition='outside')
fig.update_layout(uniformtext_minsize=8)

Dashboard Layout

fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=('Revenue', 'Users', 'Conversion', 'Churn'),
    specs=[
        [{"type": "scatter"}, {"type": "bar"}],
        [{"type": "pie"}, {"type": "scatter"}]
    ]
)

fig.add_trace(go.Scatter(x=dates, y=revenue), row=1, col=1)
fig.add_trace(go.Bar(x=categories, y=users), row=1, col=2)
fig.add_trace(go.Pie(labels=labels, values=values), row=2, col=1)
fig.add_trace(go.Scatter(x=dates, y=churn), row=2, col=2)

fig.update_layout(height=800, showlegend=False)

{% endif %}

3. Design Principles

Color Palette

# Categorical (distinct groups)
CATEGORICAL = ['#2563eb', '#16a34a', '#dc2626', '#9333ea', '#f59e0b']

# Sequential (ordered values)
SEQUENTIAL = ['#eff6ff', '#bfdbfe', '#60a5fa', '#2563eb', '#1d4ed8']

# Diverging (positive/negative)
DIVERGING = ['#dc2626', '#fca5a5', '#f5f5f5', '#86efac', '#16a34a']

# Colorblind-safe palette
COLORBLIND_SAFE = ['#0077bb', '#33bbee', '#009988', '#ee7733', '#cc3311']

Typography and Labels

# Clear, informative titles
ax.set_title('Monthly Revenue Growth (YoY %)', fontsize=14, fontweight='bold')

# Axis labels with units
ax.set_xlabel('Date')
ax.set_ylabel('Revenue ($M)')

# Remove chartjunk
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Annotations for key points
ax.annotate(
    'Product Launch',
    xy=(launch_date, peak_value),
    xytext=(launch_date, peak_value * 1.1),
    arrowprops=dict(arrowstyle='->', color='gray'),
    fontsize=10
)

Small Multiples

# Faceted plots for comparison
fig, axes = plt.subplots(2, 3, figsize=(15, 10), sharey=True)

for ax, region in zip(axes.flat, regions):
    data = df[df['region'] == region]
    ax.plot(data['date'], data['value'])
    ax.set_title(region)

# Consistent y-axis makes comparison easy
plt.suptitle('Sales by Region', fontsize=14)
plt.tight_layout()

4. Common Chart Types

Distribution Visualization

import seaborn as sns

# Histogram with KDE
fig, ax = plt.subplots(figsize=(10, 6))
sns.histplot(df['value'], kde=True, ax=ax)
ax.axvline(df['value'].mean(), color='red', linestyle='--', label='Mean')
ax.axvline(df['value'].median(), color='green', linestyle='--', label='Median')
ax.legend()

# Box plot comparison
fig, ax = plt.subplots(figsize=(10, 6))
sns.boxplot(data=df, x='category', y='value', ax=ax)

# Violin plot (distribution + density)
fig, ax = plt.subplots(figsize=(10, 6))
sns.violinplot(data=df, x='category', y='value', ax=ax)

Time Series

# Multiple metrics with different scales
fig, ax1 = plt.subplots(figsize=(12, 6))

# Primary axis
color1 = '#2563eb'
ax1.set_xlabel('Date')
ax1.set_ylabel('Revenue ($)', color=color1)
ax1.plot(df['date'], df['revenue'], color=color1, label='Revenue')
ax1.tick_params(axis='y', labelcolor=color1)

# Secondary axis
ax2 = ax1.twinx()
color2 = '#16a34a'
ax2.set_ylabel('Users', color=color2)
ax2.plot(df['date'], df['users'], color=color2, label='Users')
ax2.tick_params(axis='y', labelcolor=color2)

fig.tight_layout()

Heatmaps

# Correlation matrix
fig, ax = plt.subplots(figsize=(10, 8))

corr_matrix = df.select_dtypes(include=[np.number]).corr()

sns.heatmap(
    corr_matrix,
    annot=True,
    fmt='.2f',
    cmap='RdBu_r',
    center=0,
    ax=ax,
    square=True
)

ax.set_title('Feature Correlation Matrix')

5. Dashboard Design

Layout Principles

┌────────────────────────────────────────────────────┐
│  TITLE / DATE RANGE                          FILTERS│
├────────────────────────────────────────────────────┤
│  KPI 1   │  KPI 2   │  KPI 3   │  KPI 4           │
├──────────┴──────────┴──────────┴──────────────────┤
│                                                    │
│              MAIN CHART (60% of space)             │
│                                                    │
├──────────────────────────┬─────────────────────────┤
│  SUPPORTING CHART 1      │  SUPPORTING CHART 2     │
│                          │                         │
└──────────────────────────┴─────────────────────────┘

KPI Cards

def create_kpi_card(value, label, change_pct, ax):
    """Create a KPI card visualization."""
    ax.text(0.5, 0.7, f'{value:,.0f}', fontsize=28, ha='center', fontweight='bold')
    ax.text(0.5, 0.4, label, fontsize=12, ha='center', color='gray')

    # Change indicator
    color = 'green' if change_pct >= 0 else 'red'
    arrow = '↑' if change_pct >= 0 else '↓'
    ax.text(0.5, 0.15, f'{arrow} {abs(change_pct):.1f}%', fontsize=14, ha='center', color=color)

    ax.axis('off')

6. Storytelling with Data

Narrative Structure

Context: What's the situation?
Conflict: What's the problem/opportunity?
Resolution: What does the data reveal?
Action: What should we do?

Annotation Strategy

# Highlight key insights
ax.annotate(
    'Sales dropped 40% during\nlockdown period',
    xy=(lockdown_start, lockdown_value),
    xytext=(lockdown_start - 30, lockdown_value + 1000),
    fontsize=10,
    arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.2'),
    bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5)
)

# Use color to direct attention
ax.fill_between(dates[highlight_start:highlight_end],
                values[highlight_start:highlight_end],
                color='#fef3c7', alpha=0.5)

Quick Reference

Chart Selection Matrix

Data Type	Comparison	Distribution	Relationship
Categorical	Bar	-	Grouped bar
Numeric	Line	Histogram	Scatter
Time	Line	-	Line
Geographic	Choropleth	-	-

Dos and Don'ts

DO	DON'T
Start y-axis at 0 for bars	Truncate y-axis to exaggerate
Use consistent colors	Rainbow every chart
Label axes clearly	Assume units are obvious
Highlight key insights	Show all data equally
Use white space	Cram in too much

Export for Different Uses

# For presentations (high res)
plt.savefig('chart.png', dpi=300, bbox_inches='tight')

# For web (smaller file)
plt.savefig('chart.png', dpi=100, bbox_inches='tight')

# Vector format (scalable)
plt.savefig('chart.svg', format='svg', bbox_inches='tight')

# For print (CMYK PDF)
plt.savefig('chart.pdf', format='pdf', bbox_inches='tight')

Related Skills

pandas-data-analysis - Data preparation for visualization
dashboard-design - Interactive dashboard patterns
presentation-skills - Presenting data effectively

data-visualization

Data Visualization Guide

Core Philosophy

Chart Selection Guide