wehub-resource-sync
306 lines
11 KiB
Markdown
306 lines
11 KiB
Markdown
---
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name: data-visualization
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description: Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
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user-invocable: false
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---
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# Data Visualization Skill
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Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations.
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## Chart Selection Guide
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### Choose by Data Relationship
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| What You're Showing | Best Chart | Alternatives |
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|---|---|---|
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| **Trend over time** | Line chart | Area chart (if showing cumulative or composition) |
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| **Comparison across categories** | Vertical bar chart | Horizontal bar (many categories), lollipop chart |
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| **Ranking** | Horizontal bar chart | Dot plot, slope chart (comparing two periods) |
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| **Part-to-whole composition** | Stacked bar chart | Treemap (hierarchical), waffle chart |
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| **Composition over time** | Stacked area chart | 100% stacked bar (for proportion focus) |
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| **Distribution** | Histogram | Box plot (comparing groups), violin plot, strip plot |
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| **Correlation (2 variables)** | Scatter plot | Bubble chart (add 3rd variable as size) |
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| **Correlation (many variables)** | Heatmap (correlation matrix) | Pair plot |
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| **Geographic patterns** | Choropleth map | Bubble map, hex map |
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| **Flow / process** | Sankey diagram | Funnel chart (sequential stages) |
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| **Relationship network** | Network graph | Chord diagram |
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| **Performance vs. target** | Bullet chart | Gauge (single KPI only) |
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| **Multiple KPIs at once** | Small multiples | Dashboard with separate charts |
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### When NOT to Use Certain Charts
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- **Pie charts**: Avoid unless <6 categories and exact proportions matter less than rough comparison. Humans are bad at comparing angles. Use bar charts instead.
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- **3D charts**: Never. They distort perception and add no information.
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- **Dual-axis charts**: Use cautiously. They can mislead by implying correlation. Clearly label both axes if used.
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- **Stacked bar (many categories)**: Hard to compare middle segments. Use small multiples or grouped bars instead.
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- **Donut charts**: Slightly better than pie charts but same fundamental issues. Use for single KPI display at most.
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## Python Visualization Code Patterns
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### Setup and Style
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```python
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import matplotlib.pyplot as plt
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import matplotlib.ticker as mticker
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import seaborn as sns
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import pandas as pd
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import numpy as np
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# Professional style setup
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plt.style.use('seaborn-v0_8-whitegrid')
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plt.rcParams.update({
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'figure.figsize': (10, 6),
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'figure.dpi': 150,
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'font.size': 11,
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'axes.titlesize': 14,
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'axes.titleweight': 'bold',
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'axes.labelsize': 11,
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'xtick.labelsize': 10,
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'ytick.labelsize': 10,
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'legend.fontsize': 10,
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'figure.titlesize': 16,
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})
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# Colorblind-friendly palettes
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PALETTE_CATEGORICAL = ['#4C72B0', '#DD8452', '#55A868', '#C44E52', '#8172B3', '#937860']
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PALETTE_SEQUENTIAL = 'YlOrRd'
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PALETTE_DIVERGING = 'RdBu_r'
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```
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### Line Chart (Time Series)
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```python
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fig, ax = plt.subplots(figsize=(10, 6))
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for label, group in df.groupby('category'):
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ax.plot(group['date'], group['value'], label=label, linewidth=2)
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ax.set_title('Metric Trend by Category', fontweight='bold')
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ax.set_xlabel('Date')
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ax.set_ylabel('Value')
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ax.legend(loc='upper left', frameon=True)
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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# Format dates on x-axis
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fig.autofmt_xdate()
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plt.tight_layout()
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plt.savefig('trend_chart.png', dpi=150, bbox_inches='tight')
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```
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### Bar Chart (Comparison)
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```python
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fig, ax = plt.subplots(figsize=(10, 6))
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# Sort by value for easy reading
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df_sorted = df.sort_values('metric', ascending=True)
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bars = ax.barh(df_sorted['category'], df_sorted['metric'], color=PALETTE_CATEGORICAL[0])
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# Add value labels
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for bar in bars:
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width = bar.get_width()
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ax.text(width + 0.5, bar.get_y() + bar.get_height()/2,
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f'{width:,.0f}', ha='left', va='center', fontsize=10)
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ax.set_title('Metric by Category (Ranked)', fontweight='bold')
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ax.set_xlabel('Metric Value')
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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plt.tight_layout()
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plt.savefig('bar_chart.png', dpi=150, bbox_inches='tight')
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```
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### Histogram (Distribution)
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```python
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fig, ax = plt.subplots(figsize=(10, 6))
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ax.hist(df['value'], bins=30, color=PALETTE_CATEGORICAL[0], edgecolor='white', alpha=0.8)
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# Add mean and median lines
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mean_val = df['value'].mean()
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median_val = df['value'].median()
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ax.axvline(mean_val, color='red', linestyle='--', linewidth=1.5, label=f'Mean: {mean_val:,.1f}')
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ax.axvline(median_val, color='green', linestyle='--', linewidth=1.5, label=f'Median: {median_val:,.1f}')
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ax.set_title('Distribution of Values', fontweight='bold')
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ax.set_xlabel('Value')
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ax.set_ylabel('Frequency')
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ax.legend()
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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plt.tight_layout()
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plt.savefig('histogram.png', dpi=150, bbox_inches='tight')
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```
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### Heatmap
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```python
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fig, ax = plt.subplots(figsize=(10, 8))
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# Pivot data for heatmap format
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pivot = df.pivot_table(index='row_dim', columns='col_dim', values='metric', aggfunc='sum')
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sns.heatmap(pivot, annot=True, fmt=',.0f', cmap='YlOrRd',
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linewidths=0.5, ax=ax, cbar_kws={'label': 'Metric Value'})
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ax.set_title('Metric by Row Dimension and Column Dimension', fontweight='bold')
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ax.set_xlabel('Column Dimension')
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ax.set_ylabel('Row Dimension')
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plt.tight_layout()
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plt.savefig('heatmap.png', dpi=150, bbox_inches='tight')
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```
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### Small Multiples
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```python
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categories = df['category'].unique()
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n_cats = len(categories)
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n_cols = min(3, n_cats)
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n_rows = (n_cats + n_cols - 1) // n_cols
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fig, axes = plt.subplots(n_rows, n_cols, figsize=(5*n_cols, 4*n_rows), sharex=True, sharey=True)
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axes = axes.flatten() if n_cats > 1 else [axes]
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for i, cat in enumerate(categories):
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ax = axes[i]
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subset = df[df['category'] == cat]
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ax.plot(subset['date'], subset['value'], color=PALETTE_CATEGORICAL[i % len(PALETTE_CATEGORICAL)])
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ax.set_title(cat, fontsize=12)
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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# Hide empty subplots
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for j in range(i+1, len(axes)):
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axes[j].set_visible(False)
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fig.suptitle('Trends by Category', fontsize=14, fontweight='bold', y=1.02)
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plt.tight_layout()
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plt.savefig('small_multiples.png', dpi=150, bbox_inches='tight')
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```
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### Number Formatting Helpers
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```python
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def format_number(val, format_type='number'):
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"""Format numbers for chart labels."""
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if format_type == 'currency':
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if abs(val) >= 1e9:
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return f'${val/1e9:.1f}B'
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elif abs(val) >= 1e6:
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return f'${val/1e6:.1f}M'
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elif abs(val) >= 1e3:
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return f'${val/1e3:.1f}K'
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else:
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return f'${val:,.0f}'
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elif format_type == 'percent':
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return f'{val:.1f}%'
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elif format_type == 'number':
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if abs(val) >= 1e9:
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return f'{val/1e9:.1f}B'
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elif abs(val) >= 1e6:
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return f'{val/1e6:.1f}M'
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elif abs(val) >= 1e3:
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return f'{val/1e3:.1f}K'
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else:
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return f'{val:,.0f}'
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return str(val)
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# Usage with axis formatter
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ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, p: format_number(x, 'currency')))
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```
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### Interactive Charts with Plotly
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```python
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import plotly.express as px
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import plotly.graph_objects as go
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# Simple interactive line chart
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fig = px.line(df, x='date', y='value', color='category',
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title='Interactive Metric Trend',
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labels={'value': 'Metric Value', 'date': 'Date'})
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fig.update_layout(hovermode='x unified')
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fig.write_html('interactive_chart.html')
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fig.show()
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# Interactive scatter with hover data
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fig = px.scatter(df, x='metric_a', y='metric_b', color='category',
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size='size_metric', hover_data=['name', 'detail_field'],
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title='Correlation Analysis')
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fig.show()
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```
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## Design Principles
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### Color
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- **Use color purposefully**: Color should encode data, not decorate
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- **Highlight the story**: Use a bright accent color for the key insight; grey everything else
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- **Sequential data**: Use a single-hue gradient (light to dark) for ordered values
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- **Diverging data**: Use a two-hue gradient with neutral midpoint for data with a meaningful center
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- **Categorical data**: Use distinct hues, maximum 6-8 before it gets confusing
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- **Avoid red/green only**: 8% of men are red-green colorblind. Use blue/orange as primary pair
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### Typography
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- **Title states the insight**: "Revenue grew 23% YoY" beats "Revenue by Month"
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- **Subtitle adds context**: Date range, filters applied, data source
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- **Axis labels are readable**: Never rotated 90 degrees if avoidable. Shorten or wrap instead
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- **Data labels add precision**: Use on key points, not every single bar
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- **Annotation highlights**: Call out specific points with text annotations
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### Layout
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- **Reduce chart junk**: Remove gridlines, borders, backgrounds that don't carry information
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- **Sort meaningfully**: Categories sorted by value (not alphabetically) unless there's a natural order (months, stages)
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- **Appropriate aspect ratio**: Time series wider than tall (3:1 to 2:1); comparisons can be squarer
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- **White space is good**: Don't cram charts together. Give each visualization room to breathe
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### Accuracy
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- **Bar charts start at zero**: Always. A bar from 95 to 100 exaggerates a 5% difference
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- **Line charts can have non-zero baselines**: When the range of variation is meaningful
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- **Consistent scales across panels**: When comparing multiple charts, use the same axis range
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- **Show uncertainty**: Error bars, confidence intervals, or ranges when data is uncertain
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- **Label your axes**: Never make the reader guess what the numbers mean
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## Accessibility Considerations
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### Color Blindness
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- Never rely on color alone to distinguish data series
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- Add pattern fills, different line styles (solid, dashed, dotted), or direct labels
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- Test with a colorblind simulator (e.g., Coblis, Sim Daltonism)
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- Use the colorblind-friendly palette: `sns.color_palette("colorblind")`
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### Screen Readers
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- Include alt text describing the chart's key finding
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- Provide a data table alternative alongside the visualization
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- Use semantic titles and labels
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### General Accessibility
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- Sufficient contrast between data elements and background
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- Text size minimum 10pt for labels, 12pt for titles
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- Avoid conveying information only through spatial position (add labels)
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- Consider printing: does the chart work in black and white?
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### Accessibility Checklist
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Before sharing a visualization:
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- [ ] Chart works without color (patterns, labels, or line styles differentiate series)
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- [ ] Text is readable at standard zoom level
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- [ ] Title describes the insight, not just the data
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- [ ] Axes are labeled with units
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- [ ] Legend is clear and positioned without obscuring data
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- [ ] Data source and date range are noted
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