Data Visualization Style Guide
Design Philosophy
- •Clarity over decoration - Every visual element should aid comprehension
- •Data-ink ratio - Remove non-essential visual elements
- •Sort logically - Rankings should be descending (highest first)
- •Consistent styling - Same palette, fonts, and spacing across all charts
Color Palette
Sequential (for ranked/ordered data)
Use gradient from dark (high values) to light (low values):
python
COLORS_SEQUENTIAL = ['#1a6642', '#2d8659', '#4a9f6e', '#6bb885', '#8fd4a0']
Categorical (for distinct groups)
python
COLORS_CATEGORICAL = ['#1a6642', '#4a7c94', '#d4a03c', '#8b5a6b', '#5d7a5d', '#6b7b8a']
Semantic
python
COLORS_SEMANTIC = {
'highlight': '#1a6642', # Forest green
'warning': '#d4a03c', # Amber
'error': '#b55a4a', # Muted red
}
Standard Chart Setup
Always apply this style at the start of chart creation:
python
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
def apply_chart_style():
"""Apply consistent styling to all charts."""
plt.rcParams.update({
'figure.figsize': (10, 6),
'figure.facecolor': 'white',
'font.family': 'monospace',
'axes.spines.top': False,
'axes.spines.right': False,
'axes.edgecolor': '#cccccc',
'axes.titlesize': 14,
'axes.titleweight': 'bold',
'axes.labelsize': 11,
'grid.color': '#e8e8e8',
'grid.linewidth': 0.5,
})
def create_gradient_colors(n, reverse=False):
"""Generate n colors from dark green to light green."""
colors = ['#1a6642', '#8fd4a0']
if reverse:
colors = colors[::-1]
cmap = LinearSegmentedColormap.from_list('green', colors, N=n)
return [cmap(i/(n-1)) for i in range(n)]
def add_bar_labels(ax, bars, fmt='{:,.0f}'):
"""Add formatted value labels to horizontal bar chart."""
for bar in bars:
width = bar.get_width()
ax.annotate(fmt.format(width),
xy=(width, bar.get_y() + bar.get_height()/2),
xytext=(5, 0), textcoords='offset points',
ha='left', va='center', fontsize=9, color='#2d2d2d')
Chart Type Selection
| Analysis Type | Recommended Chart | Key Style Points |
|---|---|---|
| Ranking ("Top N...") | Horizontal bar | Sort descending, gradient colors, data labels outside |
| Trend over time | Line chart | 2pt line width, hollow circle markers |
| Part-to-whole | Donut chart | 40% ring width, start angle 90° |
| Distribution | Histogram | 20-30 bins, single color |
| Comparison | Grouped bar | Categorical colors, legend outside |
Horizontal Bar Chart Template
python
apply_chart_style()
fig, ax = plt.subplots()
# Sort descending (ascending=True for horizontal puts highest at top)
data = data.sort_values('value', ascending=True)
colors = create_gradient_colors(len(data), reverse=True)
bars = ax.barh(data['label'], data['value'], color=colors)
add_bar_labels(ax, bars)
ax.set_xlabel('Value')
ax.set_title('Top Items by Value')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout()
plt.savefig('/home/user/chart.png', dpi=150, bbox_inches='tight')
plt.close()
Line Chart Template
python
apply_chart_style()
fig, ax = plt.subplots()
ax.plot(data['date'], data['value'],
color='#1a6642', linewidth=2,
marker='o', markersize=6,
markerfacecolor='white', markeredgewidth=1.5)
ax.set_xlabel('Date')
ax.set_ylabel('Value')
ax.set_title('Trend Over Time')
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('/home/user/trend.png', dpi=150, bbox_inches='tight')
plt.close()
Donut Chart Template
python
apply_chart_style()
fig, ax = plt.subplots()
colors = create_gradient_colors(len(data))
wedges, texts, autotexts = ax.pie(
data['value'],
labels=data['label'],
colors=colors,
autopct='%1.1f%%',
startangle=90,
wedgeprops={'width': 0.4, 'edgecolor': 'white', 'linewidth': 2}
)
ax.set_title('Distribution by Category')
plt.tight_layout()
plt.savefig('/home/user/donut.png', dpi=150, bbox_inches='tight')
plt.close()
Always Save Charts
python
plt.tight_layout()
plt.savefig('/home/user/chart.png', dpi=150, bbox_inches='tight', facecolor='white')
plt.close()