Data Visualization Skill

Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations.

Chart Selection Guide

Choose by Data Relationship

What You're Showing	Best Chart	Alternatives
Trend over time	Line chart	Area chart (if showing cumulative or composition)
Comparison across categories	Vertical bar chart	Horizontal bar (many categories), lollipop chart
Ranking	Horizontal bar chart	Dot plot, slope chart (comparing two periods)
Part-to-whole composition	Stacked bar chart	Treemap (hierarchical), waffle chart
Composition over time	Stacked area chart	100% stacked bar (for proportion focus)
Distribution	Histogram	Box plot (comparing groups), violin plot, strip plot
Correlation (2 variables)	Scatter plot	Bubble chart (add 3rd variable as size)
Correlation (many variables)	Heatmap (correlation matrix)	Pair plot
Geographic patterns	Choropleth map	Bubble map, hex map
Flow / process	Sankey diagram	Funnel chart (sequential stages)
Relationship network	Network graph	Chord diagram
Performance vs. target	Bullet chart	Gauge (single KPI only)
Multiple KPIs at once	Small multiples	Dashboard with separate charts

When NOT to Use Certain Charts

•Pie charts: Avoid unless <6 categories and exact proportions matter less than rough comparison. Humans are bad at comparing angles. Use bar charts instead.
•3D charts: Never. They distort perception and add no information.
•Dual-axis charts: Use cautiously. They can mislead by implying correlation. Clearly label both axes if used.
•Stacked bar (many categories): Hard to compare middle segments. Use small multiples or grouped bars instead.
•Donut charts: Slightly better than pie charts but same fundamental issues. Use for single KPI display at most.

Python Visualization Code Patterns

Setup and Style

python

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

# Professional style setup
plt.style.use('seaborn-v0_8-whitegrid')
plt.rcParams.update({
    'figure.figsize': (10, 6),
    'figure.dpi': 150,
    'font.size': 11,
    'axes.titlesize': 14,
    'axes.titleweight': 'bold',
    'axes.labelsize': 11,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'legend.fontsize': 10,
    'figure.titlesize': 16,
})

# Use colorblind-safe palettes
# For categorical: 'tab10', 'Set2', or seaborn's default palette
# For sequential: 'viridis', 'plasma', 'Blues', 'Greens'
# For diverging: 'RdBu', 'RdYlBu', 'PiYG'

Line Chart (Time Series)

python

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

for label, group in df.groupby('category'):
    ax.plot(group['date'], group['value'], label=label, linewidth=2)

ax.set_title('Metric Trend by Category', fontweight='bold')
ax.set_xlabel('Date')
ax.set_ylabel('Value')
ax.legend(loc='upper left', frameon=True)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Format dates on x-axis
fig.autofmt_xdate()

plt.tight_layout()
plt.savefig('trend_chart.png', dpi=150, bbox_inches='tight')

Bar Chart (Comparison)

python

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

# Sort by value for easy reading
df_sorted = df.sort_values('metric', ascending=True)

bars = ax.barh(df_sorted['category'], df_sorted['metric'], color='steelblue')

# Add value labels
for bar in bars:
    width = bar.get_width()
    ax.text(width + 0.5, bar.get_y() + bar.get_height()/2,
            f'{width:,.0f}', ha='left', va='center', fontsize=10)

ax.set_title('Metric by Category (Ranked)', fontweight='bold')
ax.set_xlabel('Metric Value')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.savefig('bar_chart.png', dpi=150, bbox_inches='tight')

Histogram (Distribution)

python

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

ax.hist(df['value'], bins=30, color='steelblue', edgecolor='white', alpha=0.8)

# Add mean and median lines
mean_val = df['value'].mean()
median_val = df['value'].median()
ax.axvline(mean_val, color=NU_CRIMSON_PURPLE_HEX, linestyle='--', linewidth=1.5, label=f'Mean: {mean_val:,.1f}')
ax.axvline(median_val, color='darkslateblue', linestyle='--', linewidth=1.5, label=f'Median: {median_val:,.1f}')

ax.set_title('Distribution of Values', fontweight='bold')
ax.set_xlabel('Value')
ax.set_ylabel('Frequency')
ax.legend()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.savefig('histogram.png', dpi=150, bbox_inches='tight')

Heatmap

python

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

# Pivot data for heatmap format
pivot = df.pivot_table(index='row_dim', columns='col_dim', values='metric', aggfunc='sum')

sns.heatmap(pivot, annot=True, fmt=',.0f', cmap=WARM,
            linewidths=0.5, ax=ax, cbar_kws={'label': 'Metric Value'})

ax.set_title('Metric by Row Dimension and Column Dimension', fontweight='bold')
ax.set_xlabel('Column Dimension')
ax.set_ylabel('Row Dimension')

plt.tight_layout()
plt.savefig('heatmap.png', dpi=150, bbox_inches='tight')

Small Multiples

python

categories = df['category'].unique()
n_cats = len(categories)
n_cols = min(3, n_cats)
n_rows = (n_cats + n_cols - 1) // n_cols

fig, axes = plt.subplots(n_rows, n_cols, figsize=(5*n_cols, 4*n_rows), sharex=True, sharey=True)
axes = axes.flatten() if n_cats > 1 else [axes]

for i, cat in enumerate(categories):
    ax = axes[i]
    subset = df[df['category'] == cat]
    ax.plot(subset['date'], subset['value'], color=PALETTE_CATEGORICAL[i % len(PALETTE_CATEGORICAL)])
    ax.set_title(cat, fontsize=12)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

# Hide empty subplots
for j in range(i+1, len(axes)):
    axes[j].set_visible(False)

fig.suptitle('Trends by Category', fontsize=14, fontweight='bold', y=1.02)
plt.tight_layout()
plt.savefig('small_multiples.png', dpi=150, bbox_inches='tight')

Number Formatting Helpers

python

def format_number(val, format_type='number'):
    """Format numbers for chart labels."""
    if format_type == 'currency':
        if abs(val) >= 1e9:
            return f'${val/1e9:.1f}B'
        elif abs(val) >= 1e6:
            return f'${val/1e6:.1f}M'
        elif abs(val) >= 1e3:
            return f'${val/1e3:.1f}K'
        else:
            return f'${val:,.0f}'
    elif format_type == 'percent':
        return f'{val:.1f}%'
    elif format_type == 'number':
        if abs(val) >= 1e9:
            return f'{val/1e9:.1f}B'
        elif abs(val) >= 1e6:
            return f'{val/1e6:.1f}M'
        elif abs(val) >= 1e3:
            return f'{val/1e3:.1f}K'
        else:
            return f'{val:,.0f}'
    return str(val)

# Usage with axis formatter
ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, p: format_number(x, 'currency')))

Interactive Charts with Plotly

python

import plotly.express as px
import plotly.graph_objects as go

# Simple interactive line chart
fig = px.line(df, x='date', y='value', color='category',
              title='Interactive Metric Trend',
              labels={'value': 'Metric Value', 'date': 'Date'})
fig.update_layout(hovermode='x unified')
fig.write_html('interactive_chart.html')
fig.show()

# Interactive scatter with hover data
fig = px.scatter(df, x='metric_a', y='metric_b', color='category',
                 size='size_metric', hover_data=['name', 'detail_field'],
                 title='Correlation Analysis')
fig.show()

Design Principles

Color

•Use color purposefully: Color should encode data, not decorate
•Colorblind safety first: Use colorblind-safe palettes like 'tab10', 'Set2', or ColorBrewer palettes
•Sequential data: Use perceptually uniform colormaps like 'viridis', 'plasma', 'cividis', or single-hue like 'Blues', 'Greens'
•Diverging data: Use 'RdBu', 'RdYlBu', 'PiYG' for data with meaningful center point (e.g., zero, neutral)
•Categorical data: Limit to 8-10 distinct colors maximum. Use seaborn's 'tab10' or 'Set2' palettes
•Highlight the story: Use a consistent accent color for key insights; desaturate or gray out supporting data
•Test your colors: View charts in grayscale to ensure they're still readable

Typography

•Title states the insight: "Revenue grew 23% YoY" beats "Revenue by Month"
•Subtitle adds context: Date range, filters applied, data source
•Axis labels are readable: Never rotated 90 degrees if avoidable. Shorten or wrap instead
•Data labels add precision: Use on key points, not every single bar
•Annotation highlights: Call out specific points with text annotations

Layout

•Reduce chart junk: Remove gridlines, borders, backgrounds that don't carry information
•Sort meaningfully: Categories sorted by value (not alphabetically) unless there's a natural order (months, stages)
•Appropriate aspect ratio: Time series wider than tall (3:1 to 2:1); comparisons can be squarer
•White space is good: Don't cram charts together. Give each visualization room to breathe

Accuracy

•Bar charts start at zero: Always. A bar from 95 to 100 exaggerates a 5% difference
•Line charts can have non-zero baselines: When the range of variation is meaningful
•Consistent scales across panels: When comparing multiple charts, use the same axis range
•Show uncertainty: Error bars, confidence intervals, or ranges when data is uncertain
•Label your axes: Never make the reader guess what the numbers mean