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

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, })

Colorblind-friendly palettes

PALETTE_CATEGORICAL = ['#4C72B0', '#DD8452', '#55A868', '#C44E52', '#8172B3', '#937860'] PALETTE_SEQUENTIAL = 'YlOrRd' PALETTE_DIVERGING = 'RdBu_r'

Line Chart (Time Series)

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)

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=PALETTE_CATEGORICAL[0])

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)

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

ax.hist(df['value'], bins=30, color=PALETTE_CATEGORICAL[0], 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='red', linestyle='--', linewidth=1.5, label=f'Mean: {mean_val:,.1f}') ax.axvline(median_val, color='green', 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

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='YlOrRd', 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

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=(5n_cols, 4n_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

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

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

• Highlight the story: Use a bright accent color for the key insight; grey everything else

• Sequential data: Use a single-hue gradient (light to dark) for ordered values

• Diverging data: Use a two-hue gradient with neutral midpoint for data with a meaningful center

• Categorical data: Use distinct hues, maximum 6-8 before it gets confusing

• Avoid red/green only: 8% of men are red-green colorblind. Use blue/orange as primary pair

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

Accessibility Considerations

Color Blindness

• Never rely on color alone to distinguish data series

• Add pattern fills, different line styles (solid, dashed, dotted), or direct labels

• Test with a colorblind simulator (e.g., Coblis, Sim Daltonism)

• Use the colorblind-friendly palette: sns.color_palette("colorblind")

Screen Readers

• Include alt text describing the chart's key finding

• Provide a data table alternative alongside the visualization

• Use semantic titles and labels

General Accessibility

• Sufficient contrast between data elements and background

• Text size minimum 10pt for labels, 12pt for titles

• Avoid conveying information only through spatial position (add labels)

• Consider printing: does the chart work in black and white?

Accessibility Checklist

Before sharing a visualization:

• Chart works without color (patterns, labels, or line styles differentiate series)

• Text is readable at standard zoom level

• Title describes the insight, not just the data

• Axes are labeled with units

• Legend is clear and positioned without obscuring data

• Data source and date range are noted