Data Visualization

What This Skill Does

• The Data Visualization skill provides guidance on chart selection, Python visualization code patterns, design principles, and accessibility considerations.
• It helps users create effective and informative data visualizations.
• This skill is useful for anyone who needs to present data in a clear and compelling way.

When to Use

• Choose the best chart type for a specific data relationship.
• Create a line chart to show trends over time.
• Generate a bar chart to compare categories.
• Visualize part-to-whole composition with a stacked bar chart.
• Show data distribution using a histogram.
• Identify correlations between variables with a scatter plot.

Key Features

• Provides a chart selection guide based on data relationships.
• Offers Python visualization code patterns.
• Includes design principles for effective visualizations.
• Covers accessibility considerations for data visualization.
• Warns against misuse of pie charts and 3D charts.
• Provides guidance on using dual-axis charts effectively.

Manual Installation

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

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.rc Params.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 = 'Yl Or Rd' PALETTE_DIVERGING = 'Rd Bu_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='Yl Or Rd', 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.
• Func Formatter(lambda x, p: format_number(x, 'currency'))).

Interactive