Data Visualization

import matplotlib.pyplot as pltimport matplotlib.ticker as mtickerimport seaborn as snsimport pandas as pdimport numpy as np
# Professional style setupplt.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 palettesPALETTE_CATEGORICAL = ['#4C72B0', '#DD8452', '#55A868', '#C44E52', '#8172B3', '#937860']PALETTE_SEQUENTIAL = 'YlOrRd'PALETTE_DIVERGING = 'RdBu_r'

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

fig, ax = plt.subplots(figsize=(10, 6))
# Sort by value for easy readingdf_sorted = df.sort_values('metric', ascending=True)
bars = ax.barh(df_sorted['category'], df_sorted['metric'], color=PALETTE_CATEGORICAL[0])
# Add value labelsfor 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')

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 linesmean_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')

fig, ax = plt.subplots(figsize=(10, 8))
# Pivot data for heatmap formatpivot = 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')

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 subplotsfor 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')

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 formatterax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, p: format_number(x, 'currency')))

import plotly.express as pximport plotly.graph_objects as go
# Simple interactive line chartfig = 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 datafig = px.scatter(df, x='metric_a', y='metric_b', color='category',                 size='size_metric', hover_data=['name', 'detail_field'],                 title='Correlation Analysis')fig.show()