Chart Generator Skill

Generate charts and visualizations from data using various charting libraries and formats.

Instructions

You are a data visualization expert. When invoked:

1. Analyze Data:

   • Understand data structure and types
   • Identify appropriate chart types
   • Detect data patterns and trends
   • Calculate aggregations and statistics
   • Determine visualization goals

2. Generate Charts:

   • Create bar, line, pie, scatter plots
   • Generate heatmaps and tree maps
   • Create histograms and box plots
   • Build time series visualizations
   • Design multi-dimensional charts

3. Style and Customize:

   • Apply color schemes and themes
   • Add labels, legends, and annotations
   • Format axes and gridlines
   • Customize tooltips and interactions
   • Ensure accessibility and readability

4. Export and Embed:

   • Save as PNG, SVG, PDF
   • Generate interactive HTML charts
   • Embed in markdown reports
   • Create chart APIs
   • Support responsive design

Usage Examples

@chart-generator data.csv --type bar
@chart-generator --line --time-series
@chart-generator --pie --group-by category
@chart-generator --scatter x:age y:income
@chart-generator --heatmap --correlation
@chart-generator --interactive --html

Chart Types and Use Cases

When to Use Each Chart Type

Chart Type	Best For	Example Use Case
Bar Chart	Comparing categories	Sales by product
Line Chart	Trends over time	Revenue over months
Pie Chart	Part-to-whole relationships	Market share
Scatter Plot	Relationships between variables	Height vs Weight
Histogram	Distribution of values	Age distribution
Box Plot	Statistical distribution	Salary ranges by department
Heatmap	Matrix data, correlations	Feature correlations
Area Chart	Cumulative trends	Stacked revenue streams
Bubble Chart	3-dimensional data	Sales vs Profit vs Market Share
Treemap	Hierarchical data	Disk space usage

Python - Matplotlib

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

def create_bar_chart(data, x_col, y_col, title='Bar Chart', output='chart.png'):
    """
    Create a bar chart
    """
    plt.figure(figsize=(10, 6))

    if isinstance(data, pd.DataFrame):
        x = data[x_col]
        y = data[y_col]
    else:
        x = data['labels']
        y = data['values']

    bars = plt.bar(x, y, color='steelblue', alpha=0.8)

    # Add value labels on bars
    for bar in bars:
        height = bar.get_height()
        plt.text(bar.get_x() + bar.get_width()/2., height,
                f'{height:.1f}',
                ha='center', va='bottom')

    plt.title(title, fontsize=16, fontweight='bold')
    plt.xlabel(x_col if isinstance(data, pd.DataFrame) else 'Category', fontsize=12)
    plt.ylabel(y_col if isinstance(data, pd.DataFrame) else 'Value', fontsize=12)
    plt.xticks(rotation=45, ha='right')
    plt.grid(axis='y', alpha=0.3)
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

def create_line_chart(data, x_col, y_col, title='Line Chart', output='chart.png'):
    """
    Create a line chart
    """
    plt.figure(figsize=(12, 6))

    if isinstance(data, pd.DataFrame):
        x = data[x_col]
        y = data[y_col]
    else:
        x = data['x']
        y = data['y']

    plt.plot(x, y, marker='o', linewidth=2, markersize=6, color='steelblue')

    # Add grid
    plt.grid(True, alpha=0.3)

    plt.title(title, fontsize=16, fontweight='bold')
    plt.xlabel(x_col if isinstance(data, pd.DataFrame) else 'X', fontsize=12)
    plt.ylabel(y_col if isinstance(data, pd.DataFrame) else 'Y', fontsize=12)
    plt.xticks(rotation=45, ha='right')
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

def create_pie_chart(data, labels_col, values_col, title='Pie Chart', output='chart.png'):
    """
    Create a pie chart
    """
    plt.figure(figsize=(10, 8))

    if isinstance(data, pd.DataFrame):
        labels = data[labels_col]
        values = data[values_col]
    else:
        labels = data['labels']
        values = data['values']

    # Create color palette
    colors = plt.cm.Set3(np.linspace(0, 1, len(labels)))

    # Create pie chart
    wedges, texts, autotexts = plt.pie(
        values,
        labels=labels,
        autopct='%1.1f%%',
        startangle=90,
        colors=colors,
        explode=[0.05] * len(labels)  # Slightly separate slices
    )

    # Style percentage text
    for autotext in autotexts:
        autotext.set_color('white')
        autotext.set_fontweight('bold')
        autotext.set_fontsize(10)

    plt.title(title, fontsize=16, fontweight='bold')
    plt.axis('equal')
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

def create_scatter_plot(data, x_col, y_col, color_col=None, size_col=None,
                       title='Scatter Plot', output='chart.png'):
    """
    Create a scatter plot
    """
    plt.figure(figsize=(10, 8))

    if isinstance(data, pd.DataFrame):
        x = data[x_col]
        y = data[y_col]
        c = data[color_col] if color_col else None
        s = data[size_col] if size_col else 50
    else:
        x = data['x']
        y = data['y']
        c = None
        s = 50

    scatter = plt.scatter(x, y, c=c, s=s, alpha=0.6, cmap='viridis')

    if color_col:
        plt.colorbar(scatter, label=color_col)

    # Add trend line
    z = np.polyfit(x, y, 1)
    p = np.poly1d(z)
    plt.plot(x, p(x), "r--", alpha=0.8, label='Trend')

    plt.title(title, fontsize=16, fontweight='bold')
    plt.xlabel(x_col if isinstance(data, pd.DataFrame) else 'X', fontsize=12)
    plt.ylabel(y_col if isinstance(data, pd.DataFrame) else 'Y', fontsize=12)
    plt.grid(True, alpha=0.3)
    plt.legend()
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

def create_histogram(data, column, bins=30, title='Histogram', output='chart.png'):
    """
    Create a histogram
    """
    plt.figure(figsize=(10, 6))

    if isinstance(data, pd.DataFrame):
        values = data[column]
    else:
        values = data

    n, bins, patches = plt.hist(values, bins=bins, color='steelblue',
                                 alpha=0.7, edgecolor='black')

    # Add mean line
    mean_val = np.mean(values)
    plt.axvline(mean_val, color='red', linestyle='dashed', linewidth=2,
                label=f'Mean: {mean_val:.2f}')

    # Add median line
    median_val = np.median(values)
    plt.axvline(median_val, color='green', linestyle='dashed', linewidth=2,
                label=f'Median: {median_val:.2f}')

    plt.title(title, fontsize=16, fontweight='bold')
    plt.xlabel(column if isinstance(data, pd.DataFrame) else 'Value', fontsize=12)
    plt.ylabel('Frequency', fontsize=12)
    plt.legend()
    plt.grid(axis='y', alpha=0.3)
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

def create_box_plot(data, columns, title='Box Plot', output='chart.png'):
    """
    Create a box plot
    """
    plt.figure(figsize=(10, 6))

    if isinstance(data, pd.DataFrame):
        data_to_plot = [data[col].dropna() for col in columns]
        labels = columns
    else:
        data_to_plot = data
        labels = [f'Group {i+1}' for i in range(len(data))]

    bp = plt.boxplot(data_to_plot, labels=labels, patch_artist=True)

    # Color boxes
    for patch in bp['boxes']:
        patch.set_facecolor('lightblue')
        patch.set_alpha(0.7)

    plt.title(title, fontsize=16, fontweight='bold')
    plt.ylabel('Value', fontsize=12)
    plt.grid(axis='y', alpha=0.3)
    plt.xticks(rotation=45, ha='right')
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

def create_heatmap(data, title='Heatmap', output='chart.png'):
    """
    Create a heatmap (correlation matrix)
    """
    plt.figure(figsize=(10, 8))

    if isinstance(data, pd.DataFrame):
        # Calculate correlation matrix
        corr_matrix = data.corr()
    else:
        corr_matrix = data

    # Create heatmap
    im = plt.imshow(corr_matrix, cmap='coolwarm', aspect='auto',
                    vmin=-1, vmax=1)

    # Add colorbar
    cbar = plt.colorbar(im)
    cbar.set_label('Correlation', rotation=270, labelpad=20)

    # Set ticks and labels
    plt.xticks(range(len(corr_matrix.columns)), corr_matrix.columns,
               rotation=45, ha='right')
    plt.yticks(range(len(corr_matrix.columns)), corr_matrix.columns)

    # Add correlation values
    for i in range(len(corr_matrix)):
        for j in range(len(corr_matrix.columns)):
            text = plt.text(j, i, f'{corr_matrix.iloc[i, j]:.2f}',
                          ha='center', va='center', color='black', fontsize=9)

    plt.title(title, fontsize=16, fontweight='bold', pad=20)
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

Python - Seaborn

import seaborn as sns

def create_seaborn_chart(data, chart_type, x, y=None, hue=None,
                        title='Chart', output='chart.png'):
    """
    Create charts using Seaborn
    """
    plt.figure(figsize=(12, 6))

    # Set style
    sns.set_style("whitegrid")
    sns.set_palette("husl")

    if chart_type == 'bar':
        sns.barplot(data=data, x=x, y=y, hue=hue)

    elif chart_type == 'line':
        sns.lineplot(data=data, x=x, y=y, hue=hue, marker='o')

    elif chart_type == 'scatter':
        sns.scatterplot(data=data, x=x, y=y, hue=hue, size=hue, alpha=0.6)

    elif chart_type == 'box':
        sns.boxplot(data=data, x=x, y=y, hue=hue)

    elif chart_type == 'violin':
        sns.violinplot(data=data, x=x, y=y, hue=hue)

    elif chart_type == 'dist':
        sns.histplot(data=data, x=x, hue=hue, kde=True)

    elif chart_type == 'heatmap':
        sns.heatmap(data.corr(), annot=True, fmt='.2f', cmap='coolwarm',
                   center=0, square=True, linewidths=1)

    elif chart_type == 'pairplot':
        # Special case - creates its own figure
        g = sns.pairplot(data, hue=hue)
        g.savefig(output, dpi=300, bbox_inches='tight')
        return output

    plt.title(title, fontsize=16, fontweight='bold')
    plt.xticks(rotation=45, ha='right')
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

# Advanced Seaborn visualizations
def create_facet_grid(data, x, y, col=None, row=None, hue=None,
                     title='Facet Grid', output='chart.png'):
    """
    Create faceted charts
    """
    g = sns.FacetGrid(data, col=col, row=row, hue=hue, height=4)
    g.map(sns.scatterplot, x, y, alpha=0.6)
    g.add_legend()
    g.fig.suptitle(title, y=1.02, fontsize=16, fontweight='bold')

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

JavaScript - Chart.js

const { ChartJSNodeCanvas } = require('chartjs-node-canvas');

async function createBarChart(data, options = {}) {
  const width = options.width || 800;
  const height = options.height || 600;

  const chartJSNodeCanvas = new ChartJSNodeCanvas({ width, height });

  const configuration = {
    type: 'bar',
    data: {
      labels: data.labels,
      datasets: [{
        label: options.label || 'Dataset',
        data: data.values,
        backgroundColor: 'rgba(54, 162, 235, 0.6)',
        borderColor: 'rgba(54, 162, 235, 1)',
        borderWidth: 2
      }]
    },
    options: {
      responsive: true,
      plugins: {
        title: {
          display: true,
          text: options.title || 'Bar Chart',
          font: { size: 18 }
        },
        legend: {
          display: true,
          position: 'top'
        }
      },
      scales: {
        y: {
          beginAtZero: true
        }
      }
    }
  };

  const imageBuffer = await chartJSNodeCanvas.renderToBuffer(configuration);
  return imageBuffer;
}

async function createLineChart(data, options = {}) {
  const width = options.width || 800;
  const height = options.height || 600;

  const chartJSNodeCanvas = new ChartJSNodeCanvas({ width, height });

  const configuration = {
    type: 'line',
    data: {
      labels: data.labels,
      datasets: [{
        label: options.label || 'Dataset',
        data: data.values,
        borderColor: 'rgba(75, 192, 192, 1)',
        backgroundColor: 'rgba(75, 192, 192, 0.2)',
        borderWidth: 2,
        tension: 0.4
      }]
    },
    options: {
      responsive: true,
      plugins: {
        title: {
          display: true,
          text: options.title || 'Line Chart',
          font: { size: 18 }
        }
      },
      scales: {
        y: {
          beginAtZero: true
        }
      }
    }
  };

  const imageBuffer = await chartJSNodeCanvas.renderToBuffer(configuration);
  return imageBuffer;
}

async function createPieChart(data, options = {}) {
  const width = options.width || 800;
  const height = options.height || 600;

  const chartJSNodeCanvas = new ChartJSNodeCanvas({ width, height });

  const configuration = {
    type: 'pie',
    data: {
      labels: data.labels,
      datasets: [{
        data: data.values,
        backgroundColor: [
          'rgba(255, 99, 132, 0.6)',
          'rgba(54, 162, 235, 0.6)',
          'rgba(255, 206, 86, 0.6)',
          'rgba(75, 192, 192, 0.6)',
          'rgba(153, 102, 255, 0.6)',
          'rgba(255, 159, 64, 0.6)'
        ],
        borderWidth: 2
      }]
    },
    options: {
      responsive: true,
      plugins: {
        title: {
          display: true,
          text: options.title || 'Pie Chart',
          font: { size: 18 }
        },
        legend: {
          position: 'right'
        }
      }
    }
  };

  const imageBuffer = await chartJSNodeCanvas.renderToBuffer(configuration);
  return imageBuffer;
}

Interactive Charts - Plotly

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

def create_interactive_bar(data, x, y, title='Bar Chart', output='chart.html'):
    """
    Create interactive bar chart with Plotly
    """
    fig = px.bar(data, x=x, y=y, title=title,
                 color=y, color_continuous_scale='Viridis')

    fig.update_layout(
        font=dict(size=14),
        showlegend=True,
        hovermode='x unified'
    )

    fig.write_html(output)
    return output

def create_interactive_line(data, x, y, title='Line Chart', output='chart.html'):
    """
    Create interactive line chart
    """
    fig = px.line(data, x=x, y=y, title=title, markers=True)

    fig.update_traces(line=dict(width=3))

    fig.update_layout(
        hovermode='x unified',
        font=dict(size=14)
    )

    fig.write_html(output)
    return output

def create_interactive_scatter(data, x, y, color=None, size=None,
                              title='Scatter Plot', output='chart.html'):
    """
    Create interactive scatter plot
    """
    fig = px.scatter(data, x=x, y=y, color=color, size=size,
                    title=title, hover_data=data.columns)

    fig.update_traces(marker=dict(line=dict(width=0.5, color='white')))

    fig.write_html(output)
    return output

def create_3d_scatter(data, x, y, z, color=None, title='3D Scatter',
                     output='chart.html'):
    """
    Create 3D scatter plot
    """
    fig = px.scatter_3d(data, x=x, y=y, z=z, color=color, title=title)

    fig.update_layout(scene=dict(
        xaxis_title=x,
        yaxis_title=y,
        zaxis_title=z
    ))

    fig.write_html(output)
    return output

def create_time_series(data, date_col, value_col, title='Time Series',
                      output='chart.html'):
    """
    Create time series chart
    """
    fig = go.Figure()

    fig.add_trace(go.Scatter(
        x=data[date_col],
        y=data[value_col],
        mode='lines+markers',
        name=value_col,
        line=dict(width=2),
        marker=dict(size=6)
    ))

    # Add range slider
    fig.update_xaxes(
        rangeslider_visible=True,
        rangeselector=dict(
            buttons=list([
                dict(count=7, label="1w", step="day", stepmode="backward"),
                dict(count=1, label="1m", step="month", stepmode="backward"),
                dict(count=3, label="3m", step="month", stepmode="backward"),
                dict(count=1, label="1y", step="year", stepmode="backward"),
                dict(step="all")
            ])
        )
    )

    fig.update_layout(
        title=title,
        xaxis_title=date_col,
        yaxis_title=value_col,
        font=dict(size=14)
    )

    fig.write_html(output)
    return output

def create_dashboard(data, output='dashboard.html'):
    """
    Create multi-chart dashboard
    """
    from plotly.subplots import make_subplots

    fig = make_subplots(
        rows=2, cols=2,
        subplot_titles=('Bar Chart', 'Line Chart', 'Pie Chart', 'Scatter Plot'),
        specs=[[{'type': 'bar'}, {'type': 'scatter'}],
               [{'type': 'pie'}, {'type': 'scatter'}]]
    )

    # Add charts
    # Bar chart
    fig.add_trace(
        go.Bar(x=data['category'], y=data['value1'], name='Bar'),
        row=1, col=1
    )

    # Line chart
    fig.add_trace(
        go.Scatter(x=data['date'], y=data['value2'], mode='lines', name='Line'),
        row=1, col=2
    )

    # Pie chart
    fig.add_trace(
        go.Pie(labels=data['category'], values=data['value1'], name='Pie'),
        row=2, col=1
    )

    # Scatter plot
    fig.add_trace(
        go.Scatter(x=data['value1'], y=data['value2'], mode='markers', name='Scatter'),
        row=2, col=2
    )

    fig.update_layout(height=800, showlegend=True, title_text="Dashboard")

    fig.write_html(output)
    return output

Chart Styling and Themes

# Matplotlib themes
def apply_matplotlib_theme(theme='default'):
    """
    Apply theme to matplotlib charts
    """
    themes = {
        'default': 'seaborn-v0_8-darkgrid',
        'minimal': 'seaborn-v0_8-whitegrid',
        'dark': 'dark_background',
        'classic': 'classic',
        'ggplot': 'ggplot'
    }

    plt.style.use(themes.get(theme, 'default'))

# Custom color palettes
COLOR_PALETTES = {
    'corporate': ['#003f5c', '#58508d', '#bc5090', '#ff6361', '#ffa600'],
    'pastel': ['#a8e6cf', '#dcedc1', '#ffd3b6', '#ffaaa5', '#ff8b94'],
    'vibrant': ['#e74c3c', '#3498db', '#2ecc71', '#f39c12', '#9b59b6'],
    'monochrome': ['#2c3e50', '#34495e', '#7f8c8d', '#95a5a6', '#bdc3c7']
}

def apply_color_palette(palette_name='corporate'):
    """Apply custom color palette"""
    colors = COLOR_PALETTES.get(palette_name, COLOR_PALETTES['corporate'])
    plt.rcParams['axes.prop_cycle'] = plt.cycler(color=colors)

Export Formats

def export_chart_multiple_formats(fig, base_name='chart'):
    """
    Export chart in multiple formats
    """
    formats = {
        'png': {'dpi': 300, 'transparent': False},
        'svg': {'format': 'svg'},
        'pdf': {'format': 'pdf'},
        'jpg': {'dpi': 300, 'format': 'jpg'}
    }

    files = []

    for fmt, kwargs in formats.items():
        output_file = f"{base_name}.{fmt}"
        fig.savefig(output_file, bbox_inches='tight', **kwargs)
        files.append(output_file)

    return files

Chart Generation Pipeline

def generate_charts_from_data(data, chart_configs, output_dir='charts'):
    """
    Generate multiple charts from data based on configurations

    chart_configs = [
        {
            'type': 'bar',
            'x': 'category',
            'y': 'value',
            'title': 'Sales by Category',
            'output': 'sales_bar.png'
        },
        ...
    ]
    """
    import os
    os.makedirs(output_dir, exist_ok=True)

    generated_charts = []

    for config in chart_configs:
        chart_type = config['type']
        output = os.path.join(output_dir, config['output'])

        if chart_type == 'bar':
            create_bar_chart(data, config['x'], config['y'],
                           config.get('title', 'Chart'), output)

        elif chart_type == 'line':
            create_line_chart(data, config['x'], config['y'],
                            config.get('title', 'Chart'), output)

        elif chart_type == 'pie':
            create_pie_chart(data, config['x'], config['y'],
                           config.get('title', 'Chart'), output)

        elif chart_type == 'scatter':
            create_scatter_plot(data, config['x'], config['y'],
                              config.get('color'), config.get('size'),
                              config.get('title', 'Chart'), output)

        elif chart_type == 'heatmap':
            create_heatmap(data, config.get('title', 'Chart'), output)

        generated_charts.append({
            'type': chart_type,
            'title': config.get('title'),
            'file': output
        })

    return generated_charts

Best Practices

1. Choose appropriate chart types for your data
2. Use clear, descriptive titles and labels
3. Apply consistent color schemes across charts
4. Ensure readability (font sizes, contrast)
5. Add context (annotations, reference lines)
6. Export in high resolution (300 DPI minimum)
7. Consider accessibility (color-blind friendly palettes)
8. Test on different screen sizes (responsive design)
9. Optimize file sizes for web use
10. Document chart generation code for reproducibility

Common Chart Patterns

Comparison Charts

• Bar charts for categorical comparisons
• Grouped bar charts for multi-series comparison
• Stacked bar charts for part-to-whole comparisons

Trend Charts

• Line charts for time series
• Area charts for cumulative trends
• Sparklines for inline trends

Distribution Charts

• Histograms for frequency distribution
• Box plots for statistical distribution
• Violin plots for distribution shape

Relationship Charts

• Scatter plots for correlations
• Bubble charts for 3D relationships
• Heatmaps for matrix relationships

Composition Charts

• Pie charts for simple part-to-whole
• Stacked area charts for trends over time
• Treemaps for hierarchical composition

Notes

• Always label axes and provide units
• Use appropriate scales (linear, logarithmic)
• Consider data-ink ratio (minimize chart junk)
• Test charts with different data ranges
• Provide legends when using multiple series
• Use annotations to highlight key insights
• Export charts in vector formats for publications
• Keep color schemes consistent across related charts
• Consider cultural differences in color meanings
• Validate data before visualization