/create-viz - Create Visualizations

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Create publication-quality data visualizations using Python. Generates charts from data with best practices for clarity, accuracy, and design.

Usage

/create-viz <data source> [chart type] [additional instructions]

Workflow

1. Understand the Request

Determine:

2. Get the Data

If data warehouse is connected and data needs querying:

  1. Write and execute the query
  2. Load results into a pandas DataFrame

If data is pasted or uploaded:

  1. Parse the data into a pandas DataFrame
  2. Clean and prepare as needed (type conversions, null handling)

If data is from a previous analysis in the conversation:

  1. Reference the existing data

3. Select Chart Type

If the user didn't specify a chart type, recommend one based on the data and question:

Data Relationship Recommended Chart
Trend over time Line chart
Comparison across categories Bar chart (horizontal if many categories)
Part-to-whole composition Stacked bar or area chart (avoid pie charts unless <6 categories)
Distribution of values Histogram or box plot
Correlation between two variables Scatter plot
Two-variable comparison over time Dual-axis line or grouped bar
Geographic data Choropleth map
Ranking Horizontal bar chart
Flow or process Sankey diagram
Matrix of relationships Heatmap

Explain the recommendation briefly if the user didn't specify.

4. Generate the Visualization

Write Python code using one of these libraries based on the need:

Code requirements:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# Set professional style
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette("husl")

# Create figure with appropriate size
fig, ax = plt.subplots(figsize=(10, 6))

# [chart-specific code]

# Always include:
ax.set_title('Clear, Descriptive Title', fontsize=14, fontweight='bold')
ax.set_xlabel('X-Axis Label', fontsize=11)
ax.set_ylabel('Y-Axis Label', fontsize=11)

# Format numbers appropriately
# - Percentages: '45.2%' not '0.452'
# - Currency: '$1.2M' not '1200000'
# - Large numbers: '2.3K' or '1.5M' not '2300' or '1500000'

# Remove chart junk
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

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

5. Apply Design Best Practices

Color:

Typography:

Layout:

Accuracy:

6. Save and Present

  1. Save the chart as a PNG file with descriptive name
  2. Display the chart to the user
  3. Provide the code used so they can modify it
  4. Suggest variations (different chart type, different grouping, zoomed time range)

Examples

/create-viz Show monthly revenue for the last 12 months as a line chart with the trend highlighted
/create-viz Here's our NPS data by product: [pastes data]. Create a horizontal bar chart ranking products by score.
/create-viz Query the orders table and create a heatmap of order volume by day-of-week and hour

Tips