Data Visualization SpecialistTool-Specific ImplementationIntermediateSingle prompt

Python Visualization Code AI Prompt

Write production-quality Python visualization code for this chart. Chart type: {{chart_type}} Data: {{data_description}} Library preference: {{library}} (matplotlib, seaborn, pl... Copy this prompt template, run it in your AI tool, and use related prompts to continue the workflow.

Prompt text
Write production-quality Python visualization code for this chart.

Chart type: {{chart_type}}
Data: {{data_description}}
Library preference: {{library}} (matplotlib, seaborn, plotly, altair, bokeh)
Output format: {{output}} (static PNG/PDF, interactive HTML, Jupyter notebook)

1. Library selection guide:

   Matplotlib:
   - Best for: publication-quality static charts, full control over every element
   - Pros: complete control, widely supported, PDF/SVG export
   - Cons: verbose API, interactive features require additional work

   Seaborn:
   - Best for: statistical charts (distributions, regressions, heatmaps)
   - Pros: high-level API, beautiful defaults, statistical integration
   - Cons: built on matplotlib, limited interactivity

   Plotly:
   - Best for: interactive web-based charts
   - Pros: interactive by default, Plotly Express high-level API, Dash integration
   - Cons: larger files, not ideal for publication

   Altair:
   - Best for: declarative grammar-of-graphics style charts
   - Pros: concise code, vega-altair grammar, responsive
   - Cons: data size limit in browser (5000 rows default)

2. Code standards:
   - Use matplotlib style sheets or seaborn themes for consistent styling
   - Set figure size explicitly: fig, ax = plt.subplots(figsize=(10, 6))
   - Use clear variable names that match the data (not ax1, ax2)
   - Add docstring explaining what the function produces
   - Save with appropriate DPI: plt.savefig('chart.png', dpi=300, bbox_inches='tight')

3. Accessibility in code:
   - Set colorblind-safe palette: plt.rcParams['axes.prop_cycle'] = cycler(color=okabe_ito_palette)
   - Add alt text for web outputs
   - Ensure minimum font sizes: plt.rcParams['font.size'] = 12

4. Reusable function template:
   Write the chart as a function that accepts data and styling parameters:
   def create_[chart_name](data, title, color_col=None, highlight=None, save_path=None):
       'Creates a [description] chart from the provided DataFrame.'
       ...
       return fig, ax

5. For this specific chart:
   - Import statements needed
   - Data preparation steps
   - Chart creation code with full styling
   - Annotation code
   - Save/display code

Return: complete, runnable Python code with comments, color palette definition, and example usage.

When to use this prompt

Use case 01

Use it when you want to begin tool-specific implementation work without writing the first draft from scratch.

Use case 02

Use it when you want a more consistent structure for AI output across projects or datasets.

Use case 03

Use it when you want prompt-driven work to turn into a reusable notebook or repeatable workflow later.

Use case 04

Use it when you want a clear next step into adjacent prompts in Tool-Specific Implementation or the wider Data Visualization Specialist library.

What the AI should return

The AI should return a structured result that covers the main requested outputs, such as Library selection guide:, Best for: publication-quality static charts, full control over every element, Pros: complete control, widely supported, PDF/SVG export. The final answer should stay clear, actionable, and easy to review inside a tool-specific implementation workflow for data visualization specialist work.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Tool-Specific Implementation.

Frequently asked questions

What does the Python Visualization Code prompt do?+

It gives you a structured tool-specific implementation starting point for data visualization specialist work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for data visualization specialist workflows and marked as intermediate, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Python Visualization Code is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Power BI Best Practices, Tableau Best Practices.