Gemini Data Analysis intermediate

Chart Type Recommender

Added Apr 2, 2026

You are a data visualization expert who follows the principles of Edward Tufte and Stephen Few. I need to visualize the following data for a [AUDIENCE_TYPE] audience: [DATA_DESCRIPTION]. The visualization will be used for [PURPOSE] and displayed in [CONTEXT]. Recommend the optimal chart type by analyzing: the nature of the data (categorical, temporal, quantitative, geographic, hierarchical), the relationship being shown (comparison, composition, distribution, trend, correlation, flow), the number of data points and dimensions, and the audience's data literacy level. For your recommendation, provide: your top recommended chart type with a detailed justification, two alternative chart types ranked by suitability with trade-off analysis, specific design guidance including axis labels, color usage, annotations, and grid lines, common mistakes to avoid with this chart type and data combination, accessibility considerations for colorblind users and screen readers, a sample implementation snippet using [VIZ_TOOL], and an explanation of what NOT to use and why (the anti-recommendation). If the data would benefit from a small multiples approach or a combined visualization, explain that option as well.

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About This Prompt

This prompt provides expert-level data visualization guidance by analyzing your specific data and recommending the optimal chart type with detailed justification. Instead of guessing whether to use a bar chart or a line chart, you get a recommendation backed by data visualization principles from leading experts. The output includes design specifications, accessibility guidance, common mistakes to avoid, and working code snippets. It also explains what chart types to avoid and why, which is just as valuable as knowing what to use. This is essential for data analysts, researchers, product managers, and anyone who needs to communicate data effectively to stakeholders.

Variables to Customize


[AUDIENCE_TYPE]

Who will view the visualization

Example: non-technical executive leadership team


[DATA_DESCRIPTION]

Description of the data to visualize

Example: quarterly revenue broken down by 5 product lines over the past 3 years, with target vs actual comparisons


[PURPOSE]

What decision or insight the visualization should support

Example: identifying which product lines are underperforming relative to targets and showing trend direction


[CONTEXT]

Where the visualization will appear

Example: a quarterly board presentation slide deck


[VIZ_TOOL]

Preferred visualization tool or library

Example: Python matplotlib with seaborn

Tips for Best Results

Describe your data in detail including the number of categories, time periods, and data points for the best recommendation
Consider your audience's data literacy when choosing between simpler and more complex chart types
Always test your visualization with someone unfamiliar with the data to check if the message comes through clearly

Example Output

## Recommended: Grouped Bar Chart with Target Line Overlay

### Why This Chart Type
Your data has two key dimensions: 5 categorical product lines and 12 temporal periods (quarters). For an executive audience, grouped bar charts are immediately readable and allow quick comparison across products within each quarter. The target overlay adds a clear benchmark without adding cognitive load.

### Alternative 1: Small Multiples Line Chart (Suitability: 8/10)
Five separate line charts, one per product line, each showing actual vs. target. Better for trend analysis but requires more space and makes cross-product comparison harder.

### Alternative 2: Slope Chart (Suitability: 6/10)
Good for showing change between two time points but loses the quarterly granularity.

### Anti-Recommendation: Stacked Bar Chart
Do NOT use stacked bars here. With 5 product lines, the middle segments become impossible to compare accurately because they share neither a common baseline nor a common top.

### Design Specifications:
- Color: Use a 5-color sequential palette; highlight underperforming lines in red...

data-visualization charts analytics data-science presentation

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