Visualization AI Prompts

Create an exploratory visualization dashboard for this dataset using matplotlib and seaborn. Include: - Histogram for each numeric column (up to 6, pick the most interesting) - Bar chart showing the top 10 values of the highest-cardinality categorical column - Correlation heatmap for all numeric columns - Line chart showing the trend over time if a date column exists Layout: a 2×2 or 2×3 grid of subplots. Style: clean white background, readable font sizes, meaningful titles and axis labels. Do not use default matplotlib styles — apply a clean, minimal look.

Create a ranked bar chart for the most important categorical breakdown in this dataset: 1. Identify the best categorical column to use as the dimension (highest analytical value) 2. Identify the primary numeric metric to measure 3. Calculate the metric per category and sort from highest to lowest 4. Create a horizontal bar chart (easier to read category labels) 5. Color the top 3 bars in a highlight color, the rest in a neutral color 6. Add data labels at the end of each bar showing the exact value 7. Add a vertical dashed line at the average value 8. Title the chart: '[Metric] by [Category] — [period]' Use a clean, minimal style with no unnecessary gridlines or chart elements.

Create a well-designed correlation heatmap for the numeric columns in this dataset: 1. Compute the Pearson correlation matrix for all numeric columns 2. Plot as a heatmap using a diverging colormap: dark blue for strong positive correlation, dark red for strong negative, white for zero 3. Show only the lower triangle (remove redundant upper triangle) 4. Add the correlation coefficient value inside each cell, rounded to 2 decimal places 5. Bold or highlight cells where |r| > 0.7 6. Sort columns and rows so that highly correlated variables are clustered together (use hierarchical clustering on the correlation matrix) 7. Set figure size so all labels are readable without overlapping Add a subtitle explaining what the strongest correlation means in business terms.

Create a complete chart pack for a {{report_type}} report on {{subject}} covering {{time_range}}. The pack should include exactly these chart types in order: 1. An overview trend chart: {{primary_metric}} over time with 30-day moving average 2. A breakdown chart: {{primary_metric}} by {{dimension_column}} as a ranked bar chart 3. A comparison chart: current {{time_range}} vs prior {{time_range}} for top 5 {{dimension_column}} values 4. A composition chart: share of {{primary_metric}} by {{segment_column}} as a donut chart 5. A scatter or correlation chart: {{primary_metric}} vs {{secondary_metric}} with regression line Style requirements: - Consistent color palette across all 5 charts: primary color {{brand_color}}, accent {{accent_color}} - All charts use the same font family and font sizes - Each chart has a title, subtitle (one-sentence insight), and data source label - Export as a 5-page PDF or a 5-slide PowerPoint layout

Create a visualization comparing the distribution of a key metric across multiple groups or segments: 1. Identify the primary numeric metric and the best grouping column in the dataset 2. Create a violin plot showing the full distribution shape per group 3. Overlay individual data points using a strip plot (jittered) for transparency 4. Add a box plot overlay to show median and quartiles clearly 5. Annotate each group with its median value and sample size (n=) 6. Sort groups from highest to lowest median 7. Use a color palette that distinguishes groups clearly without being distracting Add a title that describes what comparison is being shown.

Create an executive-ready KPI dashboard for this dataset. 1. Identify the 4–6 most important business metrics from the column names and data context 2. For each metric, create a time-series line chart with: - A trend line (7-day rolling average) - An annotation marking the most significant change point - Clear title, axis labels, and current value callout 3. Add a summary row at the top showing: current value, % change vs prior period, and a directional arrow (▲▼) 4. Use a consistent color palette — brand-neutral (blues and grays) 5. Layout should be presentation-ready (16:9 aspect ratio)

Step 1: Identify the single most important insight in this dataset that deserves a visual treatment. Step 2: Choose the most appropriate chart type for that insight and justify the choice. Step 3: Create the primary chart with full production styling: clean theme, annotated key points, descriptive title, subtitle with the insight stated explicitly. Step 4: Create one supporting chart that provides necessary context or comparison for the primary chart. Step 5: Write a 3-sentence data caption for the primary chart that a non-technical reader can understand — what is shown, what is the key finding, and what action does it suggest.

Create a calendar heatmap to reveal daily and weekly patterns in this time series: 1. Aggregate the primary metric by day 2. Create a GitHub-style calendar heatmap where: - Rows are days of the week (Mon–Sun) - Columns are weeks - Cell color intensity represents the metric value (lighter = lower, darker = higher) 3. Use a sequential color palette (e.g. light yellow to dark green) 4. Add month labels along the top 5. Add a color bar legend showing the value scale 6. Annotate the 3 highest and 3 lowest days with their exact values Below the chart, answer: Is there a clear day-of-week pattern? Is there a clear seasonal pattern across months?

Step 1: Analyze the dataset and identify the top 3 most important insights — each should be a specific, quantified finding that could drive a business decision. Step 2: For each insight, select the single best chart type to communicate it visually. Justify your choice in one sentence. Step 3: Build chart 1 with full production styling: meaningful title, subtitle stating the insight explicitly, annotated key data points, clean minimal theme. Step 4: Build charts 2 and 3 in the same visual style as chart 1, ensuring a consistent look across all three. Step 5: Arrange all three charts into a single figure with a shared title. Write a 50-word executive caption that tells the complete story across all three charts in sequence. Step 6: Export the figure at 300 DPI. Suggest the most appropriate slide or document format for sharing with a non-technical audience.

Create a visualization of missing data patterns in this dataset: 1. Generate a heatmap where rows are observations and columns are variables — missing values shown in a distinct color (e.g. red), present values in white or light gray 2. Sort columns left to right by missing value percentage (most missing on the left) 3. Add a bar chart below the heatmap showing the missing percentage per column 4. Add annotations for any columns with more than 20% missing values 5. Check for patterns: are missing values random, or do they cluster in certain rows or time periods? Write a 2-sentence interpretation: what is the overall completeness of the dataset, and is the missing data pattern random or systematic?

Create a compact multi-metric summary dashboard using sparklines: 1. Identify the top 6–8 business metrics in this dataset 2. For each metric, create one row in a summary table containing: - Metric name - Current value (latest period) - Change vs prior period: absolute and percentage, with colored arrow (▲ green / ▼ red) - A sparkline — a tiny inline line chart showing the last 12 periods of trend - A status indicator: ✅ On track / ⚠️ Watch / 🔴 Alert (based on whether the trend is improving or deteriorating) 3. Sort metrics by business importance, not alphabetically 4. Use a clean table layout — no heavy borders, subtle row alternation 5. The whole dashboard should fit on a single A4 page or slide This format is designed for a weekly business review where space is limited.

Create a composition chart showing how a total is broken down across categories: 1. Identify the best categorical column for the breakdown (5–8 categories ideal) 2. Calculate each category's share of the total 3. Create a donut chart (not a pie — donut is cleaner and leaves room for a center label) 4. Place the total value and a label ('Total [metric]') in the center of the donut 5. Show percentage labels on each segment, but only if the segment is larger than 3% (suppress tiny labels) 6. Group all segments smaller than 2% into an 'Other' category 7. Use a qualitative color palette — no gradients, each category a distinct color 8. Add a clean legend outside the chart Also create a companion table showing: category, count, percentage, ranked from largest to smallest.

Create an annotated scatter plot showing the relationship between two key variables: 1. Identify the two most interesting numeric variables to compare (ideally a cause and an effect) 2. Create a scatter plot with: - Each point representing one row - A linear regression line with confidence band (95%) - The R² value displayed in the top corner 3. Color points by a categorical variable if one exists (e.g. region, product type) 4. Label the top 5 and bottom 5 outlier points with their identifier 5. Add axis labels that describe what each variable measures, including units 6. If the relationship is non-linear, also fit and plot a polynomial regression line Write a one-sentence interpretation below the chart: what does the relationship mean in business terms?

Create a comparison chart for {{metric}} broken down by {{segment_column}}. Chart specifications: - Chart type: {{chart_type}} — bar chart for categorical comparison, line chart for time-series comparison, box plot for distribution comparison - Time range: {{time_range}} - Highlight the top 3 and bottom 3 segments with distinct colors - Add a dashed reference line at the overall average value - Annotate the highest and lowest data points with their exact values - Title: '{{metric}} by {{segment_column}} — {{time_range}}' - Export at 300 DPI suitable for a slide deck

Create a clean time series chart for the primary metric in this dataset: 1. Plot the raw values as a thin line in a neutral color 2. Overlay a 7-day rolling average as a thicker, more prominent line 3. Add a horizontal reference line at the overall average 4. Annotate the global maximum and minimum points with their values and dates 5. Shade the area under the rolling average line with low opacity 6. Use a minimal style: no gridlines on the x-axis, light gridlines on y-axis, clean legend Title the chart with the metric name and the date range shown.

Create a waterfall chart to show how a metric changed between two periods: 1. Identify the starting value (e.g. last month's total) and ending value (this month's total) 2. Decompose the change into its contributing factors — what drove the increase or decrease? (look for dimension columns like region, product, channel) 3. Build a waterfall chart where: - First bar: starting value (blue) - Middle bars: positive contributors (green, going up) and negative contributors (red, going down) - Final bar: ending value (blue) 4. Add value labels on top of each bar 5. Add a dashed horizontal reference line at the starting value 6. Sort contributing factors from largest positive to largest negative Title the chart: '[Metric]: [Start Period] to [End Period] — What Changed and Why'