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Introduction to LLM AI Charts and Maps for Excel
It is the same charting engine as the Power BI visual, hosted in Excel: you select a range, say what you would like to see — or say nothing — and a real, interactive chart is drawn in the worksheet. The chart is an object on the sheet, it follows your cells, and it is saved inside the workbook, so it redraws for anyone who opens the file.
This page is the deep dive: how a range becomes a chart, what the sample table and the example charts are for, what linking an account does, how your own AI model reaches Excel, and the chart catalog the engine draws from. The button-by-button reference lives in Help, and the short list of things the two hosts genuinely do differently is here.
Table of Contents
What it does, and where the chart lives
LLM AI Charts and Maps for Excel writes a chart for the data you select and draws it in the worksheet. It is a content add-in: it lives in an object on the sheet rather than in a side pane, which is why the chart can be positioned, resized and printed like anything else in the workbook — and why it is the chart itself you are looking at, not a picture of one pasted out of a chat window.
The chart is real code, saved inside the workbook. Three consequences are worth having up front, because they shape everything below:
- It redraws from your cells, instantly and offline. Edit the bound range and the chart repaints from the code already in the file — no call to us, nothing spent.
- It travels with the file. Send the workbook to a colleague and the chart draws for them with nothing installed and no sign-in.
- Nothing is a dead end. Every version is kept on your account, the code can be read and edited, and an earlier version can be pulled back into the workbook at any time.
Binding a range — and what “the used range” means
There are no data wells here. A worksheet has no model to fill them from, so the range is the data: point the add-in at a rectangle whose first row is the column headers, and every column under that header row becomes a field the chart can use.
Menu > Use selection binds whatever you have selected right now, and the range summary under the heading tells you what it read. Two habits save most of the trouble people hit here:
- Select the headers too. A selection that starts one row too low makes your first data row the column names.
- Or select nothing and let it take the used range. With no explicit selection the add-in reads the sheet's used range — the rectangle Excel considers occupied, from the first used cell to the last. That is the friendly default on a tidy sheet with one table on it, and the wrong answer on a sheet that also carries a title, a stray note three columns over, or two tables side by side. On a sheet like that, select the block you mean.
It charts what you can see. The read takes the visible cells of the range, so rows hidden by a filter or by hand, and hidden columns, are not in the chart — which is almost always what you wanted, and occasionally a surprise worth knowing about. The one case it refuses is a hidden header row: it says so rather than guessing at names.
Two more facts about raw rows, both of which the Power BI visual is spared because a semantic model has already answered them:
- Aggregation is yours to set. Nothing in a sheet has been summarised, so the add-in decides how to summarise a measure per group. The default sums amounts and averages rates; if a column is a percentage, a rate or an index, set Aggregation to Average or Median. Summing a percentage is the classic wrong answer.
- Dates have to be typed as dates. Excel hands dates over as serial numbers, and the add-in reads the cell formatting to tell a date from a number. A date column formatted as General arrives as a plain number and gets charted like one: format it as a date in the sheet and bind again.
Your rows never leave the workbook. A generation request carries the measured shape of the range — your column headers, the type inferred for each one, aggregate statistics and a few short category labels — plus the size of the object, your settings, your locale and anything you typed. See what's sent shows you the exact payload before anything is sent; the full disclosure is in the privacy policy for the add-in.
Seeing it work before you sign in
Three things work with no account at all, and between them they answer “is this any good?” before you commit to anything:
- Insert sample data writes a small demo table to a new sheet and binds to it, so there is something real in front of the add-in within a couple of clicks.
- Show an example chart draws a finished chart over that sample table. Nothing of yours is measured or sent — the data is ours — which is why it needs no acceptance and no sign-in. It is the product doing the actual thing, not a screenshot.
- What fits? reads the columns in your range and lists the chart types that suit them, ranked, with the ones that do not fit explained. It reaches no AI and costs nothing. Because it measures your columns, it asks you to accept the terms first.
And viewing is always free of charge. A workbook that already carries a chart draws it for anyone who opens it, with no add-in to set up and no account. What an active license decides is whether that render carries a watermark — the three cases are set out in Help. Rendering is never refused.
Generating, adjusting, and keeping versions
With data bound, the chart-type list opens by itself, ranked for your columns. Highlight a row to ask for that type, or leave every row unhighlighted and let the engine choose. The “What would you like to see?” box is optional: “revenue by region, as bars”, “dark theme, no gridlines, label the top five” — or nothing at all, which is a perfectly good way to work.
Generate is the step that spends a credit; everything before it is free of charge. Progress streams in the pane, Stop is there while it runs, and the chart draws in the object — usually inside a minute.
After that, the two buttons that matter are Adjust… and Different take. Adjust takes a sentence and keeps your chart while it applies the change, rather than starting over; Different take keeps the data and the chart type and asks for a visibly different rendering of it, for when the type is right and the execution is not. Change type goes back to the list with your data still bound, and spends nothing until you generate.
Every generation is a version held on your account. Menu > Edit the code opens that chart on your account page in the browser, where the code can be read, edited and saved as a new version; typing a version number into the Version setting brings that version back into the workbook. Fetching stored code never calls a model and never mints a new version, so moving between versions costs nothing.
The add-in has no format pane — a content add-in gets none — so the knobs live in a Settings overlay, and they travel with the chart inside the workbook. The distinction to learn first is that a few settings apply now and repaint the chart on screen, while the rest steer what the engine makes and therefore take effect on your next Generate. The full table is in Help.
Signing in, and the link your workbook holds
Generating needs a linked account; viewing never does. Link your account opens our own sign-in page in a dialog — the same sign-in as the rest of the site, multi-factor and all — and the pane comes back naming who it is linked to and how many credits are left.
There is no license key to type into the add-in, on purpose. A typed key is a permanent credential sitting in a file that gets emailed around. What the workbook stores instead is a short-lived link: it is scoped to this one mechanism, it expires on its own rather than sliding forward while you work, and revoking it leaves your license untouched. Open the workbook a week later and Generate will ask you to re-link — two clicks, and it needs your account credentials, which is exactly the control: whoever has the file does not have your account.
None of that touches rendering. A chart draws from the code in the workbook whether the link is live, expired or was never there. If you are ever unsure which state you are in, the pane says so in words rather than leaving you to infer it from a failure.
Licenses are per organization, not per named seat, and one license covers both hosts — the Excel add-in and the Power BI visual, on one account, drawing on the same credits. Licenses and credits are bought on our site rather than through Microsoft, and the current figures are there and on the price list.
Bringing your own AI model
If your organization wants generations to run through its AI endpoint — your Azure OpenAI deployment, your OpenAI or Anthropic key — that works in Excel, and it is set up once, on your account: not in the add-in. Connect the key on your account page (Account → LLM Chart), switch the provider on, and pin it as your account's model.
From then on every generation the account makes — from Excel or from the Power BI visual — is routed to your endpoint, because the override is applied on our server rather than chosen by the client. The Excel pane has no key field and simply shows the default model, which is cosmetic: the routing is already decided by the account it is linked to.
The reasons to do it are the usual two: the model call goes to an endpoint your organization already sanctions, and you pay your AI provider directly, so a generation costs a low fixed service fee instead of the usual credit cost. The setup itself is described step by step in the Power BI section — Setup Bring Your Own Model — and the account it configures is the same account Excel links to.
Chart Catalog
The engine can render the chart types below, and the add-in draws from the same catalog and the same picker as the Power BI visual. You don't pick from this list directly — you describe what you want (or simply let the AI decide) and it chooses the best fit for your data's shape. This catalog is generated live from the engine's current chart metadata, so it always reflects what's actually available in the deployed build.
One honest caveat for Excel: the add-in renders the D3 lane, so a type that only exists as a Python, Plotly or Vega rendering in the Power BI visual is not offered here. The chart-type list inside the add-in is always the authority for a given range — it ranks what fits your columns, in this host, before anything is generated.
| Chart type | What it's good for | Works best with |
|---|---|---|
| 3D scatter plot | A rotatable cube in which every row is ONE point placed by three continuous measures on the x, y and z axes, with optional colour by category and size by a fourth measure. The depth is real: the reader turns the cube by dragging it, and nearer points are drawn larger and more opaque so the third axis still reads in a still image | Three continuous measures whose joint structure is the question, on a tile large enough to turn. To read exact values off three or more measures, Pair plot (scatter matrix) and Parallel coordinates are both more precise |
| Animated bar chart | Horizontal bar chart played over an ordered time dimension - bars re-rank and slide period by period, the classic 'bar chart race' | Requires one categorical, one measure and a time column with 5 or more ordered periods; best for a leaderboard that changes over time (top products by year, teams by season, countries by decade) |
| Animated treemap | Treemap whose cell areas animate across an ordered time dimension, showing how a hierarchy's composition shifts period by period | Requires a 2-level categorical hierarchy plus an additive measure observed at 3 or more ordered time points |
| Animated World (Bubbles) | Animated World (Bubbles): one bubble per place on a world basemap, positioned by coordinates or by country, whose size and colour animate period by period with play/pause and a scrubber - the place never moves, its measure does | Best for a measure per place observed at 3 or more ordered time points - revenue by country by year, openings by city by quarter, throughput by port by month - when the question is how each place changed rather than where it ranks |
| Animated World Choropleth | Animated World Choropleth: a filled world map whose country shading animates across an ordered time dimension, revealing how a measure shifts across countries period by period | Best for a country-level measure observed at 3 or more ordered time points — watch how sales, users, or population shift across countries year by year |
| Annotated heatmap | Heatmap with annotations for each cell | Requires two variables: categorical × categorical with values |
| Arc diagram | Nodes arranged along a single straight axis; edges drawn as semi-circular arcs above/below the axis to show pairwise relationships | Best for 5-50 nodes and a sparse-to-medium-density set of pairwise relationships |
| Area chart | Area chart to show magnitude over time or another ordered axis, one filled region per series | Requires ordered categories or time with a continuous variable |
| Autocorrelation plot | Autocorrelation (ACF) correlogram for time series lag-structure analysis, with the partial autocorrelation (PACF) where the renderer computes it | Requires a datetime series with one continuous variable |
| Bar chart (horizontal) | Horizontal bars; useful for long category names | Requires one categorical and one numeric variable |
| Bar chart (vertical) | Vertical bars; good for categorical vs numeric values | Requires one categorical and one numeric variable |
| Basic Sankey | Visualizes flows from one categorical group (sources) to another categorical group (targets) | Requires two categorical (nodes) and a continuous variable |
| Beeswarm chart | Displays distributions of a numeric variable across categories, one dot per observation packed sideways so no two overlap | Best for showing distribution density across small categorical sets |
| Binned 2D heatmap | Binned 2D heatmap: two continuous variables binned into a rectangular grid with cell colour encoding the count or an aggregate, revealing density where individual points would overplot | Best for dense scatter data where points overplot: thousands of observations of two continuous variables, where the question is where the mass is rather than where each point is |
| Binned bubble matrix | A grid of bubbles at the intersections of two BANDED axes — categorical/ordinal dimensions, or continuous variables binned into ordered ranges (e.g. income <30K / 30-50K / ...). Each cell holds ONE bubble whose size encodes an AGGREGATED measure (mean/sum/median) for the rows in that cell, and an optional independent color can encode a second measure; empty cells stay blank | Two banded/categorical axes (or two continuous variables split into ranges) plus an aggregatable size measure per cell, with optional independent color. Choose this over a continuous Bubble chart when raw points would overplot into a blob, or when the axes are naturally grouped |
| Bivariate USA Choropleth (by state) | Bivariate USA Choropleth (by state): US states coloured by TWO measures at once on a filled Albers-USA map - a 3x3 colour matrix of thirds, so where the two measures disagree is visible in one look | Best for two state-level measures that might disagree: revenue vs return rate, income vs cost of living, cases vs vaccinations - at least 24 states and two measures that do not simply track each other |
| Bivariate World Choropleth | Bivariate World Choropleth: countries coloured by TWO measures at once on a filled world map - a 3x3 colour matrix of thirds, so where the two measures disagree is visible in one look | Best for two country-level measures that might disagree: wealth vs inequality, revenue vs margin, cases vs deaths - at least 24 countries and two measures that do not simply track each other |
| Box plot | Box plot showing distribution and outliers of a continuous variable | Requires one continuous variable (plus optional category) |
| Bubble chart | A continuous bubble chart: each data row is ONE mark plotted at its raw (x, y) position on two CONTINUOUS numeric axes, with marker size encoding a third continuous measure (and optional color a fourth). The axes stay continuous — values are never grouped into bands | Three continuous variables (x, y, size); one mark per row on continuous axes. If you want both axes grouped into bands with one aggregated bubble per cell, pick Binned bubble matrix instead |
| Bullet chart | A compact KPI bar showing a measure against a target marker and qualitative range bands (e.g. poor/satisfactory/good), packing actual-vs-target-vs-context into one small row | Best for actual-vs-target KPIs with thresholds (revenue vs goal, SLA vs target) — a denser, clearer replacement for gauges |
| Bump chart | Plots each series' RANK (not raw value) across ordered time points, with crossing lines showing how positions rise and fall over time | Best for leaderboard/position dynamics: how a handful of regions, products, or teams change rank quarter to quarter |
| Calendar heatmap | GitHub-style grid showing daily values arranged in week/month calendar format with color intensity | Requires daily datetime data with associated numeric values; best for activity/contribution tracking over time |
| Candlestick chart | Financial chart showing open, high, low, and close values over time | Best for financial or trading data |
| Card | A single headline number, optionally with a small label or sub-text underneath. The dataset is summarized to ONE value (sum / mean / count / max / latest) and rendered large enough to read at a glance — ideal for KPI tiles and very small viewports where a richer chart would be illegible | One numeric value that summarizes the dataset; small viewports |
| Card with embedded | Card with embedded: a headline number with one small drawing inside the card carrying one more channel - against its target, over time, across comparable measures, split by a small category, or spread across a large one - the form chosen to fit the data and the tile | One headline value that deserves one more channel beside it: attainment with its target, a metric with its history, a profile of a few comparable scores, or a total with its split across up to eight categories; small tiles |
| Card with multiple KPI | A grid (or stack) of N labeled value tiles, each showing one headline number derived from a distinct numeric column. Ideal for dashboards that need to surface several independent metrics at once on a small tile — Revenue / Users / Conversion / etc. — and especially well-suited to long-narrow or short-wide viewports where stacking N small cards reads far better than a single stretched chart | Multiple independent numeric metrics that should be surfaced together; KPI dashboards; long-narrow / short-wide viewports |
| Card with sparkline | A headline number paired with a tiny inline trendline (sparkline) that shows recent history of that value. Combines the at-a-glance readability of a Card with a compact visual of how the metric is trending — ideal for KPI tiles in small viewports | One numeric metric trending over an ordered dimension; small viewports |
| Chord diagram | Circular visualization showing flows or relationships between entities arranged around a ring, connected by curved arcs | Requires matrix data showing bidirectional flows between entities; best for migration, trade, or relationship intensity data |
| Circular heatmap | Circular heatmap to visualize values in radial layout | Requires two dimensions - a cyclical one such as hour, weekday or month around the angle and a second category or date for the rings - and one measure for the colour |
| Clustered heatmap | Heatmap with clustering of rows and columns | Requires two variables for matrix values |
| Connected scatterplot | A scatterplot of two measures whose points are connected in time order, tracing the path the relationship takes over time (with an arrowhead or start/end markers showing direction) | Best for the joint trajectory of two related metrics over time (e.g. units vs revenue, unemployment vs inflation) where the path shape is the insight |
| Custom small multiples | Repeats the same chart across facets for categorical comparison | Best when comparing patterns across categories |
| Cycle plot | Splits a time series into one mini-panel per seasonal sub-period (e.g. a small line for each quarter across years), so within-period trend and across-period seasonality are both visible at once | Best for multi-year monthly/quarterly/weekly data where the question is seasonality vs trend ('is Q4 always highest, and is each Q4 growing?') |
| Delta KPI | One headline number beside the value it is being compared with, and the change between them stated twice - as an amount and as a percentage - with the direction shown but coloured only when the data says which way is better | Two comparable measures, or one measure at two points in time - this period against last, actual against plan, after against before |
| Dendrogram (tree diagram) | Hierarchical tree layout: each node is a categorical entity; edges connect parents to children at orthogonal angles | Best for 2-4 hierarchy levels with ≤ ~80 leaves |
| Density contour plot | Contour plot for density estimation of two continuous variables | Requires two continuous variables |
| Donut chart | Donut chart showing category proportions | Requires one categorical variable with proportions |
| Dumbbell chart | Shows change between two numeric values per category using connected points | Best for before/after or comparison scenarios |
| ECDF plot | Empirical cumulative distribution function plot | Requires one continuous variable |
| Error band | Error band: a measured central line with a shaded band showing spread (confidence interval, standard deviation or min-max) around it along a continuous or temporal axis | Best for repeated measurements over time or over a continuous axis: latency trials, sensor readings, A/B metrics - anywhere the SPREAD matters as much as the average |
| Error bar plot | Plot with error bars for uncertainty in measurements | Requires a category and a measure with a spread to show - repeated rows per category or a second measure holding the error |
| Event plot | Event plot to show occurrence times of events | Requires a date or ordered value for each event, with an optional category that splits the events into lanes |
| Flippable multi-card | Flippable multi-card: a set of KPI panels that page through a shared carousel of faces - the category's values when a dimension is bound, the measures themselves when not - opening on the face that most deserves attention | Best when more headline numbers compete for one tile than fit at once: several measures side by side, or one or two measures paged through a small category, with the details on hover |
| Funnel chart | Ordered, tapering horizontal bars showing how a quantity drops off from one sequential stage to the next, with each stage's width encoding its value and the narrowing communicating conversion/attrition | Best for conversion/attrition pipelines: signup funnels, sales pipeline stages, checkout flow drop-off |
| Gantt chart | Gantt chart: one horizontal bar per task spanning its start and end dates on a time axis, grouped by phase, showing schedule and overlap at a glance | Best for project and phase schedules: tasks with a start and an end date, optionally grouped by phase, workstream or owner |
| Gauge | A radial arc showing one value against a maximum the data or the user stated, with the number printed large at the centre and an optional target tick on the arc | One value against a ceiling the data states - a percentage, a rate, or a measure that has a target |
| Grouped bar chart | Grouped bars to compare subcategories across categories | Requires one categorical, one subcategory, and one numeric variable |
| Heatmap | Heatmap showing one measure as colour across a grid of two categorical or ordinal fields | Requires two categorical or ordinal fields for the rows and columns, and one measure for the cell colour |
| Hexbin plot | Hexbin plot for density of two continuous variables | Requires two continuous variables |
| Histogram | Histogram of a continuous variable | Requires one continuous variable |
| Horizon chart | Compact time series visualization using layered bands to show magnitude | Best for dense time series dashboards |
| Horizontal/vertical span plot | Span plot drawing each category's low-to-high or start-to-end interval as a floating bar on one shared axis | Requires a category and two numeric bounds per category - a low and a high, or a start and an end - on one shared scale |
| Icicle plot | Rectangular flat sibling of the sunburst; nested categorical hierarchy rendered as stacked rows or columns of proportionally-sized rectangles | Best for 2-4 levels of hierarchy with ≤ ~50 leaves; an alternative to a sunburst when horizontal/vertical scanning is preferred |
| Joint plot | Joint plot for two variables combining scatter and distribution plots | Requires two continuous variables |
| KDE plot (1D & 2D) | Kernel density estimate plot | Requires one continuous variable (or two for 2D KDE) |
| Lag plot (scatter of value vs lag) | Lag plot for autocorrelation of time series | Requires a datetime series with one continuous variable |
| Line chart | Use a line chart for one or more continuous series over ordered categories; multiple measures or a series category yield multiple lines | One or more continuous series over an ordered/temporal axis |
| Linear gauge | A single horizontal track with a filled portion showing one value against a stated maximum, the unfilled remainder making the ceiling visible - the form that stays readable in a short, wide tile | One value against a ceiling the data states, in a short wide tile - percent complete, utilisation, progress to a target |
| Lollipop chart | A cleaner alternative to a bar chart: a thin stem from the baseline to a dot that marks each category's value, reducing ink and clutter when ranking many categories | Best as a cleaner bar replacement for ranking many categories by a single measure |
| Marimekko / Mosaic plot | Two-dimensional proportional area chart: column widths encode one categorical total and within-column segment heights encode a second categorical's share | Best for 3-10 outer × 3-10 inner categories with a positive measure |
| Mermaid diagram | Mermaid diagram: boxes, arrows and labels laid out automatically by Mermaid - a flowchart or process map from source-target rows (with swimlanes from a grouping column), an org tree or mind map from nested categories, or a timeline from dated events | A source and a target column (a process, a hand-off chain, a dependency list), two to four nested categories (an org or product hierarchy), or a date beside an event name (milestones, releases, history) - up to a few dozen boxes |
| Motion bubble chart | A bubble chart played over time: each entity is ONE bubble that TRAVELS across two continuous axes period by period, sized by a third measure - the axes stay fixed, so a bubble moves only when its own values do | Requires one categorical whose members are the bubbles, two measures for position and a time column with 3 or more ordered periods; best for watching entities travel - plans by subscribers and churn, countries by wealth and lifespan, teams by cost and output |
| Multi-level Sankey | Visualizes flows across more than two categorical stages, showing how quantities split and rejoin as they move through a sequence of categories or processes | Requires three (or more) categorical and one continuous variable |
| Network diagram | Force-directed graph visualization showing nodes and edges representing relationships or connections | Requires node and edge data (source-target pairs); best for relationship, dependency, or social network visualization |
| Normalized stacked bar chart | A horizontal stacked bar where every bar is scaled to the same full width, so the MIX is comparable across categories of very different size | Best with two categoricals and one additive measure when the question is what the composition is rather than how big the total is |
| North America (Bubbles) | North America (Bubbles): each row plotted as a bubble at its latitude/longitude coordinates on a North America basemap - bubble size encodes one measure, bubble color a second measure or a category | Best for point-located data with latitude and longitude columns: stores, cities, facilities, or events across North America, with a measure for bubble size and optionally a second measure or category for color |
| Origin-Destination Flow Map | Origin-Destination Flow Map: each route drawn as a great-circle arc from its origin to its destination on a world basemap, with arc width encoding the size of the flow and a light-to-dark gradient showing which way it runs | Best for movement between places: flight routes, shipping and trade lanes, migration, commutes or transfers, where each row names where something started and where it ended plus a measure of how much moved |
| Packed circles | Hierarchical bubble layout where every node is a circle, packed without overlap inside its parent circle; sized by a numeric measure | Best with one numeric measure plus 1-2 categorical groupings producing 20-150 leaves |
| Pair plot (scatter matrix) | Pair plot for multiple continuous variables showing pairwise relationships | Requires at least three continuous variables |
| Parallel coordinates | Multivariate visualization with one parallel vertical axis per dimension; each row is rendered as a polyline connecting its value on every axis | Best for 4-8 numeric dimensions and up to ~1000 rows (heavier overlap with more) |
| Pareto chart | Pareto chart: categories ranked descending as bars with a cumulative-percentage line on a secondary axis and an 80% reference, exposing the vital few causes behind most of the effect | Best for finding the vital few: defect/complaint/incident counts by cause, revenue concentration by product or customer (the 80/20 view) |
| Pie chart | Pie chart showing proportion of categories | Requires one categorical variable with proportions |
| Polar plot | Polar plot for circular data with continuous variables | Requires angle and radius (two continuous variables) |
| Progress ring | A single arc sweeping a full circle to show one value as a fraction of a stated ceiling, with the percentage in the middle - the KPI form that survives a small square tile | One value against a ceiling the data states, in a small or square tile - percent complete, attainment, utilisation |
| Radar (spider) chart | Radar chart comparing multiple continuous variables across categories | Requires multiple continuous variables across categories |
| Range-compare time series | Range-compare time series: one measure over time with two draggable range handles, and a strip of KPI tiles above that recompute live for the selected window - its total, the value at each handle, and the percent change between them | Best for a single measure tracked over many dates when the story is a chosen window: drag the two handles to total a period and compare its endpoints, like revenue across a campaign or sensor readings around an incident |
| Regression plot (line with CI) | Regression line plot with confidence intervals | Requires two continuous variables - a predictor and a response |
| Ridgeline plots | Overlapping density plots stacked vertically to compare distributions | Best with many observations per category |
| Rose (Coxcomb) chart | Rose (Coxcomb) chart: categories placed around a circle with each sector's RADIUS encoding its magnitude, the polar counterpart of a bar chart and the natural reading for directional or cyclical categories | Best for cyclical or directional categories: wind direction, hour of day, month of year, compass sectors - anywhere the categories wrap around rather than running left to right |
| Rug plot | Rug plot to show individual data points along an axis | Requires one continuous variable |
| Sankey alluvial flow | Show how quantities move or transition between categories across multiple stages, with link widths emphasizing the relative size of each flow | Requires at least two categorical variables |
| Scatter plot | Scatter plot of two continuous variables | Requires two continuous variables |
| Seasonal decomposition | Splits one time series into trend, seasonal and residual components in aligned stacked panels | Requires a datetime series with one continuous variable and at least two full seasonal cycles |
| Slope chart | Connects each series' value at two points with a straight line, so the slope's direction and steepness encode the magnitude of change between them | Best for two-period change across many categories: YoY by region/segment, before/after comparisons, ranking shifts between two snapshots |
| Spiral plot | Wraps a long time series around an Archimedean spiral so each full loop is one period (year/week), making periodic and seasonal patterns visually pop | Best for long, finely-grained, strongly periodic series (years of daily data) where the cyclic pattern is the story |
| Stacked area chart | Stacked area chart to show multiple continuous series over categories | Requires ordered categories with multiple continuous series |
| Stacked bar chart | Stacked bars for cumulative category comparisons | Requires one categorical, one subcategory, and one numeric variable |
| Stem plot | Stem plot showing each value as a stem from a baseline along an ordered sequence of discrete samples | Requires one measure read along an ordered sequence - a time step, a row index or an ordered category |
| Step plot | Step plot for discrete changes over ordered categories | Requires ordered categories with one continuous variable |
| Streamgraph | Smooth, organic stacked-area variant centered on a baseline that flexes around zero, used to compare flows of multiple categorical series over time | Best with 3-12 categorical series and a continuous time axis with at least 15-30 evenly-spaced points |
| Strip plot | Strip / jitter plot — categorical groups on one axis, continuous values on the other, with a small random horizontal offset on the category axis to spread overlapping points | Requires one categorical and one continuous variable; jitter on the category axis prevents overplot at the same value |
| Sunburst chart | Sunburst chart for hierarchical categorical data | Requires hierarchical categorical data |
| Tabular with embedded | Tabular with embedded: one row per category, attribute columns rendered as decorated value-cards and small horizontal charts (range bars, lollipops, sparklines) | Best for a small set of items compared across several attributes at once (a forecast/roster/scorecard table) |
| Ternary plot | A triangle whose three corners are three measures sharing a total, with every row placed inside it by how it splits between them - so clusters in the mix are visible at a glance instead of row by row | Three measures that add up to the same total on every row - product mix, budget allocation, vote share, channel split |
| Thermometer | A vertical bounded tube filled from a bulb at the base, with the value at the fill head and a target mark across the track - the one scalar form that WANTS a tall narrow tile | One value against a ceiling the data states, in a tall narrow tile - a sidebar rail or a column of KPIs down a page edge |
| Time series plot | Time series plot: continuous data over datetime | Requires a datetime axis with at least one continuous variable |
| Trail | Trail: a line over time whose THICKNESS varies to encode a second measure, showing a trajectory and its magnitude together in one mark | Best when a series has both a level and a weight: price with traded volume, sentiment with mention count, a metric with the sample size behind it |
| Treemap | Treemap for hierarchical categorical data visualization | Requires hierarchical categorical data with values |
| US hex-tile cartogram | Every US state drawn as one equal hexagon in a grid, so each state carries the same visual weight regardless of its real size | Best with one row per US state and a single measure, when the question is which states are high or low rather than where they are |
| USA Choropleth (by state) | USA Choropleth (by state): US states shaded by a measure on a filled Albers-USA map, revealing state-level geographic patterns | Best for state-level US metrics: sales, users, or population by state, any measure that varies by US state (keyed to state names or USPS codes) |
| USA Choropleth (by ZIP-3) | USA Choropleth (by ZIP-3): three-digit ZIP prefix regions shaded by a measure on a filled US map - 5-digit ZIPs are grouped into their ~900 three-digit prefix areas (e.g. 90210 into region 902), revealing regional postal patterns (this is a REGIONAL zip map, not one polygon per 5-digit ZIP) | Best for regional US ZIP analysis: metrics by 3-digit ZIP prefix area, any measure that varies across US postal regions (keyed to 5-digit ZIP codes, grouped to their 3-digit prefix) |
| Variance chart (budget vs actual) | Variance chart: for every period or unit the ACTUAL against a BUDGET, PLAN or TARGET the data names, with the difference drawn as its own marks - absolute variance bars and percentage pins under solid-vs-hollow value bars, or a shaded difference between two lines over a long time axis | One category or period column plus two measures where one names itself the reference - Revenue and Revenue Budget, Cost and Cost Plan, Attained and Quota; finance reporting, budget reviews, plan attainment |
| Violin plot | Violin plot showing distribution of continuous variable with density | Requires one continuous variable (plus optional category) |
| Voronoi treemap | Treemap variant using Voronoi cells (organic polygons) instead of axis-aligned rectangles, sized to encode a numeric measure | Best for 5-30 categories with one non-negative measure; an alternative to a plain treemap when the organic look is desired |
| Waffle chart | Grid of squares showing proportions as colored cells within a fixed grid (typically 10x10 for percentages) | Best for showing part-to-whole relationships with small number of categories; more accurate than pie charts |
| Waterfall chart | Shows cumulative effect of sequential positive and negative values, with floating bars connecting to running total | Best for financial data showing contribution to total (revenue bridges, P&L analysis, budget variance) |
| What-if projection | What-if projection: one measure over time, then a projected continuation the reader steers with an on-chart growth-rate slider and a horizon slider - the end value and the period a target is reached print beside the assumption, and a bound What-If parameter can own the rate instead | A dated measure with at least six periods when the question is what happens next under an assumption the reader chooses - revenue against a target, headcount, subscribers; bind a What-If parameter measure to drive the rate from a slicer |
| What-if scenarios | What-if scenarios: one measure over time, then its compound-growth projection at every scenario rate on one plot - the reader steps or plays through the scenarios and the emphasised projection glides to each, with the end value and the period a target is reached printed beside it; a bound What-If parameter column supplies the scenarios | A dated measure with at least six periods when the question is how far apart the outcomes of several assumptions land - revenue under a range of growth rates, headcount, subscribers; bind a What-If parameter column to draw exactly the range you defined |
| Word cloud | Text visualization where word size is proportional to frequency or importance score | Requires text data or pre-computed word-frequency pairs; best for qualitative text exploration |
| World (Bubbles) | World (Bubbles): each row plotted as a bubble on a world basemap - positioned by its latitude/longitude when the data has them, or by its COUNTRY when it does not - with bubble size encoding one measure and color a second measure or a category | Best for point or country-level data spanning more than one continent: offices, shipments, users or revenue by country, with a measure for bubble size and optionally a second measure or category for color |
| World Choropleth | World Choropleth: countries shaded by a measure on a filled world map, revealing geographic and regional patterns across the globe | Best for country-level metrics: sales, users, or population by country, any measure that varies by nation (keyed to country names or ISO codes) |
| World tile-grid cartogram | Every country drawn as one equal square in a grid, so a small country carries the same visual weight as a large one | Best with one row per country and a single measure, when the question is which countries are high or low rather than where they are |
What differs from the Power BI visual
One engine, one account, one license — but the two hosts are not the same place, and pretending otherwise only costs you time looking for things that are not there. The differences are set out, line by line, in what is different in Excel: where the data comes from, why Aggregation matters here, what clicking a mark does, where the settings live, and what a colleague sees when you send them the file.
The short version: in Excel you select a range and get a chart on the sheet that travels with the workbook; in Power BI you fill data wells and get a visual that cross-filters the report and travels with the .pbix. The chart catalog, the picker, the plain-English refinement, the version history and the account are the same on both sides.
Where to go next: the button-by-button reference is in Help, the product page is here, and the Power BI visual's own deep dive is here.