Comprehensive Guide to Different Types of Charts in Excel

Open any business report and you’ll find rows and columns of numbers that mean very little at first glance. A chart changes that instantly. It turns a wall of figures into a shape your eyes can read in seconds, whether that’s a rising sales trend, a shrinking market share, or an outlier that needs investigating. In Excel, charts are not just decoration, they are a core analytical tool, and knowing which type to use for which situation is a skill every commerce student needs before stepping into a finance, marketing, or operations role.

Excel offers a large family of chart types, and Microsoft groups them by the kind of question they help answer, such as comparing values, showing composition, tracking trends, or spotting relationships between variables, as explained in its official guide to available chart types. This post walks through the major categories you’ll encounter in your syllabus and in real spreadsheets: column, line, pie, bar, area, XY scatter, stock, and surface charts, along with their common sub-types.

Why the right chart choice matters 🔗

Picking a chart isn’t about what looks attractive. It’s about matching the visual to the story your data is telling. Research on graphical perception has shown that humans judge position along a common scale far more accurately than they judge angles, areas, or color intensity, which is why bar and column charts tend to outperform pie charts for precise comparisons, a finding detailed in UC Berkeley’s data visualization guide. Before inserting any chart in Excel, it helps to ask a simple question: am I comparing categories, showing a trend over time, displaying parts of a whole, or looking for a relationship between two variables? The answer usually points you straight to the right chart family.

Column charts 🔗

Column charts display data using vertical bars and are one of the most frequently used chart types in Excel because they make comparisons across categories easy to read. Data arranged in rows or columns on a worksheet plots naturally onto the horizontal and vertical axes, with categories typically sitting on the horizontal axis and values on the vertical axis, according to Microsoft’s chart type reference.

Clustered column chart 🔗

This is the default column chart. It places bars for different data series side by side within each category, which is ideal when you want to compare, say, quarterly sales of three different products next to each other.

Stacked column chart 🔗

Instead of placing bars next to each other, a stacked column chart piles them on top of one another so each column shows the total value along with how individual components contribute to that total. This works well for showing, for example, total revenue broken down by region within each year.

3D column chart 🔗

This adds a depth axis and a three-dimensional look to the columns. It can look impressive in a presentation, but it often makes it harder to read exact values accurately because of the perspective distortion, so it’s best used sparingly and mainly for visual impact rather than precise analysis.

Line charts 🔗

Line charts connect data points with a continuous line and are the standard choice for showing trends over time, such as monthly expenses, stock prices, or website traffic. Because the eye can trace a line smoothly, this chart type is particularly good at revealing the direction and rate of change in data.

Stacked line chart 🔗

This variant stacks multiple series on top of each other, useful for seeing cumulative trends, though it can get visually cluttered if too many series are added.

Line with markers 🔗

Adding markers at each data point makes individual values easier to pinpoint, which is handy when the audience needs to read specific figures rather than just the overall trend.

3D line chart 🔗

This renders each data series on its own depth plane in three dimensions. It’s less common in business reporting because the added visual complexity rarely improves clarity.

Pie charts 🔗

A pie chart displays a single data series as proportional slices of a circle, making it suitable for showing how individual categories contribute to one whole, such as market share by competitor or expense heads within a monthly budget.

Standard pie chart 🔗

The classic circular chart with each slice representing a percentage of the total. It works best with a small number of categories, ideally under six or seven, otherwise the slices become too thin to compare.

Doughnut chart 🔗

Functionally similar to a pie chart but with a hole in the centre, a doughnut chart can display more than one data series as concentric rings, which is useful for comparing composition across two time periods side by side.

3D pie chart 🔗

This tilts the pie into a three-dimensional view. Visualization researchers generally caution against 3D pie charts because the perspective can distort slice sizes and mislead the viewer about actual proportions.

Bar charts 🔗

Bar charts are essentially column charts rotated ninety degrees, using horizontal bars instead of vertical ones. They’re particularly useful when category names are long, since horizontal bars leave more room for text labels, or when you want to emphasise ranking, such as top-performing sales regions listed from highest to lowest.

Clustered bar chart 🔗

Places multiple data series as horizontal bars grouped by category, good for comparing a few metrics across many categories at once.

Stacked bar chart 🔗

Stacks series within each horizontal bar to show both totals and the contribution of each component, similar in logic to a stacked column chart but read left to right.

3D bar chart 🔗

Adds depth for visual effect, though it carries the same readability trade-offs as other 3D variants.

Area charts 🔗

An area chart is essentially a line chart with the space below the line filled in with colour. This filled area emphasises the magnitude of change over time and works well when you want to highlight cumulative totals or the volume of change, not just its direction.

Stacked area chart 🔗

Layers multiple filled series on top of one another, useful for showing how several categories combine to form a total trend, such as cumulative sales across product lines over a year.

3D area chart 🔗

Adds a depth dimension purely for visual styling; as with other 3D types, it’s better suited to presentation slides than to detailed analysis.

XY scatter charts 🔗

Unlike the charts above, an XY scatter chart plots two numeric variables against each other rather than categories against values. It’s the go-to chart for spotting correlation, clusters, or outliers, for example, examining whether advertising spend correlates with sales revenue across different markets.

Scatter with smooth lines 🔗

Connects data points with a curved line, useful when the underlying relationship is expected to follow a continuous pattern rather than jump between points.

Scatter with markers only 🔗

Shows only the individual data points without connecting lines, which is the purest way to look for clusters or outliers without implying a trend that may not exist.

Bubble chart 🔗

A variation of the scatter chart that adds a third variable, represented by the size of each bubble. This lets you compare three dimensions of data at once, such as product price, sales volume, and market share, in a single view.

Stock charts 🔗

Stock charts are purpose-built for tracking price movements over time, most commonly used for equity or commodity price analysis. Excel requires the data to be arranged in a specific column order for these charts to render correctly.

High-low-close chart 🔗

Requires exactly three series, in the order high, low, and close, and is commonly used to illustrate stock price trends and volatility over a period, as noted in this Excel charts reference.

Open-high-low-close chart 🔗

Adds an opening price to the mix, giving a fuller picture of each trading period’s price range and movement.

Volume charts 🔗

These layer trading volume as a secondary axis alongside price data, which helps analysts judge whether a price move was backed by strong market participation or happened on relatively light trading.

Surface charts 🔗

A surface chart plots data across a three-dimensional surface, much like a topographic map shows elevation, with colours and patterns representing value ranges rather than distinct data series. It’s especially useful for finding the optimal combination between two sets of variables, such as identifying the pricing and advertising spend combination that maximises profit.

3D surface chart 🔗

Displays a full three-dimensional view of how two variables interact, which can be visualised as a stretched sheet draped over the data points, similar to how it’s described in Excel’s surface chart documentation.

Wireframe and contour charts 🔗

A wireframe surface chart shows the same three-dimensional relationship as an outline without solid colour fill, while a contour chart presents the same data as a flat, top-down view, similar to a topographic map viewed from above. Both are more specialised and appear less often in standard business reporting than in scientific or engineering contexts.

Matching the chart to the message 🔗

With so many options, the fastest way to choose correctly is to start with your objective rather than your data format. This approach, sometimes called starting from the message rather than the data, is a common recommendation across data visualization practice, including guidance from George Washington University’s data visualization resource.

It’s also worth remembering that a well-labelled two-dimensional chart usually communicates more clearly than a flashy 3D one. As Atlassian’s guide to essential chart types points out, the goal of any chart is to encode values through position, length, or angle in a way the audience can decode accurately, and unnecessary visual complexity works against that goal rather than for it.

What do you think? 🔗

What do you think? The next time you’re handed a spreadsheet at an internship or a college project, which of these chart types would you reach for first to make sense of the numbers? And can you think of a situation where a 3D chart might genuinely add value instead of just making the data harder to read?

References

1. https://support.microsoft.com/en-us/office/available-chart-types-in-office-a6187218-807e-4103-9e0a-27cdb19afb90
2. https://guides.lib.berkeley.edu/data-visualization/type
3. https://support.microsoft.com/en-us/excel/available-chart-types-in-office
4. https://www.tutorialspoint.com/excel_charts/excel_charts_stock.htm
5. https://www.tutorialspoint.com/excel_charts/excel_charts_surface.htm
6. https://libguides.gwu.edu/dataviz/best_practices
7. https://www.atlassian.com/data/charts/essential-chart-types-for-data-visualization