The Ultimate Guide to Types of Charts and Their Uses

Quick Answer: There are many types of charts to choose from, and there is no single “best” one — the right choice depends on what you want your data to say. Use bar or column charts to compare categories, line charts to show change over time, pie or donut charts for parts of a whole, scatter plots for relationships between variables, and specialized types like Sankey diagrams, treemaps, or choropleth maps for flow, hierarchy, or geographic data. Matching the chart type to your data and your message is what makes a visualization easy to read and trust.

Introduction

Picking the right chart type can make your data visualization clear, useful, and easy to read. Get it wrong, and even strong data can feel confusing. With so many types of charts available, it helps to know what each one does best. Some charts compare categories, some show trends, and others reveal relationships or distribution. This guide gives you a practical overview so you can match your data, purpose, and audience with the chart that fits best.

Understanding Chart Types and Their Importance

Charts turn raw data into a visual form that people can understand faster. Instead of scanning rows of numbers, you can see patterns, differences, and movement at a glance. That is why data visualization matters.

Still, not every chart type works for every task. How you show the data should match what you want readers to notice. Are you comparing groups, showing time series data, or exploring a relationship? The next sections explain the basics and help you choose with more confidence. For more on the storytelling side of this, see how to tell a story with your data.

What is a Chart?

A chart is a visual way to show information from raw data. It takes numbers, categories, or both and turns them into shapes, lines, bars, points, or boxes. That makes the numbers easier to scan than a plain table.

Each chart type uses a different visual method. A bar chart uses length. A line graph connects values over a continuous path. A scatter chart places each data point on two axes. Even a single value can sometimes be shown without a chart if that is the clearest option.

What matters is not just appearance. A chart helps you communicate what the data means. It can reveal trends, outliers, gaps, and comparisons that may stay hidden in a spreadsheet. In simple terms, a chart helps people see the story inside the numbers.

Charts vs Graphs: Is There a Difference?

People often ask what are different types of charts versus graphs, since the two words get used interchangeably in everyday conversation. In practice, a graph usually refers to a plotted relationship between values — line graphs and scatter graphs are the clearest examples, since both plot points against numeric axes. “Chart” is the broader term, covering bar charts, pie charts, treemaps, and every other visual format alongside graphs. So when someone asks what are the different types of graphs and charts, the honest answer is that graphs are technically one category inside the larger world of charts, not a separate thing sitting next to it. For everyday use, though, most people use the two words as synonyms, and this guide does too.

Why Do Chart Types Matter in Data Visualization?

The right chart type makes data visualization easier to understand. The wrong one can hide the main point. If you want to show data changes over time, a line chart is usually a better choice than a pie chart. If you want to compare categories, bars often work better than circles or decorative shapes.

When asking, “How do I choose the right chart type for my data?” start with your goal. Ask yourself what the chart should help readers notice first.

• Use a line chart for trends and time series data.
• Use bars or columns for category comparisons.
• Use scatter plots when you want to study relationships.

Your audience matters too. Familiar charts are often easier for a general readership. Basic forms may look simple, but they usually support faster understanding and clearer decisions. For more on framing data for an audience, see our storytelling tips for presentations.

How Charts Help Interpret Data Effectively

Charts support better interpretation of data because they reduce effort. Instead of reading every value one by one, you can compare shapes, positions, and patterns. That saves time and helps you focus on meaning rather than just numbers.

A strong chart type also guides attention. A line chart highlights movement. A box plot draws attention to spread. A scatter plot shows whether each data point forms a pattern or breaks away from the rest. That visual structure helps you spot change, balance, and exceptions.

This is why charts are useful for both exploration and communication. You can use them to find insights for yourself or explain findings to others who may never see the raw data. In both cases, the chart helps turn information into understanding.

Key Factors in Selecting the Right Chart

Choosing the best chart type for your data set starts with a few basic questions. What type of data do you have? What conclusion should the reader draw? Are you showing absolute values, parts of a whole, distribution, or change over time? These questions narrow the options quickly. Once you’re comfortable with the different types of charts available, this decision becomes much faster.

Your audience also shapes the answer. If the chart is for your own exploration, you can use more technical forms. If it is for a broader audience, a familiar layout is often a good choice. Clear reading matters more than novelty.

Keep these factors in mind:

• The type of data: categorical, numeric, or time-based
• The main goal: comparison, trend, relationship, or distribution
• The number of variables or data sets involved
• The reading context, such as reports, presentations, or small screens

The Most Common Types of Charts Explained

Some chart forms appear again and again because they solve common problems well. In data visualization, the most common chart types are usually bar, column, line, pie, area, and scatter plot. They cover comparison, trend, share, and relationship.

You do not need dozens of options for most work. In many cases, a small set of reliable choices is enough. The sections below explain how each chart type works and when it makes sense to use it. If you’d rather start from a pre-built design than a blank canvas, SlideUpLift’s chart and diagram templates cover most of the types below.

Bar Chart

A bar chart shows values with the length of bars. Each bar represents a category or measured group, so it is great for comparing data sets. The format is direct, easy to scan, and familiar to most readers.

Horizontal bars are especially useful when category labels are long or when you have many items to display. They often work better than columns on small screens because they give labels more room. If exact comparison matters, a bar chart is usually a strong option.

So how is it different from a line chart or pie chart? A bar chart compares categories. A line chart shows continuous change, often over time. A pie chart focuses on parts of a whole. If your goal is side-by-side comparison, the bar chart is usually the clearest pick.

Column Chart

A column chart is the vertical version of a bar chart. It uses upright bars rising from a baseline, making it a familiar tool in data visualization for showing category values. The height of each column represents the value on the vertical axis.

This chart works well when you have a small number of categories or a few points in time. It is often a good fit for annual totals, monthly counts, or comparisons where labels are short. Readers can quickly see which columns are tallest or shortest.

Space matters, though. With too many categories, a column chart can become crowded. Labels may overlap, and the visual balance can suffer. In those cases, a horizontal bar chart is often easier to read. When used carefully, though, columns remain one of the most useful and familiar choices.

Line Chart

A line chart connects values with straight lines across a continuous scale. It is one of the best tools for showing change, especially with time series data. If you want readers to notice direction, pace, and overall movement, this is often the best option.

When people ask, “Which chart should I use to show trends over time?” the simple line chart is usually the answer. It helps readers see rises, drops, and long-term patterns. A line graph can also support projections or future expectations because the path makes movement easy to follow.

Multiple lines can compare several series at once, though too many can create clutter. If that happens, small multiples may help. In general, use a line chart when your message depends on continuous change rather than isolated category comparison.

Pie Chart

A pie chart shows parts of a whole by dividing a circle into slices. Each slice represents a share of the total, so the chart clearly signals that proportion is the main message. In data visualization, that can be useful when you want to emphasize percentages rather than raw counts.

Its strength is also its limit. While a pie chart makes part-to-whole relationships obvious, slice sizes are not always easy to compare with precision. Small differences can be hard to judge, especially when there are many categories. That is why bars often work better for tight comparisons.

The main differences are simple. Bar charts compare categories. Line charts show movement over time. Pie charts show shares of a whole. If your message is about composition, use pie carefully. If comparison matters more, choose a bar chart instead.

Area Chart

An area chart starts from a line chart and fills the space between the line and a baseline. That added shading gives more visual weight to totals and makes the chart feel stronger than a simple line. It is useful when you want to show trend over time with an added sense of volume.

The stacked area chart is especially helpful when you need to show both the total and the contribution of different parts across time. It can reveal how a whole changes and how each component rises or falls within it. This makes it useful for internal breakdowns over time.

That said, area charts can be harder to read than lines when precision matters. Overlapping layers may hide detail. Use them when the broad movement and composition are more important than exact comparisons between many categories.

Scatter Plot

A scatter plot places points on two axes to compare two numeric variables. Each point represents one observation, making the scatter chart a strong tool for studying relationship, correlation, and outliers. It is one of the most useful chart types for exploring data rather than simply presenting totals.

Use a scatter plot when you want to know whether two measures move together. Do higher values on one axis tend to match higher values on the other? Are there unusual points or gaps? This chart helps answer those questions in a visual way.

Among the most common chart types, scatter plots are best when your goal is relationship. Use bars for categories, lines for trends, pies for shares, and scatter plots for correlation. They are especially useful when patterns are not obvious from tables alone.

Comparison Charts for Categories

When your main goal is to compare different categories, you need a chart type that puts values side by side clearly. Some options highlight totals, while others focus on subgroup differences, targets, or gaps between values.

A good comparison chart depends on what you want readers to compare first. Do you care about totals, rank, difference, or category breakdown? The following chart types each handle data sets in a slightly different way, so choice matters.

Grouped Bar Chart

A grouped bar chart takes the basic bar chart and places related bars side by side within each category. This makes it a useful comparison chart when you want to compare subgroups directly rather than just overall totals. Readers can look across each cluster and spot differences quickly.

This layout works well when each category contains two or three related measures. For example, it can compare several groups across countries, products, or years without stacking values on top of one another. Because the bars are separate, subgroup comparison is easier than in stacked forms.

There is a trade-off, though. A grouped bar chart does not help much with comparing total category sums. If total size matters most, a regular bar chart may be better. Use the grouped version when side-by-side subgroup reading is your priority.

Stacked Bar Chart

A stacked bar chart divides each bar into segments. That means it can show both a category total and the parts of a whole inside it. As a comparison chart, it works well when you want readers to see how each group is built from smaller components.

This format is often useful for survey results, composition, or other cases where the full amount matters and the breakdown matters too. A reader can compare total bar lengths and also see how much each segment contributes to the whole. It is compact and space-efficient.

Still, only the first segment shares a common baseline, so comparing middle segments across bars can be harder. If precise subgroup comparison matters more than total composition, a grouped bar chart is often easier to read. Use stacking when structure within the whole is central.

Bullet Chart

A bullet chart, also called a bullet graph, adds context to a single bar. It usually combines the main value with a target marker and background bands that show performance ranges. That makes it a good choice when one number needs quick evaluation rather than simple display.

Instead of showing a value alone, the chart answers a better question: How is this value performing against a goal? That is why bullet charts are often used for dashboards or reports with multiple metrics. They are more compact than decorative gauge designs and usually easier to compare.

If your audience needs to judge progress, not just read a value, a bullet chart is useful. It works best when the benchmark matters as much as the number itself. In that case, the added context makes the chart more informative without taking much space. For the metrics that usually sit in this format, see these sales review metrics.

Dot Plot

A dot plot shows values with points instead of bars. Each data point marks a category value on a shared scale, making this a clean comparison chart for grouped information. Because dots use position rather than bar length, the display can feel lighter and less crowded.

This is helpful when a zero baseline is not useful or when space is limited. A dot plot can also handle more categories than a regular bar chart without looking heavy. Readers can compare the position of points across groups and focus on alignment rather than filled shapes.

You can think of it as a stripped-down bar chart. It is still comparing categories, but in a more compact way. If your goal is simple comparison with less visual weight, a dot plot is often a strong alternative to bars.

Dumbbell Chart

A dumbbell chart connects two points for each category with a line. That simple structure makes the difference between the two values easy to see. As a comparison chart, it is especially useful when the gap between two measures matters more than the measures on their own.

This can be a clear way to show before-and-after values, two groups, or two points in time. The line linking the dots focuses attention on distance, not just position. That makes comparison more direct than using two separate bars in some cases.

It is best for pairs. If you have many values per category, a grouped bar chart or dot plot may be more practical. But when you need to emphasize change or contrast between two numbers, the dumbbell chart offers a neat and readable solution.

Pictogram

A pictogram uses repeated icons or symbols to represent values. As a comparison chart, it can make data feel more visual and approachable, especially for a general audience. One icon may stand for a fixed amount, such as 1,000 people or 10 units.

This style can be more engaging than plain bars, but it often sacrifices precision. Comparing exact amounts across different categories is harder when readers must count or estimate symbols. That means pictograms work best when the message is broad and the values do not need close reading.

If you want a playful way to compare different categories, a pictogram can help. Still, if accuracy and speed matter most, bar charts or dot plots are usually stronger. Use icons when memorability and simple proportion matter more than exact detail. Ready-made infographic templates and data icons save the drawing work.

Radar Chart

A radar chart, sometimes called a spider chart, places several measures on spokes around a center point. It connects values into a shape, allowing readers to compare multiple variables within the same set of data. The visual form can quickly suggest strengths and weaknesses across dimensions.

This chart can look striking, especially when comparing two or more profiles. You might see where one set of data extends farther on one axis and falls shorter on another. That can create a useful overview when the main interest is overall pattern rather than exact numeric reading.

That precision comes at a cost, though: radar charts are not the easiest charts to read precisely. Angles and radial distance can be harder to judge than simple bars or dots. They are best used when shape comparison matters more than exact measurement and when the number of variables is limited.

Charts for Showing Trends Over Time

Time adds direction to data. When your message depends on movement, rise, decline, or timing, you need a chart that makes trend easy to see. A line chart is often the starting point, but it is not the only option.

Different chart types bring out different kinds of time series data. Some show continuous movement, some focus on start and end points, and others explain steps, tasks, or cumulative change. The following charts cover the most useful choices.

Line Chart for Time Series

A line chart is the standard choice for time series data because it shows continuous movement clearly. By connecting values across dates or periods, it helps readers follow a trend from one point to the next without distraction. That makes it useful for anything from prices to temperatures.

The visual path matters here. Rising lines suggest growth, falling lines suggest decline, and flat stretches suggest stability. Because the structure is so familiar, readers can quickly understand what is happening. It is often the best first choice when your chart needs to show development over time.

Use care when adding many lines. Too much overlap can reduce clarity. If that happens, small multiples or a simpler view may work better. Still, for one or a few series, the line chart remains one of the clearest and most effective tools available.

Area Chart for Temporal Analysis

An area chart can support temporal analysis when you want to show both direction and magnitude. It follows the same basic structure as a line chart, but the filled area adds visual emphasis. That makes totals feel more substantial and can help readers sense volume over time.

This is especially useful when the data represents accumulation, contribution, or total presence across periods. In stacked form, the chart can show how several parts build a total and how their shares shift. That helps reveal both overall trend and internal movement.

The trade-off is readability. With multiple layers, some values become hard to compare precisely. If your priority is exact series comparison, a line chart may be better. If your priority is showing evolving composition in a strong visual way, the area chart is a solid option.

Slope Chart

A slope chart focuses on change between two points in time. Instead of showing every step in between, it connects the starting value and ending value with a straight line. That makes the direction and size of data changes easy to read.

This chart is useful when the in-between movement is not important. If your goal is to compare where categories started and where they ended, a slope chart strips away extra detail and highlights the final shift. That can make messy multi-line charts easier to understand.

It works best with a limited number of categories. Too many lines can still create clutter. But when you want to show trend in the simplest possible way between two moments, this format is efficient, direct, and easy to compare across categories. The same logic applies when you build a roadmap in PowerPoint.

Waterfall Chart

A waterfall chart shows how sequential increases and decreases lead from a starting value to an ending value. It is designed to explain data changes step by step, which makes it especially useful for financial data such as profit, revenue, or cost breakdowns.

Each bar represents a positive or negative contribution. Readers can see what pushed the total higher, what pulled it lower, and how the final result was reached. This structure is valuable when a single net value needs explanation rather than simple reporting.

Because the chart emphasizes accumulation and subtraction, it is more informative than a standard bar chart for process-like totals. Use it when you need to show why the final number changed. It is less about trend over many periods and more about cause within a sequence.

Gantt Chart

A Gantt chart is a chart type used in project management to show tasks across time. Each task appears as a horizontal bar, with the bar length indicating duration. This layout helps readers see when activities start, how long they last, and how different tasks overlap.

Unlike a line chart, a Gantt chart is not about numeric trend. It is about scheduling. That makes it useful for planning timelines, tracking stages, and understanding dependencies in a workflow. If your data is about work over time rather than value over time, this chart fits better.

Its strength is clarity around sequence and timing. Teams can see which jobs run together and which must happen first. For projects with several stages, a Gantt chart provides a practical overview that is easier to follow than a plain task list. See how to make a Gantt chart in PowerPoint, or start from ready-made Gantt chart templates. If you are building one for a deck, see how to create a timeline in PowerPoint or start from timeline templates.

Step Chart

A step chart is a variation of the line chart where changes appear as flat segments followed by sharp jumps. This structure works well when values stay constant for a while and then shift suddenly. In that case, the step form shows the data more truthfully than a smooth line.

It is useful for rates, thresholds, and any measure that changes at specific moments rather than gradually. The chart still shows trend, but it emphasizes exact change points. Readers can see both the timing and the size of each jump.

If your data changes continuously, a regular line chart is usually better. But when the numbers hold steady between updates, the step chart is more accurate. It keeps the familiar feel of a line chart while making discrete changes easier to interpret. For a broader look at sequencing project work, see this project timeline guide.

Correlation and Relationship Chart Types

Some charts are built to answer a different question: how do variables relate to each other? Instead of comparing totals or trends, these visuals focus on correlation, clustering, density, and association between measures.

Scatter plots are the foundation here, but several related forms add extra detail. Some introduce a third variable, some help with crowded data, and others show patterns across grids or surfaces. The following chart types help make relationships easier to study.

Scatter Plot Uses

A scatter plot is one of the best tools for studying correlation. Each data point represents an observation placed according to two numeric values. When the points form a clear pattern, you can often see whether the variables move together, move apart, or show no strong link.

This makes the scatter plot useful for both exploration and explanation. It can reveal clusters, gaps, and unusual points that deserve attention. If readers need to understand whether a relationship exists, the layout gives a direct visual answer without forcing them through a table.

You can also extend the chart with a third variable through color, shape, or order. That extra layer adds context while keeping the basic structure intact. For two-variable analysis, though, the simple scatter plot remains the clearest starting point.

Bubble Chart for Multi-Variable Relationships

A bubble chart builds on the scatter plot by adding size as a third variable. The point position still shows the relationship between two measures, but bubble area adds another layer of meaning. This makes it useful when you need to compare three numeric dimensions at once.

That extra detail can be powerful. You can see not only where observations fall, but also which ones carry more weight. In one view, readers can compare location, pattern, and scale. This makes the bubble chart a practical extension of the basic scatter plot.

The catch is that adding size increases reading difficulty. People are better at comparing position than area, so exact values may be harder to judge. Use a bubble chart when the third variable adds important context and broad comparison matters more than precise measurement.

Connected Scatter Plot

A connected scatter plot links points in a scatter-style layout with lines. This creates a path through the data and can reveal sequence or trend while still showing the relationship between two variables. It is a useful tool when order matters as much as position.

The added line changes how the chart reads. Instead of viewing the points as independent observations, readers can follow a progression. That makes the chart helpful when data moves through stages or time, but the relationship between the two axes still matters.

This blend of relationship and movement is what a connected scatter plot is most useful for. It is more complex than a standard scatter plot, so it works best when your audience is ready for a richer view. If not, a simpler scatter plot may communicate more clearly.

Heatmap

A heat map uses a grid of cells colored by value. The two axes can represent numeric ranges or categories, and the color shows how strong or weak the value is in each position. In data visualization, this makes the chart useful for comparing many combinations at once.

A heat map can work as an alternative to scatter plots when there are too many points to view clearly. Instead of showing each observation, it summarizes density or value within cells. That helps reveal patterns across large data sets without heavy overlap.

This chart is useful when you want readers to see where concentration, intensity, or repeated combinations occur. Darker or stronger color often signals higher values. It sacrifices the detail of individual points, but it can reveal broad structure much more clearly in crowded datasets.

Hexagonal Binning

Hexagonal binning is a method for handling crowded scatter plot data. Instead of showing every data point, it groups nearby values into hexagon cells. The color or shade of each cell then represents how many observations fall inside that area.

This helps when overplotting hides the real pattern. In a dense scatter plot, too many points can overlap and make important structure hard to see. Hexagonal binning reduces that visual noise while still showing where observations cluster. It turns many individual marks into a readable summary.

Use it when you have a lot of data and the point cloud becomes too dense for normal scatter reading. You lose detail on single observations, but you gain a clearer view of distribution across the two-variable space. For large datasets, that trade-off can be worth it.

Contour Plot

A contour plot shows levels of equal value across a two-dimensional space using lines or bands. This type of chart helps readers see where intensity rises, falls, or forms peaks and valleys. It is like reading a surface through layers rather than points.

This can be useful when the goal is not to inspect individual observations but to understand the shape of a field or distribution. The lines help reveal zones of similar value, making the pattern easier to interpret than a cloud of dots in some cases.

It is a more specialized type of chart, so it works best when the audience is comfortable with abstract visual forms. If your data represents smooth variation across space or two continuous dimensions, a contour plot can present that structure in a compact and informative way.

Part-to-Whole & Hierarchical Types of Charts

Some data is about composition. You may need to show how a total splits into pieces or how categories sit inside larger categories. In those cases, the chart type should make parts of a whole obvious.

A second challenge is structure. When you have hierarchical data, the chart should also reflect levels and visual hierarchy. The next chart types range from simple stacked forms to nested displays that show both proportion and parent-child relationships. For the design side of this, see these visual hierarchy examples.

Stacked Column Chart

A stacked column chart is the vertical version of a stacked bar chart. Each column represents a total, and the segments inside it show how that total is divided. It is useful when you want to show part-to-whole relationships across a small number of time points or categories.

This chart helps readers see both the full height of each total and the contribution of each segment. It is often used when comparing composition across months, years, or groups. Because the structure is vertical, it works well when the sequence runs left to right.

Like other stacked charts, only some segments are easy to compare precisely because they do not all share the same baseline. If detailed segment comparison matters most, grouped columns may work better. If the whole and its parts are the real story, stacking works well.

Diverging Stacked Bar Chart

A diverging stacked bar chart places segments on both sides of a central baseline. This is useful when some values are positive, and others are negative, or when responses naturally split around a midpoint. It still shows part-to-whole structure, but in a balanced form.

This layout is often effective for survey-style scales where answers range from favorable to unfavorable. Readers can quickly see how much of each category falls on either side of the center. That makes overall sentiment easier to understand than in a regular stacked bar.

Its strength is contrast. Instead of only showing composition, it highlights direction as well. Use this chart when categories need to be understood as moving away from a neutral center. It is a practical way to show both distribution and balance in one view.

Types of Pie Charts (Pie Chart Variants)

There are several types of pie charts worth knowing, and most of them follow the same subtypes Excel and PowerPoint build in natively: pie, 3-D pie, pie of pie, and bar of pie, plus the doughnut chart, which is covered on its own below.

• 3-D pie: adds a tilted, three-dimensional look to a standard pie. It is mostly decorative — the added depth makes slice sizes harder to judge accurately, so use it only when style matters more than precision.
• Exploded pie: pulls one or more slices away from the rest of the circle to draw attention to them. Useful when a single category is the point of the chart, such as highlighting the top-performing product in a lineup.
• Pie of pie: breaks the smaller slices of a pie out into a second, linked pie so minor categories stay readable. Good when you have several small categories that would otherwise be too thin to label on one circle.
• Bar of pie: the same idea as pie of pie, but the breakout uses a stacked bar instead of a second circle. This works well when the smaller categories need more vertical room to label clearly.

As a rule of thumb, a pie chart works best with a single data series, no negative values, and no more than about seven slices — beyond that, precision drops fast and a bar chart becomes the safer choice.

Beyond these four core subtypes, a few presentation-friendly variants are also common:

• The doughnut chart, which removes the center and can create space for a label
• Multiple small pies, which place several part-to-whole views side by side
• Proportionally sized pies, which change the overall circle size as well as slice size

Use them when proportion is the main message and simplicity matters more than exact comparison.

Donut Chart

A donut chart is one of the most recognizable types of pie chart, working like a pie chart but with the middle removed. The empty center can make the design feel lighter and may also create space for a total or label. It still shows parts of a whole through slice size.

Its main benefit is presentation rather than accuracy. Readers often find the form attractive and easy to recognize, but like the pie chart, it is not ideal for fine comparison. Small differences between slices remain hard to judge, especially when there are many segments.

Use a donut chart when your goal is to emphasize proportion in a simple, compact way. If precise comparison matters more than visual style, a bar chart is often better. If the message is about share and you want a cleaner circular form, the donut chart works well.

Marimekko Chart

A Marimekko chart is a comparison chart that shows both width and height as meaningful values. That means it can display the total size of each category and the internal breakdown within it at the same time. This makes it useful when you need to compare data sets in both absolute and relative terms.

For example, one category can appear wider because it is larger overall, while its internal segments still show percentage breakdown. That combination is powerful because it lets readers see share and scale in one view rather than splitting the message across two charts.

The downside is complexity. A Marimekko chart is harder to read than bars or stacks because both dimensions carry meaning. Use it when dual comparison is essential, and your audience can handle a slightly more demanding visual format.

Treemap

A treemap shows hierarchical data through nested rectangles. The size of each rectangle represents value, and larger boxes can contain smaller ones inside them. This makes the chart useful for showing parts of a whole across multiple levels at once.

Its big advantage is structure. You are not just seeing which categories are large or small. You are also seeing how subcategories belong to parent groups. That makes a treemap a strong choice when the hierarchy itself matters, not just the totals.

On the downside, rectangles are not always easy to compare precisely, especially when shapes vary. A treemap works best when you want an overview of relative size and nested organization. If exact ranking matters, bars may be easier. If visual hierarchy matters, treemaps are very effective.

Sunburst Diagram 

A sunburst chart displays hierarchical data in concentric rings. The center represents the top level, and outer rings show lower levels. Segment size reflects value, so the chart communicates both parts of a whole and parent-child structure in one circular layout.

This makes it visually strong for nested composition. Readers can follow categories outward and see how each branch contributes to the total. Compared with a treemap, the sunburst chart emphasizes hierarchy through rings rather than nested boxes, which some audiences find more intuitive.

The trade-off is precision. Angular segments are harder to compare than bars, and deeper levels can become crowded. Use a sunburst chart when the goal is to present layered structure and relative contribution in an engaging way. If exact comparison matters more, choose a simpler form.

Circular Treemap

A circular treemap is a type of chart that represents hierarchical data using nested circles rather than rectangles. Like a standard treemap, it aims to show both structure and relative size, but it does so with a softer, more organic visual style.

This can make the chart visually appealing, especially when you want to emphasize grouping. Circles inside circles can suggest containment clearly, and larger circles naturally stand out. It still communicates parent-child relationships and relative importance across levels.

The challenge is readability. Circles do not pack space as efficiently as rectangles, and comparing their sizes can be harder. Use a circular treemap when the hierarchy is more important than exact value comparison and when visual impact supports your communication goal.

Waffle Chart

A waffle chart shows parts of a whole using a grid of small squares, often based on 100 cells. Each square represents one unit or percentage point, making the structure easy to understand at a glance. It offers a simple, grid-based alternative to pies.

Its main advantage is clarity around proportion. Readers can count or estimate filled cells more easily than they can compare angled slices in a circular chart. The grid also makes percentages feel concrete, which can help general audiences connect with the message.

That said, waffle charts are best for simple shares and a small number of categories. Too many colors or segments can reduce clarity. Use them when you want to show proportion in a friendly, memorable way without relying on a pie chart.

Icon Array

An icon array uses repeated symbols to represent a total and highlight a portion of it. It is another visual method for showing parts of a whole, often in a way that feels more human and concrete than abstract shapes. This can make percentages easier to grasp than an abstract slice or bar.

For example, instead of showing 20 percent as a slice or bar, the chart might highlight 20 icons out of 100. That can help readers connect the number to people or units. The format works especially well when the goal is simple explanation rather than detailed analysis.

As with pictograms, precision can suffer when too many categories are included. Use an icon array when your audience benefits from a direct, unit-based display. If you need stronger comparison across several groups, bars or waffle charts may be a better choice. Icon arrays show up often in infographics in PowerPoint.

Distribution Chart Types

Sometimes the question is not which category is biggest or how a trend changed. Instead, you need to understand distribution. How are values spread out? Where do they cluster? Are there outliers or long tails across the range of values?

That is where distribution-focused charts help. Each chart type below reveals spread in a different way, from simple frequency counts to richer shapes and summaries. Together, they help you move beyond totals and see how data behaves internally.

Histogram

A histogram looks like a bar chart, but it serves a different purpose. Instead of comparing separate categories, it groups continuous numeric values into ranges and shows how many observations fall into each one. That makes it ideal for viewing frequency distribution across a range of values.

If you are deciding between a histogram and a bar chart, ask what your x-axis represents. If it shows categories like products or regions, use a bar chart. If it shows numeric intervals like age ranges or score bands, a histogram is usually the right choice.

The bars in a histogram touch because the bins are continuous. That signals distribution, not category separation. Use this chart when you want to understand spread, clustering, and shape in numeric data rather than compare distinct groups.

Box Plot

A box plot, also called a whisker plot, summarizes distribution using a box and whiskers. The box shows the central portion of the data, while the whiskers extend toward the outer values. This makes it a compact tool in data visualization for comparing multiple groups.

Its biggest strength is efficiency. Instead of plotting every observation, it gives a quick summary of spread, center, and possible outliers. That is especially useful when you have several groups and want to compare their distributions side by side without clutter.

The trade-off is detail. A box plot does not show the full shape of the distribution as clearly as a histogram or violin plot. Use it when summary comparison matters more than density shape, and when space or simplicity is important.

Violin Plot

A violin plot shows distribution using a mirrored density shape around a central line. It is an alternative to the box plot when you want to compare the form of different groups rather than only summary statistics. It can reveal where values cluster within each group, not just the summary statistics.

Because the shape widens where data is denser, readers can see more than just spread. They can notice whether the distribution is narrow, broad, or uneven. This makes the violin plot useful when the overall contour of the data matters.

It is a more advanced chart, so it may be less familiar to a general audience. Some versions include box-style markers on top to add summary information. Use a violin plot when distribution shape is important and your readers can handle a slightly more technical visual.

Density Curve

A density curve is a smooth way to show distribution. Instead of placing observations into bins like a histogram, it creates a continuous line that represents where values are concentrated. In data visualization, this can make patterns easier to spot by reducing noise.

Each data point contributes to the final curve, so the result gives a broad sense of the underlying shape. Peaks show common values, while long tails show where values stretch outward. This can be useful when you want to understand the signal in the data rather than focus on exact counts.

The smoothness is helpful, but it also means the chart may suggest values that do not actually appear in the data. Use a density curve when you want a cleaner view of shape and concentration, especially as an alternative to a histogram.

Strip Plot

A strip plot shows each data point individually along a single scale, often with slight spreading to avoid overlap. This makes it a simple but effective way to explore distribution, especially when the dataset is not too large. You can see every observation rather than just a summary.

That detail is the main strength of the strip plot. Readers can spot clusters, gaps, and outliers directly because no values are hidden inside bins or boxes. It offers a more transparent view than some summary charts, especially for smaller groups.

The downside is crowding. With a large number of observations, the chart can become messy. Use a strip plot when the number of values is moderate and when seeing each data point matters more than having a compact summary.

Beeswarm Plot

A beeswarm plot is a refined version of a strip plot. It shows every observation, but it arranges points carefully so they do not overlap. This creates a cloud-like display that communicates distribution while preserving individual values. It is useful for balancing detail and readability.

Because the points are spread based on collision rather than random jitter, the structure often looks cleaner than a strip plot. Readers can still identify clusters, outliers, and dense regions, but the chart remains organized enough to compare groups.

It works best for small to medium datasets. With very large volumes of data, summary charts like histograms or density curves may scale better. Use a beeswarm plot when you want to keep every observation visible while still showing the shape of the distribution clearly.

Specialized Charts for Data Exploration

Not every chart fits into the standard categories of comparison, trend, or distribution. Some chart types are more specialized and are designed for particular structures such as flows, hierarchies, or repeated views. These can support deeper data exploration.

They are not always the best choice for a broad audience, but they are worth knowing. In the right setting, they reveal information that basic chart templates cannot show as clearly. The next group covers several of these practical specialist options. For how these fit into a deck, see the main types of slides guide.

Funnel Chart

A funnel chart is often used to show stages in a process where quantities decrease along the way. It appears frequently in business settings, such as visitor, user, or pipeline tracking. The width of each stage suggests how many people or items remain at that point.

As a chart type, it is useful when the process metaphor matters. Readers can quickly see drop-off from stage to stage, which makes it helpful for data exploration in workflows. The narrowing shape supports the idea of filtering or conversion through a sequence.

Still, the tapered form can make exact comparison harder than necessary. A bar chart can often communicate the same information more clearly. Use a funnel chart when the process story is central and the audience benefits from the visual metaphor. Funnels pair naturally with KPI examples for tracking each stage, and with funnel analysis templates for building the slide itself.

Pyramid Chart

A pyramid chart usually refers to a mirrored bar layout that compares two groups across ordered categories. The population pyramid is the best-known example, showing age groups split by sex. As a chart type, it is useful when two sides of a structure need to be compared directly.

The mirrored design helps readers see balance and imbalance at the same time. One side can be compared with the other within each band, while the full shape reveals the broader pattern. That makes the chart especially useful for demographic structure.

Although the layout is specialized, it is easy to interpret once the axes are clear. Use a pyramid chart when your data naturally divides into two opposing groups across shared categories. For that purpose, it is both compact and visually informative.

Sankey Diagram

A Sankey diagram is a chart type used to show data flow. It connects stages or categories with bands whose width represents volume, making it useful for tracking movement through a system. This can apply to energy, money, traffic, or other flows where quantity changes across paths.

Its strength is in showing transfer. Readers can see where things start, where they go, and how much moves along each route. That makes the Sankey diagram especially effective when understanding pathways matters more than reading exact values from a table.

Because of its visual complexity, it works best when the number of flows is manageable. Too many connections can reduce clarity. Still, when you need to communicate movement between stages or categories, few charts show data flow as clearly or as memorably.

Organizational Chart

An organizational chart shows hierarchical data through levels of boxes connected by lines. It is one of the clearest examples of a chart type designed around structure rather than numbers. Each box represents a role, team, or unit, and the links show reporting or parent-child relationships.

Its purpose is straightforward: it helps readers understand how a system is arranged. That makes it useful in companies, institutions, and any setting where formal structure matters. Unlike treemaps or sunburst charts, the focus here is on relationship order, not value size.

Use an organizational chart when hierarchy itself is the message. It is less about comparing quantities and more about showing position and connection. For that reason, it remains one of the most practical ways to present layered structure clearly. Browse types of organizational chart templates, or start from an org chart template directly. You can also build one in Google Slides.

Matrix Chart

A matrix chart arranges categories in rows and columns so readers can compare intersections between two dimensions. As a comparison chart, it is useful when relationships between category pairs matter more than single totals. Each cell may hold a value, symbol, or color-coded result.

This type of chart is practical for structured comparison. It can help readers scan across categories and spot where matches, gaps, or strong values appear. In that sense, it works well when you need a grid-based view rather than a single-axis display like bars.

A matrix chart is not always the fastest chart type for a general audience, but it can be very effective in analysis and reporting. Use it when your message depends on comparing combinations rather than one variable at a time. The BCG matrix is a well-known example of this format applied to portfolio strategy.

Small Multiples

Small multiples repeat the same chart type across a grid of panels, with each panel showing a different category or series. This is a powerful way to compare many groups without forcing them into one crowded chart. Each panel keeps the scale and design consistent.

This approach works well when a single chart becomes too messy. For example, too many lines in one view can create confusion, but separate mini-charts can restore clarity. Readers compare patterns from panel to panel instead of trying to untangle overlap in one space.

Use small multiples when consistency matters and the audience needs to compare several trends or categories side by side. They are especially helpful when preserving a familiar chart type, like a line chart, is better than switching to something more complex.

Parallel Coordinates

Parallel coordinates are a chart type for showing many variables at once. Instead of using one x-axis and one y-axis, the chart places several vertical axes side by side. Each observation is drawn as a line crossing those axes at the right values.

This creates a compact form of data visualization for multi-variable analysis. Readers can compare patterns across dimensions and look for lines that move together, separate, or cluster. It is useful when the dataset has more variables than a regular scatter plot can reasonably show.

The chart can become dense quickly, so it is better suited to exploration than simple communication. Use parallel coordinates when your goal is to study complex patterns across many measures. For a mainstream audience, a simpler chart may often be easier to understand.

Geospatial Chart Types

When data is tied to place, a chart alone may not be enough. A geospatial view helps readers connect numbers to actual regions, routes, or points on the ground. That can make patterns more meaningful and easier to remember.

Different map-based visuals serve different goals. Some color regions, some mark locations, and some show movement between places. The next chart types cover the most useful forms of geospatial data visualization and when each type of map helps most.

Choropleth Map

A choropleth map is a type of map that fills geographic regions with different colors based on value. In geospatial displays, it is one of the most common ways to show variation across states, counties, provinces, or other administrative areas. Darker or lighter shades usually represent higher or lower values.

This format works well when your data is already grouped by region. Readers can quickly see where values are concentrated and which areas stand out. It is especially useful for rates, percentages, or region-level measures rather than individual locations.

The main limitation is that it shows area, not exact position within the area. Large regions may draw more attention than small ones even when the values are similar. Use a choropleth map when your message is about geographic differences between defined regions.

Symbol Map

A symbol map places markers on a map to represent data at specific locations. As a geospatial chart type, it is useful when the exact position of each place matters more than coloring the entire region. Each symbol may simply mark presence or may vary by shape or size.

This makes symbol maps a strong choice for plotting many locations such as schools, stores, or event sites. Readers can see where points are clustered and where they are spread out. The method is direct and often easier to understand than a more abstract regional display.

If the map becomes overcrowded, clarity can suffer. But for location-based datasets with meaningful coordinates, a symbol map is often the best way to show what is where. It keeps the focus on place, not just territory.

Bubble Map

A bubble map is a type of map that places circles over locations and scales them by value. It combines geospatial position with magnitude, making it useful when you want readers to see both where something happens and how much is involved at each point.

This chart type works well for a limited number of locations. Larger bubbles stand out immediately, so readers can spot high-value points quickly. It is a practical option when a plain symbol map needs an added measure of size without switching to region shading.

There is a trade-off, though. Overlapping bubbles can make crowded areas hard to read, and comparing circle sizes is not always precise. Use a bubble map when location and approximate scale matter together, and when the number of points stays manageable.

Heatmap Map

A heat map can also be used as a type of map. Instead of coloring administrative regions, it overlays intensity across geographic space to show where values are concentrated. This is useful for revealing hotspots rather than exact boundaries.

This form works well when you have many points and want to show density across an area. For example, it can highlight where events, visits, or activity cluster within a city or region. The focus is on concentration rather than individual sites.

The trade-off is precision. Readers may not know the exact count at each location, but they will quickly understand where activity is strongest. Use a heat map when hotspot detection matters more than point-by-point detail or regional comparison.

Flow Map

A flow map is a type of map used to show movement between places. Lines or bands connect origins and destinations, helping readers see direction and volume across geographic space. This is useful for travel, migration, shipping, or other route-based patterns.

Its power comes from combining place with transfer. Readers can identify where flows begin, where they end, and how strong they are. This makes the chart useful when geography and movement matter at the same time, rather than simply plotting static values on a map.

Too many routes can create clutter, so selection matters. Use a flow map when your goal is to reveal how things move across space. If the main question is “where” plus “to where,” this chart is often more informative than a standard geographic display.

Cartogram

A cartogram is a type of map that changes the size of regions based on data instead of keeping normal geography. This can make values such as population or volume more visible than they would be on a standard map where land area dominates.

The main idea is emphasis. Large-value regions grow, and smaller-value regions shrink, helping readers focus on what matters in the data rather than the shape of the land. This can reveal patterns that would be hidden if physical area remained unchanged.

Because geography becomes distorted, cartograms can be harder to recognize at first. They work best when readers already know the map or when the purpose is clearly explained. Use one when region size should reflect data value rather than physical space.

Chart Types Found in Microsoft Excel

Microsoft Excel offers many familiar options for everyday chart work. If you build reports, presentations, or quick analysis, knowing the common Excel chart choices can save time and improve clarity. Many users start here before exploring more specialized tools.

The platform includes standard forms and some less common ones as well. Understanding what each Excel chart type is good for helps you move beyond default chart templates and make better visual decisions. Let’s look at the main options first. (If you’re building in PowerPoint or Google Slides rather than Excel itself, see the dedicated section on that further down.)

Common Excel Chart Types

When people ask what chart types are available in Microsoft Excel or Office, the answer starts with the basics. The most common chart types include bar, column, line, pie, area, and scatter. These cover most everyday needs and appear in many built-in chart templates. A Combo chart, which pairs two chart types on one plot — a common example is bars for monthly totals with a line overlaid for a running average — is also one of Excel’s most-used business charts and worth knowing alongside the basics.

Each Excel chart suits a different purpose. Use bars or columns to compare categories, a line chart for time series data, a pie chart for parts of a whole, and a scatter plot for relationships. These familiar forms are usually enough for reports, dashboards, and first-pass analysis.

For simple sample data, think of monthly sales for a line chart, product totals for a bar chart, market share for a pie chart, or height and weight pairs for a scatter plot. Excel makes these options easy to access, but good results still depend on choosing the right format.

Less Common Excel Chart Types

Excel also includes less common chart types that many users overlook. These can be useful when the data has a specific structure or when basic bars and lines do not explain enough. Knowing them gives you more flexibility without leaving Microsoft Excel. Excel for Microsoft 365 also includes a Map chart for coloring geographic regions by value, similar to the choropleth map covered earlier in this guide, though this option is only available in the Microsoft 365 versions of Excel on Windows, Mac, and the web, not in older standalone versions.

Some less common chart types are still practical. Waterfall charts help explain step-by-step change. Funnel charts fit pipeline views. Bubble charts add a third variable. Pareto charts, a sorted histogram subtype with a cumulative line, rank causes by frequency. Box-style summaries and specialty financial views are also worth knowing when the data calls for them.

How to Access Chart Templates in Excel

Finding chart templates in Excel takes just a few clicks. Begin by opening Excel and navigating to the “Insert” tab on the ribbon, where “Recommended Charts” suggests options based on your selected data, and the full “Charts” group lets you pick any chart type directly. To reuse a chart’s formatting later, right-click it and choose “Save as Template” — from then on, it’s available under All Charts > Templates when you insert a new chart.

Using Excel’s Recommended Charts Feature

Excel’s recommended charts feature streamlines the data visualization process by suggesting appropriate chart types based on your selected data. By simply highlighting your data points, Excel provides tailored options that match the type of data you’re working with. This feature is particularly useful when dealing with various data sets, as it helps identify the best visual representation—be it a bar chart, line graph, or pie chart. Continually refining your choice by exploring these suggestions ensures that your data narrative is clear and engaging for your audience.

Chart Types in PowerPoint and Google Slides

PowerPoint’s charting engine is built on the same foundation as Excel, so most of the types of charts in PowerPoint match the Excel list above — bar, column, line, pie, area, scatter, plus specialized options like waterfall, funnel, and radar. Treat this as a strong overlap rather than a perfect match, though: features like Map charts and Dynamic Charts, which are tied to Excel’s Tables, dynamic arrays, and PivotTables, don’t carry over to PowerPoint the same way. If you’re comparing different types of charts in PowerPoint for a report or a client deck, the safest approach is to check that your specific chart type is listed under PowerPoint’s own Insert > Chart dialog before you build around it.

How to Insert a Chart in PowerPoint

Go to Insert > Chart, pick a chart type from the dialog, and PowerPoint opens a small linked spreadsheet where you edit the underlying numbers. Update the data there, and the chart on your slide updates to match. Google Slides also uses Insert > Chart, but the data lives in a separate linked Google Sheets file rather than an embedded spreadsheet, and its native chart-editing options are more limited than PowerPoint’s or Excel’s. For a step-by-step walkthrough, see how to make a graph in PowerPoint. For the Google Slides version, see how to make a chart in Google Slides.

Template or Build From Scratch?

For a one-off internal report, PowerPoint’s native chart tool is fine. For anything presentation-facing — a client deck, a board update, a pitch — a pre-designed chart template saves the formatting work and keeps your slide’s style consistent, which is why most teams reach for a template library instead of styling each chart by hand.

Choosing the Right Chart Type for Your Data

Choosing a chart type gets easier when you work backwards from the message. Numeric values that move together usually belong in a line chart or a scatter plot. Categories that need side-by-side comparison usually belong in bars or columns. Shares of a single total belong in a pie or donut chart, and only when there are few enough slices to read.

The shape of the question matters as much as the shape of the data. If you are asking how something changed, use a line chart. If you are asking how values are spread out, use a histogram. If you are asking whether two measures are related, use a scatter plot. If you are asking how several groups compare on more than one measure, a bubble chart can carry the extra variable without adding a second chart.

Chart Selection at a Glance

Understanding Numeric vs. Categorical Data

Numeric data is anything you can measure or count: revenue, temperature, age, response time. Because the values sit on a scale, they work well in line charts, scatter plots, and histograms, where position carries meaning.

Categorical data is made up of distinct groups: regions, products, departments, survey answers. There is no scale between the categories, so length is the clearer encoding. That points to bar and column charts, or a pie chart when the categories add up to a single whole.

Knowing which type you have removes most of the guesswork. A histogram of product names makes no sense, and neither does a bar chart of continuous measurements without binning them first.

When to Use a Histogram vs. Bar Chart

Histograms and bar charts look alike, but they answer different questions. A histogram groups continuous numbers into bins and shows how many observations land in each one, which is what you want when spread, clustering, or skew is the point. A bar chart compares separate categories, which is what you want when the question is which group is largest.

Selecting Charts for Highlighting Differences

When the gap between values is the point, bars and columns are usually the safest choice. Length is easy to compare, so readers can rank categories at a glance without working at it.

For a difference between two specific values, a dumbbell chart puts the emphasis on the distance itself rather than on either endpoint. When a number needs to be judged against a target instead of against other numbers, a bullet chart adds that context in the same space. And when the difference sits in a relationship rather than a total, a scatter plot will show clusters and outliers that a bar chart hides.

Choosing Charts for Patterns or Trends

For movement over time, a line chart is the default and usually the right one. It shows direction, pace, and turning points without asking the reader to interpret anything unfamiliar.

Patterns that are not about time need different tools. A scatter plot reveals relationships and clusters between two measures. An area chart or stacked bar chart shows how the composition of a total shifts, which is useful when both the whole and its parts are moving. When a single chart gets too crowded to read, small multiples keep the same format and split the series across panels instead.

Avoiding Common Pitfalls in Chart Selection

Most bad charts come from a small set of repeated mistakes. The most common is reaching for a complex format when a simple one would do: a sunburst or violin plot on a small data set adds difficulty without adding insight.

The second is ignoring what the audience already knows. A familiar chart that is read correctly beats an elegant one that is misread. If a format needs a paragraph of explanation before it makes sense, it is probably the wrong choice for a report or a slide.

The third is a mismatch between the chart and the data. Pie charts break down with many categories, negative values, or zeros. Line charts imply continuity that categorical data does not have. Stacked charts make middle segments hard to compare because they do not share a baseline. Checking the format against the data before designing anything prevents most of these.

Conclusion

There are a lot of chart types, but choosing between them comes down to two questions: what kind of data do you have, and what do you want the reader to notice first. Numeric or categorical decides the family. Comparison, trend, composition, relationship, or distribution decides the format inside it.

The familiar options carry most of the work. Bars compare, lines track change, pie and donut charts show shares, and scatter plots show relationships. The specialized formats in this guide are worth reaching for when the data genuinely calls for them, not by default.

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