How to choose the best chart type to present your dataset

The perfect chart type for your goal

Data visualization is the art of transforming information into a visual context, such as a map or graph. Through your journey in analyzing and preparing your data, you’ll often need to visually present your findings using some type of chart to make sense of the facts, numbers, and measurements.

After all, a picture is worth a thousand words (or in our case, a chart). However, to get the best use of a chart representation, you need to make sure you chose the perfect chart type to communicate your purpose and intentions. Choosing the perfect chart type for your goal can save you a lot of time and effort in order to better explain your data.

What is Your Goal?

To choose the best chart type you first need to decide what your desired goal from this data presentation is. There are 4 basic presentation types that can help reach your goal:

  • Comparison
    Evaluate and compare values between two or more data categories.
  • Distribution
    Visualize deviations or anomalies.
  • Relationship
    Show the relationship, correlation, or connections between two or more variables.
  • Composition
    Show how a total value can be divided into parts or highlight the significance of each part relative to the total value.

Now that you decided on your desired goal, it is time to pick a proper chart for it. Let’s review each chart type and what it is best suited for.

Column charts

Suitable for: Comparison and Distribution.

When to use: To compare different values when specific values are important, and when it’s expected that users will compare individual values between columns.

Column charts display data using rectangular bars, where the length of the bar is proportional to the data value. They enable you to compare values for different categories or observe the changes in value over a period of time for a single category, especially when the change is significant.

Example: Average arrival delay of airline flights per month.


A common variation of column charts is Histograms.

Suitable for: Comparison and Distribution.

When to use: to present distribution and relationships of a single variable across a set of categories.

Example use case: Number of items sold for each price category.

Line Charts

Suitable for: Comparison, Distribution, and Relationship.

When to use: with continuous datasets.

Line charts are one of the most commonly used chart types. They’re best used to observe the change in a certain trend over time. Line charts can also be a good alternative to column charts when the chart is small.

Example use case: Daily global streams of popular songs from 2018 to 2019.

Scatter Plots

Suitable for: Distribution and Relationship.

When to use: primarily used for correlation and distribution analysis.

A scatter plot is a way to visualize how multi-dimensional data are distributed across certain values. It’s also a way to visualize the relationship between two different attributes of multi-dimensional data. Scatter plots are best used for showing the relationship between two different variables where one correlates to another (or doesn’t).

Example use case: marketing spending vs. revenue.

Pie Charts

Suitable for: Composition.

When to use: to visualize a part to a whole relationship or a composition.

A pie chart is usually used to represents numbers in percentages; however, they aren’t meant for individual section comparisons or to represent exact values.

As delicious as pies are in real life, they aren’t generally preferred in data visualization. The human mind thinks linearly, and most of us can’t judge well when it comes to angles and areas. Thus, when possible, try to avoid using pie charts.

Example use case: Distribution of visitors by types of tourist attractions (historic houses, museums…etc) in a certain city.

Stacked Bar and Area Charts

Suitable for: Composition.

When to use: to show a composition.

A stacked bar or area graph is a way to visualize the composition of a group as it changes over time (or some other quantitative variable).

Use it with care! It can get messy very quickly. Don’t use too many composition items (maximum three or four), and make sure the parts of the composition are relatively similar in size.

Example use case: Probability of American names in the previous 30 years (as shown in the image above).


Before choosing a chart type, think about what you aim to present with your chart.

  • If you aim to compare values column charts, histograms and line charts are the best option.
  • If you aim to visualize deviations or anomalies in your data column charts, histograms, line charts, and scatter plots can help you with that.
  • If you aim to understand the relationships between your variables, you can line charts and scatter plots.
  • Finally, if you aim to interpret the composition in your data, stacked columns, stacked area graphs, and pie charts are there to help; however, keep pie charts as your last option.

Written By: Mariam Mahran

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Our team has been at the forefront of Artificial Intelligence and Machine Learning research for more than 15 years and we're using our collective intelligence to help others learn, understand and grow using these new technologies in ethical and sustainable ways.

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