Box and Whisker charts are a great way to explore how your data is spread out. They help you quickly spot things like medians, ranges, and outliers, without needing to dig through numbers.
In this blog, we will walk you through building a Box and Whisker chart in Sigma using sample loan data. You’ll learn how to filter the data, set up the chart, and apply a few formatting tips to make your insights stand out.
By the end of this blog, you will not only know how to create a box plot in Sigma, but also how to use it to uncover meaningful insights from your data.
Understanding Box & Whisker Charts and Their Key Components
A Box and Whisker Chart, also called a Box Plot, is a statistical visualization used to display the distribution of a dataset. It helps identify key summary statistics like the median, quartiles, and outliers.
Box – Represents the interquartile range (IQR), which contains the middle 50% of the data.
Median (
Q2 / 50th Percentile) – The middle value of the dataset when arranged in ascending order.Lower Quartile (
Q1 / 25th Percentile) – The median of the lower half of the dataset. It marks the point below which 25% of the data falls.Upper Quartile (
Q3 / 75th Percentile) – The median of the upper half of the dataset. It marks the point below which 75% of the data falls and above which 25% of the data lies.
Whiskers – Lines extending from the box that show variability outside the IQR. Extend to the smallest and largest values within 1.5 times the IQR.
Maximum – The data point with the highest value below
Q3 + 1.5 * IQRMinimum – The data point with the lowest value above
Q1 - 1.5 * IQR
Outliers – Individual points that fall outside the whiskers, representing extreme values.
The benefits of the Box and Whisker chart are as follows:
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Clear Data Distribution Visualization:
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Displays the spread, skewness, and overall shape of the dataset.
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Helps in understanding how data is concentrated and whether it is symmetric or skewed.
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Outlier Detection:
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Easily identifies outliers, which appear as points outside the whiskers.
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Helps in analyzing anomalies that could affect data interpretation.
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Comparison of Multiple Datasets:
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 Multiple box plots can be placed side by side to compare distributions.
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Useful for analyzing variations between different categories or groups.
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Compact and Comprehensive Summary:
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Provides a five-number summary (minimum, Q1, median, Q3, and maximum) in a single view.
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Helps in quick decision-making without needing to analyze individual data points.
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How to Create a Box and Whisker Chart
Step 1: Connect to Data
Open Sigma in your browser. From the left-hand menu, click Create New, then select Workbook. This will open a blank canvas — your workspace for building visualizations.
Connect to the data: In this example, we’ll use a finance dataset from the Sigma Sample Database.
To import the data:
Click on the Data option.
Select Table from the menu.
Then click on Tables and Datasets to browse and choose your dataset.
You’ll be prompted to select a data source. Choose
FINANCE, then click onLENDING_CLUB, and finally select theLOANStable.
Now that you can see the Loans table, let’s focus on plotting the Box & Whisker Chart specifically for Joint Applications. To do this, filter out the other application types:
Click on the Filters & Controls option in the top-right corner of the Loans table.
Click the + icon and select the Application Type field.
Then, choose Joint App to apply the filter.
Next, rename the Loan Amnt column to Loan Amount for better clarity. Just double-click the column name and update it.
With that, your dataset is now clean and ready for building the Box and Whisker Chart.
Step 2: Build the Box and Whisker Chart
Now that your data is filtered, you’re ready to build the Box and Whisker chart.
Select Create Child Element on your existing table, then choose Chart. This will open a pane where you can choose the chart type. By default, a Bar Chart is selected. To change it, open the chart type drop-down in the visualization pane and select Box.
Next, let’s add a categorical field to the X-axis. In this case, we’ll use the Application Purpose field.
There are three ways to add a field or column to a chart property in Sigma:
Drag and drop the column directly onto the X-axis property.
Click the + icon next to the X-axis, then select the desired column from the list.
Click the + icon, choose Add New Column, and write a custom expression if you want to create a new calculated field.
Now, add the Loan Amount field to the Y-axis in the chart configuration panel. Then, make sure to uncheck the Aggregate Values option to display individual data points correctly in the Box and Whisker chart.
You should now see a Box Plot of Loan Amount by Application Purpose displayed on your chart.Â
Step 2.1: Key Insights
Here are the Key insights of this Box Plot chart
Small Business loans have the highest median loan amount, while Vacation loans have the lowest median among all categories.
Small Business loans also show a larger interquartile range (IQR) of $20,000, indicating greater variability in loan amounts among applicants.
Categories like Car, Medical, Moving, and Vacation have multiple high-end outliers, suggesting that while most applicants request smaller amounts, some are approved for significantly larger loans.
Most loan types are positively skewed (right-skewed). This means the majority of values are clustered at the lower end, with a longer upper whisker caused by a few large loan amounts.
For example, Car, Moving, and Vacation loans are positively skewed, indicating that while most applicants request smaller loans, a few request much larger ones.
Small Business loans appear symmetric, as the box plot shows both halves of the box (above and below the median) are of equal length—$10,000 each.
This reflects a more balanced distribution and highlights that Small Business loans have the most concentrated distribution, with an interquartile range of $20,000.
Step 3: Aggregation
For distribution analysis, it’s usually best to use None as the aggregate. This lets the box plot reflect individual values across the group on the X-axis but Sigma has a limitation where Box and Whisker charts will not render if the number of data points exceeds 25,000. To overcome this, you need to aggregate the data before plotting.
Using the right aggregation ensures your Box and Whisker chart accurately represents the spread and outliers in your data.
To Aggregate the data – Check the Aggregate Value option under Y-Axis Section, it will prompt you to add one more column to aggregate,
Click on + icon under Split By section and select Sub Grade column
Step 4: Element Formatting
Now that your Box and Whisker chart is ready, let’s make it a bit easier to read.
Start by formatting the Loan Amount column, just right-click on it, go to Format, and select SI Units. This will display the numbers in a cleaner, more readable format (like 10K instead of 10,000).
Next, let’s jump over to the Format tab to fine-tune the overall look and feel of the chart.
Under the Element Style section, adjust the Padding, set the Border Thickness
to 2, and choose a border color that fits your theme.In the Title section, update the chart title to:
Loan Amount by Application Type and GradeUnder the X-Axis section, make sure the Show Axis Title option is checked.
In the Y-Axis section, check Show Axis Title and update it to Loan Amount, then set the Grid Line option to Hide for a cleaner look.
Best Practice for Box and Whisker Chart
Use the Right Grouping on X-Axis
Select a categorical field like Region, Loan Type, or Month to compare distributions across meaningful segments.
Aggregate When Data Exceeds 25,000 Rows
Sigma limits Box and Whisker charts to 25,000 points. Use aggregation
(likeAVG,SUM, or grouping by category/date) to reduce row count.
Set Y-Axis Aggregation to None for Raw Distributions
If row count is within limits, set aggregation to None to visualize the full spread, quartiles, and outliers accurately.
Label Clearly and Customize Tooltips
Use clear axis titles and units, and enable tooltips to display median, quartiles, and outlier values for better understanding.
 Use Filters to Make Charts Interactive
Add filters like Date Range, Category, or Region to let users explore specific slices of the data dynamically.
Closing
Box and Whisker charts in Sigma are a powerful way to visualize data distributions, spot outliers, and compare variability across categories. By understanding how to structure your data, apply the right aggregation, and use best practices, you can turn complex datasets into clear, actionable insights.Â
Whether you’re analyzing sales, financial metrics, or operational performance, this chart type helps you go beyond averages and uncover the full story in your data.
Ready to implement Sigma’s best practices?
Contact phData to learn how we can help you maximize the value of your data.
