A whisker plot (formally called a box and whisker plot) is built from five numbers pulled from your dataset: the minimum, first quartile, median, third quartile, and maximum. Once you have those five values, you can draw one by hand in minutes or generate one instantly in Excel, Google Sheets, or Python. Here’s how to do it each way.
The Five Numbers You Need
Every whisker plot is a visual snapshot of the same five statistics, often called the five-number summary:
- Minimum: the smallest value in your dataset (excluding outliers)
- Q1 (first quartile): the median of the lower half of your data, marking the 25th percentile
- Median (Q2): the middle value of the entire dataset
- Q3 (third quartile): the median of the upper half of your data, marking the 75th percentile
- Maximum: the largest value in your dataset (excluding outliers)
The box spans from Q1 to Q3, and the distance between them is the interquartile range, or IQR. That box captures the middle 50% of your data. The two lines extending from the box are the “whiskers,” stretching down to the minimum and up to the maximum.
How to Draw One by Hand
Let’s walk through an example with a small dataset: 3, 7, 8, 12, 14, 18, 21, 25, 28, 30.
Step 1: Sort Your Data
Arrange every value from smallest to largest. The example above is already sorted. If your data isn’t, reorder it before doing anything else.
Step 2: Find the Five-Number Summary
Start with the median. With 10 values, the median falls between the 5th and 6th numbers: (14 + 18) / 2 = 16. Now split the data in half. The lower half is 3, 7, 8, 12, 14. Its median (Q1) is 8. The upper half is 18, 21, 25, 28, 30. Its median (Q3) is 25. The minimum is 3 and the maximum is 30.
Your five-number summary: 3, 8, 16, 25, 30.
Step 3: Draw the Number Line and Box
Draw a horizontal number line that covers your full data range. Above it, draw a rectangle (the box) from Q1 to Q3, so from 8 to 25 in this example. Draw a vertical line inside the box at the median, 16.
Step 4: Add the Whiskers
From the left edge of the box (Q1), draw a horizontal line to the minimum value, 3. From the right edge (Q3), draw a line to the maximum, 30. Cap each whisker with a short vertical tick. Add a title, and you’re done.
How Outliers Change the Whiskers
The standard convention limits whisker length to 1.5 times the IQR. Any data point beyond that boundary is plotted as an individual dot and treated as a potential outlier. Here’s how it works with the example above.
The IQR is Q3 minus Q1: 25 – 8 = 17. Multiply that by 1.5 to get 25.5. The lower fence is Q1 minus 25.5, which is -17.5. The upper fence is Q3 plus 25.5, which is 50.5. Since all 10 values fall within those fences, there are no outliers here and the whiskers extend to the true minimum and maximum.
If your dataset contained a value like 75, it would exceed the upper fence of 50.5. The whisker would stop at the highest value still inside the fence, and 75 would appear as a standalone dot. This rule, sometimes called the 1.5 IQR rule, is the default outlier detection method in most statistics software.
How to Make One in Excel
Excel 365 and Excel 2016 or later have a built-in box and whisker chart type, so you don’t need to calculate anything manually. Select the cells containing your data (one column per group if you’re comparing multiple datasets). Go to the Insert tab on the ribbon, click the Statistical chart icon, and choose Box and Whisker. Excel automatically calculates the quartiles, median, and outlier fences for you.
You can click on any element of the resulting chart to format it. Right-clicking the box lets you toggle whether to show the mean, change whisker behavior, or adjust colors. If you’re comparing multiple groups, select all columns before inserting the chart and Excel will place them side by side.
How to Make One in Google Sheets
Google Sheets doesn’t have a native box plot chart type, so you need a workaround using the candlestick chart. The tradeoff: candlestick charts don’t display the median line, so the result is an approximation rather than a true box plot.
First, calculate four values using formulas. If your data is in cells A2 through A11:
- Minimum: =MIN(A2:A11)
- Q1: =QUARTILE(A2:A11,1)
- Q3: =QUARTILE(A2:A11,3)
- Maximum: =MAX(A2:A11)
Enter these in a single row, with a label in the first column. You can add multiple rows for multiple datasets. Then select those cells, go to Insert, choose Chart, and change the chart type to Candlestick Chart. The “box” will span from Q1 to Q3, and the “wicks” will extend to the minimum and maximum. For a true box plot with median and outlier detection, Python or Excel is a better choice.
How to Make One in Python
Python’s matplotlib library generates a full box plot in two lines of code. Install matplotlib if you haven’t (pip install matplotlib), then:
import matplotlib.pyplot as plt
plt.boxplot([3, 7, 8, 12, 14, 18, 21, 25, 28, 30])
plt.show()
This produces a complete box and whisker plot with the median line, quartile box, whiskers capped at 1.5 times the IQR, and outliers plotted as individual points. To compare multiple groups, pass a list of lists: plt.boxplot([group1, group2, group3]). The tick_labels parameter lets you name each group on the x-axis.
Useful options include showmeans=True to add a marker at the mean, patch_artist=True to fill the box with color, and orientation=’horizontal’ to flip the plot on its side. The whis parameter lets you change the whisker multiplier from the default 1.5 if your field uses a different convention.
Reading What the Plot Tells You
Once your plot is drawn, its shape reveals how your data is distributed. When the median line sits in the center of the box and both whiskers are roughly equal length, your data is symmetric. When the median is closer to the bottom of the box and the lower whisker is shorter, the data is skewed right, meaning a tail of higher values stretches upward. When the median sits near the top and the upper whisker is shorter, the data is skewed left.
A tall box means the middle 50% of your data is widely spread. A narrow box means those values cluster tightly. Whisker plots are especially useful when you place several side by side to compare groups, because you can instantly see differences in center, spread, and symmetry that would be hard to spot in a table of numbers.