Stem and Leaf Plot Calculator
Paste or type your numbers below - separated by commas, spaces, or new lines - and the calculator instantly builds a stem and leaf plot that shows every value in your dataset. You also get the mean, median, mode, range, quartiles, interquartile range, and standard deviation. Switch to back-to-back mode to compare two datasets on a shared stem axis, or turn on split stems to give each stem row a finer resolution.
What is a stem and leaf plot?
A stem and leaf plot (also called a stem-and-leaf diagram or stemplot) is a table that displays a dataset so that every individual value is still visible. Each number is split into a stem (the leading digit or digits, usually tens) and a leaf (the final digit, usually the ones). The stems run down the left column in order, and all the leaves for each stem are listed to the right, also in order. The resulting layout looks like a sideways histogram but lets you read every exact data value, not just a count per bin. That makes it uniquely useful when you want both the shape of the distribution and the raw numbers at the same time.
How to read and build a stem and leaf plot
To build the plot: first sort your data from smallest to largest. Then identify the scale. For two-digit integers the stem is the tens digit and the leaf is the ones digit (scale = 10). For three-digit integers the stem is the hundreds and tens digits and the leaf is the ones digit (scale = 100). For decimals, scale the data so the leaves remain single digits. Once you have your stems listed in a column, write each leaf in the row that matches its stem. To read back an original value, combine the stem and the leaf: a stem of 4 and a leaf of 7 represents the value 47 when the scale is 10. Negative values work the same way - a stem of -3 and a leaf of 2 is -32. The split-stems option doubles the number of rows by using two rows per stem: one for leaves 0-4 and one for leaves 5-9, which spreads out a dense plot.
Back-to-back stem and leaf plots
A back-to-back stem and leaf plot places two datasets on the same stem axis. The stems are in the center column, the leaves of the first dataset extend to the left (written in reverse order so the smallest leaf is nearest the stem), and the leaves of the second dataset extend to the right in normal order. This layout makes it easy to compare the shape, center, and spread of two groups - for example, test scores for two classes, times for two athletes, or yields from two treatment groups. You can visually see whether the medians differ, whether one dataset is more spread out, and whether the shapes are similar.
Key statistics: what the numbers tell you
The stem and leaf plot gives you the shape of the distribution visually, and the summary statistics give you precise measures. The mean (average) is sensitive to extreme values, while the median (middle value) is not, so a large gap between them signals skew. The interquartile range (IQR = Q3 - Q1) measures the spread of the middle 50% of values and is robust to outliers. The standard deviation measures how spread all values are around the mean. When you apply Tukey's fence rule (lower fence = Q1 - 1.5 x IQR, upper fence = Q3 + 1.5 x IQR) to flag potential outliers, values outside those limits stand out immediately on the stem and leaf plot.
Descriptive statistics summary
| Statistic | Description | Formula |
|---|---|---|
| Mean | Arithmetic average of all values | Sum / n |
| Median | Middle value of the sorted dataset | Value at position (n+1)/2 |
| Mode | Most frequently occurring value(s) | Highest frequency value |
| Range | Difference between maximum and minimum | Max - Min |
| Q1 | Lower quartile (25th percentile) | Median of lower half |
| Q3 | Upper quartile (75th percentile) | Median of upper half |
| IQR | Interquartile range: middle 50% spread | Q3 - Q1 |
| Std Dev (s) | Typical distance from the mean | sqrt(SS / (n-1)) |
| Variance (s²) | Squared average deviation from the mean | SS / (n-1) |
Standard statistics derived from the stem and leaf plot data. All values update as you change the input.
Frequently asked questions
What is the difference between a stem and leaf plot and a histogram?
A histogram groups data into bins and shows a count for each bin, which means you lose the individual values. A stem and leaf plot keeps every data value visible: the stem is the bin, and the leaves show exactly which values are in it. For small to medium datasets (up to a few hundred values), the stem and leaf plot gives you more information. For very large datasets a histogram is more practical.
How do you handle decimal numbers in a stem and leaf plot?
For data with one decimal place (such as 3.7), the stem is the whole number (3) and the leaf is the tenths digit (7). For two decimal places (such as 3.74), you can round to one decimal, or multiply all values by 10 and interpret the plot on a different scale. This calculator automatically selects a sensible scale based on the magnitude of your data, so decimal values are handled correctly without any manual adjustment.
Can a stem and leaf plot handle negative numbers?
Yes. Negative stems work exactly like positive ones. For example, if your data includes -32, the stem is -3 and the leaf is 2. The stems are still listed in order from smallest (most negative) to largest, so the visual shape remains intact.
What does "split stems" mean?
Split stems divides each stem row into two: the first row holds leaves 0 through 4 (marked with an asterisk on the stem), and the second row holds leaves 5 through 9 (marked with a period). This doubles the number of rows and spreads out the data, which helps when many values share a stem and the plot otherwise looks too crowded. Use split stems when a standard plot has long rows that are hard to read.
How do I use a back-to-back stem and leaf plot?
Switch the "Plot type" selector to "Back-to-back", then enter your second dataset in the box that appears. Both datasets are plotted on the same stem axis - leaves for the first dataset extend to the left and leaves for the second extend to the right. Read the plots by scanning across each stem row: if the left side is consistently longer, the first dataset has more values in that range. Compare the row with the most leaves in each column to visually find each dataset's mode, and note whether the two datasets are centered on the same stem.
How many values can the calculator handle?
There is no hard limit in this calculator. In practice, stem and leaf plots are most readable with 10 to 200 values. Above that, the leaves become too dense to scan visually and a histogram or box plot is usually clearer. For very small datasets (fewer than 5 values) the shape is not meaningful, but the statistics are still accurate.