AI Stem and Leaf Plot Maker

Display quantitative data in semi-tabular format. Stems and leaves at a glance.

Stem-and-Leaf Plot AI

Hi! I'm your Stem-and-Leaf Plot analysis assistant.

• Analyze your data layout — I'll help you read stems, leaves, and distribution shape • Generate sample data — e.g. "generate 25 two-digit numbers for a stem-and-leaf plot" • Explain — ask me "how does a stem-and-leaf plot differ from a histogram?"

Try: "Help me read this plot" or "Generate 30 test scores between 40 and 99"

How to Use This AI Stem and Leaf Plot Maker

  1. Enter your data. Paste numeric values (whole numbers work best). The tool automatically determines stem (leading digits) and leaf (trailing digit).
  2. Generate the plot. Click Generate to display the stem-and-leaf arrangement with frequency counts.
  3. Read the output. Each row shows a stem, a vertical bar, and leaves (final digits of each value). The shape resembles a sideways histogram.
  4. Chat with AI. Ask the assistant to interpret the distribution, identify clusters, or compare it with other chart types.
  5. Export. Download results as SVG, CSV, XLSX, or Markdown.

What is a Stem-and-Leaf Plot?

A stem-and-leaf plot (or stemplot) displays quantitative data in a semi-tabular format where each value is split into a stem (typically the leading digit(s)) and a leaf (typically the final digit). For example, the number 73 is split into stem=7 and leaf=3.

What makes stem-and-leaf plots unique is that they preserve every original data value while simultaneously showing the distribution shape — essentially a hybrid between a table and a histogram. They are particularly popular in educational settings for teaching descriptive statistics, and remain useful for quality control dashboards and quick exploratory analysis of small datasets.

For larger datasets (100+ values), the distribution shape remains visible but individual leaves become less meaningful. In those cases, a histogram may be more practical, though the stem-and-leaf still provides an audit trail of exact values.

Frequently Asked Questions

When should I use a stem-and-leaf plot instead of a histogram?
Stem-and-leaf plots are ideal for small to medium datasets (under 100 values) because they preserve the exact original values while showing distribution shape — each 'leaf' is an actual digit from your data. For larger datasets, histograms are more practical as they aggregate data into bins.
What do the stem and leaf represent?
The 'stem' represents the leading digit(s) — typically tens, hundreds, or thousands — while the 'leaf' represents the trailing (final) digit. For example, the value 73 would be split into stem=7 and leaf=3.

Computation Method

  • Quartiles are calculated using linear interpolation (method 7 from Hyndman & Fan, 1996), consistent with Python's NumPy and pandas defaults.
  • Outlier detection uses the Tukey fences method: lower fence = Q1 − 1.5 × IQR, upper fence = Q3 + 1.5 × IQR.
  • Chart scaling caps the display axis at the upper fence value so the visualization remains clear even when extreme outliers are present.

References

  • Hyndman, R. J. & Fan, Y. (1996). “Sample Quantiles in Statistical Packages.” The American Statistician, 50(4), 361–365.
  • Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley.