Histogram & Box Plot Generator

Upload a CSV file or paste data, pick a numeric column, and instantly see how it’s distributed. This tool computes summary statistics, draws a histogram with automatically-chosen bin widths, and a parallel box plot showing quartiles and outliers — all in your browser. No data leaves your device.

What is a histogram?

A histogram is a chart that groups numeric data into intervals (bins) and shows how many values fall into each bin. It’s the most common way to visualize the distribution of a single continuous variable. By looking at the shape of a histogram you can answer questions like:

• Is the data roughly symmetric (bell-shaped) or skewed?
• Are there multiple peaks (modes), suggesting subgroups in the data?
• Are there outliers or unexpected gaps?
• How spread out are the values around the typical value?

How bin widths are chosen

The default uses the Freedman-Diaconis rule: bin width = 2 · IQR · n⁻¹ᐟ³, where IQR is the interquartile range and n is the sample size. This rule is robust to outliers because it uses IQR (a resistant measure of spread) rather than standard deviation. For tiny datasets (n < 30) we fall back to Sturges’ rule (log₂(n) + 1 bins).

You can also override the bin count manually — try several values to see how the shape changes. Too few bins hide structure; too many create noise.

What is a box plot?

A box plot (also called box-and-whisker plot) is a compact summary of a distribution showing five key values:

• Median (the line inside the box): the middle value when data is sorted.
• Q1 and Q3 (box edges): the 25th and 75th percentiles. The box spans the middle 50% of the data.
• Whiskers: typically extend to the most extreme value within 1.5 · IQR of the quartiles.
• Outliers (dots beyond whiskers): values further than 1.5 · IQR from Q1 or Q3.

Box plots are excellent for comparing several groups side-by-side, or for spotting outliers quickly. This tool draws a box plot of the selected column right next to the histogram.

How to use this tool

1. Upload your data: drag and drop a CSV file, click “Choose file”, or paste data into the textarea. The tool auto-detects delimiters and headers, and handles US (1,234.56) or EU (1.234,56) number formats.
2. Pick a column: only numeric columns are listed. Switch between columns to compare distributions.
3. Adjust bin count (optional): start with auto, then try 10, 20, 30 to see how the shape changes.
4. Read the summary: count, mean, standard deviation, quartiles, IQR, range, skewness, and kurtosis are computed for the selected column.

Common use cases

• Quality control: visualize measurement error or product dimensions to detect drift.
• Survey analysis: see how respondents’ ages, scores, or ratings are distributed.
• Finance: examine the distribution of daily returns, transaction sizes, or wait times.
• Sciences: explore experimental data before running formal statistical tests.

Privacy & accuracy

All parsing and computation happens locally in your browser. Nothing is uploaded to a server. The maximum file size is 5 MB. For very large datasets (>100,000 rows) rendering may be slow; consider sampling.

References

• Freedman, D., Diaconis, P. (1981). On the histogram as a density estimator: L₂ theory. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete 57, 453–476.
• Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley.
• Sturges, H. A. (1926). The choice of a class interval. Journal of the American Statistical Association.