Create a Histogram with Kernel Density Estimate online with InstaNANO
The InstaNANO Graph Plotter can create a histogram with kernel density estimate directly in your browser. The interactive graph above is already initialized with example data. Replace those values with your own table, assign the supported column roles, adjust the graph settings, and inspect the result on the same page.
A histogram with a kernel density estimate pairs binned observations with a smooth distribution summary. Use it for an exploratory view of sample shape while retaining the observed bin counts.
Required data columns and series controls
| Column role | Supported layout | How InstaNANO uses it |
|---|---|---|
| Y-axis | One or more numeric sample columns | Each enabled column is treated as one distribution or probability sample. |
Each Y-axis Color and Name row defines one displayed sample. There is no X-axis input: the graph derives the horizontal position, probability, or category position from the values.
How to make this graph
- Start with the working example. Review the graph and table loaded above so the relationship between the columns and plotted marks is visible before you replace the values.
- Enter or paste your data. Keep the columns in the supported order shown in the table below the graph. Use the Axis row to assign each column’s role.
- Name and distinguish the series. Where the current graph supports them, use the Name row for the displayed series label and the Color row for its color.
- Check the scientific context. Add meaningful axis titles and units, review scale choices, and confirm that row order, missing values, uncertainty columns, or normalization match the intended interpretation.
- Refine and export. Use the available graph controls described below. When the figure is ready, sign in to export a PNG with Download Graph or preserve an editable
.instananofile with Save Project.
What the loaded example demonstrates
The starter table contains two small hardness samples, “Alloy A” and “Alloy B”. It demonstrates separate numeric value columns and should not be used to infer a population distribution from the example alone.
What InstaNANO calculates and renders
The renderer draws a density histogram and overlays a kernel estimate. Its current bandwidth is derived from the sample standard deviation and sample size, then applied to a compact-support kernel.
The Bins control changes the histogram; the KDE is generated from the finite numeric values currently in each Y-axis series.
Scientific instruments and research data suited to this graph
Use it to inspect whether a measured distribution appears concentrated, broad, skewed, or potentially multi-peaked before choosing a formal distributional analysis.
Broad instrument and data coverage
- Microscopy-derived populations: Particle, grain, pore, fiber, cell, droplet, or defect sizes; aspect ratios; circularity; orientation; spacing; and other numerical features extracted from SEM, TEM, STEM, AFM, optical, or confocal images.
- Spectroscopy and diffraction features: Peak positions, widths, areas, intensity ratios, chemical shifts, binding energies, lifetimes, and other values extracted from repeated XRD, Raman, FTIR, UV–visible, PL, XPS, NMR, EELS, or EDS measurements.
- Surface, dimensional, and thermal measurements: Roughness, height, thickness, dimensional deviation, transition temperature, mass loss, heat-flow metrics, and local temperature values collected across samples or locations.
- Mechanical, electrical, electrochemical, and transport properties: Hardness, strength, modulus, conductivity, resistance, capacity, efficiency, diffusivity, permeability, and replicate device or specimen measurements.
- Reliability, process, biological, and computational samples: Failure times, cycle life, reaction yields, sensor readings, assay values, population measurements, Monte Carlo outputs, residuals, and parameter ensembles.
Representative questions for this exact graph
- Particle-size or materials-property samples inspected for broadening or possible multimodality.
- Process measurements explored for skewness before selecting a statistical model.
- Two numeric samples compared with both normalized bins and a smooth descriptive density.
This coverage is not limited to the instruments named above. InstaNANO can plot other experimental, microscopy-derived, computational, simulation, and processed research data when the required column roles are present and this graph’s encoding is scientifically appropriate.
Data checks before interpretation
- Retain the sample size and units for every numeric column.
- Inspect missing, censored, rounded, or excluded observations before interpreting a derived distribution summary.
Frequently asked questions about Histogram with Kernel Density Estimate
Is the KDE curve a fitted physical or probability model?
The smooth curve depends on the automatic bandwidth and should not be treated as a fitted physical model. Sparse samples and outliers can alter both the histogram and the estimate.
Can I plot data from instruments or research workflows not listed here?
Yes. The instrument and dataset examples on this page are representative rather than a fixed compatibility list. InstaNANO can create a histogram with kernel density estimate from other experimental, computational, simulation, and processed research data when the table follows these roles: Y-axis (one or more numeric sample columns). Microscopy use refers to numeric profiles, coordinates, dimensions, counts, intensities, distributions, or map values extracted from images; the graph does not convert a raw micrograph into numerical data.
Can I use this histogram with kernel density estimate in a publication, thesis, report, poster, or presentation?
Yes, after scientific and visual review. InstaNANO can be used to prepare a histogram with kernel density estimate for a journal manuscript, thesis or dissertation, technical report, poster, conference slide, classroom presentation, or other formal scientific communication. After sign-in, Download Graph exports a PNG, defaults to 600 dpi, and offers a transparent-background option. For this graph, report sample size, units, exclusions, censoring, and the descriptive or diagnostic calculation represented by the graph. Also check final dimensions, font size, line and symbol weight, color accessibility, caption content, and the destination’s required file format. The current export is PNG, so a journal that requires TIFF, EPS, PDF, SVG, or another format needs a journal-specific workflow. InstaNANO supports figure preparation, but it cannot guarantee that a journal, conference, institution, or publisher will accept a figure.
Choose a related graph when needed
Choose Density Histogram for the normalized bins alone, Violin Plot for a grouped distribution display, or Normal Q-Q for a focused normality diagnostic.
Plot your data or compare graph types
Edit the loaded Histogram with Kernel Density Estimate example above to test your own values. If this encoding does not match the scientific question, compare the complete graph-type directory or return to the online graph plotter.