Choose the web application or Python

Use Phitter Web when you want to inspect a dataset immediately, share no setup instructions, or keep the workflow inside the browser. Use the Python library when the fit belongs in a notebook, script, reproducible study, or larger data pipeline.

The two routes complement one another: explore interactively first, then reproduce the selected analysis in code when necessary.

Fit a dataset in the browser

  1. Open Fit a dataset.
  2. Paste observations or import a TXT, CSV, XLSX, or remote file.
  3. Select a continuous or discrete fit. Continuous data can take any value in an interval; discrete data represent countable outcomes such as 0, 1, 2, and so on.
  4. Review the confidence level and, for continuous data, the histogram bins. Candidate selection is available in the Python API; the web tool evaluates its available catalogue.
  5. Run the fit and inspect the ranked results, estimated parameters, tests, and plots.

The web input requires at least 20 observations. This example uses 500 synthetic observations; meeting the minimum alone does not establish statistical reliability.

The calculation runs through Python and Pyodide in the browser. Your sample does not need to be sent to a Phitter analysis server.

Fit a dataset in Python

Phitter requires Python 3.9 or newer. Install the package from PyPI:

pip install phitter

The shortest continuous fit evaluates the available continuous distributions:

import phitter

import numpy as np

data = np.random.default_rng(42).lognormal(mean=1.2, sigma=0.55, size=500)

phi = phitter.Phitter(data=data)
phi.fit(n_workers=1)

print(phi.best_distribution)
print(phi.summarize(10))

For a more controlled analysis, specify the family, confidence level, histogram bins, and candidates:

phi = phitter.Phitter(
    data=data,
    fit_type="continuous",
    num_bins=15,
    confidence_level=0.95,
    distributions_to_fit=["beta", "normal", "triangular"],
)

phi.fit(n_workers=1)

Read the result in the right order

Begin with data quality and the empirical plots. Then compare the top candidates and their estimated parameters. Check goodness-of-fit results and inspect the histogram, CDF, and Q–Q plots. Finally, ask whether the support and tail behaviour make sense for the phenomenon you are modelling.

Do not choose a distribution solely because it has the smallest error value. A fitted model should be numerically credible and substantively defensible.

Reproduce and cite the analysis

Record the Phitter version, fitting options, candidate set, and sample-preparation decisions. For academic work, cite the JOSS paper using its DOI and keep the code or input data needed to repeat the fit.

Detailed documentation