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In SciPy, scipy.special.gamma evaluates the mathematical gamma function Γ(z), while scipy.stats.gamma models a gamma-distributed random variable. Use the first to calculate a gamma-function value; use the second for distribution tasks such as densities, probabilities, quantiles, or random samples. The APIs are related through the distribution’s density, but they are not interchangeable.

Choose the SciPy gamma API for your task

What you need Use Example result
Evaluate Γ(z), including generalized factorial calculations scipy.special.gamma A gamma-function value
Calculate a gamma distribution’s density, CDF, quantile, or random variate scipy.stats.gamma A probability, density, quantile, or sample
Calculate a gamma distribution’s CDF or survival probability directly scipy.special.gdtr or scipy.special.gdtrc A CDF or upper-tail probability

Calculate the mathematical gamma function

Import gamma from scipy.special. It accepts scalar or array-like inputs:

from scipy.special import gamma

values = gamma([0, 0.5, 1, 5])

The gamma function is defined by Γ(z) = ∫₀∞ tz−1e−tdt for Re(z) > 0 and extended elsewhere by analytic continuation. It generalizes the factorial: Γ(n + 1) = n! for natural numbers n, and it follows the recurrence Γ(z + 1) = zΓ(z). See the SciPy special.gamma reference.

Select a related function when the expression calls for it

For log-domain work, gammaln returns the logarithm of the absolute gamma value, while loggamma is the principal branch of the complex logarithm. gammasgn gives the sign. These functions are not interchangeable; choose the one matching the quantity in your formula. SciPy also provides reciprocal gamma, rgamma, and regularized incomplete gamma functions and their inverses. The SciPy special-function index lists these related functions.

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Account for poles and SciPy version

In current SciPy documentation, gamma returns NaN at negative integer poles. At zero, signed zero affects the result: gamma(-0.0) is negative infinity, while gamma(+0.0) is positive infinity. SciPy documents this behavior as fixed in version 1.15; before 1.15, the function returned positive infinity at each pole. If a reciprocal-gamma factor appears in a formula, prefer rgamma rather than relying on a pole in a denominator, because pole behavior can affect the result. Check the documentation and release notes for the SciPy version you use before treating this as a migration guarantee. See the versioned behavior notes.

Use the gamma probability distribution

For distribution calculations, import gamma from scipy.stats. SciPy parameterizes the distribution with shape a and scale scale. If your formula uses a rate λ, convert it with scale=1/λ:

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from scipy.stats import gamma

shape = 2.0
rate = 3.0
distribution = gamma(a=shape, scale=1 / rate)
probability = distribution.cdf(1.0)

The resulting probability is the probability that a variable from this distribution is at or below 1.0. The standardized density for positive shape a and nonnegative x is xa−1e−x/Γ(a); the general distribution API also applies loc and scale. SciPy’s gamma-distribution tutorial and probability-distribution tutorial describe the model and parameter conventions.

Use the distribution methods for common tasks

The frozen distribution object in the example keeps the chosen parameters together. Its continuous-distribution interface provides methods for density, cumulative probability, quantiles, and random variates. For an upper-tail probability, use its sf method rather than subtracting a CDF from 1; the survival function directly represents the probability of exceeding a threshold.

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Calculate a gamma CDF or upper tail with special functions

SciPy also provides direct CDF and survival-function calls. Their arguments are ordered as rate, shape, x, unlike stats.gamma, where shape is the named parameter and rate must be converted to scale:

from scipy.special import gdtr, gdtrc

cdf_value = gdtr(rate, shape, x)
tail_probability = gdtrc(rate, shape, x)

For rate λ, shape a, and threshold x, SciPy documents these equivalences:

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  • gdtr(rate, shape, x) corresponds to gamma(shape, scale=1/rate).cdf(x).
  • gdtrc(rate, shape, x) corresponds to gamma(shape, scale=1/rate).sf(x).

SciPy notes that these direct functions can often be faster for small arrays or individual values; that is a documentation qualification, not a guarantee for every workload. See the gdtr reference and gdtrc reference.

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Avoid shape, scale, and rate mix-ups

  • In scipy.stats.gamma, pass the shape as a. If your model is expressed with rate λ, use scale=1/λ.
  • In gdtr and gdtrc, the first positional argument is rate and the second is shape.
  • When translating a textbook or another library’s parameterization, identify whether its second parameter is scale or rate before writing the SciPy call.

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