---
id: geometric_mean_py_datascience
name: geometric_mean
kind: function
lang: py
domain: datascience
version: "1.0.0"
purity: pure
signature: "def geometric_mean(values: list[float]) -> float"
description: "Geometric mean of positive elements via exp(mean(log(x))). Non-positive values are filtered out. Returns math.nan if no positives."
tags: [statistics, mean, geometric, distribution, lognormal]
uses_functions: []
uses_types: []
returns: []
returns_optional: false
error_type: ""
imports: [math, numpy]
example: |
  from geometric_mean import geometric_mean
  result = geometric_mean([1, 2, 4, 8])  # ~2.828 (2^1.5)
tested: true
tests:
  - "test_geometric_mean_powers_of_two"
  - "test_geometric_mean_filters_non_positive"
  - "test_geometric_mean_empty_returns_nan"
  - "test_geometric_mean_all_negative_returns_nan"
  - "test_geometric_mean_single_positive"
test_file_path: "python/functions/datascience/tests/test_geometric_mean.py"
file_path: "python/functions/datascience/geometric_mean.py"
params:
  - name: values
    desc: "List of numeric values. Non-positive elements are silently ignored."
output: "Geometric mean as float, computed over positive elements only. Returns math.nan if there are no positive values."
source_repo: "internal:footprint_aurgi"
source_license: "internal-aurgi"
source_file: "aurgi_mapas/generar_pdf_reporte.py:126"
---

## Ejemplo

```python
from geometric_mean import geometric_mean

geometric_mean([1, 2, 4, 8])   # 2.828... (= 2^1.5)
geometric_mean([1, -2, 3])     # exp((log(1)+log(3))/2) — ignores -2
geometric_mean([])              # math.nan
geometric_mean([-1, -2])       # math.nan — no positives
```

## Notas

Apropiado para distribuciones lognormales o datos multiplicativos (precios, ratios, crecimientos). Equivalente a la raiz n-esima del producto pero numericamente mas estable via log-space.