---
id: trimmed_mean_py_datascience
name: trimmed_mean
kind: function
lang: py
domain: datascience
version: "1.0.0"
purity: pure
signature: "def trimmed_mean(values: list[float], trim: float = 0.05) -> float"
description: "Arithmetic mean after cutting the bottom and top trim percentiles. Returns math.nan for empty input."
tags: [statistics, mean, robust, trimming, outliers]
uses_functions: []
uses_types: []
returns: []
returns_optional: false
error_type: ""
imports: [math, numpy]
example: |
  from trimmed_mean import trimmed_mean
  result = trimmed_mean([1, 2, 3, 4, 5, 100], 0.1)  # ~3.5
tested: true
tests:
  - "test_trimmed_mean_basic"
  - "test_trimmed_mean_empty_returns_nan"
  - "test_trimmed_mean_no_trim"
  - "test_trimmed_mean_single_element"
  - "test_trimmed_mean_uniform"
test_file_path: "python/functions/datascience/tests/test_trimmed_mean.py"
file_path: "python/functions/datascience/trimmed_mean.py"
params:
  - name: values
    desc: "List of numeric values to average."
  - name: trim
    desc: "Fraction to cut from each tail before averaging (0 <= trim < 0.5). Default 0.05."
output: "Trimmed arithmetic mean as float. Returns math.nan if values is empty or all values are trimmed away."
source_repo: "internal:footprint_aurgi"
source_license: "internal-aurgi"
source_file: "aurgi_mapas/generar_pdf_reporte.py:117"
---

## Ejemplo

```python
from trimmed_mean import trimmed_mean

trimmed_mean([1, 2, 3, 4, 5, 100], 0.1)  # ~3.5 (100 is trimmed)
trimmed_mean([], 0.05)                     # math.nan
trimmed_mean([5.0, 5.0, 5.0], 0.0)        # 5.0
```

## Notas

Usa numpy.percentile para calcular los umbrales lo y hi, luego filtra valores dentro del rango [lo, hi]. Util para calcular promedios robustos cuando hay valores extremos en la distribucion.
