pvanalytics.quality.outliers.hampel(data, window=5, max_deviation=3.0, scale=None)#

Identify outliers by the Hampel identifier.

The Hampel identifier is computed according to 1.

  • data (Series) – The data in which to find outliers.

  • window (int or offset, default 5) – The size of the rolling window used to compute the Hampel identifier.

  • max_deviation (float, default 3.0) – Any value with a Hampel identifier > max_deviation standard deviations from the median is considered an outlier.

  • scale (float, optional) – Scale factor used to estimate the standard deviation as \(MAD / scale\). If scale=None (default), then the scale factor is taken to be scipy.stats.norm.ppf(3/4.) (approx. 0.6745), and \(MAD / scale\) approximates the standard deviation of Gaussian distributed data.


True for each value that is an outlier according to its Hampel identifier.

Return type




Pearson, R.K., Neuvo, Y., Astola, J. et al. Generalized Hampel Filters. EURASIP J. Adv. Signal Process. 2016, 87 (2016). https://doi.org/10.1186/s13634-016-0383-6

Examples using pvanalytics.quality.outliers.hampel#

Hampel Outlier Detection

Hampel Outlier Detection