class_docstr: Running skew using Welford's algorithm.
  
  Parameters
  ----------
  bias
      If `False`, then the calculations are corrected for statistical bias.
  
  Examples
  --------
  
  >>> from river import stats
  >>> import numpy as np
  
  >>> np.random.seed(42)
  >>> X = np.random.normal(loc=0, scale=1, size=10)
  
  >>> skew = stats.Skew(bias=False)
  >>> for x in X:
  ...     skew.update(x)
  ...     print(skew.get())
  0.0
  0.0
  -1.4802398132849872
  0.5127437186677888
  0.7803466510704751
  1.056115628922055
  0.5057840774320389
  0.3478402420400934
  0.4536710660918704
  0.4123070197493227
  
  >>> skew = stats.Skew(bias=True)
  >>> for x in X:
  ...     skew.update(x)
  ...     print(skew.get())
  0.0
  0.0
  -0.6043053732501439
  0.2960327239981376
  0.5234724473423674
  0.7712778043924866
  0.39022088752624845
  0.278892645224261
  0.37425953513864063
  0.3476878073823696
  
  References
  ----------
  [^1]: [Wikipedia article on algorithms for calculating variance](https://www.wikiwand.com/en/Algorithms_for_calculating_variance#/Covariance)
code: def name(self):
      return "skew"
  
file_path: river/river/stats/skew.py
imports: from __future__ import annotations
  from river import stats
  from river.stats import _rust_stats
label_desc: Use image node_id 2 for calling the Skew obj's underlying member method code with example usage: obj.name() and returns: str