sklearn.utils.sparsefuncs
.mean_variance_axis¶

sklearn.utils.sparsefuncs.
mean_variance_axis
(X, axis, weights=None, return_sum_weights=False)[source]¶ Compute mean and variance along an axis on a CSR or CSC matrix.
 Parameters
 Xsparse matrix of shape (n_samples, n_features)
Input data. It can be of CSR or CSC format.
 axis{0, 1}
Axis along which the axis should be computed.
 weightsndarray of shape (n_samples,) or (n_features,), default=None
if axis is set to 0 shape is (n_samples,) or if axis is set to 1 shape is (n_features,). If it is set to None, then samples are equally weighted.
New in version 0.24.
 return_sum_weightsbool, default=False
If True, returns the sum of weights seen for each feature if
axis=0
or each sample ifaxis=1
.New in version 0.24.
 Returns
 meansndarray of shape (n_features,), dtype=floating
Featurewise means.
 variancesndarray of shape (n_features,), dtype=floating
Featurewise variances.
 sum_weightsndarray of shape (n_features,), dtype=floating
Returned if
return_sum_weights
isTrue
.