cellxgene_census.experimental.pp.mean_variance¶
- cellxgene_census.experimental.pp.mean_variance(query: ExperimentAxisQuery, layer: str = 'raw', axis: int = 0, calculate_mean: bool = False, calculate_variance: bool = False, ddof: int = 1, nnz_only: bool = False) DataFrame ¶
Calculate mean and/or variance along the
obs
axis from query results. Calculations are done in an accumulative chunked fashion. For the mean and variance calculations, the total number of elements (N) is, by default, the corresponding dimension size: for column-wise calculations (axis = 0
) N is number of rows, for row-wise calculations (axis = 1
) N is number of columns. For metrics calculated only on nnz (explicitly stored)values of the sparse matrix, specifynnz_only=True
.- Parameters:
query – A SOMA query, specifying the obs/var selection over which mean and variance are calculated.
layer – X layer used, e.g.,
raw
axis – Axis or axes along which the statistics are computed
calculate_mean – If
True
it calculates mean, otherwise skips calculationcalculate_variance – If
True
it calculates variance, otherwise skips calculationddof – “Delta Degrees of Freedom”: the divisor used in the calculation for variance is
N - ddof
, whereN
represents the number of elements.nnz_only – If
True
mean and variance will only be calculated over explicitly stored values in the sparse matrix. Defaults toFalse
.
- Returns:
Pandas DataFrame indexed by the
soma_joinid
and with columnsmean
(ifcalculate_mean = True
), andvariance
(ifcalculate_variance = True
)
Lifecycle
experimental