scanpy.pp.sample

scanpy.pp.sample#

scanpy.pp.sample(data, fraction=None, *, n=None, rng=None, copy=False, replace=False, axis='obs', p=None)[source]#

Sample observations or variables with or without replacement.

Parameters:

data AnnData | ndarray | csr_matrix | csc_matrix | Array: The (annotated) data matrix of shape n_obs × n_vars. Rows correspond to cells and columns to genes.
fraction float | None (default: None): Sample to this fraction of the number of observations or variables. (All of them, even if there are 0s/Falses in p.) This can be larger than 1.0, if replace=True. See axis and replace.
n int | None (default: None): Sample to this number of observations or variables. See axis.
rng Generator | BitGenerator | int | integer | Sequence[int] | SeedSequence | None (default: None): Random seed to change subsampling.
copy bool (default: False): If an AnnData is passed, determines whether a copy is returned.
replace bool (default: False): If True, samples are drawn with replacement.
axis Literal['obs', 0, 'var', 1] (default: 'obs'): Sample observations (axis 0) or variables (axis 1).
p str | ndarray[tuple[Any, ...], dtype[bool]] | ndarray[tuple[Any, ...], dtype[floating]] | None (default: None): Drawing probabilities (floats) or mask (bools). Either an axis-sized array, or the name of a column. If p is an array of probabilities, it must sum to 1.

Return type:

Returns:

If isinstance(data, AnnData) and copy=False, this function returns None. Otherwise:

data[indices, :] | data[:, indices] (depending on axis): If data is array-like or copy=True, returns the subset.
indicesnumpy.ndarray: If data is array-like, also returns the indices into the original.