skbio.table.Table

class skbio.table.Table(data, observation_ids, sample_ids, observation_metadata=None, sample_metadata=None, table_id=None, type=None, create_date=None, generated_by=None, observation_group_metadata=None, sample_group_metadata=None, validate=True, observation_index=None, sample_index=None, **kwargs)

The (canonically pronounced ‘teh’) Table.

Give in to the power of the Table!

Creates an in-memory representation of a BIOM file. BIOM version 1.0 is based on JSON to provide the overall structure for the format while versions 2.0 and 2.1 are based on HDF5. For more information see [1] and [2]

Raises:

TableException

When an invalid table type is provided.

Notes

Allowed table types are None, “OTU table”, “Pathway table”, “Function table”, “Ortholog table”, “Gene table”, “Metabolite table”, “Taxon table”

References

[1]

http://biom-format.org/documentation/biom_format.html

[2]

D. McDonald, et al. “The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome” GigaScience 2012 1:7

Attributes:

shape

The shape of the underlying contingency matrix

dtype

The type of the objects in the underlying contingency matrix

nnz

Number of non-zero elements of the underlying contingency matrix

matrix_data

The sparse matrix object

type

table_id

create_date

generated_by

format_version

Attributes

default_write_format
dtype	The type of the objects in the underlying contingency matrix
matrix_data	The sparse matrix object
nnz	Number of non-zero elements of the underlying contingency matrix
shape	The shape of the underlying contingency matrix

Built-ins

__eq__(other)	Equality is determined by the data matrix, metadata, and IDs
__ge__(value, /)	Return self>=value.
__getitem__(args)	Handles row or column slices
__getstate__(/)	Helper for pickle.
__gt__(value, /)	Return self>value.
__iter__()	See biom.table.Table.iter
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(other)	Return self!=value.
__str__()	Stringify self

Methods

add_group_metadata(group_md[, axis])	Take a dict of group metadata and add it to an axis
add_metadata(md[, axis])	Take a dict of metadata and add it to an axis.
align_to(other[, axis])	Align self to other over a requested axis
align_to_dataframe(metadata[, axis])	Aligns dataframe against biom table, only keeping common ids.
align_tree(tree[, axis])	Aligns biom table against tree, only keeping common ids.
collapse(f[, collapse_f, norm, ...])	Collapse partitions in a table by metadata or by IDs
concat(others[, axis])	Concatenate tables if axis is disjoint
copy()	Returns a copy of the table
data(id[, axis, dense])	Returns data associated with an id
del_metadata([keys, axis])	Remove metadata from an axis
delimited_self([delim, header_key, ...])	Return self as a string in a delimited form
descriptive_equality(other)	For use in testing, describe how the tables are not equal
exists(id[, axis])	Returns whether id exists in axis
filter(ids_to_keep[, axis, invert, inplace])	Filter a table based on a function or iterable.
from_adjacency(lines)	Parse an adjacency format into BIOM
from_hdf5(h5grp[, ids, axis, parse_fs, ...])	Parse an HDF5 formatted BIOM table
from_json(json_table[, data_pump, ...])	Parse a biom otu table type
from_tsv(lines, obs_mapping, sample_mapping, ...)	Parse a tab separated (observation x sample) formatted BIOM table
get_table_density()	Returns the fraction of nonzero elements in the table.
get_value_by_ids(obs_id, samp_id)	Return value in the matrix corresponding to (obs_id, samp_id)
group_metadata([axis])	Return the group metadata of the given axis
head([n, m])	Get the first n rows and m columns from self
ids([axis])	Return the ids along the given axis
index(id, axis)	Return the index of the identified sample/observation.
is_empty()	Check whether the table is empty
iter([dense, axis])	Yields (value, id, metadata)
iter_data([dense, axis])	Yields axis values
iter_pairwise([dense, axis, tri, diag])	Pairwise iteration over self
length([axis])	Return the length of an axis
max([axis])	Get the maximum nonzero value over an axis
merge(other[, sample, observation, ...])	Merge two tables together
metadata([id, axis])	Return the metadata of the identified sample/observation.
metadata_to_dataframe(axis)	Convert axis metadata to a Pandas DataFrame
min([axis])	Get the minimum nonzero value over an axis
nonzero()	Yields locations of nonzero elements within the data matrix
nonzero_counts(axis[, binary])	Get nonzero summaries about an axis
norm([axis, inplace])	Normalize in place sample values by an observation, or vice versa.
pa([inplace])	Convert the table to presence/absence data
partition(f[, axis, remove_empty, ignore_none])	Yields partitions
rankdata([axis, inplace, method])	Convert values to rank abundances from smallest to largest
read(file[, format])	Create a new Table instance from a file.
reduce(f, axis)	Reduce over axis using function f
remove_empty([axis, inplace])	Remove empty samples or observations from the table
sort([sort_f, axis])	Return a table sorted along axis
sort_order(order[, axis])	Return a new table with axis in order
subsample(n[, axis, by_id, ...])	Randomly subsample without replacement.
sum([axis])	Returns the sum by axis
to_anndata([dense, dtype, transpose])	Convert Table to AnnData format
to_dataframe([dense])	Convert matrix data to a Pandas SparseDataFrame or DataFrame
to_hdf5(h5grp, generated_by[, compress, ...])	Store CSC and CSR in place
to_json(generated_by[, direct_io, creation_date])	Returns a JSON string representing the table in BIOM format.
to_tsv([header_key, header_value, ...])	Return self as a string in tab delimited form
transform(f[, axis, inplace])	Iterate over axis, applying a function f to each vector.
transpose()	Transpose the contingency table
update_ids(id_map[, axis, strict, inplace])	Update the ids along the given axis.
write(file[, format])	Write an instance of Table to a file.