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biom.table.Table

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biom.table.Table

class biom.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, **kwargs)

The (canonically pronounced ‘teh’) Table.

Give in to the power of the Table!

Attributes

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

Methods

__getitem__(args) Handles row or column slices
_extract_data_from_tsv(lines[, delim, ...]) Parse a classic table into (sample_ids, obs_ids, data, metadata,
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.
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_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]) Yields partitions
rankdata([axis, inplace, method]) Convert values to rank abundances from smallest to largest
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_dataframe() Convert matrix data to a Pandas SparseDataFrame
to_hdf5(h5grp, generated_by[, compress, ...]) Store CSC and CSR in place
to_json(generated_by[, direct_io]) 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

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