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, 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 [R1] and [R2]
Raises: TableException
When an invalid table type is provided.
References
[R1] (1, 2) http://biom-format.org/documentation/biom_format.html [R2] (1, 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 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. 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]) 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, with_replacement]) 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]) 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.