rotifer.db.ncbi.TaxonomyCursor#

class rotifer.db.ncbi.TaxonomyCursor(readers=['ete3', 'entrez'], writers=[], progress=True, tries=3, sleep_between_tries=1, batch_size=None, threads=None, *args, **kwargs)[source]#

Bases: SequentialDelegatorCursor

Fetch NCBI Taxonomy data as dataframes.

Taxonomy identifiers are first searched in the local ETE toolkit copy of the NCBI Taxonomy database and only the identifiers not found there are sent to NCBI Entrez.

Parameters:

readerslist of str, default ['ete3', 'entrez']: Backend reader modules, tried in order.
writerslist of str, default []: Backend writer modules.
progressbool, default True: Whether to print a progress bar.
triesint, default 3: Number of attempts to download data.
sleep_between_triesint, default 1: Number of seconds to wait between download attempts.
batch_sizeint, optional: Number of accessions per batch.
threadsint, optional: Number of simultaneous threads.

Attributes:

taxcolslist of str: Columns of the returned dataframes: taxid, organism, superkingdom, lineage, classification and alternative_taxids.

See also

rotifer.db.local.ete3.TaxonomyCursor: the local backend
rotifer.db.ncbi.entrez.TaxonomyCursor: the remote backend

Examples

>>> from rotifer.db import ncbi
>>> tc = ncbi.TaxonomyCursor(progress=False)
>>> t = tc.fetchall([2599])

__getitem__(accessions, *args, **kwargs)[source]#

Fetch taxonomy data for one or more taxids, dictionary style.

Parameters:

accessionsint, str or iterable: NCBI Taxonomy identifiers.

Returns:

pandas.DataFrame: One row per taxon, with the columns listed in taxcols.

Examples

>>> from rotifer.db import ncbi
>>> tc = ncbi.TaxonomyCursor(progress=False)
>>> t = tc[2599]

fetchall(accessions, *args, **kwargs)[source]#

Fetch taxonomy data for all taxids as one dataframe.

Parameters:

accessionslist: NCBI Taxonomy identifiers.

Returns:

pandas.DataFrame: One row per taxon, with the columns listed in taxcols.

getids(obj, *args, **kwargs)[source]#

Extract taxonomy identifiers from taxonomy dataframes.

Parameters:

objpandas.DataFrame or list of pandas.DataFrame: Taxonomy dataframes produced by the cursor.

Returns:

set of str: All identifiers in the taxid and alternative_taxids columns.