rotifer.db.ncbi.entrez.TaxonomyCursor#

class rotifer.db.ncbi.entrez.TaxonomyCursor(progress=True, tries=3, sleep_between_tries=1, batch_size=20, threads=10)[source]#

Fetch NCBI Taxonomy records with the E-utilities.

Taxonomy entries are downloaded as XML with EFetch and returned as dataframes.

Parameters:

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, default 20: Number of accessions per batch.
threadsint, default 10: Number of simultaneous threads. Capped at 3 without an NCBI API key and at 10 with one.

Attributes:

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

See also

rotifer.db.ncbi.TaxonomyCursor: delegator that prefers the local ETE toolkit database

Examples

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

fetchall(accessions)[source]#

Fetch taxonomy data for all taxids as one dataframe.

Parameters:

Returns:

getids(obj)[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.

parser(stream, accession)[source]#

Parse an EFetch XML stream into a taxonomy dataframe.

Parameters:

streamfile-like: Open stream returned by the fetcher.
accessionstr or iterable of str: The queried taxonomy identifiers, kept for interface compatibility.

Returns:

list of pandas.DataFrame: A single element list holding the parsed dataframe.

Raises: