ncbi_taxonomy.names

Python

In [1]:

import bedrock_bio as bb

rel = bb.load_table('ncbi_taxonomy.names')

df = rel.limit(5).pl()  # Polars
df = rel.limit(5).df()  # Pandas
df.head()

Out[1]:

	sources	taxon_id	name	unique_name	name_class
0	[{'url': 'https://ftp.ncbi.nlm.nih.gov/pub/tax...	1	all	None	synonym
1	[{'url': 'https://ftp.ncbi.nlm.nih.gov/pub/tax...	1	root	None	scientific name
2	[{'url': 'https://ftp.ncbi.nlm.nih.gov/pub/tax...	10	Cellvibrio (ex Winogradsky 1929) Blackall et a...	None	authority
3	[{'url': 'https://ftp.ncbi.nlm.nih.gov/pub/tax...	10	Cellvibrio (ex Winogradsky 1929) Blackall et a...	None	authority
4	[{'url': 'https://ftp.ncbi.nlm.nih.gov/pub/tax...	10	Cellvibrio	None	scientific name

R

In [1]:

library(bedrockbio)

lf <- load_table("ncbi_taxonomy.names")
df <- head(lf, 5) |> collect()

head(df)

Out[1]:

Sample output (5 rows, showing 4 of 5 columns)
   taxon_id | name                     | unique_name | name_class
   -------- | ------------------------ | ----------- | ---------------
0  1        | all                      | —           | synonym
1  1        | root                     | —           | scientific name
2  10       | Cellvibrio (ex Winograd… | —           | authority
3  10       | Cellvibrio (ex Winograd… | —           | authority
4  10       | Cellvibrio               | —           | scientific name

Description

Scientific names, synonyms, common names, and other NCBI Taxonomy names, one row per taxon name and name class.

Partitions

Not partitioned.

Schema

Column	Type	Description
sources	STRUCT(url TEXT, filename TEXT, last_modified_at TIMESTAMPTZ, sha256 TEXT)[]	Source provenance for the row.
taxon_id	TEXT	Taxonomy ID associated with the name.
name	TEXT	Taxon name text.
unique_name	TEXT	NCBI’s disambiguating name variant, if supplied.
name_class	TEXT	NCBI name class, such as scientific name, synonym, or common name.