ncbi_taxonomy.full_name_lineages

Python

In [1]:

import bedrock_bio as bb

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

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

Out[1]:

	sources	taxon_id	scientific_name	ancestor_names
0	[{'url': 'https://ftp.ncbi.nlm.nih.gov/pub/tax...	1	root	[]
1	[{'url': 'https://ftp.ncbi.nlm.nih.gov/pub/tax...	10	Cellvibrio	[cellular organisms, Bacteria, Pseudomonadati,...
2	[{'url': 'https://ftp.ncbi.nlm.nih.gov/pub/tax...	100	Ancylobacter aquaticus	[cellular organisms, Bacteria, Pseudomonadati,...
3	[{'url': 'https://ftp.ncbi.nlm.nih.gov/pub/tax...	100000	Herbaspirillum sp. BA12	[cellular organisms, Bacteria, Pseudomonadati,...
4	[{'url': 'https://ftp.ncbi.nlm.nih.gov/pub/tax...	1000000	Microbacterium sp. 6.11-VPa	[cellular organisms, Bacteria, Bacillati, Acti...

R

In [1]:

library(bedrockbio)

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

head(df)

Out[1]:

Sample output (5 rows, showing 2 of 4 columns)
   taxon_id | scientific_name
   -------- | ------------------------
0  1        | root
1  10       | Cellvibrio
2  100      | Ancylobacter aquaticus
3  100000   | Herbaspirillum sp. BA12
4  1000000  | Microbacterium sp. 6.11…

Description

Full NCBI Taxonomy name lineages, one row per taxon.

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 for the node.
scientific_name	TEXT	Scientific name of the node.
ancestor_names	TEXT[]	Ancestor names, split from NCBI’s semicolon-delimited lineage in order from the root to the parent. Empty for the root node.