Jupyter Notebook

Query & search registries

This guide walks through different ways of querying & searching LaminDB registries.

# pip install 'lamindb[bionty]'
!lamin init --storage ./test-registries --modules bionty
Hide code cell output 
→ initialized lamindb: testuser1/test-registries

Let’s start by creating a few exemplary datasets and saving them into a LaminDB instance using, e.g., ingest_mini_immuno_datasets().

import lamindb as ln

ln.track("Wc8F4siRSKMZ")

ln.Artifact(ln.core.datasets.file_jpg_paradisi05(), key="images/my_image.jpg").save()
ln.Artifact(ln.core.datasets.file_fastq(), key="raw/my_fastq.fastq.gz").save()
ln.Artifact.from_df(ln.core.datasets.df_iris(), key="iris.parquet").save()
ln.examples.ingest_mini_immuno_datasets()
Hide code cell output 
→ connected lamindb: testuser1/test-registries

→ created Transform('Wc8F4siRSKMZ0000'), started new Run('949qZTWi...') at 2025-05-22 14:45:59 UTC

→ notebook imports: bionty==1.3.2 lamindb==1.5.2

/opt/hostedtoolcache/Python/3.13.3/x64/lib/python3.13/site-packages/pandera/_pandas_deprecated.py:146: FutureWarning: Importing pandas-specific classes and functions from the
top-level pandera module will be **removed in a future version of pandera**.
If you're using pandera to validate pandas objects, we highly recommend updating
your import:

```
# old import
import pandera as pa

# new import
import pandera.pandas as pa
```

If you're using pandera to validate objects from other compatible libraries
like pyspark or polars, see the supported libraries section of the documentation
for more information on how to import pandera:

https://pandera.readthedocs.io/en/stable/supported_libraries.html

To disable this warning, set the environment variable:

```
export DISABLE_PANDERA_IMPORT_WARNING=True
```

  warnings.warn(_future_warning, FutureWarning)

import lamindb as ln

Get an overview

The easiest way to get an overview over all artifacts is by typing df(), which returns the 100 latest artifacts in the Artifact registry.

import lamindb as ln

ln.Artifact.df()
Hide code cell output
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
5 5N61onK2FErvtI4S0000 examples/dataset2.h5ad None .h5ad dataset AnnData 26896 RKJjWbINYNIwYU8BxCejMw None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:14.584000+00:00 1 None 1
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad None .h5ad dataset AnnData 31672 FB3CeMjmg1ivN6HDy6wsSg None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:11.644000+00:00 1 None 1
3 8Fs5XN0buN3Uoxgn0000 iris.parquet None .parquet dataset DataFrame 5131 s-hxJHE31HpN050-AEaLBw None 150.0 md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.934000+00:00 1 None 1
2 NR1Ri1FIhpqU7b660000 raw/my_fastq.fastq.gz None .fastq.gz None None 20 hi7ZmAzz8sfMd3vIQr-57Q None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.808000+00:00 1 None 1
1 Ht1DKBy6J5K1Sac80000 images/my_image.jpg None .jpg None None 29358 r4tnqmKI_SjrkdLzpuWp4g None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.798000+00:00 1 None 1

You can include fields from other registries.

ln.Artifact.df(
    include=[
        "created_by__name",
        "ulabels__name",
        "cell_types__name",
        "feature_sets__itype",
        "suffix",
    ]
)
Hide code cell output
uid key created_by__name ulabels__name cell_types__name feature_sets__itype suffix
id
5 5N61onK2FErvtI4S0000 examples/dataset2.h5ad Test User1 {Experiment 2, IFNG, DMSO} {T cell, B cell} {Feature, bionty.Gene.ensembl_gene_id} .h5ad
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad Test User1 {Experiment 1, IFNG, DMSO} {CD8-positive, alpha-beta T cell, T cell, B cell} {Feature, bionty.Gene.ensembl_gene_id} .h5ad
3 8Fs5XN0buN3Uoxgn0000 iris.parquet Test User1 {None} {None} {None} .parquet
2 NR1Ri1FIhpqU7b660000 raw/my_fastq.fastq.gz Test User1 {None} {None} {None} .fastq.gz
1 Ht1DKBy6J5K1Sac80000 images/my_image.jpg Test User1 {None} {None} {None} .jpg

You can include information about which artifact measures which feature.

df = ln.Artifact.df(features=True)
ln.view(df)  # optionally use ln.view() to see dtypes
Hide code cell output 
→ queried for all categorical features with dtype 'cat[ULabel...'] and non-categorical features: (7) ['perturbation', 'sample_note', 'temperature', 'experiment', 'date_of_study', 'study_note', 'study_metadata']
uidkeyperturbationtemperatureexperimentdate_of_studystudy_notestudy_metadata
idstrstrcat[ULabel]floatcat[ULabel]datestrdict
55N61onK2FErvtI4S0000examples/dataset2.h5ad{'IFNG', 'DMSO'}22.6Experiment 22025-02-13nan{'detail1': '456', 'detail2': 2}
4RJ7UJz7cqr0odLZh0000examples/dataset1.h5ad{'IFNG', 'DMSO'}21.6Experiment 12024-12-01We had a great time performing this study and the results look compelling.{'detail1': '123', 'detail2': 1}
38Fs5XN0buN3Uoxgn0000iris.parquetnannannannannannan
2NR1Ri1FIhpqU7b660000raw/my_fastq.fastq.gznannannannannannan
1Ht1DKBy6J5K1Sac80000images/my_image.jpgnannannannannannan

The flattened table that includes information from all relevant registries is easier to understand than the normalized data.

ln.view()
Hide code cell output 
****************

* module: core *

****************

Artifact
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
5 5N61onK2FErvtI4S0000 examples/dataset2.h5ad None .h5ad dataset AnnData 26896 RKJjWbINYNIwYU8BxCejMw None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:14.584000+00:00 1 None 1
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad None .h5ad dataset AnnData 31672 FB3CeMjmg1ivN6HDy6wsSg None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:11.644000+00:00 1 None 1
3 8Fs5XN0buN3Uoxgn0000 iris.parquet None .parquet dataset DataFrame 5131 s-hxJHE31HpN050-AEaLBw None 150.0 md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.934000+00:00 1 None 1
2 NR1Ri1FIhpqU7b660000 raw/my_fastq.fastq.gz None .fastq.gz None None 20 hi7ZmAzz8sfMd3vIQr-57Q None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.808000+00:00 1 None 1
1 Ht1DKBy6J5K1Sac80000 images/my_image.jpg None .jpg None None 29358 r4tnqmKI_SjrkdLzpuWp4g None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.798000+00:00 1 None 1 
Feature
uid name dtype is_type unit description array_rank array_size array_shape proxy_dtype synonyms _expect_many _curation space_id type_id run_id created_at created_by_id _aux _branch_code
id
9 x3r9RPfI1SDi study_metadata dict None None None 0 0 None None None True None 1 None 1 2025-05-22 14:46:01.255000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
8 QTST08elLsBz study_note str None None None 0 0 None None None True None 1 None 1 2025-05-22 14:46:01.248000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
7 8WkMT9dl5YHR date_of_study date None None None 0 0 None None None True None 1 None 1 2025-05-22 14:46:01.242000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
6 mHfkBPUiSZPH experiment cat[ULabel] None None None 0 0 None None None True None 1 None 1 2025-05-22 14:46:01.235000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
5 EKx6yQFpVNLh temperature float None None None 0 0 None None None True None 1 None 1 2025-05-22 14:46:01.228000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
4 ahqCsFiyTJXW cell_type_by_model cat[bionty.CellType] None None None 0 0 None None None True None 1 None 1 2025-05-22 14:46:01.221000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
3 PHj7eoYrSNgr cell_type_by_expert cat[bionty.CellType] None None None 0 0 None None None True None 1 None 1 2025-05-22 14:46:01.215000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1 
FeatureValue
value hash space_id feature_id run_id created_at created_by_id _aux _branch_code
id
1 21.6 None 1 5 1 2025-05-22 14:46:11.697000+00:00 1 None 1
2 2024-12-01 None 1 7 1 2025-05-22 14:46:11.704000+00:00 1 None 1
3 We had a great time performing this study and ... None 1 8 1 2025-05-22 14:46:11.706000+00:00 1 None 1
4 {'detail1': '123', 'detail2': 1} nJ33A6k51yp-1ZlqFabWdw 1 9 1 2025-05-22 14:46:11.708000+00:00 1 None 1
5 22.6 None 1 5 1 2025-05-22 14:46:14.629000+00:00 1 None 1
6 2025-02-13 None 1 7 1 2025-05-22 14:46:14.636000+00:00 1 None 1
7 {'detail1': '456', 'detail2': 2} QAU2Is6uXBBgz8zC_p-rAQ 1 9 1 2025-05-22 14:46:14.638000+00:00 1 None 1 
Run
uid name started_at finished_at reference reference_type _is_consecutive _status_code space_id transform_id report_id _logfile_id environment_id initiated_by_run_id created_at created_by_id _aux _branch_code
id
1 949qZTWiZC86FFbYMFjK None 2025-05-22 14:45:59.493106+00:00 None None None None 0 1 1 None None None None 2025-05-22 14:45:59.494000+00:00 1 None 1 
Schema
uid name description n is_type itype otype dtype hash minimal_set ordered_set maximal_set _curation slot space_id type_id validated_by_id composite_id run_id created_at created_by_id _aux _branch_code
id
1 0000000000000000 valid_features None -1 False Feature None None kMi7B_N88uu-YnbTLDU-DA True False False None None 1 None None None NaN 2025-05-22 14:46:06.521000+00:00 1 {'af': {'2': True}} 1
2 0000000000000001 valid_ensembl_gene_ids None -1 False bionty.Gene.ensembl_gene_id None num 1gocc_TJ1RU2bMwDRK-WUA True False False None None 1 None None None NaN 2025-05-22 14:46:07.038000+00:00 1 {'af': {'2': True}} 1
3 0000000000000002 anndata_ensembl_gene_ids_and_valid_features_in... None -1 False Composite AnnData num GTxxM36n9tocphLfdbNt9g True False False None None 1 None None None NaN 2025-05-22 14:46:07.047000+00:00 1 {'af': {'2': True}} 1
4 8eywLHHA9Hj3ybgS None None 4 False Feature None None RL2sArDBZNakjG3HVXB-zw True False False None None 1 None None None 1.0 2025-05-22 14:46:11.667000+00:00 1 {'af': {'2': False}} 1
5 E3RWzggHRaa7MCVU None None 3 False bionty.Gene.ensembl_gene_id None num WlLDN3zWgqWe_JijdKPOlg True False False None None 1 None None None 1.0 2025-05-22 14:46:11.678000+00:00 1 {'af': {'2': False}} 1
6 ijZtdfWLaJFw6tgq None None 2 False Feature None None IyKnrT0QADeuk2Zt_el5HQ True False False None None 1 None None None 1.0 2025-05-22 14:46:14.601000+00:00 1 {'af': {'2': False}} 1
7 oHlYiRa53JwXI9Ou None None 3 False bionty.Gene.ensembl_gene_id None num E_omq1L6l9JkW_T50wgyfg True False False None None 1 None None None 1.0 2025-05-22 14:46:14.610000+00:00 1 {'af': {'2': False}} 1 
Storage
uid root description type region instance_uid space_id run_id created_at created_by_id _aux _branch_code
id
1 DSO6PPXIbs5c /home/runner/work/lamindb/lamindb/docs/test-re... None local None hlGq1WkbeSSf 1 None 2025-05-22 14:45:55.327000+00:00 1 None 1 
Transform
uid key description type source_code hash reference reference_type space_id _template_id version is_latest created_at created_by_id _aux _branch_code
id
1 Wc8F4siRSKMZ0000 registries.ipynb Query & search registries notebook None None None None 1 None None True 2025-05-22 14:45:59.485000+00:00 1 None 1 
ULabel
uid name is_type description reference reference_type space_id type_id run_id created_at created_by_id _aux _branch_code
id
3 gWecFeoO Experiment 1 False None None None 1 None 1 2025-05-22 14:46:01.284000+00:00 1 None 1
4 KflyEjzf Experiment 2 False None None None 1 None 1 2025-05-22 14:46:01.284000+00:00 1 None 1
1 rE8GlsYe DMSO False None None None 1 None 1 2025-05-22 14:46:01.271000+00:00 1 None 1
2 SdnqP1n4 IFNG False None None None 1 None 1 2025-05-22 14:46:01.271000+00:00 1 None 1 
******************

* module: bionty *

******************

CellType
uid name ontology_id abbr synonyms description space_id source_id run_id created_at created_by_id _aux _branch_code
id
4 4bKGljt0 cell CL:0000000 None None A Material Entity Of Anatomical Origin (Part O... 1 32 1 2025-05-22 14:46:08.248000+00:00 1 None 1
5 22LvKd01 T cell CL:0000084 None T-cell|T-lymphocyte|T lymphocyte A Type Of Lymphocyte Whose Defining Characteri... 1 32 1 2025-05-22 14:46:08.248000+00:00 1 None 1
6 2K93w3xO motile cell CL:0000219 None None A Cell That Moves By Its Own Activities. 1 32 1 2025-05-22 14:46:08.248000+00:00 1 None 1
7 2cXC7cgF single nucleate cell CL:0000226 None None A Cell With A Single Nucleus. 1 32 1 2025-05-22 14:46:08.248000+00:00 1 None 1
8 4WnpvUTH eukaryotic cell CL:0000255 None None Any Cell That Only Exists In Eukaryota. 1 32 1 2025-05-22 14:46:08.248000+00:00 1 None 1
9 X6c7osZ5 lymphocyte CL:0000542 None None A Lymphocyte Is A Leukocyte Commonly Found In ... 1 32 1 2025-05-22 14:46:08.248000+00:00 1 None 1
10 3VEAlFdi leukocyte CL:0000738 None white blood cell|leucocyte An Achromatic Cell Of The Myeloid Or Lymphoid ... 1 32 1 2025-05-22 14:46:08.248000+00:00 1 None 1 
Gene
uid symbol stable_id ensembl_gene_id ncbi_gene_ids biotype synonyms description space_id source_id organism_id run_id created_at created_by_id _aux _branch_code
id
4 iFxDa8hoEWuW CD38 None ENSG00000004468 952 protein_coding CADPR1 CD38 molecule 1 11 1 1 2025-05-22 14:46:14.574000+00:00 1 None 1
1 6Aqvc8ckDYeN CD8A None ENSG00000153563 925 protein_coding P32|CD8|CD8ALPHA CD8 subunit alpha 1 11 1 1 2025-05-22 14:46:11.634000+00:00 1 None 1
2 1j4At3x7akJU CD4 None ENSG00000010610 920 protein_coding T4|LEU-3 CD4 molecule 1 11 1 1 2025-05-22 14:46:11.634000+00:00 1 None 1
3 3bhNYquOnA4s CD14 None ENSG00000170458 929 protein_coding CD14 molecule 1 11 1 1 2025-05-22 14:46:11.634000+00:00 1 None 1 
Organism
uid name ontology_id scientific_name synonyms description space_id source_id run_id created_at created_by_id _aux _branch_code
id
1 1dpCL6Td human NCBITaxon:9606 Homo sapiens None None 1 1 1 2025-05-22 14:46:08.890000+00:00 1 None 1 
Source
uid entity organism name in_db currently_used description url md5 source_website space_id dataframe_artifact_id version run_id created_at created_by_id _aux _branch_code
id
53 5Xov8Lap bionty.Disease all mondo False False Mondo Disease Ontology http://purl.obolibrary.org/obo/mondo/releases/... None https://mondo.monarchinitiative.org 1 None 2024-02-06 None 2025-05-22 14:45:55.571000+00:00 1 None 1
54 69lnSXfR bionty.Disease all mondo False False Mondo Disease Ontology http://purl.obolibrary.org/obo/mondo/releases/... None https://mondo.monarchinitiative.org 1 None 2024-01-03 None 2025-05-22 14:45:55.571000+00:00 1 None 1
55 4ss2Hizg bionty.Disease all mondo False False Mondo Disease Ontology http://purl.obolibrary.org/obo/mondo/releases/... None https://mondo.monarchinitiative.org 1 None 2023-08-02 None 2025-05-22 14:45:55.571000+00:00 1 None 1
56 Hgw08Vk3 bionty.Disease all mondo False False Mondo Disease Ontology http://purl.obolibrary.org/obo/mondo/releases/... None https://mondo.monarchinitiative.org 1 None 2023-04-04 None 2025-05-22 14:45:55.571000+00:00 1 None 1
57 UUZUtULu bionty.Disease all mondo False False Mondo Disease Ontology http://purl.obolibrary.org/obo/mondo/releases/... None https://mondo.monarchinitiative.org 1 None 2023-02-06 None 2025-05-22 14:45:55.571000+00:00 1 None 1
58 7DH1aJIr bionty.Disease all mondo False False Mondo Disease Ontology http://purl.obolibrary.org/obo/mondo/releases/... None https://mondo.monarchinitiative.org 1 None 2022-10-11 None 2025-05-22 14:45:55.571000+00:00 1 None 1
59 4kswnHVF bionty.Disease human doid False True Human Disease Ontology http://purl.obolibrary.org/obo/doid/releases/2... None https://disease-ontology.org 1 None 2024-05-29 None 2025-05-22 14:45:55.571000+00:00 1 None 1

Auto-complete records

For registries with less than 100k records, auto-completing a Lookup object is the most convenient way of finding a record.

import bionty as bt

# query the database for all ulabels or all cell types
ulabels = ln.ULabel.lookup()
cell_types = bt.CellType.lookup()
Show me a screenshot

With auto-complete, we find a ulabel:

study1 = ulabels.experiment_1
study1
Hide code cell output 
ULabel(uid='gWecFeoO', name='Experiment 1', is_type=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-05-22 14:46:01 UTC)

Get one record

get errors if more than one matching records are found.

print(study1.uid)

# by uid
ln.ULabel.get(study1.uid)

# by field
ln.ULabel.get(name="Experiment 1")
Hide code cell output 
gWecFeoO

ULabel(uid='gWecFeoO', name='Experiment 1', is_type=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-05-22 14:46:01 UTC)

Query records by fields

Filter for all artifacts annotated by a ulabel:

ln.Artifact.filter(ulabels=study1).df()
Hide code cell output
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad None .h5ad dataset AnnData 31672 FB3CeMjmg1ivN6HDy6wsSg None 3 md5 True False 1 1 3 None True 1 2025-05-22 14:46:11.644000+00:00 1 None 1

To access the results encoded in a filter statement, execute its return value with one of:

  • df(): A pandas DataFrame with each record in a row.

  • all(): A QuerySet.

  • one(): Exactly one record. Will raise an error if there is none. Is equivalent to the .get() method shown above.

  • one_or_none(): Either one record or None if there is no query result.

Note

filter() returns a QuerySet.

The registries in LaminDB are Django Models and any Django query works.

LaminDB re-interprets Django’s API for data scientists.

What does this have to do with SQL?

Under the hood, any .filter() call translates into a SQL select statement.

LaminDB’s registries are object relational mappers (ORMs) that rely on Django for all the heavy lifting.

Of note, .one() and .one_or_none() are the two parts of LaminDB’s API that are borrowed from SQLAlchemy. In its first year, LaminDB built on SQLAlchemy.

Query datasets by features

The Artifact registry is the only registry that additionally allows to query by features.

ln.Artifact.filter(perturbation="DMSO").df(features=True)

→ queried for all categorical features with dtype 'cat[ULabel...'] and non-categorical features: (7) ['perturbation', 'sample_note', 'temperature', 'experiment', 'date_of_study', 'study_note', 'study_metadata']
uid key perturbation temperature experiment date_of_study study_note study_metadata
id
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad {IFNG, DMSO} 21.6 Experiment 1 2024-12-01 We had a great time performing this study and ... {'detail1': '123', 'detail2': 1}
5 5N61onK2FErvtI4S0000 examples/dataset2.h5ad {IFNG, DMSO} 22.6 Experiment 2 2025-02-13 NaN {'detail1': '456', 'detail2': 2}

You can also query for nested dictionary-like features.

ln.Artifact.filter(study_metadata__detail1="123").df()
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad None .h5ad dataset AnnData 31672 FB3CeMjmg1ivN6HDy6wsSg None 3 md5 True False 1 1 3 None True 1 2025-05-22 14:46:11.644000+00:00 1 None 1 
ln.Artifact.filter(study_metadata__detail2=2).df()
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
5 5N61onK2FErvtI4S0000 examples/dataset2.h5ad None .h5ad dataset AnnData 26896 RKJjWbINYNIwYU8BxCejMw None 3 md5 True False 1 1 3 None True 1 2025-05-22 14:46:14.584000+00:00 1 None 1

Query runs by parameters

Here is an example for querying by parameters: Query by run parameters.

Search for records

You can search every registry via search(). For example, the Artifact registry.

ln.Artifact.search("iris").df()
Hide code cell output
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
3 8Fs5XN0buN3Uoxgn0000 iris.parquet None .parquet dataset DataFrame 5131 s-hxJHE31HpN050-AEaLBw None 150 md5 True False 1 1 None None True 1 2025-05-22 14:46:00.934000+00:00 1 None 1

Here is more background on search and examples for searching the entire cell type ontology: How does search work?

Filter operators

You can qualify the type of comparison in a query by using a comparator.

Below follows a list of the most import, but Django supports about two dozen field comparators field__comparator=value.

and

ln.Artifact.filter(suffix=".h5ad", ulabels=study1).df()
Hide code cell output
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad None .h5ad dataset AnnData 31672 FB3CeMjmg1ivN6HDy6wsSg None 3 md5 True False 1 1 3 None True 1 2025-05-22 14:46:11.644000+00:00 1 None 1

less than/ greater than

Or subset to artifacts greater than 10kB. Here, we can’t use keyword arguments, but need an explicit where statement.

ln.Artifact.filter(ulabels=study1, size__gt=1e4).df()
Hide code cell output
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad None .h5ad dataset AnnData 31672 FB3CeMjmg1ivN6HDy6wsSg None 3 md5 True False 1 1 3 None True 1 2025-05-22 14:46:11.644000+00:00 1 None 1

in

ln.Artifact.filter(suffix__in=[".jpg", ".fastq.gz"]).df()
Hide code cell output
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
1 Ht1DKBy6J5K1Sac80000 images/my_image.jpg None .jpg None None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 True False 1 1 None None True 1 2025-05-22 14:46:00.798000+00:00 1 None 1
2 NR1Ri1FIhpqU7b660000 raw/my_fastq.fastq.gz None .fastq.gz None None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 True False 1 1 None None True 1 2025-05-22 14:46:00.808000+00:00 1 None 1

order by

ln.Artifact.filter().order_by("created_at").df()
Hide code cell output
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
1 Ht1DKBy6J5K1Sac80000 images/my_image.jpg None .jpg None None 29358 r4tnqmKI_SjrkdLzpuWp4g None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.798000+00:00 1 None 1
2 NR1Ri1FIhpqU7b660000 raw/my_fastq.fastq.gz None .fastq.gz None None 20 hi7ZmAzz8sfMd3vIQr-57Q None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.808000+00:00 1 None 1
3 8Fs5XN0buN3Uoxgn0000 iris.parquet None .parquet dataset DataFrame 5131 s-hxJHE31HpN050-AEaLBw None 150.0 md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.934000+00:00 1 None 1
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad None .h5ad dataset AnnData 31672 FB3CeMjmg1ivN6HDy6wsSg None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:11.644000+00:00 1 None 1
5 5N61onK2FErvtI4S0000 examples/dataset2.h5ad None .h5ad dataset AnnData 26896 RKJjWbINYNIwYU8BxCejMw None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:14.584000+00:00 1 None 1 
# reverse ordering
ln.Artifact.filter().order_by("-created_at").df()
Hide code cell output
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
5 5N61onK2FErvtI4S0000 examples/dataset2.h5ad None .h5ad dataset AnnData 26896 RKJjWbINYNIwYU8BxCejMw None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:14.584000+00:00 1 None 1
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad None .h5ad dataset AnnData 31672 FB3CeMjmg1ivN6HDy6wsSg None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:11.644000+00:00 1 None 1
3 8Fs5XN0buN3Uoxgn0000 iris.parquet None .parquet dataset DataFrame 5131 s-hxJHE31HpN050-AEaLBw None 150.0 md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.934000+00:00 1 None 1
2 NR1Ri1FIhpqU7b660000 raw/my_fastq.fastq.gz None .fastq.gz None None 20 hi7ZmAzz8sfMd3vIQr-57Q None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.808000+00:00 1 None 1
1 Ht1DKBy6J5K1Sac80000 images/my_image.jpg None .jpg None None 29358 r4tnqmKI_SjrkdLzpuWp4g None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.798000+00:00 1 None 1 
ln.Artifact.filter().order_by("key").df()
Hide code cell output
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad None .h5ad dataset AnnData 31672 FB3CeMjmg1ivN6HDy6wsSg None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:11.644000+00:00 1 None 1
5 5N61onK2FErvtI4S0000 examples/dataset2.h5ad None .h5ad dataset AnnData 26896 RKJjWbINYNIwYU8BxCejMw None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:14.584000+00:00 1 None 1
1 Ht1DKBy6J5K1Sac80000 images/my_image.jpg None .jpg None None 29358 r4tnqmKI_SjrkdLzpuWp4g None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.798000+00:00 1 None 1
3 8Fs5XN0buN3Uoxgn0000 iris.parquet None .parquet dataset DataFrame 5131 s-hxJHE31HpN050-AEaLBw None 150.0 md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.934000+00:00 1 None 1
2 NR1Ri1FIhpqU7b660000 raw/my_fastq.fastq.gz None .fastq.gz None None 20 hi7ZmAzz8sfMd3vIQr-57Q None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.808000+00:00 1 None 1 
# reverse ordering
ln.Artifact.filter().order_by("-key").df()
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
2 NR1Ri1FIhpqU7b660000 raw/my_fastq.fastq.gz None .fastq.gz None None 20 hi7ZmAzz8sfMd3vIQr-57Q None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.808000+00:00 1 None 1
3 8Fs5XN0buN3Uoxgn0000 iris.parquet None .parquet dataset DataFrame 5131 s-hxJHE31HpN050-AEaLBw None 150.0 md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.934000+00:00 1 None 1
1 Ht1DKBy6J5K1Sac80000 images/my_image.jpg None .jpg None None 29358 r4tnqmKI_SjrkdLzpuWp4g None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.798000+00:00 1 None 1
5 5N61onK2FErvtI4S0000 examples/dataset2.h5ad None .h5ad dataset AnnData 26896 RKJjWbINYNIwYU8BxCejMw None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:14.584000+00:00 1 None 1
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad None .h5ad dataset AnnData 31672 FB3CeMjmg1ivN6HDy6wsSg None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:11.644000+00:00 1 None 1

contains

ln.Transform.filter(description__contains="search").df().head(5)
Hide code cell output
uid key description type source_code hash reference reference_type space_id _template_id version is_latest created_at created_by_id _aux _branch_code
id
1 Wc8F4siRSKMZ0000 registries.ipynb Query & search registries notebook None None None None 1 None None True 2025-05-22 14:45:59.485000+00:00 1 None 1

And case-insensitive:

ln.Transform.filter(description__icontains="Search").df().head(5)
Hide code cell output
uid key description type source_code hash reference reference_type space_id _template_id version is_latest created_at created_by_id _aux _branch_code
id
1 Wc8F4siRSKMZ0000 registries.ipynb Query & search registries notebook None None None None 1 None None True 2025-05-22 14:45:59.485000+00:00 1 None 1

startswith

ln.Transform.filter(description__startswith="Query").df()
Hide code cell output
uid key description type source_code hash reference reference_type space_id _template_id version is_latest created_at created_by_id _aux _branch_code
id
1 Wc8F4siRSKMZ0000 registries.ipynb Query & search registries notebook None None None None 1 None None True 2025-05-22 14:45:59.485000+00:00 1 None 1

or

ln.Artifact.filter(ln.Q(suffix=".jpg") | ln.Q(suffix=".fastq.gz")).df()
Hide code cell output
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
1 Ht1DKBy6J5K1Sac80000 images/my_image.jpg None .jpg None None 29358 r4tnqmKI_SjrkdLzpuWp4g None None md5 True False 1 1 None None True 1 2025-05-22 14:46:00.798000+00:00 1 None 1
2 NR1Ri1FIhpqU7b660000 raw/my_fastq.fastq.gz None .fastq.gz None None 20 hi7ZmAzz8sfMd3vIQr-57Q None None md5 True False 1 1 None None True 1 2025-05-22 14:46:00.808000+00:00 1 None 1

negate/ unequal

ln.Artifact.filter(~ln.Q(suffix=".jpg")).df()
Hide code cell output
uid key description suffix kind otype size hash n_files n_observations _hash_type _key_is_virtual _overwrite_versions space_id storage_id schema_id version is_latest run_id created_at created_by_id _aux _branch_code
id
2 NR1Ri1FIhpqU7b660000 raw/my_fastq.fastq.gz None .fastq.gz None None 20 hi7ZmAzz8sfMd3vIQr-57Q None NaN md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.808000+00:00 1 None 1
3 8Fs5XN0buN3Uoxgn0000 iris.parquet None .parquet dataset DataFrame 5131 s-hxJHE31HpN050-AEaLBw None 150.0 md5 True False 1 1 NaN None True 1 2025-05-22 14:46:00.934000+00:00 1 None 1
4 RJ7UJz7cqr0odLZh0000 examples/dataset1.h5ad None .h5ad dataset AnnData 31672 FB3CeMjmg1ivN6HDy6wsSg None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:11.644000+00:00 1 None 1
5 5N61onK2FErvtI4S0000 examples/dataset2.h5ad None .h5ad dataset AnnData 26896 RKJjWbINYNIwYU8BxCejMw None 3.0 md5 True False 1 1 3.0 None True 1 2025-05-22 14:46:14.584000+00:00 1 None 1