adata CLI

A command-line tool for exploring huge AnnData stores (.h5ad and .zarr) without loading them fully into memory. Streams data directly from disk for efficient inspection of structure, metadata, and matrices.

Features

• Streaming access to very large .h5ad and .zarr stores
• Auto-detects .h5ad files vs .zarr directories
• Chunked processing for dense and sparse matrices (CSR/CSC)
• Reads every AnnData on-disk layout, from 0.7.x through the current spec, and always writes the current one
• Converts between HDF5 and Zarr (v2 and v3) in either direction
• Rich terminal output with progress indicators, kept on stderr so results pipe cleanly

Documentation: cellgeni.github.io/adata-cli

Installation

pip install pyadata-cli

The command is adata. The distribution is named pyadata-cli because adata-cli was already taken on PyPI by an unrelated project.

From source with uv:

git clone https://github.com/cellgeni/adata-cli.git
cd adata-cli
uv sync

For development and testing:

uv sync --extra dev

Alternative with pip:

git clone https://github.com/cellgeni/adata-cli.git
cd adata-cli
pip install .

For development and testing with pip:

pip install -e ".[dev]"

Commands (Overview)

Run help at any level (e.g. adata --help, adata export --help).

• view – AnnData-aware inspection: store layout, shapes, and encodings; supports drilling into paths like obsm/X_pca or uns.
• ls – list the contents of any HDF5 or Zarr store as a tree, with no AnnData assumptions (works on .loom and plain .h5); -1 emits bare paths for piping.
• create – write a new, empty AnnData store for import to fill in.
• subset – stream and write a filtered copy, selected by obs/var name lists (--obs/--var) or by expression (--obs-query/--var-query).
• split – write one store per distinct value of an annotation column, with a CSV manifest.
• concat – concatenate stores along the obs axis, with --join inner|outer and merge strategies for var and uns.
• export – extract data from a store; subcommands: dataframe (any dataframe group to CSV), array (dense to .npy), sparse (CSR/CSC to .mtx), dict (JSON), image (PNG). Results go to stdout when no --output is given.
• import – write new data into a store at any path; subcommands: dataframe (CSV), array (.npy), sparse (.mtx), dict (JSON), image (PNG/JPEG/TIFF).

Building a store from scratch

adata create out.h5ad --obs-names cells.txt --var-names genes.txt
adata import sparse    out.h5ad X            counts.mtx --inplace
adata import dataframe out.h5ad obs          cells.csv  --inplace -i cell_id
adata import array     out.h5ad obsm/X_umap  umap.npy   --inplace
adata import dict      out.h5ad uns/params   params.json --inplace

Filtering without a name list

adata subset data.h5ad -o cortex.h5ad --obs-query "cluster == Cortex_2"
adata subset data.h5ad -o big.h5ad    -q "n_counts > 1000 and cluster in A,B"
adata split  data.h5ad --by sample -o per_sample/
adata concat per_sample/*.h5ad -o merged.h5ad --join outer --label sample

Documentation

• Get started — a short tutorial
• Command reference — every command and flag
• Element spec: HDF5 / Zarr — the on-disk format, and what this tool does with it
• Testing — how the suite is organised, and how compatibility is verified against six anndata releases
• Changelog

Docker

A docker image is available on QUAY: quay.io/cellgeni/adata-cli:latest. Pull and run with:

docker run --rm -it -v /path/to/data:/data quay.io/cellgeni/adata-cli:latest view /data/your_file.h5ad