Zarr Python

uv pip install zarr

uv pip install s3fs  # For S3
uv pip install gcsfs  # For Google Cloud Storage

import zarr
import numpy as np

# Create a 2D array with chunking and compression
z = zarr.create_array(
    store="data/my_array.zarr",
    shape=(10000, 10000),
    chunks=(1000, 1000),
    dtype="f4"
)

# Write data using NumPy-style indexing
z[:, :] = np.random.random((10000, 10000))

# Read data
data = z[0:100, 0:100]  # Returns NumPy array

# Create empty array
z = zarr.zeros(shape=(10000, 10000), chunks=(1000, 1000), dtype='f4',
               store='data.zarr')

# Create filled arrays
z = zarr.ones((5000, 5000), chunks=(500, 500))
z = zarr.full((1000, 1000), fill_value=42, chunks=(100, 100))

# Create from existing data
data = np.arange(10000).reshape(100, 100)
z = zarr.array(data, chunks=(10, 10), store='data.zarr')

# Create like another array
z2 = zarr.zeros_like(z)  # Matches shape, chunks, dtype of z

# Open array (read/write mode by default)
z = zarr.open_array('data.zarr', mode='r+')

# Read-only mode
z = zarr.open_array('data.zarr', mode='r')

# The open() function auto-detects arrays vs groups
z = zarr.open('data.zarr')  # Returns Array or Group

# Write entire array
z[:] = 42

# Write slices
z[0, :] = np.arange(100)
z[10:20, 50:60] = np.random.random((10, 10))

# Read data (returns NumPy array)
data = z[0:100, 0:100]
row = z[5, :]

# Advanced indexing
z.vindex[[0, 5, 10], [2, 8, 15]]  # Coordinate indexing
z.oindex[0:10, [5, 10, 15]]       # Orthogonal indexing
z.blocks[0, 0]                     # Block/chunk indexing

# Resize array
z.resize(15000, 15000)  # Expands or shrinks dimensions

# Append data along an axis
z.append(np.random.random((1000, 10000)), axis=0)  # Adds rows

# Configure chunk size (aim for ~1MB per chunk)
# For float32 data: 1MB = 262,144 elements = 512×512 array
z = zarr.zeros(
    shape=(10000, 10000),
    chunks=(512, 512),  # ~1MB chunks
    dtype='f4'
)

# If accessing rows frequently (first dimension)
z = zarr.zeros((10000, 10000), chunks=(10, 10000))  # Chunk spans columns

# If accessing columns frequently (second dimension)
z = zarr.zeros((10000, 10000), chunks=(10000, 10))  # Chunk spans rows

# For mixed access patterns (balanced approach)
z = zarr.zeros((10000, 10000), chunks=(1000, 1000))  # Square chunks

from zarr.codecs import ShardingCodec, BytesCodec
from zarr.codecs.blosc import BloscCodec

# Create array with sharding
z = zarr.create_array(
    store='data.zarr',
    shape=(100000, 100000),
    chunks=(100, 100),  # Small chunks for access
    shards=(1000, 1000),  # Groups 100 chunks per shard
    dtype='f4'
)

from zarr.codecs.blosc import BloscCodec
from zarr.codecs import GzipCodec, ZstdCodec

# Default: Blosc with Zstandard
z = zarr.zeros((1000, 1000), chunks=(100, 100))  # Uses default compression

# Configure Blosc codec
z = zarr.create_array(
    store='data.zarr',
    shape=(1000, 1000),
    chunks=(100, 100),
    dtype='f4',
    codecs=[BloscCodec(cname='zstd', clevel=5, shuffle='shuffle')]
)

# Available Blosc compressors: 'blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib', 'zstd'

# Use Gzip compression
z = zarr.create_array(
    store='data.zarr',
    shape=(1000, 1000),
    chunks=(100, 100),
    dtype='f4',
    codecs=[GzipCodec(level=6)]
)

# Disable compression
z = zarr.create_array(
    store='data.zarr',
    shape=(1000, 1000),
    chunks=(100, 100),
    dtype='f4',
    codecs=[BytesCodec()]  # No compression
)

# Optimal for numeric scientific data
codecs=[BloscCodec(cname='zstd', clevel=5, shuffle='shuffle')]

# Optimal for speed
codecs=[BloscCodec(cname='lz4', clevel=1)]

# Optimal for compression ratio
codecs=[GzipCodec(level=9)]

from zarr.storage import LocalStore

# Explicit store creation
store = LocalStore('data/my_array.zarr')
z = zarr.open_array(store=store, mode='w', shape=(1000, 1000), chunks=(100, 100))

# Or use string path (creates LocalStore automatically)
z = zarr.open_array('data/my_array.zarr', mode='w', shape=(1000, 1000),
                    chunks=(100, 100))

from zarr.storage import MemoryStore

# Create in-memory store
store = MemoryStore()
z = zarr.open_array(store=store, mode='w', shape=(1000, 1000), chunks=(100, 100))

# Data exists only in memory, not persisted

from zarr.storage import ZipStore

# Write to ZIP file
store = ZipStore('data.zip', mode='w')
z = zarr.open_array(store=store, mode='w', shape=(1000, 1000), chunks=(100, 100))
z[:] = np.random.random((1000, 1000))
store.close()  # IMPORTANT: Must close ZipStore

# Read from ZIP file
store = ZipStore('data.zip', mode='r')
z = zarr.open_array(store=store)
data = z[:]
store.close()

import s3fs
import zarr

# S3 storage
s3 = s3fs.S3FileSystem(anon=False)  # Use credentials
store = s3fs.S3Map(root='my-bucket/path/to/array.zarr', s3=s3)
z = zarr.open_array(store=store, mode='w', shape=(1000, 1000), chunks=(100, 100))
z[:] = data

# Google Cloud Storage
import gcsfs
gcs = gcsfs.GCSFileSystem(project='my-project')
store = gcsfs.GCSMap(root='my-bucket/path/to/array.zarr', gcs=gcs)
z = zarr.open_array(store=store, mode='w', shape=(1000, 1000), chunks=(100, 100))

# Create root group
root = zarr.group(store='data/hierarchy.zarr')

# Create sub-groups
temperature = root.create_group('temperature')
precipitation = root.create_group('precipitation')

# Create arrays within groups
temp_array = temperature.create_array(
    name='t2m',
    shape=(365, 720, 1440),
    chunks=(1, 720, 1440),
    dtype='f4'
)

precip_array = precipitation.create_array(
    name='prcp',
    shape=(365, 720, 1440),
    chunks=(1, 720, 1440),
    dtype='f4'
)

# Access using paths
array = root['temperature/t2m']

# Visualize hierarchy
print(root.tree())
# Output:
# /
#  ├── temperature
#  │   └── t2m (365, 720, 1440) f4
#  └── precipitation
#      └── prcp (365, 720, 1440) f4

# Create group with h5py-style methods
root = zarr.group('data.zarr')
dataset = root.create_dataset('my_data', shape=(1000, 1000), chunks=(100, 100),
                              dtype='f4')

# Access like h5py
grp = root.require_group('subgroup')
arr = grp.require_dataset('array', shape=(500, 500), chunks=(50, 50), dtype='i4')

# Add attributes to array
z = zarr.zeros((1000, 1000), chunks=(100, 100))
z.attrs['description'] = 'Temperature data in Kelvin'
z.attrs['units'] = 'K'
z.attrs['created'] = '2024-01-15'
z.attrs['processing_version'] = 2.1

# Attributes are stored as JSON
print(z.attrs['units'])  # Output: K

# Add attributes to groups
root = zarr.group('data.zarr')
root.attrs['project'] = 'Climate Analysis'
root.attrs['institution'] = 'Research Institute'

# Attributes persist with the array/group
z2 = zarr.open('data.zarr')
print(z2.attrs['description'])

import numpy as np
import zarr

z = zarr.zeros((1000, 1000), chunks=(100, 100))

# Use NumPy functions directly
result = np.sum(z, axis=0)  # NumPy operates on Zarr array
mean = np.mean(z[:100, :100])

# Convert to NumPy array
numpy_array = z[:]  # Loads entire array into memory

import dask.array as da
import zarr

# Create large Zarr array
z = zarr.open('data.zarr', mode='w', shape=(100000, 100000),
              chunks=(1000, 1000), dtype='f4')

# Load as Dask array (lazy, no data loaded)
dask_array = da.from_zarr('data.zarr')

# Perform computations (parallel, out-of-core)
result = dask_array.mean(axis=0).compute()  # Parallel computation

# Write Dask array to Zarr
large_array = da.random.random((100000, 100000), chunks=(1000, 1000))
da.to_zarr(large_array, 'output.zarr')

import xarray as xr
import zarr

# Open Zarr store as Xarray Dataset (lazy loading)
ds = xr.open_zarr('data.zarr')

# Dataset includes coordinates and metadata
print(ds)

# Access variables
temperature = ds['temperature']

# Perform labeled operations
subset = ds.sel(time='2024-01', lat=slice(30, 60))

# Write Xarray Dataset to Zarr
ds.to_zarr('output.zarr')

# Create from scratch with coordinates
ds = xr.Dataset(
    {
        'temperature': (['time', 'lat', 'lon'], data),
        'precipitation': (['time', 'lat', 'lon'], data2)
    },
    coords={
        'time': pd.date_range('2024-01-01', periods=365),
        'lat': np.arange(-90, 91, 1),
        'lon': np.arange(-180, 180, 1)
    }
)
ds.to_zarr('climate_data.zarr')

from zarr import ThreadSynchronizer
import zarr

# For multi-threaded writes
synchronizer = ThreadSynchronizer()
z = zarr.open_array('data.zarr', mode='r+', shape=(10000, 10000),
                    chunks=(1000, 1000), synchronizer=synchronizer)

# Safe for concurrent writes from multiple threads
# (when writes don't span chunk boundaries)

from zarr import ProcessSynchronizer
import zarr

# For multi-process writes
synchronizer = ProcessSynchronizer('sync_data.sync')
z = zarr.open_array('data.zarr', mode='r+', shape=(10000, 10000),
                    chunks=(1000, 1000), synchronizer=synchronizer)

# Safe for concurrent writes from multiple processes