Datasets

Datasets are very similar to NumPy arrays. They are homogeneous collections of data elements, with an immutable datatype and (hyper)rectangular shape. Unlike NumPy arrays, they support a variety of transparent storage features such as compression, error-detection, and chunked I/O.

They are represented in h5py by a thin proxy class which supports familiar NumPy operations like slicing, along with a variety of descriptive attributes:

• shape attribute

• size attribute

• ndim attribute

• dtype attribute

• nbytes attribute

h5py supports most NumPy dtypes, and uses the same character codes (e.g. 'f', 'i8') and dtype machinery as Numpy. See FAQ for the list of dtypes h5py supports.

Creating datasets

New datasets are created using either Group.create_dataset() or Group.require_dataset(). Existing datasets should be retrieved using the group indexing syntax (dset = group["name"]).

To initialise a dataset, all you have to do is specify a name, shape, and optionally the data type (defaults to 'f'):

>>> dset = f.create_dataset("default", (100,))
>>> dset = f.create_dataset("ints", (100,), dtype='i8')

Note

This is not the same as creating an Empty dataset.

You may also initialize the dataset to an existing NumPy array by providing the data parameter:

>>> arr = np.arange(100)
>>> dset = f.create_dataset("init", data=arr)

Keywords shape and dtype may be specified along with data; if so, they will override data.shape and data.dtype. It’s required that (1) the total number of points in shape match the total number of points in data.shape, and that (2) it’s possible to cast data.dtype to the requested dtype.

Reading & writing data

HDF5 datasets re-use the NumPy slicing syntax to read and write to the file. Slice specifications are translated directly to HDF5 “hyperslab” selections, and are a fast and efficient way to access data in the file. The following slicing arguments are recognized:

• Indices: anything that can be converted to a Python long

• Slices (i.e. [:] or [0:10])

• Field names, in the case of compound data

• At most one Ellipsis (...) object

• An empty tuple (()) to retrieve all data or scalar data

Here are a few examples (output omitted).

>>> dset = f.create_dataset("MyDataset", (10,10,10), 'f')
>>> dset[0,0,0]
>>> dset[0,2:10,1:9:3]
>>> dset[:,::2,5]
>>> dset[0]
>>> dset[1,5]
>>> dset[0,...]
>>> dset[...,6]
>>> dset[()]

There’s more documentation on what parts of numpy’s fancy indexing are available in h5py.

For compound data, it is advised to separate field names from the numeric slices:

>>> dset.fields("FieldA")[:10]   # Read a single field
>>> dset[:10]["FieldA"]          # Read all fields, select in NumPy

It is also possible to mix indexing and field names (dset[:10, "FieldA"]), but this might be removed in a future version of h5py.

To retrieve the contents of a scalar dataset, you can use the same syntax as in NumPy: result = dset[()]. In other words, index into the dataset using an empty tuple.

For simple slicing, broadcasting is supported:

>>> dset[0,:,:] = np.arange(10)  # Broadcasts to (10,10)

Broadcasting is implemented using repeated hyperslab selections, and is safe to use with very large target selections. It is supported for the above “simple” (integer, slice and ellipsis) slicing only.

Warning

Currently h5py does not support nested compound types, see GH1197 for more information.

Multiple indexing

Indexing a dataset once loads a numpy array into memory. If you try to index it twice to write data, you may be surprised that nothing seems to have happened:

>>> f = h5py.File('my_hdf5_file.h5', 'w')
>>> dset = f.create_dataset("test", (2, 2))
>>> dset[0][1] = 3.0  # No effect!
>>> print(dset[0][1])
0.0

The assignment above only modifies the loaded array. It’s equivalent to this:

>>> new_array = dset[0]
>>> new_array[1] = 3.0
>>> print(new_array[1])
3.0
>>> print(dset[0][1])
0.0

To write to the dataset, combine the indexes in a single step:

>>> dset[0, 1] = 3.0
>>> print(dset[0, 1])
3.0

Length and iteration

As with NumPy arrays, the len() of a dataset is the length of the first axis, and iterating over a dataset iterates over the first axis. However, modifications to the yielded data are not recorded in the file. Resizing a dataset while iterating has undefined results.

On 32-bit platforms, len(dataset) will fail if the first axis is bigger than 2**32. It’s recommended to use Dataset.len() for large datasets.

Chunked storage

An HDF5 dataset created with the default settings will be contiguous; in other words, laid out on disk in traditional C order. Datasets may also be created using HDF5’s chunked storage layout. This means the dataset is divided up into regularly-sized pieces which are stored haphazardly on disk, and indexed using a B-tree.

Chunked storage makes it possible to resize datasets, and because the data is stored in fixed-size chunks, to use compression filters.

To enable chunked storage, set the keyword chunks to a tuple indicating the chunk shape:

>>> dset = f.create_dataset("chunked", (1000, 1000), chunks=(100, 100))

Data will be read and written in blocks with shape (100,100); for example, the data in dset[0:100,0:100] will be stored together in the file, as will the data points in range dset[400:500, 100:200].

Chunking has performance implications. It’s recommended to keep the total size of your chunks between 10 KiB and 1 MiB, larger for larger datasets. Also keep in mind that when any element in a chunk is accessed, the entire chunk is read from disk.

Since picking a chunk shape can be confusing, you can have h5py guess a chunk shape for you:

>>> dset = f.create_dataset("autochunk", (1000, 1000), chunks=True)

Auto-chunking is also enabled when using compression or maxshape, etc., if a chunk shape is not manually specified.

The iter_chunks method returns an iterator that can be used to perform chunk by chunk reads or writes:

>>> for s in dset.iter_chunks():
>>>     arr = dset[s]  # get numpy array for chunk

Resizable datasets

In HDF5, datasets can be resized once created up to a maximum size, by calling Dataset.resize(). You specify this maximum size when creating the dataset, via the keyword maxshape:

>>> dset = f.create_dataset("resizable", (10,10), maxshape=(500, 20))

Any (or all) axes may also be marked as “unlimited”, in which case they may be increased up to the HDF5 per-axis limit of 2**64 elements. Indicate these axes using None:

>>> dset = f.create_dataset("unlimited", (10, 10), maxshape=(None, 10))

Note

Resizing an array with existing data works differently than in NumPy; if any axis shrinks, the data in the missing region is discarded. Data does not “rearrange” itself as it does when resizing a NumPy array.

Filter pipeline

Chunked data may be transformed by the HDF5 filter pipeline. The most common use is applying transparent compression. Data is compressed on the way to disk, and automatically decompressed when read. Once the dataset is created with a particular compression filter applied, data may be read and written as normal with no special steps required.

Enable compression with the compression keyword to Group.create_dataset():

>>> dset = f.create_dataset("zipped", (100, 100), compression="gzip")

Options for each filter may be specified with compression_opts:

>>> dset = f.create_dataset("zipped_max", (100, 100), compression="gzip", compression_opts=9)

Lossless compression filters

GZIP filter ("gzip")

Available with every installation of HDF5, so it’s best where portability is required. Good compression, moderate speed. compression_opts sets the compression level and may be an integer from 0 to 9, default is 4.

LZF filter ("lzf")

Available with every installation of h5py (C source code also available). Low to moderate compression, very fast. No options.

SZIP filter ("szip")

Patent-encumbered filter used in the NASA community. Not available with all installations of HDF5 due to legal reasons. Consult the HDF5 docs for filter options.

Custom compression filters

In addition to the compression filters listed above, compression filters can be dynamically loaded by the underlying HDF5 library. This is done by passing a filter number to Group.create_dataset() as the compression parameter. The compression_opts parameter will then be passed to this filter.

See also

hdf5plugin

A Python package of several popular filters, including Blosc, LZ4 and ZFP, for convenient use with h5py

HDF5 Filter Plugins

A collection of filters as a single download from The HDF Group

Registered filter plugins

The index of publicly announced filter plugins

Note

The underlying implementation of the compression filter will have the H5Z_FLAG_OPTIONAL flag set. This indicates that if the compression filter doesn’t compress a block while writing, no error will be thrown. The filter will then be skipped when subsequently reading the block.

Scale-Offset filter

Filters enabled with the compression keywords are lossless; what comes out of the dataset is exactly what you put in. HDF5 also includes a lossy filter which trades precision for storage space.

Works with integer and floating-point data only. Enable the scale-offset filter by setting Group.create_dataset() keyword scaleoffset to an integer.

For integer data, this specifies the number of bits to retain. Set to 0 to have HDF5 automatically compute the number of bits required for lossless compression of the chunk. For floating-point data, indicates the number of digits after the decimal point to retain.

Warning

Currently the scale-offset filter does not preserve special float values (i.e. NaN, inf), see https://forum.hdfgroup.org/t/scale-offset-filter-and-special-float-values-nan-infinity/3379 for more information and follow-up.

Shuffle filter

Block-oriented compressors like GZIP or LZF work better when presented with runs of similar values. Enabling the shuffle filter rearranges the bytes in the chunk and may improve compression ratio. No significant speed penalty, lossless.

Enable by setting Group.create_dataset() keyword shuffle to True.

Fletcher32 filter

Adds a checksum to each chunk to detect data corruption. Attempts to read corrupted chunks will fail with an error. No significant speed penalty. Obviously shouldn’t be used with lossy compression filters.

Enable by setting Group.create_dataset() keyword fletcher32 to True.

Multi-Block Selection

The full H5Sselect_hyperslab API is exposed via the MultiBlockSlice object. This takes four elements to define the selection (start, count, stride and block) in contrast to the built-in slice object, which takes three elements. A MultiBlockSlice can be used in place of a slice to select a number of (count) blocks of multiple elements separated by a stride, rather than a set of single elements separated by a step.

For an explanation of how this slicing works, see the HDF5 documentation.

For example:

>>> dset[...]
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])
>>> dset[MultiBlockSlice(start=1, count=3, stride=4, block=2)]
array([ 1,  2,  5,  6,  9, 10])

They can be used in multi-dimensional slices alongside any slicing object, including other MultiBlockSlices. For a more complete example of this, see the multiblockslice_interleave.py example script.

Fancy indexing

A subset of the NumPy fancy-indexing syntax is supported. Use this with caution, as the underlying HDF5 mechanisms may have different performance than you expect.

For any axis, you can provide an explicit list of points you want; for a dataset with shape (10, 10):

>>> dset.shape
(10, 10)
>>> result = dset[0, [1,3,8]]
>>> result.shape
(3,)
>>> result = dset[1:6, [5,8,9]]
>>> result.shape
(5, 3)

The following restrictions exist:

• Selection coordinates must be given in increasing order

• Duplicate selections are ignored

• Very long lists (> 1000 elements) may produce poor performance

NumPy boolean “mask” arrays can also be used to specify a selection. The result of this operation is a 1-D array with elements arranged in the standard NumPy (C-style) order. Behind the scenes, this generates a laundry list of points to select, so be careful when using it with large masks:

>>> arr = numpy.arange(100).reshape((10,10))
>>> dset = f.create_dataset("MyDataset", data=arr)
>>> result = dset[arr > 50]
>>> result.shape
(49,)

Changed in version 2.10: Selecting using an empty list is now allowed. This returns an array with length 0 in the relevant dimension.

Creating and Reading Empty (or Null) datasets and attributes

HDF5 has the concept of Empty or Null datasets and attributes. These are not the same as an array with a shape of (), or a scalar dataspace in HDF5 terms. Instead, it is a dataset with an associated type, no data, and no shape. In h5py, we represent this as either a dataset with shape None, or an instance of h5py.Empty. Empty datasets and attributes cannot be sliced.

To create an empty attribute, use h5py.Empty as per Attributes:

>>> obj.attrs["EmptyAttr"] = h5py.Empty("f")

Similarly, reading an empty attribute returns h5py.Empty:

>>> obj.attrs["EmptyAttr"]
h5py.Empty(dtype="f")

Empty datasets can be created either by defining a dtype but no shape in create_dataset:

>>> grp.create_dataset("EmptyDataset", dtype="f")

or by data to an instance of h5py.Empty:

>>> grp.create_dataset("EmptyDataset", data=h5py.Empty("f"))

An empty dataset has shape defined as None, which is the best way of determining whether a dataset is empty or not. An empty dataset can be “read” in a similar way to scalar datasets, i.e. if empty_dataset is an empty dataset:

>>> empty_dataset[()]
h5py.Empty(dtype="f")

The dtype of the dataset can be accessed via <dset>.dtype as per normal. As empty datasets cannot be sliced, some methods of datasets such as read_direct will raise a TypeError exception if used on a empty dataset.

Reference

class h5py.Dataset(identifier)

Dataset objects are typically created via Group.create_dataset(), or by retrieving existing datasets from a file. Call this constructor to create a new Dataset bound to an existing DatasetID identifier.

__getitem__(args)

NumPy-style slicing to retrieve data. See Reading & writing data.

__setitem__(args)

NumPy-style slicing to write data. See Reading & writing data.

__bool__()

Check that the dataset is accessible. A dataset could be inaccessible for several reasons. For instance, the dataset, or the file it belongs to, may have been closed elsewhere.

>>> f = h5py.open(filename)
>>> dset = f["MyDS"]
>>> f.close()
>>> if dset:
...     print("datset accessible")
... else:
...     print("dataset inaccessible")
dataset inaccessible

read_direct(array, source_sel=None, dest_sel=None)

Read from an HDF5 dataset directly into a NumPy array, which can avoid making an intermediate copy as happens with slicing. The destination array must be C-contiguous and writable, and must have a datatype to which the source data may be cast. Data type conversion will be carried out on the fly by HDF5.

source_sel and dest_sel indicate the range of points in the dataset and destination array respectively. Use the output of numpy.s_[args]:

>>> dset = f.create_dataset("dset", (100,), dtype='int64')
>>> arr = np.zeros((100,), dtype='int32')
>>> dset.read_direct(arr, np.s_[0:10], np.s_[50:60])

write_direct(source, source_sel=None, dest_sel=None)

Write data directly to HDF5 from a NumPy array. The source array must be C-contiguous. Selections must be the output of numpy.s_[<args>]. Broadcasting is supported for simple indexing.

astype(dtype)

Return a wrapper allowing you to read data as a particular type. Conversion is handled by HDF5 directly, on the fly:

>>> dset = f.create_dataset("bigint", (1000,), dtype='int64')
>>> out = dset.astype('int16')[:]
>>> out.dtype
dtype('int16')

Changed in version 3.0: Allowed reading through the wrapper object. In earlier versions, astype() had to be used as a context manager:

>>> with dset.astype('int16'):
...     out = dset[:]

asstr(encoding=None, errors='strict')

Only for string datasets. Returns a wrapper to read data as Python string objects:

>>> s = dataset.asstr()[0]

encoding and errors work like bytes.decode(), but the default encoding is defined by the datatype - ASCII or UTF-8. This is not guaranteed to be correct.

New in version 3.0.

fields(names)

Get a wrapper to read a subset of fields from a compound data type:

>>> 2d_coords = dataset.fields(['x', 'y'])[:]

If names is a string, a single field is extracted, and the resulting arrays will have that dtype. Otherwise, it should be an iterable, and the read data will have a compound dtype.

New in version 3.0.

iter_chunks()

Iterate over chunks in a chunked dataset. The optional sel argument is a slice or tuple of slices that defines the region to be used. If not set, the entire dataspace will be used for the iterator.

For each chunk within the given region, the iterator yields a tuple of slices that gives the intersection of the given chunk with the selection area. This can be used to read or write data in that chunk.

A TypeError will be raised if the dataset is not chunked.

A ValueError will be raised if the selection region is invalid.

New in version 3.0.

resize(size, axis=None)

Change the shape of a dataset. size may be a tuple giving the new dataset shape, or an integer giving the new length of the specified axis.

Datasets may be resized only up to Dataset.maxshape.

len()

Return the size of the first axis.

make_scale(name='')

Make this dataset an HDF5 dimension scale.

You can then attach it to dimensions of other datasets like this:

other_ds.dims[0].attach_scale(ds)

You can optionally pass a name to associate with this scale.

virtual_sources()

If this dataset is a virtual dataset, return a list of named tuples: (vspace, file_name, dset_name, src_space), describing which parts of the dataset map to which source datasets. The two ‘space’ members are low-level SpaceID objects.

shape

NumPy-style shape tuple giving dataset dimensions.

dtype

NumPy dtype object giving the dataset’s type.

size

Integer giving the total number of elements in the dataset.

nbytes

Integer giving the total number of bytes required to load the full dataset into RAM (i.e. dset[()]). This may not be the amount of disk space occupied by the dataset, as datasets may be compressed when written or only partly filled with data. This value also does not include the array overhead, as it only describes the size of the data itself. Thus the real amount of RAM occupied by this dataset may be slightly greater.

New in version 3.0.

ndim

Integer giving the total number of dimensions in the dataset.

maxshape

NumPy-style shape tuple indicating the maximum dimensions up to which the dataset may be resized. Axes with None are unlimited.

chunks

Tuple giving the chunk shape, or None if chunked storage is not used. See Chunked storage.

compression

String with the currently applied compression filter, or None if compression is not enabled for this dataset. See Filter pipeline.

compression_opts

Options for the compression filter. See Filter pipeline.

scaleoffset

Setting for the HDF5 scale-offset filter (integer), or None if scale-offset compression is not used for this dataset. See Scale-Offset filter.

shuffle

Whether the shuffle filter is applied (T/F). See Shuffle filter.

fletcher32

Whether Fletcher32 checksumming is enabled (T/F). See Fletcher32 filter.

fillvalue

Value used when reading uninitialized portions of the dataset, or None if no fill value has been defined, in which case HDF5 will use a type-appropriate default value. Can’t be changed after the dataset is created.

external

If this dataset is stored in one or more external files, this is a list of 3-tuples, like the external= parameter to Group.create_dataset(). Otherwise, it is None.

is_virtual

True if this dataset is a virtual dataset, otherwise False.

dims

Access to Dimension Scales.

attrs

Attributes for this dataset.

id

The dataset’s low-level identifier; an instance of DatasetID.

ref

An HDF5 object reference pointing to this dataset. See Using object references.

regionref

Proxy object for creating HDF5 region references. See Using region references.

name

String giving the full path to this dataset.

file

File instance in which this dataset resides

parent

Group instance containing this dataset.