Quick Start Guide
conda install h5py
If there are wheels for your platform (mac, linux, windows on x86) and
you do not need MPI you can install
h5py via pip:
pip install h5py
With Enthought Canopy, use the GUI package manager or:
To install from source see Installation.
An HDF5 file is a container for two kinds of objects: datasets, which are array-like collections of data, and groups, which are folder-like containers that hold datasets and other groups. The most fundamental thing to remember when using h5py is:
Groups work like dictionaries, and datasets work like NumPy arrays
Suppose someone has sent you a HDF5 file,
mytestfile.hdf5. (To create this file, read Appendix: Creating a file.) The very first thing you’ll need to do is to open the file for reading:
>>> import h5py >>> f = h5py.File('mytestfile.hdf5', 'r')
>>> list(f.keys()) ['mydataset']
Based on our observation, there is one data set,
mydataset in the file.
Let us examine the data set as a Dataset object
>>> dset = f['mydataset']
The object we obtained isn’t an array, but an HDF5 dataset. Like NumPy arrays, datasets have both a shape and a data type:
>>> dset.shape (100,) >>> dset.dtype dtype('int32')
They also support array-style slicing. This is how you read and write data from a dataset in the file:
>>> dset[...] = np.arange(100) >>> dset 0 >>> dset 10 >>> dset[0:100:10] array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
Appendix: Creating a file
At this point, you may wonder how
mytestdata.hdf5 is created.
We can create a file by setting the
the File object is initialized. Some other modes are
(for read/write/create access), and
r+ (for read/write access).
A full list of file access modes and their meanings is at File Objects.
>>> import h5py >>> import numpy as np >>> f = h5py.File("mytestfile.hdf5", "w")
The File object has a couple of methods which look interesting. One of them is
as the name suggests, creates a data set of given shape and dtype
>>> dset = f.create_dataset("mydataset", (100,), dtype='i')
The File object is a context manager; so the following code works too
>>> import h5py >>> import numpy as np >>> with h5py.File("mytestfile.hdf5", "w") as f: >>> dset = f.create_dataset("mydataset", (100,), dtype='i')
Groups and hierarchical organization
“HDF” stands for “Hierarchical Data Format”. Every object in an HDF5 file
has a name, and they’re arranged in a POSIX-style hierarchy with
>>> dset.name '/mydataset'
The “folders” in this system are called groups. The
File object we
created is itself a group, in this case the root group, named
>>> f.name '/'
Creating a subgroup is accomplished via the aptly-named
create_group. But we need to open the file in the “append” mode first (Read/write if exists, create otherwise)
>>> f = h5py.File('mydataset.hdf5', 'a') >>> grp = f.create_group("subgroup")
Group objects also have the
create_* methods like File:
>>> dset2 = grp.create_dataset("another_dataset", (50,), dtype='f') >>> dset2.name '/subgroup/another_dataset'
By the way, you don’t have to create all the intermediate groups manually. Specifying a full path works just fine:
>>> dset3 = f.create_dataset('subgroup2/dataset_three', (10,), dtype='i') >>> dset3.name '/subgroup2/dataset_three'
Groups support most of the Python dictionary-style interface. You retrieve objects in the file using the item-retrieval syntax:
>>> dataset_three = f['subgroup2/dataset_three']
Iterating over a group provides the names of its members:
>>> for name in f: ... print(name) mydataset subgroup subgroup2
Membership testing also uses names:
>>> "mydataset" in f True >>> "somethingelse" in f False
You can even use full path names:
>>> "subgroup/another_dataset" in f True
There are also the familiar
iter() methods, as well as
Since iterating over a group only yields its directly-attached members,
iterating over an entire file is accomplished with the
visititems(), which take a callable:
>>> def printname(name): ... print(name) >>> f.visit(printname) mydataset subgroup subgroup/another_dataset subgroup2 subgroup2/dataset_three
For more, see Groups.
One of the best features of HDF5 is that you can store metadata right next to the data it describes. All groups and datasets support attached named bits of data called attributes.
Attributes are accessed through the
attrs proxy object, which again
implements the dictionary interface:
>>> dset.attrs['temperature'] = 99.5 >>> dset.attrs['temperature'] 99.5 >>> 'temperature' in dset.attrs True
For more, see Attributes.