![]() To read the data back, we can do it in a very similar way to when we read a numpy file: with h5py.File('random.hdf5', 'r') as f: If, for some reason, you don't use the with, never forget to add the command f.close() at the end. ![]() Even if there is an error within the with, the file will be closed. In a nutshell, it is a convenient way of opening and closing a file. If you are not familiar with the with statement, you can check out this tutorial. Datasets are holders of our data, basically the building blocks of the HDF5 format. We create a dataset called default, and we set the data as the random array created earlier. If you would like to preserve the file and still write to it, you can open it with the a attribute instead of w. We open a file called random.hdf5 with write permission, w which means that if there is already a file with the same name, it will be overwritten. We import the packages h5py and numpy and create an array with random values. Let's create a new file and save a numpy random array to it: import h5pyĭset = f.create_dataset("default", data=arr) The best way to get started is to dive into the use of the HDF5 library. It is relatively basic, but you can see the structures of the files very quickly. It is written in Java so it should work on almost any computer. The HDF5 group provides a tool called HDF5 Viewer. When working with HDF5 files, it is handy to have a tool that allows you to explore the data graphically. You can also install h5py with anaconda, which has the added benefit of a finer control on the underlying HDF5 library used: conda install h5py The command will also install numpy, in case you don't have it already in your environment. Remember that you should be using a virtual environment to perform tests: pip install h5py We can install the h5py package through pip. The HDF5 format is supported by the HDF Group, and it is based on open source standards, meaning that your data will always be accessible, even if the group disappears. All the examples that appear in this article are also available on our Github repository. We will discuss different ways of storing and organizing data and how to optimize the reading process. In this article, we will see how you can use h5py to store and retrieve data from files. H5py allows you to work with data on the hard drive just as you would with an array. Instead of loading each frame to memory, you could directly access the required data. Maybe you would like to look only at a smaller region and not the full-frame. A clear example would be a movie, which is a series of 2D arrays. Imagine you have a large array that doesn't fit in the available RAM. One of the most exciting features of the HDF5 format is that data is read from the hard drive only when it is needed. Some of the features are the same with both libraries, but we will focus on h5py. While PyTables can be thought of as implementing database-like features on top of the HDF5 specification, h5py is the natural choice when dealing with N-dimensional numpy arrays (not just tables). The first one is the one employed by Pandas under-the-hood, while the second is the one that maps the features of the HDF5 specification to numpy arrays. In Python, there are two libraries that can interface with the HDF5 format: PyTables and h5py. One essential option of the HDF5 format is that it allows attaching metadata to every element in the structure, making it ideal for generating self-explanatory files. By default, the data is stored in binary format, and the library is compatible with different data types. You can think of it as a single file with its hierarchical structure, just like a collection of folders and subfolders. If you are looking for a solution that integrates nicely with numpy and pandas, then the HDF5 format may be the solution you were seeking.Įach HDF5 file has an internal structure that allows you to search for a specific dataset. ![]() Sometimes you need to access a specific subset of the dataset, and you don't want to load it all to memory. When dealing with large amounts of data, either experimental or simulated, saving it to several text files is not very efficient.
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