Object dtype dtype(‘o’) has no native hdf5 equivalent

object dtype has no native hdf5 equivalent

In the case of NumPy arrays, the dtype attribute specifies the type of data contained in the array. The “object” dtype is a generic type that can hold any Python object.

HDF5 (Hierarchical Data Format version 5) is a file format commonly used for storing large amounts of numerical data. However, HDF5 does not have a native equivalent for the “object” dtype in NumPy.

This means that if you have an array with elements of type “object”, you cannot directly save it to an HDF5 file. You would need to convert the array to a different dtype that HDF5 supports, such as integers, floats, or strings.

Here is an example to illustrate this issue:

import numpy as np
import h5py

# Creating an array with "object" dtype
arr = np.array([1, "two", [3, 4], {"key": 5}], dtype=object)

# Trying to save the array to an HDF5 file
with h5py.File('data.h5', 'w') as file:
    file.create_dataset('myarray', data=arr)
    

The above code will result in an error because the “object” dtype is not supported by HDF5. To overcome this, you can convert the array to a more suitable dtype before saving, such as “str” dtype.

# Converting the array to "str" dtype
arr_str = arr.astype(str)

# Saving the converted array to HDF5 file
with h5py.File('data.h5', 'w') as file:
    file.create_dataset('myarray', data=arr_str)
    

In this example, we have converted the original array to “str” dtype before saving to the HDF5 file.