I get pandas error when I try to read HDF5 format files that I have created with h5py. I wonder if I am just doing something wrong?
import h5py import numpy as np import pandas as pd h5_file = h5py.File('test.h5', 'w') h5_file.create_dataset('zeros', data=np.zeros(shape=(3, 5)), dtype='f') h5_file.close() pd_file = pd.read_hdf('test.h5', 'zeros')
gives an error: TypeError: cannot create a storer if the object is not existing nor a value are passed
I tried to specify key set to '/zeros' (as I would do it with h5py when reading the file) with no luck.
If I use pandas.HDFStore to read it in, I get an empty store back:
store = pd.HDFStore('test.h5') >>> store <class 'pandas.io.pytables.HDFStore'> File path: test.h5 Empty
I have no troubles reading just created file back with h5py:
h5_back = h5py.File('test.h5', 'r') h5_back['/zeros'] <HDF5 dataset "zeros": shape (3, 5), type "<f4">
Using these versions:
Python 3.4.3 (v3.4.3:9b73f1c3e601, Feb 23 2015, 02:52:03) [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin pd.__version__ '0.16.2' h5py.__version__ '2.5.0'
Many thanks in advance, Masha
Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the “fixed” format.
Open a HDF5/H5 file in HDFView hdf5 file on your computer. Open this file in HDFView. If you click on the name of the HDF5 file in the left hand window of HDFView, you can view metadata for the file. This will be located in the bottom window of the application.
Thread safety improvements Access to all APIs, high- and low-level, are now protected by a global lock. The entire API is now believed to be thread-safe. Feedback and real-world testing is welcome.
I've worked a little on the pytables
module in pandas.io
and from what I know pandas interaction with HDF files is limited to specific structures that pandas understands. To see what these look like, you can try
import pandas as pd import numpy as np pd.Series(np.zeros((3,5),dtype=np.float32).to_hdf('test.h5','test')
If you open 'test.h5' in HDFView, you will see a path /test
with 4 items that are needed to recreate the DataFrame
.
So I think your only option for reading in NumPy arrays is to read them in directly and then convert these to Pandas objects.
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