I am referring this question to this. I am making this new thread because I did not really understand the answer given there and hopefully there is someone who could explain it more to me.

Basically my problem is like in the link there.Before, I use np.vstack and create h5 format file from it. Below are my example:

import numpy as np
import h5py
import globpath="/home/ling/test/"def runtest():data1 = [np.loadtxt(file) for file in glob.glob(path + "data1/*.csv")]data2 = [np.loadtxt(file) for file in glob.glob(path + "data2/*.csv")]stack = np.vstack((data1, data2))h5f = h5py.File("/home/ling/test/2test.h5", "w") h5f.create_dataset("test_data", data=stack)h5f.close()

This works perfectly if the size is all same. But when the size is different, it throws me error TypeError: Object dtype dtype('O') has no native HDF5 equivalent

What I understand from the answer given there, I must save the arrays as separate dataset but looking at the example snippet given; for k,v in adict.items()and grp.create_dataset(k,data=v), k should be the name of the dataset correct? like from my example, test_data? and what is v ?

Below are what it looks like for vstack and also stack

[[array([-0.07812, -0.07812, -0.07812, ..., -0.07812, -0.07812,  0.     ])array([-0.07812, -0.07812, -0.11719, ..., -0.07812, -0.07812,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([-0.07812, -0.07812, -0.07812, ..., -0.07812, -0.07812,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([ 0.03906,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([-0.07812, -0.07812, -0.07812, ..., -0.07812, -0.07812,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.11719,  0.     ])array([-0.07812, -0.07812, -0.07812, ..., -0.07812, -0.07812,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([-0.07812, -0.07812, -0.07812, ..., -0.07812, -0.07812,  0.     ])array([-0.15625, -0.07812, -0.07812, ..., -0.07812, -0.07812,  0.     ])array([-0.07812, -0.07812, -0.07812, ..., -0.07812, -0.07812,  0.     ])array([-0.11719, -0.07812, -0.07812, ..., -0.07812, -0.07812,  0.     ])array([-0.07812, -0.07812, -0.07812, ..., -0.07812, -0.15625,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([-0.07812, -0.07812, -0.07812, ..., -0.11719, -0.07812,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([-0.07812, -0.07812, -0.07812, ..., -0.07812, -0.07812,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([-0.07812, -0.11719, -0.07812, ..., -0.07812, -0.07812,  0.     ])array([-0.07812, -0.07812, -0.07812, ..., -0.07812, -0.07812,  0.     ])array([ 0.07812,  0.03906,  0.07812, ...,  0.03906,  0.07812,  0.     ])array([ 0.03906,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([-0.07812, -0.07812, -0.07812, ..., -0.07812, -0.11719,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])array([ 0.07812,  0.07812,  0.07812, ...,  0.07812,  0.07812,  0.     ])][ array([ 10.9375 ,  10.97656,  10.97656, ...,  11.05469,  11.05469,   1.     ])array([ 11.01562,  11.01562,  11.01562, ...,  11.09375,  11.09375,   1.     ])array([ 11.09375,  11.09375,  11.09375, ...,  11.09375,  11.09375,   1.     ])array([ 10.97656,  11.01562,  11.01562, ...,  11.13281,  11.09375,   1.     ])array([ 11.05469,  11.05469,  11.01562, ...,  11.09375,  11.09375,   1.     ])array([ 11.05469,  11.05469,  11.05469, ...,  11.05469,  11.05469,   1.     ])array([ 11.05469,  11.05469,  11.05469, ...,  11.05469,  11.13281,   1.     ])array([ 11.05469,  11.09375,  11.09375, ...,  11.09375,  11.09375,   1.     ])array([ 11.09375,  11.05469,  11.09375, ...,  11.05469,  11.05469,   1.     ])array([ 11.05469,  11.05469,  11.05469, ...,  11.09375,  11.09375,   1.     ])array([ 11.05469,  11.05469,  11.09375, ...,  11.05469,  11.05469,   1.     ])array([ 10.97656,  10.97656,  10.97656, ...,  11.05469,  11.05469,   1.     ])array([ 11.09375,  11.05469,  11.09375, ...,  11.09375,  11.09375,   1.     ])array([ 11.05469,  11.05469,  11.05469, ...,  11.05469,  11.05469,   1.     ])array([ 11.05469,  11.05469,  11.05469, ...,  11.09375,  11.17188,   1.     ])array([ 11.09375,  11.09375,  11.09375, ...,  10.97656,  11.09375,   1.     ])array([ 11.09375,  11.09375,  11.09375, ...,  11.05469,  11.05469,   1.     ])array([ 11.05469,  11.05469,  11.05469, ...,  11.05469,  11.05469,   1.     ])array([ 11.05469,  11.01562,  11.05469, ...,  11.01562,  11.01562,   1.     ])array([ 10.78125,  10.78125,  10.78125, ...,  11.05469,  11.05469,   1.     ])array([ 11.13281,  11.09375,  11.13281, ...,  11.09375,  11.09375,   1.     ])array([ 11.13281,  11.09375,  11.09375, ...,  11.05469,  11.05469,   1.     ])array([ 10.97656,  10.97656,  10.9375 , ...,  11.05469,  11.05469,   1.     ])array([ 11.05469,  11.09375,  11.05469, ...,  11.09375,  11.09375,   1.     ])array([ 10.9375 ,  10.9375 ,  10.9375 , ...,  11.09375,  11.09375,   1.     ])array([ 11.05469,  11.05469,  11.05469, ...,  11.05469,  11.05469,   1.     ])array([ 10.9375 ,  10.89844,  10.9375 , ...,  11.05469,  11.09375,   1.     ])array([ 10.9375 ,  10.97656,  10.97656, ...,  11.05469,  11.05469,   1.     ])array([ 10.89844,  10.89844,  10.89844, ...,  11.05469,  11.09375,   1.     ])array([ 11.05469,  11.05469,  11.05469, ...,  11.01562,  11.01562,   1.     ])]]

Thank you for your help and explanation.

Update

I solved the problem by using pandas. At first I used the exact suggestion by Pierre de Buyl but it gave me error when I tried to load/read the file/dataset. I tried with test_data = h5f["data1/file1"][:]. This gave me an error saying that Unable to open object(Object 'file1' does not exist).

I checked by reading the 2test.h5 using pandas.read_hdf and it shows that the file is empty. I searched online for other solution and I found this. I already modified it:

import numpy as np
import globimport pandas as pdpath = "/home/ling/test/"def runtest():data1 = [np.loadtxt(file) for file in glob.glob(path + "data1/*.csv")]data2 = [np.loadtxt(file) for file in glob.glob(path + "data2/*.csv")]df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)combine = df1.append(df2, ignore_index=True)# sort the NaN to the left
combinedf = combine.apply(lambda x : sorted(x, key=pd.notnull), 1)
combinedf.to_hdf('/home/ling/test/2test.h5', 'twodata')runtest()

For reading, I simply use

input_data = pd.read_hdf('2test.h5', 'twodata')
read_input = input_data.valuesread1 = read_input[:, -1] # read/get last column for example

The basic elements in a HDF5 file are groups (similar to directories) and datasets (similar to arrays).

NumPy will create an array with a lot of different inputs. When one attempts to create an array from disparate elements (i.e. different lengths), NumPy returns an array of type 'O'. Look for object_ in the NumPy reference guide. Then, there is little advantage to use NumPy as this resembles a standard Python list.

HDF5 cannot store arrays of type 'O' because it does not have generic datatypes (only some support for C struct type objects).

The most obvious solution to your problem is to store your data in HDF5 dataset, with "one dataset" per table. You retain the advantage of collecting the data in a single file and you have "dict-like" access to the elements.

Try the following code:

import numpy as np
import h5py
import globpath="/home/ling/test/"def runtest():h5f = h5py.File("/home/ling/test/2test.h5", "w") h5f.create_group('data1')h5f.create_group('data2')[h5f.create_dataset(file[:-4], data=np.loadtxt(file)) for file in glob.glob(path + "data1/*.csv")][h5f.create_dataset(file[:-4], data=np.loadtxt(file)) for file in glob.glob(path + "data2/*.csv")]h5f.close()

For reading:

h5f = h5py.File("/home/ling/test/2test.h5", "r")
test_data = h5f['data1/thefirstfilenamewithoutcsvextension'][:]