Load group from hdf5

I have an hdf5 file that contains datasets inside groups. Example:

group1/dataset1
group1/dataset2
group1/datasetX


group2/dataset1
group2/dataset2
group2/datasetX

I'm able to read each dataset independently. This is how I read a dataset from a .hdf5 file:

def hdf5_load_dataset(hdf5_filename, dsetname):
    with h5py.File(hdf5_filename, 'r') as f:
        dset = f[dsetname]
        return dset[...]

# pseudo-code of how I call the hdf5_load_dataset() function
group = {'group1':'dataset1', 'group1':'dataset2' ...}

for group in groups:
    for dataset in groups[group]:
        dset_value = hdf5_load_dataset_value(path_hdf5_file, f'{group}/{dataset}')
        # do stuff

I would like to know if it's possible to load into memory all the datasets of group1, then of group2, etc as a dictionary or similar in a single file read. My script is taking quite some time (4min) to read ~200k datasets. There are 2k groups with 100 datasets. So if I load a group in memory at once it will not overload it and I will gain in speed.

This is a pseudo-code of what I'm looking for:

for group in groups:
    dset_group_as_dict = hdf5_load_group(path_hdf5_file, f'{group}')

    for dataset in dset_group_as_dict;
        #do stuff

EDIT:

Inside each .csv file:

time, amplitude
1.000e-08, -1.432e-07
1.001e-08, 7.992e-07
1.003e-08, -1.838e-05
1.003e-08, 2.521e-05

For each .csv file in each folder I have a dataset for the time and for the amplitude. The structure of the hdfile is like this:

XY_1/impact_X/time
XY_1/impact_Y/amplitude

where

time = np.array([1.000e-08, 1.001e-08, 1.003e-08, ...])  # 500 items
amplitude = np.array([-1.432e-07, 7.992e-07, -1.838e-05, ...])  # 500 items

XY_1 is a position in space.

impact_X means that X was impacted in position XY_1 so X amplitude has changed.

So, XY_1 must be in a different group of XY_2 as well as impact_X, impact_Y etc since they represent data to a particular XY position.

I need to create plots from each or only one (time, amplitude) pair (configurable). I also need to compare the amplitudes with a "golden" array to see the differences and calculate other stuff. To perform calculation I will read all the datasets, perform calculation and save the result.

I have more than 200k .csv files for each test case, in a total is more than 5M. Using 5M reads from disk will take quite some time in this case. For the 200k files, by exporting all the .csv to a unique JSON file, it takes ~40s to execute, using .csv is taking ~4min. I can't use a unique json any longer due to memory issues when loading the single JSON file. That's why I chose hdf5 as an alternative.

EDIT 2:

How I read the csv file:

def read_csv_return_list_of_rows(csv_file, _delimiter):
    csv_file_list = list()
    with open(csv_file, 'r') as f_read:
        csv_reader = csv.reader(f_read, delimiter = _delimiter)
        for row in csv_reader:
            csv_file_list.append(row)
    return csv_file_list