Data processing as a batch way

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I ran across this code as I think it will help me process data sequentially but it yields an error and does not work out of the box.

'float' object cannot be interpreted as an integer

# Create dummy csv data
nb_samples = 110
a = np.arange(nb_samples)
df = pd.DataFrame(a, columns=['data'])
df.to_csv('data.csv', index=False)


# Create Dataset
class CSVDataset(torch.utils.data.Dataset):
    def __init__(self, path, chunksize, nb_samples):
        self.path = path
        self.chunksize = chunksize
        self.len = nb_samples / self.chunksize

    def __getitem__(self, index):
        x = next(
            pd.read_csv(
                self.path,
                skiprows=index * self.chunksize + 1,  #+1, since we skip the header
                chunksize=self.chunksize,
                names=['data']))
        x = torch.from_numpy(x.data.values)
        return x

    def __len__(self):
        return self.len


dataset = CSVDataset('data.csv', chunksize=10, nb_samples=nb_samples)
loader = DataLoader(dataset, batch_size=10, num_workers=1, shuffle=False)

for batch_idx, data in enumerate(loader):
    print('batch: {}\tdata: {}'.format(batch_idx, data))