答案是用tcas建议的。我猜压缩是在每个块上单独执行的，并且块的默认大小很小，因此数据中没有足够的冗余，压缩无法从中获益。在

下面是给出一个想法的代码：import numpy as np

import gzip

import cPickle as pkl

import h5py

a = np.random.randn(100000, 10)

b = np.hstack( [a[cnt:a.shape[0]-10+cnt+1] for cnt in range(10)] )

f_hdf5_chunk_1 = h5py.File('noise_chunk_1.hdf5', 'w')

f_hdf5_chunk_1.create_dataset('b', data = b, compression = 'gzip', compression_opts = 9, chunks = (1,100))

f_hdf5_chunk_1.close()

f_hdf5_chunk_10 = h5py.File('noise_chunk_10.hdf5', 'w')

f_hdf5_chunk_10.create_dataset('b', data = b, compression = 'gzip', compression_opts = 9, chunks = (10,100))

f_hdf5_chunk_10.close()

f_hdf5_chunk_100 = h5py.File('noise_chunk_100.hdf5', 'w')

f_hdf5_chunk_100.create_dataset('b', data = b, compression = 'gzip', compression_opts = 9, chunks = (100,100))

f_hdf5_chunk_100.close()

f_hdf5_chunk_1000 = h5py.File('noise_chunk_1000.hdf5', 'w')

f_hdf5_chunk_1000.create_dataset('b', data = b, compression = 'gzip', compression_opts = 9, chunks = (1000,100))

f_hdf5_chunk_1000.close()

f_hdf5_chunk_10000 = h5py.File('noise_chunk_10000.hdf5', 'w')

f_hdf5_chunk_10000.create_dataset('b', data = b, compression = 'gzip', compression_opts = 9, chunks = (10000,100))

f_hdf5_chunk_10000.close()

结果是：

^{pr2}$

因此，当块变小时，文件的大小也会增加。在