我将这个问题提到this。我正在制作这个新帖子，因为我并不真正理解那里给出的答案，希望有人可以向我解释更多。
基本上我的问题就像在那里的链接一样。之前，我使用np.vstack并从中创建h5格式文件。以下是我的例子：
import numpy as np
import h5py
import glob
path="/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()
如果尺寸完全相同，则效果非常好。但是当大小不同时，它会引发错误TypeError: Object dtype dtype('O') has no native HDF5 equivalent
我从那里给出的答案中理解，我必须将数组保存为单独的数据集，但要查看给出的示例代码段; for k,v in adict.items()和grp.create_dataset(k,data=v)，k应该是数据集的名称是否正确？比如我的例子，test_data？什么是v？
以下是vstack以及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. ])]]
感谢您的帮助和解释。
我通过使用pandas解决了这个问题。起初我使用了Pierre de Buyl的确切建议，但是当我尝试加载/读取文件/数据集时，它给了我错误。我尝试了test_data = h5f["data1/file1"][:]。这给了我一个错误，说Unable to open object(Object 'file1' does not exist)。
我使用2test.h5阅读pandas.read_hdf进行了检查，结果显示该文件为空。我在网上搜索其他解决方案，我发现了这个。我已经修改过了：
import numpy as np
import glob
import pandas as pd
path = "/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()
为了阅读，我只需使用
input_data = pd.read_hdf('2test.h5', 'twodata')
read_input = input_data.values
read1 = read_input[:, -1] # read/get last column for example
答案 0 :(得分：5)
HDF5文件中的基本元素是组（类似于目录）和数据集（类似于数组）。
NumPy将创建一个包含许多不同输入的数组。当一个人试图从不同的元素（即不同的长度）创建一个数组时，NumPy返回一个类型为＆＃39; O＆＃39;的数组。在NumPy reference guide中查找object_。然后，使用NumPy几乎没有什么优势，因为它类似于标准的Python列表。
HDF5无法存储类型＆＃39; O＆＃39;因为它没有通用数据类型（只支持C结构类型对象）。
您问题最明显的解决方案是将数据存储在HDF5数据集中，其中包含一个数据集＆＃34;每张桌子。您保留了在单个文件中收集数据的优势，并且您具有类似于＆＃34; dict-like＆＃34;访问元素。
请尝试以下代码：
import numpy as np
import h5py
import glob
path="/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()
阅读：
h5f = h5py.File("/home/ling/test/2test.h5", "r")
test_data = h5f['data1/thefirstfilenamewithoutcsvextension'][:]