Pandas to_hdf() TypeError: 'int' 类型的对象没有 len()答案

【问题标题】：Pandas to_hdf() TypeError: object of type 'int' has no len()Pandas to_hdf() TypeError: 'int' 类型的对象没有 len()
【发布时间】：2021-04-28 21:48:36
【问题描述】：

我想存储一个 pandas DataFrame，这样当我以后再次加载它时，我只加载它的某些列而不是整个内容。因此，我试图以 hdf 格式存储 pandas DataFrame。 DataFrame 包含一个 numpy 数组，我收到以下错误消息。

关于如何消除错误或我可以使用什么格式的任何想法？

代码：

import pandas as pd
import numpy as np

df = pd.DataFrame({"a": [1,2,3,4], "b": [1,2,3,4]})
df["c"] = [np.ones((4,4)) for i in range(4)]
df.to_hdf("test.h5", "df", format='table', data_columns=True)

错误：

TypeError                                 Traceback (most recent call last)
<ipython-input-2-ace42e5ccbb7> in <module>
----> 1 df.to_hdf("test.h5", "df", format='table', data_columns=True)

/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py in to_hdf(self, path_or_buf, key, mode, complevel, complib, append, format, index, min_itemsize, nan_rep, dropna, data_columns, errors, encoding)
   2619             data_columns=data_columns,
   2620             errors=errors,
-> 2621             encoding=encoding,
   2622         )
   2623 

/opt/conda/lib/python3.7/site-packages/pandas/io/pytables.py in to_hdf(path_or_buf, key, value, mode, complevel, complib, append, format, index, min_itemsize, nan_rep, dropna, data_columns, errors, encoding)
    278             path_or_buf, mode=mode, complevel=complevel, complib=complib
    279         ) as store:
--> 280             f(store)
    281     else:
    282         f(path_or_buf)

/opt/conda/lib/python3.7/site-packages/pandas/io/pytables.py in <lambda>(store)
    270             errors=errors,
    271             encoding=encoding,
--> 272             dropna=dropna,
    273         )
    274 

/opt/conda/lib/python3.7/site-packages/pandas/io/pytables.py in put(self, key, value, format, index, append, complib, complevel, min_itemsize, nan_rep, data_columns, encoding, errors, track_times, dropna)
   1104             errors=errors,
   1105             track_times=track_times,
-> 1106             dropna=dropna,
   1107         )
   1108 

/opt/conda/lib/python3.7/site-packages/pandas/io/pytables.py in _write_to_group(self, key, value, format, axes, index, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, nan_rep, data_columns, encoding, errors, track_times)
   1753             nan_rep=nan_rep,
   1754             data_columns=data_columns,
-> 1755             track_times=track_times,
   1756         )
   1757 

/opt/conda/lib/python3.7/site-packages/pandas/io/pytables.py in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, nan_rep, data_columns, track_times)
   4222             min_itemsize=min_itemsize,
   4223             nan_rep=nan_rep,
-> 4224             data_columns=data_columns,
   4225         )
   4226 

/opt/conda/lib/python3.7/site-packages/pandas/io/pytables.py in _create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize)
   3892                 nan_rep=nan_rep,
   3893                 encoding=self.encoding,
-> 3894                 errors=self.errors,
   3895             )
   3896             adj_name = _maybe_adjust_name(new_name, self.version)

/opt/conda/lib/python3.7/site-packages/pandas/io/pytables.py in _maybe_convert_for_string_atom(name, block, existing_col, min_itemsize, nan_rep, encoding, errors)
   4885         # we cannot serialize this data, so report an exception on a column
   4886         # by column basis
-> 4887         for i in range(len(block.shape[0])):
   4888             col = block.iget(i)
   4889             inferred_type = lib.infer_dtype(col, skipna=False)

TypeError: object of type 'int' has no len()

【问题讨论】：

一种选择是在保存之前转换整个astype(str)。根据this github page 看来，这可能是一个开放的错误

标签： python pandas hdf

【解决方案1】：

Pandas 似乎无法序列化数据框中的 numpy 数组。所以我建议将numpy 数据存储在单独的*.h5 文件中。

import pandas as pd
import numpy as np
import h5py

df = pd.DataFrame({"a": [1,2,3,4], "b": [1,2,3,4]})
df.to_hdf("pandas_data.h5", "df", format='table', data_columns=True)

c =  [np.ones((4,4)) for i in range(4)]
with h5py.File('numpy_data.h5', 'w') as hf:
    hf.create_dataset('dataset_1', data=c)

然后您可以使用以下命令重新加载该数据：'

with h5py.File('numpy_data.h5', 'r') as hf:
    c_out = hf['dataset_1'][:]

df = pd.read_hdf('pandas_data.h5', 'df')
df['c'] = list(c_out)

【讨论】：