Pandas to_hdf() TypeError: object of type 'int' has no len()
Pandas to_hdf() TypeError: object of type 'int' has no len()
我想存储一个 pandas DataFrame，这样当我稍后再次加载它时，我只加载它的某些列而不是整个东西。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()
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方式加载该数据：'
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)
我想存储一个 pandas DataFrame，这样当我稍后再次加载它时，我只加载它的某些列而不是整个东西。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()
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方式加载该数据：'
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)