Question

我有一个大的Pandas数据框（〜15GB，8300万行），我有兴趣另存为h5（或feather）文件。一列包含数字的长ID字符串，该字符串应具有字符串/对象类型。但是，即使我确保熊猫将所有列都解析为object：

df = pd.read_csv('data.csv', dtype=object)
print(df.dtypes)  # sanity check
df.to_hdf('df.h5', 'df')

> client_id                object
  event_id                 object
  account_id               object
  session_id               object
  event_timestamp          object
  # etc...

我收到此错误：

  File "foo.py", line 14, in <module>
    df.to_hdf('df.h5', 'df')
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pandas/core/generic.py", line 1996, in to_hdf
    return pytables.to_hdf(path_or_buf, key, self, **kwargs)
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pandas/io/pytables.py", line 279, in to_hdf
    f(store)
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pandas/io/pytables.py", line 273, in <lambda>
    f = lambda store: store.put(key, value, **kwargs)
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pandas/io/pytables.py", line 890, in put
    self._write_to_group(key, value, append=append, **kwargs)
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pandas/io/pytables.py", line 1367, in _write_to_group
    s.write(obj=value, append=append, complib=complib, **kwargs)
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pandas/io/pytables.py", line 2963, in write
    self.write_array('block%d_values' % i, blk.values, items=blk_items)
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pandas/io/pytables.py", line 2730, in write_array
    vlarr.append(value)
  File "/shared_directory/projects/env/lib/python3.6/site-packages/tables/vlarray.py", line 547, in append
    self._append(nparr, nobjects)
  File "tables/hdf5extension.pyx", line 2032, in tables.hdf5extension.VLArray._append
OverflowError: value too large to convert to int

显然，它试图将其转换为int并失败。

运行df.to_feather()时遇到类似的问题：

df.to_feather('df.feather')
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pandas/core/frame.py", line 1892, in to_feather
    to_feather(self, fname)
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pandas/io/feather_format.py", line 83, in to_feather
    feather.write_dataframe(df, path)
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pyarrow/feather.py", line 182, in write_feather
    writer.write(df)
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pyarrow/feather.py", line 93, in write
    table = Table.from_pandas(df, preserve_index=False)
  File "pyarrow/table.pxi", line 1174, in pyarrow.lib.Table.from_pandas
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pyarrow/pandas_compat.py", line 501, in dataframe_to_arrays
    convert_fields))
  File "/usr/lib/python3.6/concurrent/futures/_base.py", line 586, in result_iterator
    yield fs.pop().result()
  File "/usr/lib/python3.6/concurrent/futures/_base.py", line 425, in result
    return self.__get_result()
  File "/usr/lib/python3.6/concurrent/futures/_base.py", line 384, in __get_result
    raise self._exception
  File "/usr/lib/python3.6/concurrent/futures/thread.py", line 56, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pyarrow/pandas_compat.py", line 487, in convert_column
    raise e
  File "/shared_directory/projects/env/lib/python3.6/site-packages/pyarrow/pandas_compat.py", line 481, in convert_column
    result = pa.array(col, type=type_, from_pandas=True, safe=safe)
  File "pyarrow/array.pxi", line 191, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 78, in pyarrow.lib._ndarray_to_array
  File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: ('Could not convert 1542852887489 with type str: tried to convert to double', 'Conversion failed for column session_id with type object')

所以：

任何看起来像数字的东西都被强制转换为数字在存储中？
NaN的存在会影响这里发生的事情吗？
是否有替代的存储解决方案？最好是什么？

Answer 1

HDF5不是此用例的合适解决方案。如果要在单个结构中存储许多数据帧，则hdf5是更好的解决方案。打开文件时，它会有更多开销，然后使您可以有效地加载每个数据帧，也可以轻松地加载它们的切片。应该将其视为存储数据帧的文件系统。

对于时间序列事件的单个数据帧，建议的格式将是Apache Arrow项目格式之一，即feather或parquet。人们应该将它们视为基于列的（压缩）csv文件。在What are the differences between feather and parquet?下很好地列出了这两者之间的特殊权衡。

要考虑的一个特殊问题是数据类型。由于feather并非旨在通过压缩来优化磁盘空间，因此它可以为larger variety of data types提供支持。尽管parquet试图提供非常有效的压缩，但它只能支持limited subset，这将使其能够更好地处理数据压缩。

Answer 2

已经阅读了有关该主题的文章，看来问题是在处理string类型的列。我的string列包含全数字字符串和带字符的字符串的混合。 Pandas具有灵活的选项，可以将字符串保留为object，而没有声明的类型，但是当序列化为hdf5或feather时，列的内容将转换为类型（{{1 }}或str，并且不能混用。当面对足够大的混合类型库时，这两个库都将失败。

解决方案：

对于文本数据，除了double / hdf5以外，还有其他推荐的解决方案，包括：

feather
json（请注意，熊猫0.25 msgpack已弃用）
read_msgpack（已知security issues，所以要小心-但对于内部存储/传输数据帧应该没问题）
pickle，是Apache Arrow生态系统的一部分。

Here是Matthew Rocklin（parquet开发人员之一）对dask和msgpack进行比较的答案。他在自己的blog上进行了比较广泛的比较。

将大熊猫df保存到hdf

2 个答案:

解决方案：