python-3.x - 将大 Pandas df 保存到 hdf 时出现溢出错误

Question

我有一个大型 Pandas 数据帧(~15GB，83m 行)，我有兴趣将其保存为 h5(或 feather)文件。一列包含数字的长 ID 字符串，应具有字符串/对象类型。但即使我确保 Pandas 将所有列解析为 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

显然它无论如何都试图将其转换为 int 并且失败了。

运行 df.to_feather() 时，我遇到了类似的问题:

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:

是什么看起来像数字的东西被强行转换成数字
在存储？

NaN 的存在会影响这里发生的事情吗？

有替代的存储解决方案吗？什么是最好的？

Answer 1

对这个主题做了一些阅读后，问题似乎是处理 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 可以灵活地选择将字符串保留为 object ，没有声明类型，但是当序列化为 hdf5 或 feather 时，该列的内容将转换为类型(例如 0x2313143 或 0x231343143)不能混合的类型(例如 0x2313431321313313133313313133133133131331331313313313131313131313131313131313131313131313131231231231231313131314131232323131313131313131313141 或 str当遇到足够大的混合类型库时，这两个库都失败了。

将我的混合列强制转换为字符串允许我将它保存在 Feather 中，但在 HDF5 中，文件膨胀并且当我用完磁盘空间时该过程结束。

Here 是一个类似案例的答案，评论者指出(2 年前)“这个问题非常标准，但解决方案很少”。

一些背景:

Pandas 中的字符串类型被称为 double ，但这掩盖了它们可能是纯字符串或 混合数据类型 (numpy 具有内置字符串类型，但 Pandas 从不使用它们)。所以在这种情况下要做的第一件事是将所有字符串 cols 强制为字符串类型(使用 object )。但即便如此，在足够大的文件(16GB，长字符串)中，这仍然失败。为什么？

我遇到这个错误的原因是我有 长 和 高熵 字符串的数据(很多不同) (对于低熵数据，切换到 df[col].astype(str) dtype 可能是值得的。)在我的例子中，我意识到我只需要这些字符串来识别行 - 所以我可以用唯一的整数替换它们!

df[col] = df[col].map(dict(zip(df[col].unique(), range(df[col].nunique()))))

其他解决方案:

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

feather

json(注意在 Pandas 0.25 中 msgpack 已弃用)

read_msgpack(已知 security issues ，所以要小心 - 但它应该可以用于内部存储/数据帧的传输)

pickle ，Apache Arrow 生态系统的一部分。

Here 是 Matthew Rocklin( parquet 开发人员之一)比较 dask 和 msgpack 的答案。他在他的 blog 上写了一个更广泛的比较。