Pandas msgpack vs pickle

2020-02-25 05:25发布

msgpack in Pandas is supposed to be a replacement for pickle.

Per the Pandas docs on msgpack:

This is a lightweight portable binary format, similar to binary JSON, that is highly space efficient, and provides good performance both on the writing (serialization), and reading (deserialization).

I find, however, that its performance does not appear to stack up against pickle.

df = pd.DataFrame(np.random.randn(10000, 100))

>>> %timeit df.to_pickle('test.p')
10 loops, best of 3: 22.4 ms per loop

>>> %timeit df.to_msgpack('test.msg')
10 loops, best of 3: 36.4 ms per loop

>>> %timeit pd.read_pickle('test.p')
100 loops, best of 3: 10.5 ms per loop

>>> %timeit pd.read_msgpack('test.msg')
10 loops, best of 3: 24.6 ms per loop

Question: Asides from potential security issues with pickle, what are the benefits of msgpack over pickle? Is pickle still the preferred method of serializing data, or do better alternatives currently exist?

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2楼-- · 2020-02-25 05:36

Pickle is better for the following:

  1. Numerical data or anything that uses the buffer protocol (numpy arrays) (though only if you use a somewhat recent protocol=)
  2. Python specific objects like classes, functions, etc.. (although here you should look at cloudpickle)

MsgPack is better for the following:

  1. Cross language interoperation. It's an alternative to JSON with some improvements
  2. Performance on text data and Python objects. It's a decent factor faster than Pickle at this under any setting.

As @Jeff noted above this blogpost may be of interest

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