PandasCodec.encode_request can not handle missing values

[sp1thas]

While trying to handle missing values, I've noticed that json serialization is not correct:

import pandas as pd
from mlserver.codecs.pandas import PandasCodec

df = pd.DataFrame({'foo': [None, 1.0]})
PandasCodec.encode_request(df).json()

serialized request:

{
    "parameters": {
        "content_type": "pd"
    },
    "inputs": [
        {
            "name": "foo",
            "shape": [
                2,
                1
            ],
            "datatype": "FP64",
            "data": [
                NaN,
                1.0
            ]
        }
    ]
}

In case anyone else is facing the same issue, this is my quick-n-dirty way to handle it:

import pandas as pd
from mlserver.types import InferenceRequest

def replace_nan_with_none(inference_request: InferenceRequest) -> InferenceRequest:
    for i, _input in enumerate(inference_request.inputs):
        for ii, v in enumerate(_input.data.__root__):
            if pd.isna(v):
                inference_request.inputs[i].data.__root__[ii] = None
    return inference_request

In case this is a simple fix that could be handled by a newcomer like me, I would be interested to work on the bug fix.

[sp1thas]

Duplicate of #1747

[bwallima]

The change only fixes the Problem with nan values. As soon you have any text data with None value it breaks: This problem occurs as soon as there is an attempt to pass any non-numeric values. For instance, I have a df that contains text values, some of which can be None. A fix would be to check for float values in mlserver/codecs/numpy.py row 109: if isinstance(val, float) and np.isnan(val)