I think that this isn't a bug. NaN is a numerical value. If dtype=False we shouldn't infer it to be a numerical value.

But if it it is an issue I can do this one.

Yea there is certainly some ambiguity here and @jorisvandenbossche might have some thoughts but I don't necessarily think the JSON null would map better to None than it would to np.nan.

read_csv would also convert this to NaN:

>>> pd.read_csv(io.StringIO("1\nnull"))
    1
0 NaN

Complementing what @chrisstpierre said, the null type in json can be numerical, string, list, ..., as seen in https://stackoverflow.com/questions/21120999/representing-null-in-json.

So setting up as np.nan even though dtype is set explicitly to False may be beneficial when working with numerical data only but not with strings for example. I think a np.nan in a column of strings should not be desirable.

Hi all - Just curious whether stringifying None in the following example is expected behaviour?

df = pd.read_json('[null]', dtype={0: str})
print(df.values)
# [['None']]

If yes, how can I avoid this while at the same time specifying the the column to be of str type?
Thanks for your help!

take

read_csv would also convert this to NaN:

It's true that read_csv converts to NaN in a float dtype. With dtype=object (the way in read_csv to turn off dtype inference), you still get a NaN, but using object dtype (so this is different from the float dtype as what is now implemented by #37834)

Another point of comparison, when creating a Series from None without specified dtype, we also keep it as None with object dtype:

In [27]: pd.Series([None])
Out[27]: 
0    None
dtype: object