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ROADMAP: add consistent missing values for all dtypes to the roadmap #35208

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ROADMAP: add consistent missing values for all dtypes to the roadmap #35208

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ROADMAP: add consistent missing values for all dtypes to the roadmap
jorisvandenbossche Jul 10, 2020
ee62bd0
add notion of different semantics
jorisvandenbossche Aug 7, 2020
7bcb4e6
update with Tom's suggestions
jorisvandenbossche Aug 7, 2020
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26 changes: 26 additions & 0 deletions doc/source/development/roadmap.rst
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Expand Up @@ -53,6 +53,32 @@ need to implement certain operations expected by pandas users (for example
the algorithm used in, ``Series.str.upper``). That work may be done outside of
pandas.

Consistent missing value handling
---------------------------------

Currently, pandas has varying missing data interfaces depending on the data
type: pandas uses ``np.nan`` as missing value indicator in floating point data,
``np.nan`` or ``None`` in object dtype data (eg strings, or booleans with
missing values are cast to object), ``pd.NaT`` in datetimelike data. For
categorical or interval data, they return ``np.nan`` on access even when the
categories or intervals are datetime-like. Integer data cannot store missing
data or are cast to float.

Long term, we want to introduce consistent missing value handling accross the
different data types: all data types should support missing values and with the
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Not sure I agree that all data types should support missing values. Non-nullable types could be beneficial

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@TomAugspurger TomAugspurger Jul 13, 2020 •
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I agree that there's value in pandas ensuring that a column cannot contain NAs. I'm not sure where best to put that invariant: the dtype or the array / column.

But, to sidestep this issue, perhaps something like "pandas should provide consistent missing value handling for all the different kinds of data", i.e. we give you the ability, without saying that every dtype has to be nullable.

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I seem to remember that you, Tom and I had a similar discussion in another issues (but can't directly find it).

I agree that the concept of non-nullability is useful/interesting, but as Tom also mentions: non-nullability shouldn't necessarily be a property of the data type itself. Similarly as Tom is working on the "allow_duplicate_labels" flag, we could have "nullable=True/False" flags per column.

Because for the dtype itself to be non-nullable, we should ask ourselves: do we have an example of a data type (that we want to include in pandas) for which it would never be useful to be nullable?

Or to phrase the text in a different way: if a data type supports missing values, it should follow consistent semantics.

Note that right now, pandas doesn't really know the concept of non-nullable. Yes, we have some dtypes that don't support NAs (like integer dtype), but whenever some operation introduces a missing value, we simply upcast to a dtype that can store the missing value (so in practice means changing to float for integer). A proper concept of nullable=False flag wouldn't necessarily work like this, I think.

same behaviour.

To this end, a new experimental ``pd.NA`` scalar to be used as missing value
indicator has already been added in pandas 1.0 (and used in the experimental
nullable dtypes). Further work is needed to integrate this with other data
types, and to provide a path forward to make this the default in a future
version of pandas.

This has been discussed at
`github #28095 <https://github.com/pandas-dev/pandas/issues/28095>`__ (and
linked issues), and described in more detail in this
`design doc <https://hackmd.io/@jorisvandenbossche/Sk0wMeAmB>`__.

Apache Arrow interoperability
-----------------------------

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