BUG: dtype not being preserved for replace on a CategoricalDtype

[galipremsagar]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

>>> import pandas as pd
>>> pd.__version__
'1.4.2'
>>> pdf = pd.DataFrame(
...             {
...                 "a": ["one", "two", None, "three"],
...                 "b": ["one", None, "two", "three"],
...             },
...             dtype="category",
...         )
>>> pdf.dtypes
a    category
b    category
dtype: object
>>> pdf
       a      b
0    one    one
1    two    NaN
2    NaN    two
3  three  three
>>> new_df = pdf.replace(to_replace=[".", "def"], value=["_", None])
>>> new_df
       a      b
0    one    one
1    two    NaN
2    NaN    two
3  three  three
>>> new_df.dtypes
a    object
b    object
dtype: object

Issue Description

When the series is of category dtype, calling replace with above inputs is not preserving the dtype.

Expected Behavior

>>> import pandas as pd
>>> pd.__version__
'1.3.5'
>>> pdf = pd.DataFrame(
...             {
...                 "a": ["one", "two", None, "three"],
...                 "b": ["one", None, "two", "three"],
...             },
...             dtype="category",
...         )
>>> new_df = pdf.replace(to_replace=[".", "def"], value=["_", None])
>>> new_df
       a      b
0    one    one
1    two    NaN
2    NaN    two
3  three  three
>>> new_df.dtypes
a    category
b    category
dtype: object

Installed Versions

pd.show_versions()
Traceback (most recent call last):
File "", line 1, in
File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/pandas/util/_print_versions.py", line 109, in show_versions
deps = _get_dependency_info()
File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/pandas/util/_print_versions.py", line 88, in _get_dependency_info
mod = import_optional_dependency(modname, errors="ignore")
File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/pandas/compat/_optional.py", line 138, in import_optional_dependency
module = importlib.import_module(name)
File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/importlib/init.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "", line 1014, in _gcd_import
File "", line 991, in _find_and_load
File "", line 975, in _find_and_load_unlocked
File "", line 671, in _load_unlocked
File "", line 843, in exec_module
File "", line 219, in _call_with_frames_removed
File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/setuptools/init.py", line 8, in
import _distutils_hack.override # noqa: F401
File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/_distutils_hack/override.py", line 1, in
import('_distutils_hack').do_override()
File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/_distutils_hack/init.py", line 72, in do_override
ensure_local_distutils()
File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/_distutils_hack/init.py", line 59, in ensure_local_distutils
assert '_distutils' in core.file, core.file
AssertionError: /nvme/0/pgali/envs/cudfdev/lib/python3.8/distutils/core.py

[rhshadrach]

Thanks for the report @galipremsagar! Within the BlockManager, there is special logic to handle None

pandas/pandas/core/internals/blocks.py

Lines 782 to 792 in 32999a1

	else:
	if value is None:
	# gh-45601, gh-45836
	nb = self.astype(np.dtype(object), copy=False)
	if nb is self and not inplace:
	nb = nb.copy()
	putmask_inplace(nb.values, mask, value)
	return [nb]
	return self.replace(
	to_replace=to_replace, value=value, inplace=inplace, mask=mask
	)

but I'm not sure it was ever meant to handle Categorical blocks. If the code were to take the return self.replace(...) path, then the result is indeed Categorical. Further investigations and PRs to fix are welcome!

[simonjayhawkins]

Thanks @galipremsagar for the report, I noticed this while fixing #46634

The cause of this regression is #46404

from #46636 (comment)

my biggest concern (need to check this though) is categorical since the special casing was also moved in #44940.

We need a fix that preserves all the bug fixes in #44940 that were included in 1.4 and also restores the 1.3.5 behavior when None is the replacement value #45601 and #45836 while at the same time making these None cases consistent with other replacement values such as copying and splitting blocks.

This work is ongoing and being discussed in #46636, #46443 and #46393

The underlying issue is that the bug fixes introduced in 1.4 (#44940) were achieved by dispatching to Block.replace which appears handle None values differently for single replacement than is the existing behavior for None in a list like.

Ideally we would fix this on main in Block.replace, but it is my opinion that this may be difficult to backport (due to other changes that we would not normally backport for a patch release). However, if we continue with small patches like #46634 we would also need to revert the special case handling of Categorical to pre #44940 and would still not solve all the issue around copying and splitting.

There is also an extra issue on main, #45725 which needs to be fixed before we can dispatch to Block.replace

but I'm not sure it was ever meant to handle Categorical blocks.

this was done elsewhere.

[simonjayhawkins]

Looking some more, this code sample is actually a duplicate of #46634 since the replacement is a no-op?

The regression for categorical comes when there is a replacement performed.

in 1.3.5

>>> new_df = pdf.replace(to_replace=[".", "one"], value=["_", None])
>>> new_df
       a      b
0   None   None
1    two    NaN
2    NaN    two
3  three  three
>>> 
>>> new_df.dtypes
a    object
b    object
dtype: object
>>> 
>>> new_df["a"]
0     None
1      two
2      NaN
3    three
Name: a, dtype: object
>>> 
>>> new_df.dtypes.a
dtype('O')
>>> 
>>> pdf.dtypes.a
CategoricalDtype(categories=['one', 'three', 'two'], ordered=False)
>>> 
>>> 

in 1.4.0

>>> new_df = pdf.replace(to_replace=[".", "one"], value=["_", None])
>>> new_df
       a      b
0    NaN    NaN
1    two    NaN
2    NaN    two
3  three  three
>>> 
>>> new_df.dtypes
a    category
b    category
dtype: object
>>> 
>>> new_df["a"]
0      NaN
1      two
2      NaN
3    three
Name: a, dtype: category
Categories (2, object): ['three', 'two']
>>> 
>>> new_df.dtypes.a
CategoricalDtype(categories=['three', 'two'], ordered=False)
>>> 
>>> pdf.dtypes.a
CategoricalDtype(categories=['one', 'three', 'two'], ordered=False)
>>> 

in 1.4.2

>>> new_df = pdf.replace(to_replace=[".", "one"], value=["_", None])
>>> new_df
       a      b
0   None   None
1    two    NaN
2    NaN    two
3  three  three
>>> 
>>> new_df.dtypes
a    object
b    object
dtype: object
>>> 
>>> new_df["a"]
0     None
1      two
2      NaN
3    three
Name: a, dtype: object
>>> 
>>> new_df.dtypes.a
dtype('O')
>>> 
>>> pdf.dtypes.a
CategoricalDtype(categories=['one', 'three', 'two'], ordered=False)
>>> 

So in 1.4.2 we have reverted to 1.3.5 behavior and convert to object when replacing a value.

In 1.4.0 we retain the categorical dtype, but with a different CategoricalDtype (and convert the None to a missing value). I'm assuming, but not convinced, this is now the intended behavior. @jbrockmendel ?

[jbrockmendel]

In 1.4.0 we retain the categorical dtype, but with a different CategoricalDtype (and convert the None to a missing value). I'm assuming, but not convinced, this is now the intended behavior. @jbrockmendel ?

Haven't looked super-closely, but I think the if value is None check @rhshadrach linked to above was written
a) with non-Cateogorical in mind, so his suggestion of not going through that path for Categorical would make sense (though it would further complicate an already messy method)
b) specifically for cases where non-None NA values are being replaced by None, which does not appear to be the case here.

[simonjayhawkins]

moving to 1.4.4