BUG: Float Nullable dtypes not being returned when use_nullable_dtypes=True in pd.read_parquet

[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
>>> df = pd.DataFrame({'a':[1, 2, 3], 'b':[1.0, 2.0, 3]})
>>> df['c'] = pd.Series([1, 2, 3], dtype='uint8')
>>> df.to_parquet('a')
>>> pd.read_parquet('a').dtypes
a      int64
b    float64
c      uint8
dtype: object
>>> pd.read_parquet('a', use_nullable_dtypes=True).dtypes
a      Int64
b    float64
c      UInt8
dtype: object
>>> pd.__version__
'1.4.0'

Issue Description

When use_nullable_dtypes=True in parquet reader the floating column is being read as float64 instead of Float64(A nullable dtype)

Expected Behavior

pd.read_parquet('a', use_nullable_dtypes=True).dtypes
a Int64
b Float64
c UInt8
dtype: object

Installed Versions

pd.show_versions()
Traceback (most recent call last):
File "", line 1, in
File "/Users/pgali/opt/anaconda3/envs/del/lib/python3.9/site-packages/pandas/util/_print_versions.py", line 109, in show_versions
deps = _get_dependency_info()
File "/Users/pgali/opt/anaconda3/envs/del/lib/python3.9/site-packages/pandas/util/_print_versions.py", line 88, in _get_dependency_info
mod = import_optional_dependency(modname, errors="ignore")
File "/Users/pgali/opt/anaconda3/envs/del/lib/python3.9/site-packages/pandas/compat/_optional.py", line 126, in import_optional_dependency
module = importlib.import_module(name)
File "/Users/pgali/opt/anaconda3/envs/del/lib/python3.9/importlib/init.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "", line 1030, in _gcd_import
File "", line 1007, in _find_and_load
File "", line 986, in _find_and_load_unlocked
File "", line 680, in _load_unlocked
File "", line 850, in exec_module
File "", line 228, in _call_with_frames_removed
File "/Users/pgali/opt/anaconda3/envs/del/lib/python3.9/site-packages/setuptools/init.py", line 8, in
import _distutils_hack.override # noqa: F401
File "/Applications/PyCharm CE.app/Contents/plugins/python-ce/helpers/pydev/_pydev_bundle/pydev_import_hook.py", line 21, in do_import
module = self._system_import(name, *args, **kwargs)
File "/Users/pgali/opt/anaconda3/envs/del/lib/python3.9/site-packages/_distutils_hack/override.py", line 1, in
import('_distutils_hack').do_override()
File "/Users/pgali/opt/anaconda3/envs/del/lib/python3.9/site-packages/_distutils_hack/init.py", line 71, in do_override
ensure_local_distutils()
File "/Users/pgali/opt/anaconda3/envs/del/lib/python3.9/site-packages/_distutils_hack/init.py", line 59, in ensure_local_distutils
assert '_distutils' in core.file, core.file
AssertionError: /Users/pgali/opt/anaconda3/envs/del/lib/python3.9/distutils/core.py

[Jaafarben2]

Pandas primarily uses NaN to represent missing data. Because NaN is a float, this forces an array of integers with any missing values to become floating point.So pandas introduce a extension types for (int64 -> Int64, ... ), but there isn't a need to extend float64 to Float64 because float64 is nullable (NaN is a float).

>>> pd.Series([1, 2, np.nan], dtype="Float64").dtype
dtype('float64')

[galipremsagar]

Pandas primarily uses NaN to represent missing data. Because NaN is a float, this forces an array of integers with any missing values to become floating point.So pandas introduce a extension types for (int64 -> Int64, ... ), but there isn't a need to extend float64 to Float64 because float64 is nullable (NaN is a float).

>>> pd.Series([1, 2, np.nan], dtype="Float64").dtype
dtype('float64')

I don't understand. Is this documented somewhere? What I'm expecting interms of nullable dtype equivalent is pd.Float64Dtype, the one currently being returned is of numpy.dtype:

>>> df['b']
0    1.0
1    2.0
2    3.0
Name: b, dtype: float64
>>> df['b'].dtype
dtype('float64')
>>> type(df['b'].dtype)
<class 'numpy.dtype[float64]'>
>>> type(df['b'].astype(pd.Float64Dtype()).dtype)
<class 'pandas.core.arrays.floating.Float64Dtype'>
>>> df['b'].astype(pd.Float64Dtype())
0    1.0
1    2.0
2    3.0
Name: b, dtype: Float64

[Jaafarben2]

Yes, I'm sorry for using an earlier version of pandas.
Otherwise the float mapping is missing in the "mapping" dictionary on pandas\io\parquet.py
I add these two lines in the "mapping" dictionary of parquet.py and it works:

mapping = {
.
.
.
self.api.float32(): pd.Float32Dtype(),
self.api.float64(): pd.Float64Dtype(),
}

I will make a pull request about this