ENH: support downcasting of nullable EAs in pd.to_numeric (#38746)

Author: arw2019

doc/source/whatsnew/v1.3.0.rst

@@ -50,6 +50,7 @@ Other enhancements
 - Improved consistency of error message when passing an invalid ``win_type`` argument in :class:`Window` (:issue:`15969`)
 - :func:`pandas.read_sql_query` now accepts a ``dtype`` argument to cast the columnar data from the SQL database based on user input (:issue:`10285`)
 - Improved integer type mapping from pandas to SQLAlchemy when using :meth:`DataFrame.to_sql` (:issue:`35076`)
+- :func:`to_numeric` now supports downcasting of nullable ``ExtensionDtype`` objects (:issue:`33013`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/tools/numeric.py

@@ -7,6 +7,7 @@
  ensure_object,
  is_datetime_or_timedelta_dtype,
  is_decimal,
+ is_integer_dtype,
  is_number,
  is_numeric_dtype,
  is_scalar,
@@ -15,6 +16,7 @@
 from pandas.core.dtypes.generic import ABCIndex, ABCSeries
 
 import pandas as pd
+from pandas.core.arrays.numeric import NumericArray
 
 
 def to_numeric(arg, errors="raise", downcast=None):
@@ -108,6 +110,21 @@ def to_numeric(arg, errors="raise", downcast=None):
  2 2.0
  3 -3.0
  dtype: float64
+
+ Downcasting of nullable integer and floating dtypes is supported:
+
+ >>> s = pd.Series([1, 2, 3], dtype="Int64")
+ >>> pd.to_numeric(s, downcast="integer")
+ 0 1
+ 1 2
+ 2 3
+ dtype: Int8
+ >>> s = pd.Series([1.0, 2.1, 3.0], dtype="Float64")
+ >>> pd.to_numeric(s, downcast="float")
+ 0 1.0
+ 1 2.1
+ 2 3.0
+ dtype: Float32
  """
  if downcast not in (None, "integer", "signed", "unsigned", "float"):
  raise ValueError("invalid downcasting method provided")
@@ -142,6 +159,14 @@ def to_numeric(arg, errors="raise", downcast=None):
  else:
  values = arg
 
+ # GH33013: for IntegerArray & FloatingArray extract non-null values for casting
+ # save mask to reconstruct the full array after casting
+ if isinstance(values, NumericArray):
+ mask = values._mask
+ values = values._data[~mask]
+ else:
+ mask = None
+
  values_dtype = getattr(values, "dtype", None)
  if is_numeric_dtype(values_dtype):
  pass
@@ -188,6 +213,16 @@ def to_numeric(arg, errors="raise", downcast=None):
  if values.dtype == dtype:
  break
 
+ # GH33013: for IntegerArray & FloatingArray need to reconstruct masked array
+ if mask is not None:
+ data = np.zeros(mask.shape, dtype=values.dtype)
+ data[~mask] = values
+
+ from pandas.core.arrays import FloatingArray, IntegerArray
+
+ klass = IntegerArray if is_integer_dtype(data.dtype) else FloatingArray
+ values = klass(data, mask)
+
  if is_series:
  return arg._constructor(values, index=arg.index, name=arg.name)
  elif is_index:

pandas/tests/tools/test_to_numeric.py

@@ -725,3 +725,42 @@ def test_to_numeric_from_nullable_string(values, expected):
  s = Series(values, dtype="string")
  result = to_numeric(s)
  tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+ "data, input_dtype, downcast, expected_dtype",
+ (
+ ([1, 1], "Int64", "integer", "Int8"),
+ ([1.0, pd.NA], "Float64", "integer", "Int8"),
+ ([1.0, 1.1], "Float64", "integer", "Float64"),
+ ([1, pd.NA], "Int64", "integer", "Int8"),
+ ([450, 300], "Int64", "integer", "Int16"),
+ ([1, 1], "Float64", "integer", "Int8"),
+ ([np.iinfo(np.int64).max - 1, 1], "Int64", "integer", "Int64"),
+ ([1, 1], "Int64", "signed", "Int8"),
+ ([1.0, 1.0], "Float32", "signed", "Int8"),
+ ([1.0, 1.1], "Float64", "signed", "Float64"),
+ ([1, pd.NA], "Int64", "signed", "Int8"),
+ ([450, -300], "Int64", "signed", "Int16"),
+ pytest.param(
+ [np.iinfo(np.uint64).max - 1, 1],
+ "UInt64",
+ "signed",
+ "UInt64",
+ marks=pytest.mark.xfail(reason="GH38798"),
+ ),
+ ([1, 1], "Int64", "unsigned", "UInt8"),
+ ([1.0, 1.0], "Float32", "unsigned", "UInt8"),
+ ([1.0, 1.1], "Float64", "unsigned", "Float64"),
+ ([1, pd.NA], "Int64", "unsigned", "UInt8"),
+ ([450, -300], "Int64", "unsigned", "Int64"),
+ ([-1, -1], "Int32", "unsigned", "Int32"),
+ ([1, 1], "Float64", "float", "Float32"),
+ ([1, 1.1], "Float64", "float", "Float32"),
+ ),
+)
+def test_downcast_nullable_numeric(data, input_dtype, downcast, expected_dtype):
+ arr = pd.array(data, dtype=input_dtype)
+ result = pd.to_numeric(arr, downcast=downcast)
+ expected = pd.array(data, dtype=expected_dtype)
+ tm.assert_extension_array_equal(result, expected)