CLN: remove unused nan comparison functions, ensure_float (#52385)

* CLN: remove unused nan comparison functions

* CLN: remove unused ensure_float

Author: jbrockmendel

pandas/core/dtypes/common.py

@@ -66,32 +66,6 @@
 _is_scipy_sparse = None
 
 ensure_float64 = algos.ensure_float64
-
-
-def ensure_float(arr):
-    """
-    Ensure that an array object has a float dtype if possible.
-
-    Parameters
-    ----------
-    arr : array-like
-        The array whose data type we want to enforce as float.
-
-    Returns
-    -------
-    float_arr : The original array cast to the float dtype if
-                possible. Otherwise, the original array is returned.
-    """
-    if is_extension_array_dtype(arr.dtype):
-        if is_float_dtype(arr.dtype):
-            arr = arr.to_numpy(dtype=arr.dtype.numpy_dtype, na_value=np.nan)
-        else:
-            arr = arr.to_numpy(dtype="float64", na_value=np.nan)
-    elif issubclass(arr.dtype.type, (np.integer, np.bool_)):
-        arr = arr.astype(float)
-    return arr
-
-
 ensure_int64 = algos.ensure_int64
 ensure_int32 = algos.ensure_int32
 ensure_int16 = algos.ensure_int16
@@ -1735,7 +1709,6 @@ def is_all_strings(value: ArrayLike) -> bool:
     "classes",
     "classes_and_not_datetimelike",
     "DT64NS_DTYPE",
-    "ensure_float",
     "ensure_float64",
     "ensure_python_int",
     "ensure_str",

pandas/core/nanops.py

@@ -2,7 +2,6 @@
 
 import functools
 import itertools
-import operator
 from typing import (
     Any,
     Callable,
@@ -36,7 +35,6 @@
 
 from pandas.core.dtypes.common import (
     is_any_int_dtype,
-    is_bool_dtype,
     is_complex,
     is_float,
     is_float_dtype,
@@ -1688,35 +1686,6 @@ def _ensure_numeric(x):
     return x
 
 
-# NA-friendly array comparisons
-
-
-def make_nancomp(op):
-    def f(x, y):
-        xmask = isna(x)
-        ymask = isna(y)
-        mask = xmask | ymask
-
-        result = op(x, y)
-
-        if mask.any():
-            if is_bool_dtype(result):
-                result = result.astype("O")
-            np.putmask(result, mask, np.nan)
-
-        return result
-
-    return f
-
-
-nangt = make_nancomp(operator.gt)
-nange = make_nancomp(operator.ge)
-nanlt = make_nancomp(operator.lt)
-nanle = make_nancomp(operator.le)
-naneq = make_nancomp(operator.eq)
-nanne = make_nancomp(operator.ne)
-
-
 def na_accum_func(values: ArrayLike, accum_func, *, skipna: bool) -> ArrayLike:
     """
     Cumulative function with skipna support.

pandas/tests/series/test_arithmetic.py

@@ -27,10 +27,7 @@
     isna,
 )
 import pandas._testing as tm
-from pandas.core import (
-    nanops,
-    ops,
-)
+from pandas.core import ops
 from pandas.core.computation import expressions as expr
 from pandas.core.computation.check import NUMEXPR_INSTALLED
 
@@ -499,17 +496,6 @@ def test_ser_cmp_result_names(self, names, comparison_op):
             assert result.name == names[2]
 
     def test_comparisons(self):
-        left = np.random.randn(10)
-        right = np.random.randn(10)
-        left[:3] = np.nan
-
-        result = nanops.nangt(left, right)
-        with np.errstate(invalid="ignore"):
-            expected = (left > right).astype("O")
-        expected[:3] = np.nan
-
-        tm.assert_almost_equal(result, expected)
-
         s = Series(["a", "b", "c"])
         s2 = Series([False, True, False])
 

pandas/tests/test_nanops.py

@@ -1,5 +1,4 @@
 from functools import partial
-import operator
 import warnings
 
 import numpy as np
@@ -744,53 +743,6 @@ def test_nancov(self):
         self.check_nancorr_nancov_1d(nanops.nancov, targ0, targ1)
 
 
-@pytest.mark.parametrize(
-    "op,nanop",
-    [
-        (operator.eq, nanops.naneq),
-        (operator.ne, nanops.nanne),
-        (operator.gt, nanops.nangt),
-        (operator.ge, nanops.nange),
-        (operator.lt, nanops.nanlt),
-        (operator.le, nanops.nanle),
-    ],
-)
-def test_nan_comparison(request, op, nanop, disable_bottleneck):
-    arr_float = request.getfixturevalue("arr_float")
-    arr_float1 = request.getfixturevalue("arr_float")
-    targ0 = op(arr_float, arr_float1)
-    arr_nan = request.getfixturevalue("arr_nan")
-    arr_nan_nan = request.getfixturevalue("arr_nan_nan")
-    arr_float_nan = request.getfixturevalue("arr_float_nan")
-    arr_float1_nan = request.getfixturevalue("arr_float_nan")
-    arr_nan_float1 = request.getfixturevalue("arr_nan_float1")
-
-    while targ0.ndim:
-        res0 = nanop(arr_float, arr_float1)
-        tm.assert_almost_equal(targ0, res0)
-
-        if targ0.ndim > 1:
-            targ1 = np.vstack([targ0, arr_nan])
-        else:
-            targ1 = np.hstack([targ0, arr_nan])
-        res1 = nanop(arr_float_nan, arr_float1_nan)
-        tm.assert_numpy_array_equal(targ1, res1, check_dtype=False)
-
-        targ2 = arr_nan_nan
-        res2 = nanop(arr_float_nan, arr_nan_float1)
-        tm.assert_numpy_array_equal(targ2, res2, check_dtype=False)
-
-        # Lower dimension for next step in the loop
-        arr_float = np.take(arr_float, 0, axis=-1)
-        arr_float1 = np.take(arr_float1, 0, axis=-1)
-        arr_nan = np.take(arr_nan, 0, axis=-1)
-        arr_nan_nan = np.take(arr_nan_nan, 0, axis=-1)
-        arr_float_nan = np.take(arr_float_nan, 0, axis=-1)
-        arr_float1_nan = np.take(arr_float1_nan, 0, axis=-1)
-        arr_nan_float1 = np.take(arr_nan_float1, 0, axis=-1)
-        targ0 = np.take(targ0, 0, axis=-1)
-
-
 @pytest.mark.parametrize(
     "arr, correct",
     [