TST: nan->NA in non-construction tests (pandas-dev#62021)

Author: jbrockmendel

pandas/tests/arrays/categorical/test_astype.py

@@ -2,6 +2,7 @@
 import pytest
 
 from pandas import (
+    NA,
     Categorical,
     CategoricalDtype,
     CategoricalIndex,
@@ -34,7 +35,7 @@ def test_astype_nan_to_int(self, cls, values):
             array([0, 0], dtype="timedelta64[ns]"),
             array([Period("2019"), Period("2020")], dtype="period[Y-DEC]"),
             array([Interval(0, 1), Interval(1, 2)], dtype="interval"),
-            array([1, np.nan], dtype="Int64"),
+            array([1, NA], dtype="Int64"),
         ],
     )
     def test_astype_category_to_extension_dtype(self, expected):

pandas/tests/arrays/floating/conftest.py

@@ -33,7 +33,7 @@ def data_missing(dtype):
     Fixture returning array with missing data according to parametrized float
     'dtype'.
     """
-    return pd.array([np.nan, 0.1], dtype=dtype)
+    return pd.array([pd.NA, 0.1], dtype=dtype)
 
 
 @pytest.fixture(params=["data", "data_missing"])

pandas/tests/arrays/floating/test_arithmetic.py

@@ -104,9 +104,9 @@ def test_pow_array(dtype):
 def test_rpow_one_to_na():
     # https://github.com/pandas-dev/pandas/issues/22022
     # https://github.com/pandas-dev/pandas/issues/29997
-    arr = pd.array([np.nan, np.nan], dtype="Float64")
+    arr = pd.array([pd.NA, pd.NA], dtype="Float64")
     result = np.array([1.0, 2.0]) ** arr
-    expected = pd.array([1.0, np.nan], dtype="Float64")
+    expected = pd.array([1.0, pd.NA], dtype="Float64")
     tm.assert_extension_array_equal(result, expected)
 
 
@@ -187,22 +187,22 @@ def test_error_invalid_values(data, all_arithmetic_operators):
 def test_cross_type_arithmetic():
     df = pd.DataFrame(
         {
-            "A": pd.array([1, 2, np.nan], dtype="Float64"),
-            "B": pd.array([1, np.nan, 3], dtype="Float32"),
+            "A": pd.array([1, 2, pd.NA], dtype="Float64"),
+            "B": pd.array([1, pd.NA, 3], dtype="Float32"),
             "C": np.array([1, 2, 3], dtype="float64"),
         }
     )
 
     result = df.A + df.C
-    expected = pd.Series([2, 4, np.nan], dtype="Float64")
+    expected = pd.Series([2, 4, pd.NA], dtype="Float64")
     tm.assert_series_equal(result, expected)
 
     result = (df.A + df.C) * 3 == 12
     expected = pd.Series([False, True, None], dtype="boolean")
     tm.assert_series_equal(result, expected)
 
     result = df.A + df.B
-    expected = pd.Series([2, np.nan, np.nan], dtype="Float64")
+    expected = pd.Series([2, pd.NA, pd.NA], dtype="Float64")
     tm.assert_series_equal(result, expected)
 
 

pandas/tests/arrays/floating/test_construction.py

@@ -20,7 +20,7 @@ def test_floating_array_constructor():
     mask = np.array([False, False, False, True], dtype="bool")
 
     result = FloatingArray(values, mask)
-    expected = pd.array([1, 2, 3, np.nan], dtype="Float64")
+    expected = pd.array([1, 2, 3, pd.NA], dtype="Float64")
     tm.assert_extension_array_equal(result, expected)
     tm.assert_numpy_array_equal(result._data, values)
     tm.assert_numpy_array_equal(result._mask, mask)

pandas/tests/arrays/floating/test_function.py

@@ -11,7 +11,7 @@
 # np.sign emits a warning with nans, <https://github.com/numpy/numpy/issues/15127>
 @pytest.mark.filterwarnings("ignore:invalid value encountered in sign:RuntimeWarning")
 def test_ufuncs_single(ufunc):
-    a = pd.array([1, 2, -3, np.nan], dtype="Float64")
+    a = pd.array([1, 2, -3, pd.NA], dtype="Float64")
     result = ufunc(a)
     expected = pd.array(ufunc(a.astype(float)), dtype="Float64")
     tm.assert_extension_array_equal(result, expected)
@@ -24,7 +24,7 @@ def test_ufuncs_single(ufunc):
 
 @pytest.mark.parametrize("ufunc", [np.log, np.exp, np.sin, np.cos, np.sqrt])
 def test_ufuncs_single_float(ufunc):
-    a = pd.array([1.0, 0.2, 3.0, np.nan], dtype="Float64")
+    a = pd.array([1.0, 0.2, 3.0, pd.NA], dtype="Float64")
     with np.errstate(invalid="ignore"):
         result = ufunc(a)
         expected = pd.array(ufunc(a.astype(float)), dtype="Float64")
@@ -40,7 +40,7 @@ def test_ufuncs_single_float(ufunc):
 @pytest.mark.parametrize("ufunc", [np.add, np.subtract])
 def test_ufuncs_binary_float(ufunc):
     # two FloatingArrays
-    a = pd.array([1, 0.2, -3, np.nan], dtype="Float64")
+    a = pd.array([1, 0.2, -3, pd.NA], dtype="Float64")
     result = ufunc(a, a)
     expected = pd.array(ufunc(a.astype(float), a.astype(float)), dtype="Float64")
     tm.assert_extension_array_equal(result, expected)
@@ -88,7 +88,7 @@ def test_ufunc_reduce_raises(values):
     ],
 )
 def test_stat_method(pandasmethname, kwargs):
-    s = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, np.nan, np.nan], dtype="Float64")
+    s = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, pd.NA, pd.NA], dtype="Float64")
     pandasmeth = getattr(s, pandasmethname)
     result = pandasmeth(**kwargs)
     s2 = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype="float64")

pandas/tests/arrays/integer/conftest.py

@@ -1,4 +1,3 @@
-import numpy as np
 import pytest
 
 import pandas as pd
@@ -40,7 +39,7 @@ def data(dtype):
     Used to test dtype conversion with and without missing values.
     """
     return pd.array(
-        list(range(8)) + [np.nan] + list(range(10, 98)) + [np.nan] + [99, 100],
+        list(range(8)) + [pd.NA] + list(range(10, 98)) + [pd.NA] + [99, 100],
         dtype=dtype,
     )
 
@@ -53,7 +52,7 @@ def data_missing(dtype):
 
     Used to test dtype conversion with and without missing values.
     """
-    return pd.array([np.nan, 1], dtype=dtype)
+    return pd.array([pd.NA, 1], dtype=dtype)
 
 
 @pytest.fixture(params=["data", "data_missing"])

pandas/tests/arrays/integer/test_arithmetic.py

@@ -154,9 +154,9 @@ def test_pow_array():
 def test_rpow_one_to_na():
     # https://github.com/pandas-dev/pandas/issues/22022
     # https://github.com/pandas-dev/pandas/issues/29997
-    arr = pd.array([np.nan, np.nan], dtype="Int64")
+    arr = pd.array([pd.NA, pd.NA], dtype="Int64")
     result = np.array([1.0, 2.0]) ** arr
-    expected = pd.array([1.0, np.nan], dtype="Float64")
+    expected = pd.array([1.0, pd.NA], dtype="Float64")
     tm.assert_extension_array_equal(result, expected)
 
 
@@ -243,22 +243,22 @@ def test_arithmetic_conversion(all_arithmetic_operators, other):
 def test_cross_type_arithmetic():
     df = pd.DataFrame(
         {
-            "A": pd.Series([1, 2, np.nan], dtype="Int64"),
-            "B": pd.Series([1, np.nan, 3], dtype="UInt8"),
+            "A": pd.Series([1, 2, pd.NA], dtype="Int64"),
+            "B": pd.Series([1, pd.NA, 3], dtype="UInt8"),
             "C": [1, 2, 3],
         }
     )
 
     result = df.A + df.C
-    expected = pd.Series([2, 4, np.nan], dtype="Int64")
+    expected = pd.Series([2, 4, pd.NA], dtype="Int64")
     tm.assert_series_equal(result, expected)
 
     result = (df.A + df.C) * 3 == 12
     expected = pd.Series([False, True, None], dtype="boolean")
     tm.assert_series_equal(result, expected)
 
     result = df.A + df.B
-    expected = pd.Series([2, np.nan, np.nan], dtype="Int64")
+    expected = pd.Series([2, pd.NA, pd.NA], dtype="Int64")
     tm.assert_series_equal(result, expected)
 
 

pandas/tests/arrays/integer/test_construction.py

@@ -77,7 +77,7 @@ def test_integer_array_constructor():
     mask = np.array([False, False, False, True], dtype="bool")
 
     result = IntegerArray(values, mask)
-    expected = pd.array([1, 2, 3, np.nan], dtype="Int64")
+    expected = pd.array([1, 2, 3, pd.NA], dtype="Int64")
     tm.assert_extension_array_equal(result, expected)
 
     msg = r".* should be .* numpy array. Use the 'pd.array' function instead"
@@ -191,7 +191,7 @@ def test_to_integer_array_float():
 
 def test_to_integer_array_str():
     result = IntegerArray._from_sequence(["1", "2", None], dtype="Int64")
-    expected = pd.array([1, 2, np.nan], dtype="Int64")
+    expected = pd.array([1, 2, pd.NA], dtype="Int64")
     tm.assert_extension_array_equal(result, expected)
 
     with pytest.raises(

pandas/tests/arrays/integer/test_dtypes.py

@@ -52,7 +52,7 @@ def test_preserve_dtypes(op):
 
 def test_astype_nansafe():
     # see gh-22343
-    arr = pd.array([np.nan, 1, 2], dtype="Int8")
+    arr = pd.array([pd.NA, 1, 2], dtype="Int8")
     msg = "cannot convert NA to integer"
 
     with pytest.raises(ValueError, match=msg):
@@ -230,7 +230,7 @@ def test_construct_cast_invalid(dtype):
     with pytest.raises(TypeError, match=msg):
         pd.Series(arr).astype(dtype)
 
-    arr = [1.2, 2.3, 3.7, np.nan]
+    arr = [1.2, 2.3, 3.7, pd.NA]
     with pytest.raises(TypeError, match=msg):
         pd.array(arr, dtype=dtype)
 

pandas/tests/arrays/integer/test_function.py

@@ -10,7 +10,7 @@
 # np.sign emits a warning with nans, <https://github.com/numpy/numpy/issues/15127>
 @pytest.mark.filterwarnings("ignore:invalid value encountered in sign:RuntimeWarning")
 def test_ufuncs_single_int(ufunc):
-    a = pd.array([1, 2, -3, np.nan])
+    a = pd.array([1, 2, -3, pd.NA], dtype="Int64")
     result = ufunc(a)
     expected = pd.array(ufunc(a.astype(float)), dtype="Int64")
     tm.assert_extension_array_equal(result, expected)
@@ -23,7 +23,7 @@ def test_ufuncs_single_int(ufunc):
 
 @pytest.mark.parametrize("ufunc", [np.log, np.exp, np.sin, np.cos, np.sqrt])
 def test_ufuncs_single_float(ufunc):
-    a = pd.array([1, 2, -3, np.nan])
+    a = pd.array([1, 2, -3, pd.NA], dtype="Int64")
     with np.errstate(invalid="ignore"):
         result = ufunc(a)
         expected = FloatingArray(ufunc(a.astype(float)), mask=a._mask)
@@ -39,7 +39,7 @@ def test_ufuncs_single_float(ufunc):
 @pytest.mark.parametrize("ufunc", [np.add, np.subtract])
 def test_ufuncs_binary_int(ufunc):
     # two IntegerArrays
-    a = pd.array([1, 2, -3, np.nan])
+    a = pd.array([1, 2, -3, pd.NA], dtype="Int64")
     result = ufunc(a, a)
     expected = pd.array(ufunc(a.astype(float), a.astype(float)), dtype="Int64")
     tm.assert_extension_array_equal(result, expected)
@@ -99,7 +99,7 @@ def test_ufunc_reduce_raises(values):
     ],
 )
 def test_stat_method(pandasmethname, kwargs):
-    s = pd.Series(data=[1, 2, 3, 4, 5, 6, np.nan, np.nan], dtype="Int64")
+    s = pd.Series(data=[1, 2, 3, 4, 5, 6, pd.NA, pd.NA], dtype="Int64")
     pandasmeth = getattr(s, pandasmethname)
     result = pandasmeth(**kwargs)
     s2 = pd.Series(data=[1, 2, 3, 4, 5, 6], dtype="Int64")