done

lib/python3.11/site-packages/pandas/tests/frame/test_npfuncs.py

@@ -0,0 +1,89 @@
+"""
+Tests for np.foo applied to DataFrame, not necessarily ufuncs.
+"""
+import numpy as np
+
+from pandas import (
+    Categorical,
+    DataFrame,
+)
+import pandas._testing as tm
+
+
+class TestAsArray:
+    def test_asarray_homogeneous(self):
+        df = DataFrame({"A": Categorical([1, 2]), "B": Categorical([1, 2])})
+        result = np.asarray(df)
+        # may change from object in the future
+        expected = np.array([[1, 1], [2, 2]], dtype="object")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_np_sqrt(self, float_frame):
+        with np.errstate(all="ignore"):
+            result = np.sqrt(float_frame)
+        assert isinstance(result, type(float_frame))
+        assert result.index.is_(float_frame.index)
+        assert result.columns.is_(float_frame.columns)
+
+        tm.assert_frame_equal(result, float_frame.apply(np.sqrt))
+
+    def test_sum_deprecated_axis_behavior(self):
+        # GH#52042 deprecated behavior of df.sum(axis=None), which gets
+        #  called when we do np.sum(df)
+
+        arr = np.random.default_rng(2).standard_normal((4, 3))
+        df = DataFrame(arr)
+
+        msg = "The behavior of DataFrame.sum with axis=None is deprecated"
+        with tm.assert_produces_warning(
+            FutureWarning, match=msg, check_stacklevel=False
+        ):
+            res = np.sum(df)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            expected = df.sum(axis=None)
+        tm.assert_series_equal(res, expected)
+
+    def test_np_ravel(self):
+        # GH26247
+        arr = np.array(
+            [
+                [0.11197053, 0.44361564, -0.92589452],
+                [0.05883648, -0.00948922, -0.26469934],
+            ]
+        )
+
+        result = np.ravel([DataFrame(batch.reshape(1, 3)) for batch in arr])
+        expected = np.array(
+            [
+                0.11197053,
+                0.44361564,
+                -0.92589452,
+                0.05883648,
+                -0.00948922,
+                -0.26469934,
+            ]
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.ravel(DataFrame(arr[0].reshape(1, 3), columns=["x1", "x2", "x3"]))
+        expected = np.array([0.11197053, 0.44361564, -0.92589452])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.ravel(
+            [
+                DataFrame(batch.reshape(1, 3), columns=["x1", "x2", "x3"])
+                for batch in arr
+            ]
+        )
+        expected = np.array(
+            [
+                0.11197053,
+                0.44361564,
+                -0.92589452,
+                0.05883648,
+                -0.00948922,
+                -0.26469934,
+            ]
+        )
+        tm.assert_numpy_array_equal(result, expected)