TST: add copy/view test for setting columns with an array/series

pandas/conftest.py

@@ -1797,3 +1797,11 @@ def using_array_manager():
     Fixture to check if the array manager is being used.
     """
     return pd.options.mode.data_manager == "array"
+
+
+@pytest.fixture
+def using_copy_on_write():
+    """
+    Fixture to check if Copy-on-Write is enabled.
+    """
+    return False

pandas/tests/copy_view/test_setitem.py

@@ -0,0 +1,95 @@
+import numpy as np
+
+from pandas import (
+    DataFrame,
+    Index,
+    RangeIndex,
+    Series,
+)
+import pandas._testing as tm
+
+# -----------------------------------------------------------------------------
+# Copy/view behaviour for the values that are set in a DataFrame
+
+
+def test_set_column_with_array():
+    # Case: setting an array as a new column (df[col] = arr) copies that data
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    arr = np.array([1, 2, 3])
+
+    df["c"] = arr
+
+    # the array data is copied
+    assert not np.shares_memory(df["c"].values, arr)
+    # and thus modifying the array does not modify the DataFrame
+    arr[0] = 0
+    tm.assert_series_equal(df["c"], Series([1, 2, 3], name="c"))
+
+
+def test_set_column_with_series(using_copy_on_write):
+    # Case: setting a series as a new column (df[col] = s) copies that data
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    s = Series([1, 2, 3])
+
+    df["c"] = s
+
+    if using_copy_on_write:
+        # with CoW we can delay the copy
+        assert np.shares_memory(df["c"].values, s.values)
+    else:
+        # the series data is copied
+        assert not np.shares_memory(df["c"].values, s.values)
+
+    # and modifying the series does not modify the DataFrame
+    s.iloc[0] = 0
+    tm.assert_series_equal(df["c"], Series([1, 2, 3], name="c"))
+
+
+def test_set_column_with_index(using_copy_on_write):
+    # Case: setting an index as a new column (df[col] = idx) copies that data
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    idx = Index([1, 2, 3])
+
+    df["c"] = idx
+
+    # the index data is copied
+    assert not np.shares_memory(df["c"].values, idx.values)
+
+    # and thus modifying the index does not modify the DataFrame
+    idx.values[0] = 0
+    tm.assert_series_equal(df["c"], Series([1, 2, 3], name="c"))
+
+    # however, in case of a RangeIndex, we currently don't copy the cached
+    # "materialized" values
+    idx = RangeIndex(1, 4)
+    arr = idx.values
+
+    df["d"] = idx
+
+    if using_copy_on_write:
+        assert not np.shares_memory(df["d"].values, arr)
+        arr[0] = 0
+        tm.assert_series_equal(df["d"], Series([1, 2, 3], name="d"))
+    else:
+        assert np.shares_memory(df["d"].values, arr)
+        arr[0] = 0
+        tm.assert_series_equal(df["d"], Series([0, 2, 3], name="d"))
+
+
+def test_set_columns_with_dataframe(using_copy_on_write):
+    # Case: setting a DataFrame as new columns copies that data
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    df2 = DataFrame({"c": [7, 8, 9], "d": [10, 11, 12]})
+
+    df[["c", "d"]] = df2
+
+    if using_copy_on_write:
+        # with CoW we can delay the copy
+        assert np.shares_memory(df["c"].values, df2["c"].values)
+    else:
+        # the data is copied
+        assert not np.shares_memory(df["c"].values, df2["c"].values)
+
+    # and modifying the set DataFrame does not modify the original DataFrame
+    df2.iloc[0, 0] = 0
+    tm.assert_series_equal(df["c"], Series([7, 8, 9], name="c"))