done

lib/python3.11/site-packages/pandas/tests/base/test_misc.py

@@ -0,0 +1,190 @@
+import sys
+
+import numpy as np
+import pytest
+
+from pandas._config import using_string_dtype
+
+from pandas.compat import PYPY
+
+from pandas.core.dtypes.common import (
+    is_dtype_equal,
+    is_object_dtype,
+)
+
+import pandas as pd
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+def test_isnull_notnull_docstrings():
+    # GH#41855 make sure its clear these are aliases
+    doc = pd.DataFrame.notnull.__doc__
+    assert doc.startswith("\nDataFrame.notnull is an alias for DataFrame.notna.\n")
+    doc = pd.DataFrame.isnull.__doc__
+    assert doc.startswith("\nDataFrame.isnull is an alias for DataFrame.isna.\n")
+
+    doc = Series.notnull.__doc__
+    assert doc.startswith("\nSeries.notnull is an alias for Series.notna.\n")
+    doc = Series.isnull.__doc__
+    assert doc.startswith("\nSeries.isnull is an alias for Series.isna.\n")
+
+
+@pytest.mark.parametrize(
+    "op_name, op",
+    [
+        ("add", "+"),
+        ("sub", "-"),
+        ("mul", "*"),
+        ("mod", "%"),
+        ("pow", "**"),
+        ("truediv", "/"),
+        ("floordiv", "//"),
+    ],
+)
+def test_binary_ops_docstring(frame_or_series, op_name, op):
+    # not using the all_arithmetic_functions fixture with _get_opstr
+    # as _get_opstr is used internally in the dynamic implementation of the docstring
+    klass = frame_or_series
+
+    operand1 = klass.__name__.lower()
+    operand2 = "other"
+    expected_str = " ".join([operand1, op, operand2])
+    assert expected_str in getattr(klass, op_name).__doc__
+
+    # reverse version of the binary ops
+    expected_str = " ".join([operand2, op, operand1])
+    assert expected_str in getattr(klass, "r" + op_name).__doc__
+
+
+def test_ndarray_compat_properties(index_or_series_obj):
+    obj = index_or_series_obj
+
+    # Check that we work.
+    for p in ["shape", "dtype", "T", "nbytes"]:
+        assert getattr(obj, p, None) is not None
+
+    # deprecated properties
+    for p in ["strides", "itemsize", "base", "data"]:
+        assert not hasattr(obj, p)
+
+    msg = "can only convert an array of size 1 to a Python scalar"
+    with pytest.raises(ValueError, match=msg):
+        obj.item()  # len > 1
+
+    assert obj.ndim == 1
+    assert obj.size == len(obj)
+
+    assert Index([1]).item() == 1
+    assert Series([1]).item() == 1
+
+
+@pytest.mark.skipif(
+    PYPY or using_string_dtype(),
+    reason="not relevant for PyPy doesn't work properly for arrow strings",
+)
+def test_memory_usage(index_or_series_memory_obj):
+    obj = index_or_series_memory_obj
+    # Clear index caches so that len(obj) == 0 report 0 memory usage
+    if isinstance(obj, Series):
+        is_ser = True
+        obj.index._engine.clear_mapping()
+    else:
+        is_ser = False
+        obj._engine.clear_mapping()
+
+    res = obj.memory_usage()
+    res_deep = obj.memory_usage(deep=True)
+
+    is_object = is_object_dtype(obj) or (is_ser and is_object_dtype(obj.index))
+    is_categorical = isinstance(obj.dtype, pd.CategoricalDtype) or (
+        is_ser and isinstance(obj.index.dtype, pd.CategoricalDtype)
+    )
+    is_object_string = is_dtype_equal(obj, "string[python]") or (
+        is_ser and is_dtype_equal(obj.index.dtype, "string[python]")
+    )
+
+    if len(obj) == 0:
+        expected = 0
+        assert res_deep == res == expected
+    elif is_object or is_categorical or is_object_string:
+        # only deep will pick them up
+        assert res_deep > res
+    else:
+        assert res == res_deep
+
+    # sys.getsizeof will call the .memory_usage with
+    # deep=True, and add on some GC overhead
+    diff = res_deep - sys.getsizeof(obj)
+    assert abs(diff) < 100
+
+
+def test_memory_usage_components_series(series_with_simple_index):
+    series = series_with_simple_index
+    total_usage = series.memory_usage(index=True)
+    non_index_usage = series.memory_usage(index=False)
+    index_usage = series.index.memory_usage()
+    assert total_usage == non_index_usage + index_usage
+
+
+@pytest.mark.parametrize("dtype", tm.NARROW_NP_DTYPES)
+def test_memory_usage_components_narrow_series(dtype):
+    series = Series(range(5), dtype=dtype, index=[f"i-{i}" for i in range(5)], name="a")
+    total_usage = series.memory_usage(index=True)
+    non_index_usage = series.memory_usage(index=False)
+    index_usage = series.index.memory_usage()
+    assert total_usage == non_index_usage + index_usage
+
+
+def test_searchsorted(request, index_or_series_obj):
+    # numpy.searchsorted calls obj.searchsorted under the hood.
+    # See gh-12238
+    obj = index_or_series_obj
+
+    if isinstance(obj, pd.MultiIndex):
+        # See gh-14833
+        request.applymarker(
+            pytest.mark.xfail(
+                reason="np.searchsorted doesn't work on pd.MultiIndex: GH 14833"
+            )
+        )
+    elif obj.dtype.kind == "c" and isinstance(obj, Index):
+        # TODO: Should Series cases also raise? Looks like they use numpy
+        #  comparison semantics https://github.com/numpy/numpy/issues/15981
+        mark = pytest.mark.xfail(reason="complex objects are not comparable")
+        request.applymarker(mark)
+
+    max_obj = max(obj, default=0)
+    index = np.searchsorted(obj, max_obj)
+    assert 0 <= index <= len(obj)
+
+    index = np.searchsorted(obj, max_obj, sorter=range(len(obj)))
+    assert 0 <= index <= len(obj)
+
+
+@pytest.mark.filterwarnings(r"ignore:Dtype inference:FutureWarning")
+def test_access_by_position(index_flat):
+    index = index_flat
+
+    if len(index) == 0:
+        pytest.skip("Test doesn't make sense on empty data")
+
+    series = Series(index)
+    assert index[0] == series.iloc[0]
+    assert index[5] == series.iloc[5]
+    assert index[-1] == series.iloc[-1]
+
+    size = len(index)
+    assert index[-1] == index[size - 1]
+
+    msg = f"index {size} is out of bounds for axis 0 with size {size}"
+    if isinstance(index.dtype, pd.StringDtype) and index.dtype.storage == "pyarrow":
+        msg = "index out of bounds"
+    with pytest.raises(IndexError, match=msg):
+        index[size]
+    msg = "single positional indexer is out-of-bounds"
+    with pytest.raises(IndexError, match=msg):
+        series.iloc[size]