199 lines
6.1 KiB
Python
199 lines
6.1 KiB
Python
import sys
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import numpy as np
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import pytest
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from pandas.compat import (
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IS64,
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PYPY,
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)
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from pandas.core.dtypes.common import (
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is_categorical_dtype,
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is_dtype_equal,
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is_object_dtype,
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)
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import pandas as pd
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from pandas import (
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Index,
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Series,
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)
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import pandas._testing as tm
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def test_isnull_notnull_docstrings():
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# GH#41855 make sure its clear these are aliases
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doc = pd.DataFrame.notnull.__doc__
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assert doc.startswith("\nDataFrame.notnull is an alias for DataFrame.notna.\n")
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doc = pd.DataFrame.isnull.__doc__
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assert doc.startswith("\nDataFrame.isnull is an alias for DataFrame.isna.\n")
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doc = Series.notnull.__doc__
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assert doc.startswith("\nSeries.notnull is an alias for Series.notna.\n")
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doc = Series.isnull.__doc__
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assert doc.startswith("\nSeries.isnull is an alias for Series.isna.\n")
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@pytest.mark.parametrize(
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"op_name, op",
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[
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("add", "+"),
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("sub", "-"),
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("mul", "*"),
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("mod", "%"),
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("pow", "**"),
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("truediv", "/"),
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("floordiv", "//"),
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],
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)
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def test_binary_ops_docstring(frame_or_series, op_name, op):
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# not using the all_arithmetic_functions fixture with _get_opstr
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# as _get_opstr is used internally in the dynamic implementation of the docstring
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klass = frame_or_series
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operand1 = klass.__name__.lower()
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operand2 = "other"
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expected_str = " ".join([operand1, op, operand2])
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assert expected_str in getattr(klass, op_name).__doc__
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# reverse version of the binary ops
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expected_str = " ".join([operand2, op, operand1])
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assert expected_str in getattr(klass, "r" + op_name).__doc__
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def test_ndarray_compat_properties(index_or_series_obj):
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obj = index_or_series_obj
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# Check that we work.
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for p in ["shape", "dtype", "T", "nbytes"]:
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assert getattr(obj, p, None) is not None
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# deprecated properties
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for p in ["strides", "itemsize", "base", "data"]:
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assert not hasattr(obj, p)
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msg = "can only convert an array of size 1 to a Python scalar"
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with pytest.raises(ValueError, match=msg):
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obj.item() # len > 1
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assert obj.ndim == 1
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assert obj.size == len(obj)
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assert Index([1]).item() == 1
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assert Series([1]).item() == 1
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def test_array_wrap_compat():
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# Note: at time of dask 2022.01.0, this is still used by eg dask
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# (https://github.com/dask/dask/issues/8580).
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# This test is a small dummy ensuring coverage
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orig = Series([1, 2, 3], dtype="int64", index=["a", "b", "c"])
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with tm.assert_produces_warning(DeprecationWarning):
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result = orig.__array_wrap__(np.array([2, 4, 6], dtype="int64"))
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expected = orig * 2
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tm.assert_series_equal(result, expected)
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@pytest.mark.skipif(PYPY, reason="not relevant for PyPy")
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def test_memory_usage(index_or_series_obj):
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obj = index_or_series_obj
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res = obj.memory_usage()
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res_deep = obj.memory_usage(deep=True)
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is_ser = isinstance(obj, Series)
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is_object = is_object_dtype(obj) or (
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isinstance(obj, Series) and is_object_dtype(obj.index)
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)
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is_categorical = is_categorical_dtype(obj.dtype) or (
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isinstance(obj, Series) and is_categorical_dtype(obj.index.dtype)
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)
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is_object_string = is_dtype_equal(obj, "string[python]") or (
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is_ser and is_dtype_equal(obj.index.dtype, "string[python]")
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)
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if len(obj) == 0:
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if isinstance(obj, Index):
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expected = 0
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else:
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expected = 108 if IS64 else 64
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assert res_deep == res == expected
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elif is_object or is_categorical or is_object_string:
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# only deep will pick them up
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assert res_deep > res
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else:
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assert res == res_deep
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# sys.getsizeof will call the .memory_usage with
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# deep=True, and add on some GC overhead
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diff = res_deep - sys.getsizeof(obj)
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assert abs(diff) < 100
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def test_memory_usage_components_series(series_with_simple_index):
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series = series_with_simple_index
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total_usage = series.memory_usage(index=True)
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non_index_usage = series.memory_usage(index=False)
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index_usage = series.index.memory_usage()
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assert total_usage == non_index_usage + index_usage
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@pytest.mark.parametrize("dtype", tm.NARROW_NP_DTYPES)
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def test_memory_usage_components_narrow_series(dtype):
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series = tm.make_rand_series(name="a", dtype=dtype)
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total_usage = series.memory_usage(index=True)
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non_index_usage = series.memory_usage(index=False)
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index_usage = series.index.memory_usage()
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assert total_usage == non_index_usage + index_usage
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def test_searchsorted(request, index_or_series_obj):
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# numpy.searchsorted calls obj.searchsorted under the hood.
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# See gh-12238
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obj = index_or_series_obj
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if isinstance(obj, pd.MultiIndex):
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# See gh-14833
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request.node.add_marker(
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pytest.mark.xfail(
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reason="np.searchsorted doesn't work on pd.MultiIndex: GH 14833"
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)
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)
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elif obj.dtype.kind == "c" and isinstance(obj, Index):
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# TODO: Should Series cases also raise? Looks like they use numpy
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# comparison semantics https://github.com/numpy/numpy/issues/15981
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mark = pytest.mark.xfail(reason="complex objects are not comparable")
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request.node.add_marker(mark)
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max_obj = max(obj, default=0)
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index = np.searchsorted(obj, max_obj)
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assert 0 <= index <= len(obj)
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index = np.searchsorted(obj, max_obj, sorter=range(len(obj)))
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assert 0 <= index <= len(obj)
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def test_access_by_position(index_flat):
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index = index_flat
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if len(index) == 0:
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pytest.skip("Test doesn't make sense on empty data")
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series = Series(index)
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assert index[0] == series.iloc[0]
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assert index[5] == series.iloc[5]
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assert index[-1] == series.iloc[-1]
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size = len(index)
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assert index[-1] == index[size - 1]
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msg = f"index {size} is out of bounds for axis 0 with size {size}"
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if is_dtype_equal(index.dtype, "string[pyarrow]"):
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msg = "index out of bounds"
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with pytest.raises(IndexError, match=msg):
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index[size]
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msg = "single positional indexer is out-of-bounds"
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with pytest.raises(IndexError, match=msg):
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series.iloc[size]
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