597 lines
20 KiB
Python
597 lines
20 KiB
Python
"""
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This module tests the functionality of StringArray and ArrowStringArray.
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Tests for the str accessors are in pandas/tests/strings/test_string_array.py
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"""
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import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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from pandas.core.dtypes.common import is_dtype_equal
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.arrays.string_arrow import ArrowStringArray
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from pandas.util.version import Version
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@pytest.fixture
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def dtype(string_storage):
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"""Fixture giving StringDtype from parametrized 'string_storage'"""
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return pd.StringDtype(storage=string_storage)
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@pytest.fixture
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def cls(dtype):
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"""Fixture giving array type from parametrized 'dtype'"""
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return dtype.construct_array_type()
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def test_repr(dtype):
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df = pd.DataFrame({"A": pd.array(["a", pd.NA, "b"], dtype=dtype)})
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expected = " A\n0 a\n1 <NA>\n2 b"
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assert repr(df) == expected
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expected = "0 a\n1 <NA>\n2 b\nName: A, dtype: string"
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assert repr(df.A) == expected
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arr_name = "ArrowStringArray" if dtype.storage == "pyarrow" else "StringArray"
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expected = f"<{arr_name}>\n['a', <NA>, 'b']\nLength: 3, dtype: string"
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assert repr(df.A.array) == expected
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def test_none_to_nan(cls):
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a = cls._from_sequence(["a", None, "b"])
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assert a[1] is not None
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assert a[1] is pd.NA
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def test_setitem_validates(cls):
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arr = cls._from_sequence(["a", "b"])
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if cls is pd.arrays.StringArray:
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msg = "Cannot set non-string value '10' into a StringArray."
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else:
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msg = "Scalar must be NA or str"
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with pytest.raises(TypeError, match=msg):
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arr[0] = 10
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if cls is pd.arrays.StringArray:
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msg = "Must provide strings."
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else:
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msg = "Scalar must be NA or str"
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with pytest.raises(TypeError, match=msg):
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arr[:] = np.array([1, 2])
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def test_setitem_with_scalar_string(dtype):
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# is_float_dtype considers some strings, like 'd', to be floats
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# which can cause issues.
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arr = pd.array(["a", "c"], dtype=dtype)
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arr[0] = "d"
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expected = pd.array(["d", "c"], dtype=dtype)
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tm.assert_extension_array_equal(arr, expected)
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def test_setitem_with_array_with_missing(dtype):
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# ensure that when setting with an array of values, we don't mutate the
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# array `value` in __setitem__(self, key, value)
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arr = pd.array(["a", "b", "c"], dtype=dtype)
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value = np.array(["A", None])
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value_orig = value.copy()
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arr[[0, 1]] = value
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expected = pd.array(["A", pd.NA, "c"], dtype=dtype)
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tm.assert_extension_array_equal(arr, expected)
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tm.assert_numpy_array_equal(value, value_orig)
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def test_astype_roundtrip(dtype):
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ser = pd.Series(pd.date_range("2000", periods=12))
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ser[0] = None
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casted = ser.astype(dtype)
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assert is_dtype_equal(casted.dtype, dtype)
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result = casted.astype("datetime64[ns]")
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tm.assert_series_equal(result, ser)
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def test_add(dtype):
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a = pd.Series(["a", "b", "c", None, None], dtype=dtype)
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b = pd.Series(["x", "y", None, "z", None], dtype=dtype)
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result = a + b
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expected = pd.Series(["ax", "by", None, None, None], dtype=dtype)
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tm.assert_series_equal(result, expected)
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result = a.add(b)
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tm.assert_series_equal(result, expected)
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result = a.radd(b)
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expected = pd.Series(["xa", "yb", None, None, None], dtype=dtype)
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tm.assert_series_equal(result, expected)
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result = a.add(b, fill_value="-")
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expected = pd.Series(["ax", "by", "c-", "-z", None], dtype=dtype)
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tm.assert_series_equal(result, expected)
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def test_add_2d(dtype, request):
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if dtype.storage == "pyarrow":
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reason = "Failed: DID NOT RAISE <class 'ValueError'>"
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mark = pytest.mark.xfail(raises=None, reason=reason)
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request.node.add_marker(mark)
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a = pd.array(["a", "b", "c"], dtype=dtype)
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b = np.array([["a", "b", "c"]], dtype=object)
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with pytest.raises(ValueError, match="3 != 1"):
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a + b
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s = pd.Series(a)
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with pytest.raises(ValueError, match="3 != 1"):
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s + b
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def test_add_sequence(dtype):
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a = pd.array(["a", "b", None, None], dtype=dtype)
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other = ["x", None, "y", None]
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result = a + other
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expected = pd.array(["ax", None, None, None], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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result = other + a
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expected = pd.array(["xa", None, None, None], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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def test_mul(dtype, request):
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if dtype.storage == "pyarrow":
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reason = "unsupported operand type(s) for *: 'ArrowStringArray' and 'int'"
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mark = pytest.mark.xfail(raises=NotImplementedError, reason=reason)
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request.node.add_marker(mark)
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a = pd.array(["a", "b", None], dtype=dtype)
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result = a * 2
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expected = pd.array(["aa", "bb", None], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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result = 2 * a
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.xfail(reason="GH-28527")
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def test_add_strings(dtype):
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arr = pd.array(["a", "b", "c", "d"], dtype=dtype)
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df = pd.DataFrame([["t", "y", "v", "w"]])
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assert arr.__add__(df) is NotImplemented
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result = arr + df
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expected = pd.DataFrame([["at", "by", "cv", "dw"]]).astype(dtype)
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tm.assert_frame_equal(result, expected)
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result = df + arr
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expected = pd.DataFrame([["ta", "yb", "vc", "wd"]]).astype(dtype)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.xfail(reason="GH-28527")
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def test_add_frame(dtype):
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arr = pd.array(["a", "b", np.nan, np.nan], dtype=dtype)
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df = pd.DataFrame([["x", np.nan, "y", np.nan]])
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assert arr.__add__(df) is NotImplemented
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result = arr + df
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expected = pd.DataFrame([["ax", np.nan, np.nan, np.nan]]).astype(dtype)
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tm.assert_frame_equal(result, expected)
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result = df + arr
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expected = pd.DataFrame([["xa", np.nan, np.nan, np.nan]]).astype(dtype)
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tm.assert_frame_equal(result, expected)
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def test_comparison_methods_scalar(comparison_op, dtype):
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op_name = f"__{comparison_op.__name__}__"
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a = pd.array(["a", None, "c"], dtype=dtype)
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other = "a"
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result = getattr(a, op_name)(other)
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expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean"
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expected = np.array([getattr(item, op_name)(other) for item in a], dtype=object)
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expected = pd.array(expected, dtype=expected_dtype)
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tm.assert_extension_array_equal(result, expected)
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def test_comparison_methods_scalar_pd_na(comparison_op, dtype):
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op_name = f"__{comparison_op.__name__}__"
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a = pd.array(["a", None, "c"], dtype=dtype)
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result = getattr(a, op_name)(pd.NA)
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expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean"
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expected = pd.array([None, None, None], dtype=expected_dtype)
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tm.assert_extension_array_equal(result, expected)
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def test_comparison_methods_scalar_not_string(comparison_op, dtype):
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op_name = f"__{comparison_op.__name__}__"
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a = pd.array(["a", None, "c"], dtype=dtype)
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other = 42
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if op_name not in ["__eq__", "__ne__"]:
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with pytest.raises(TypeError, match="not supported between"):
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getattr(a, op_name)(other)
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return
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result = getattr(a, op_name)(other)
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expected_data = {"__eq__": [False, None, False], "__ne__": [True, None, True]}[
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op_name
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]
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expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean"
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expected = pd.array(expected_data, dtype=expected_dtype)
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tm.assert_extension_array_equal(result, expected)
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def test_comparison_methods_array(comparison_op, dtype):
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op_name = f"__{comparison_op.__name__}__"
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a = pd.array(["a", None, "c"], dtype=dtype)
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other = [None, None, "c"]
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result = getattr(a, op_name)(other)
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expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean"
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expected = np.full(len(a), fill_value=None, dtype="object")
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expected[-1] = getattr(other[-1], op_name)(a[-1])
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expected = pd.array(expected, dtype=expected_dtype)
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tm.assert_extension_array_equal(result, expected)
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result = getattr(a, op_name)(pd.NA)
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expected = pd.array([None, None, None], dtype=expected_dtype)
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tm.assert_extension_array_equal(result, expected)
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def test_constructor_raises(cls):
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if cls is pd.arrays.StringArray:
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msg = "StringArray requires a sequence of strings or pandas.NA"
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else:
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msg = "Unsupported type '<class 'numpy.ndarray'>' for ArrowExtensionArray"
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with pytest.raises(ValueError, match=msg):
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cls(np.array(["a", "b"], dtype="S1"))
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with pytest.raises(ValueError, match=msg):
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cls(np.array([]))
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if cls is pd.arrays.StringArray:
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# GH#45057 np.nan and None do NOT raise, as they are considered valid NAs
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# for string dtype
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cls(np.array(["a", np.nan], dtype=object))
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cls(np.array(["a", None], dtype=object))
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else:
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with pytest.raises(ValueError, match=msg):
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cls(np.array(["a", np.nan], dtype=object))
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with pytest.raises(ValueError, match=msg):
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cls(np.array(["a", None], dtype=object))
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with pytest.raises(ValueError, match=msg):
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cls(np.array(["a", pd.NaT], dtype=object))
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with pytest.raises(ValueError, match=msg):
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cls(np.array(["a", np.datetime64("NaT", "ns")], dtype=object))
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with pytest.raises(ValueError, match=msg):
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cls(np.array(["a", np.timedelta64("NaT", "ns")], dtype=object))
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@pytest.mark.parametrize("na", [np.nan, np.float64("nan"), float("nan"), None, pd.NA])
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def test_constructor_nan_like(na):
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expected = pd.arrays.StringArray(np.array(["a", pd.NA]))
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tm.assert_extension_array_equal(
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pd.arrays.StringArray(np.array(["a", na], dtype="object")), expected
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)
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@pytest.mark.parametrize("copy", [True, False])
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def test_from_sequence_no_mutate(copy, cls, request):
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nan_arr = np.array(["a", np.nan], dtype=object)
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expected_input = nan_arr.copy()
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na_arr = np.array(["a", pd.NA], dtype=object)
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result = cls._from_sequence(nan_arr, copy=copy)
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if cls is ArrowStringArray:
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import pyarrow as pa
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expected = cls(pa.array(na_arr, type=pa.string(), from_pandas=True))
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else:
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expected = cls(na_arr)
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tm.assert_extension_array_equal(result, expected)
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tm.assert_numpy_array_equal(nan_arr, expected_input)
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def test_astype_int(dtype):
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arr = pd.array(["1", "2", "3"], dtype=dtype)
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result = arr.astype("int64")
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expected = np.array([1, 2, 3], dtype="int64")
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tm.assert_numpy_array_equal(result, expected)
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arr = pd.array(["1", pd.NA, "3"], dtype=dtype)
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msg = r"int\(\) argument must be a string, a bytes-like object or a( real)? number"
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with pytest.raises(TypeError, match=msg):
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arr.astype("int64")
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def test_astype_nullable_int(dtype):
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arr = pd.array(["1", pd.NA, "3"], dtype=dtype)
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result = arr.astype("Int64")
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expected = pd.array([1, pd.NA, 3], dtype="Int64")
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tm.assert_extension_array_equal(result, expected)
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def test_astype_float(dtype, any_float_dtype):
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# Don't compare arrays (37974)
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ser = pd.Series(["1.1", pd.NA, "3.3"], dtype=dtype)
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result = ser.astype(any_float_dtype)
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expected = pd.Series([1.1, np.nan, 3.3], dtype=any_float_dtype)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("skipna", [True, False])
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@pytest.mark.xfail(reason="Not implemented StringArray.sum")
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def test_reduce(skipna, dtype):
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arr = pd.Series(["a", "b", "c"], dtype=dtype)
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result = arr.sum(skipna=skipna)
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assert result == "abc"
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@pytest.mark.parametrize("skipna", [True, False])
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@pytest.mark.xfail(reason="Not implemented StringArray.sum")
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def test_reduce_missing(skipna, dtype):
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arr = pd.Series([None, "a", None, "b", "c", None], dtype=dtype)
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result = arr.sum(skipna=skipna)
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if skipna:
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assert result == "abc"
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else:
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assert pd.isna(result)
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@pytest.mark.parametrize("method", ["min", "max"])
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@pytest.mark.parametrize("skipna", [True, False])
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def test_min_max(method, skipna, dtype, request):
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arr = pd.Series(["a", "b", "c", None], dtype=dtype)
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result = getattr(arr, method)(skipna=skipna)
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if skipna:
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expected = "a" if method == "min" else "c"
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assert result == expected
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else:
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assert result is pd.NA
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@pytest.mark.parametrize("method", ["min", "max"])
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@pytest.mark.parametrize("box", [pd.Series, pd.array])
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def test_min_max_numpy(method, box, dtype, request):
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if dtype.storage == "pyarrow" and box is pd.array:
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if box is pd.array:
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reason = "'<=' not supported between instances of 'str' and 'NoneType'"
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else:
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reason = "'ArrowStringArray' object has no attribute 'max'"
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mark = pytest.mark.xfail(raises=TypeError, reason=reason)
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request.node.add_marker(mark)
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arr = box(["a", "b", "c", None], dtype=dtype)
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result = getattr(np, method)(arr)
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expected = "a" if method == "min" else "c"
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assert result == expected
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def test_fillna_args(dtype, request):
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# GH 37987
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arr = pd.array(["a", pd.NA], dtype=dtype)
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res = arr.fillna(value="b")
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expected = pd.array(["a", "b"], dtype=dtype)
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tm.assert_extension_array_equal(res, expected)
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res = arr.fillna(value=np.str_("b"))
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expected = pd.array(["a", "b"], dtype=dtype)
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tm.assert_extension_array_equal(res, expected)
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if dtype.storage == "pyarrow":
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msg = "Invalid value '1' for dtype string"
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else:
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msg = "Cannot set non-string value '1' into a StringArray."
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with pytest.raises(TypeError, match=msg):
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arr.fillna(value=1)
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def test_arrow_array(dtype):
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# protocol added in 0.15.0
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pa = pytest.importorskip("pyarrow")
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data = pd.array(["a", "b", "c"], dtype=dtype)
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arr = pa.array(data)
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expected = pa.array(list(data), type=pa.string(), from_pandas=True)
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if dtype.storage == "pyarrow" and Version(pa.__version__) <= Version("11.0.0"):
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expected = pa.chunked_array(expected)
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assert arr.equals(expected)
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@td.skip_if_no("pyarrow")
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def test_arrow_roundtrip(dtype, string_storage2):
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# roundtrip possible from arrow 1.0.0
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import pyarrow as pa
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data = pd.array(["a", "b", None], dtype=dtype)
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df = pd.DataFrame({"a": data})
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table = pa.table(df)
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assert table.field("a").type == "string"
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with pd.option_context("string_storage", string_storage2):
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result = table.to_pandas()
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assert isinstance(result["a"].dtype, pd.StringDtype)
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expected = df.astype(f"string[{string_storage2}]")
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tm.assert_frame_equal(result, expected)
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# ensure the missing value is represented by NA and not np.nan or None
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assert result.loc[2, "a"] is pd.NA
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@td.skip_if_no("pyarrow")
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def test_arrow_load_from_zero_chunks(dtype, string_storage2):
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# GH-41040
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import pyarrow as pa
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data = pd.array([], dtype=dtype)
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df = pd.DataFrame({"a": data})
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table = pa.table(df)
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assert table.field("a").type == "string"
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# Instantiate the same table with no chunks at all
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table = pa.table([pa.chunked_array([], type=pa.string())], schema=table.schema)
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with pd.option_context("string_storage", string_storage2):
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result = table.to_pandas()
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assert isinstance(result["a"].dtype, pd.StringDtype)
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expected = df.astype(f"string[{string_storage2}]")
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tm.assert_frame_equal(result, expected)
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|
|
|
|
|
def test_value_counts_na(dtype):
|
|
if getattr(dtype, "storage", "") == "pyarrow":
|
|
exp_dtype = "int64[pyarrow]"
|
|
else:
|
|
exp_dtype = "Int64"
|
|
arr = pd.array(["a", "b", "a", pd.NA], dtype=dtype)
|
|
result = arr.value_counts(dropna=False)
|
|
expected = pd.Series([2, 1, 1], index=arr[[0, 1, 3]], dtype=exp_dtype, name="count")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = arr.value_counts(dropna=True)
|
|
expected = pd.Series([2, 1], index=arr[:2], dtype=exp_dtype, name="count")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_value_counts_with_normalize(dtype):
|
|
if getattr(dtype, "storage", "") == "pyarrow":
|
|
exp_dtype = "double[pyarrow]"
|
|
else:
|
|
exp_dtype = "Float64"
|
|
ser = pd.Series(["a", "b", "a", pd.NA], dtype=dtype)
|
|
result = ser.value_counts(normalize=True)
|
|
expected = pd.Series([2, 1], index=ser[:2], dtype=exp_dtype, name="proportion") / 3
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"values, expected",
|
|
[
|
|
(["a", "b", "c"], np.array([False, False, False])),
|
|
(["a", "b", None], np.array([False, False, True])),
|
|
],
|
|
)
|
|
def test_use_inf_as_na(values, expected, dtype):
|
|
# https://github.com/pandas-dev/pandas/issues/33655
|
|
values = pd.array(values, dtype=dtype)
|
|
with pd.option_context("mode.use_inf_as_na", True):
|
|
result = values.isna()
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
result = pd.Series(values).isna()
|
|
expected = pd.Series(expected)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = pd.DataFrame(values).isna()
|
|
expected = pd.DataFrame(expected)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_memory_usage(dtype):
|
|
# GH 33963
|
|
|
|
if dtype.storage == "pyarrow":
|
|
pytest.skip(f"not applicable for {dtype.storage}")
|
|
|
|
series = pd.Series(["a", "b", "c"], dtype=dtype)
|
|
|
|
assert 0 < series.nbytes <= series.memory_usage() < series.memory_usage(deep=True)
|
|
|
|
|
|
@pytest.mark.parametrize("float_dtype", [np.float16, np.float32, np.float64])
|
|
def test_astype_from_float_dtype(float_dtype, dtype):
|
|
# https://github.com/pandas-dev/pandas/issues/36451
|
|
ser = pd.Series([0.1], dtype=float_dtype)
|
|
result = ser.astype(dtype)
|
|
expected = pd.Series(["0.1"], dtype=dtype)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_to_numpy_returns_pdna_default(dtype):
|
|
arr = pd.array(["a", pd.NA, "b"], dtype=dtype)
|
|
result = np.array(arr)
|
|
expected = np.array(["a", pd.NA, "b"], dtype=object)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
|
|
def test_to_numpy_na_value(dtype, nulls_fixture):
|
|
na_value = nulls_fixture
|
|
arr = pd.array(["a", pd.NA, "b"], dtype=dtype)
|
|
result = arr.to_numpy(na_value=na_value)
|
|
expected = np.array(["a", na_value, "b"], dtype=object)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
|
|
def test_isin(dtype, fixed_now_ts):
|
|
s = pd.Series(["a", "b", None], dtype=dtype)
|
|
|
|
result = s.isin(["a", "c"])
|
|
expected = pd.Series([True, False, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s.isin(["a", pd.NA])
|
|
expected = pd.Series([True, False, True])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s.isin([])
|
|
expected = pd.Series([False, False, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s.isin(["a", fixed_now_ts])
|
|
expected = pd.Series([True, False, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_setitem_scalar_with_mask_validation(dtype):
|
|
# https://github.com/pandas-dev/pandas/issues/47628
|
|
# setting None with a boolean mask (through _putmaks) should still result
|
|
# in pd.NA values in the underlying array
|
|
ser = pd.Series(["a", "b", "c"], dtype=dtype)
|
|
mask = np.array([False, True, False])
|
|
|
|
ser[mask] = None
|
|
assert ser.array[1] is pd.NA
|
|
|
|
# for other non-string we should also raise an error
|
|
ser = pd.Series(["a", "b", "c"], dtype=dtype)
|
|
if type(ser.array) is pd.arrays.StringArray:
|
|
msg = "Cannot set non-string value"
|
|
else:
|
|
msg = "Scalar must be NA or str"
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser[mask] = 1
|
|
|
|
|
|
def test_from_numpy_str(dtype):
|
|
vals = ["a", "b", "c"]
|
|
arr = np.array(vals, dtype=np.str_)
|
|
result = pd.array(arr, dtype=dtype)
|
|
expected = pd.array(vals, dtype=dtype)
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
|
|
def test_tolist(dtype):
|
|
vals = ["a", "b", "c"]
|
|
arr = pd.array(vals, dtype=dtype)
|
|
result = arr.tolist()
|
|
expected = vals
|
|
tm.assert_equal(result, expected)
|