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894 lines
30 KiB
Python
894 lines
30 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 operator
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import numpy as np
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import pytest
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from pandas._config import using_string_dtype
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from pandas.compat import HAS_PYARROW
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from pandas.compat.pyarrow import (
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pa_version_under12p0,
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pa_version_under19p0,
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)
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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_ import StringArrayNumpySemantics
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from pandas.core.arrays.string_arrow import (
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ArrowStringArray,
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ArrowStringArrayNumpySemantics,
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)
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@pytest.fixture
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def dtype(string_dtype_arguments):
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"""Fixture giving StringDtype from parametrized storage and na_value arguments"""
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storage, na_value = string_dtype_arguments
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return pd.StringDtype(storage=storage, na_value=na_value)
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@pytest.fixture
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def dtype2(string_dtype_arguments2):
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storage, na_value = string_dtype_arguments2
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return pd.StringDtype(storage=storage, na_value=na_value)
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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 string_dtype_highest_priority(dtype1, dtype2):
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if HAS_PYARROW:
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DTYPE_HIERARCHY = [
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pd.StringDtype("python", na_value=np.nan),
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pd.StringDtype("pyarrow", na_value=np.nan),
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pd.StringDtype("python", na_value=pd.NA),
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pd.StringDtype("pyarrow", na_value=pd.NA),
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]
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else:
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DTYPE_HIERARCHY = [
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pd.StringDtype("python", na_value=np.nan),
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pd.StringDtype("python", na_value=pd.NA),
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]
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h1 = DTYPE_HIERARCHY.index(dtype1)
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h2 = DTYPE_HIERARCHY.index(dtype2)
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return DTYPE_HIERARCHY[max(h1, h2)]
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def test_dtype_constructor():
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pytest.importorskip("pyarrow")
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with tm.assert_produces_warning(FutureWarning):
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dtype = pd.StringDtype("pyarrow_numpy")
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assert dtype == pd.StringDtype("pyarrow", na_value=np.nan)
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def test_dtype_equality():
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pytest.importorskip("pyarrow")
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dtype1 = pd.StringDtype("python")
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dtype2 = pd.StringDtype("pyarrow")
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dtype3 = pd.StringDtype("pyarrow", na_value=np.nan)
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assert dtype1 == pd.StringDtype("python", na_value=pd.NA)
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assert dtype1 != dtype2
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assert dtype1 != dtype3
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assert dtype2 == pd.StringDtype("pyarrow", na_value=pd.NA)
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assert dtype2 != dtype1
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assert dtype2 != dtype3
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assert dtype3 == pd.StringDtype("pyarrow", na_value=np.nan)
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assert dtype3 == pd.StringDtype("pyarrow", na_value=float("nan"))
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assert dtype3 != dtype1
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assert dtype3 != dtype2
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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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if dtype.na_value is np.nan:
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expected = " A\n0 a\n1 NaN\n2 b"
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else:
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expected = " A\n0 a\n1 <NA>\n2 b"
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assert repr(df) == expected
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if dtype.na_value is np.nan:
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expected = "0 a\n1 NaN\n2 b\nName: A, dtype: str"
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else:
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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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if dtype.storage == "pyarrow" and dtype.na_value is pd.NA:
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arr_name = "ArrowStringArray"
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expected = f"<{arr_name}>\n['a', <NA>, 'b']\nLength: 3, dtype: string"
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elif dtype.storage == "pyarrow" and dtype.na_value is np.nan:
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arr_name = "ArrowStringArrayNumpySemantics"
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expected = f"<{arr_name}>\n['a', nan, 'b']\nLength: 3, dtype: str"
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elif dtype.storage == "python" and dtype.na_value is np.nan:
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arr_name = "StringArrayNumpySemantics"
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expected = f"<{arr_name}>\n['a', nan, 'b']\nLength: 3, dtype: str"
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else:
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arr_name = "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_dtype_repr(dtype):
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if dtype.storage == "pyarrow":
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if dtype.na_value is pd.NA:
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assert repr(dtype) == "string[pyarrow]"
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else:
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assert repr(dtype) == "<StringDtype(na_value=nan)>"
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elif dtype.na_value is pd.NA:
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assert repr(dtype) == "string[python]"
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else:
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assert repr(dtype) == "<StringDtype(storage='python', na_value=nan)>"
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def test_none_to_nan(cls, dtype):
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a = cls._from_sequence(["a", None, "b"], dtype=dtype)
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assert a[1] is not None
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assert a[1] is a.dtype.na_value
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def test_setitem_validates(cls, dtype):
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arr = cls._from_sequence(["a", "b"], dtype=dtype)
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msg = "Invalid value '10' for dtype 'str"
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with pytest.raises(TypeError, match=msg):
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arr[0] = 10
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msg = "Invalid value for dtype '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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# GH#38509 same thing for timedelta64
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ser2 = ser - ser.iloc[-1]
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casted2 = ser2.astype(dtype)
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assert is_dtype_equal(casted2.dtype, dtype)
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result2 = casted2.astype(ser2.dtype)
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tm.assert_series_equal(result2, ser2)
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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.applymarker(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):
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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"]], dtype=object)
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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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if dtype.na_value is np.nan:
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expected = np.array([getattr(item, op_name)(other) for item in a])
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if comparison_op == operator.ne:
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expected[1] = True
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else:
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expected[1] = False
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tm.assert_numpy_array_equal(result, expected.astype(np.bool_))
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else:
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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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if dtype.na_value is np.nan:
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if operator.ne == comparison_op:
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expected = np.array([True, True, True])
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else:
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expected = np.array([False, False, False])
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tm.assert_numpy_array_equal(result, expected)
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else:
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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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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="Invalid comparison|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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if dtype.na_value is np.nan:
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expected_data = {
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"__eq__": [False, False, False],
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"__ne__": [True, True, True],
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}[op_name]
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expected = np.array(expected_data)
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tm.assert_numpy_array_equal(result, expected)
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else:
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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, dtype2):
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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 = pd.array([None, None, "c"], dtype=dtype2)
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result = comparison_op(a, other)
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# ensure operation is commutative
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result2 = comparison_op(other, a)
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tm.assert_equal(result, result2)
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if dtype.na_value is np.nan and dtype2.na_value is np.nan:
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if operator.ne == comparison_op:
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expected = np.array([True, True, False])
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else:
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expected = np.array([False, False, False])
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expected[-1] = getattr(other[-1], op_name)(a[-1])
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tm.assert_numpy_array_equal(result, expected)
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else:
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max_dtype = string_dtype_highest_priority(dtype, dtype2)
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if max_dtype.storage == "python":
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expected_dtype = "boolean"
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else:
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expected_dtype = "bool[pyarrow]"
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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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@td.skip_if_no("pyarrow")
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def test_comparison_methods_array_arrow_extension(comparison_op, dtype2):
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# Test pd.ArrowDtype(pa.string()) against other string arrays
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import pyarrow as pa
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op_name = f"__{comparison_op.__name__}__"
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dtype = pd.ArrowDtype(pa.string())
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a = pd.array(["a", None, "c"], dtype=dtype)
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other = pd.array([None, None, "c"], dtype=dtype2)
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result = comparison_op(a, other)
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# ensure operation is commutative
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result2 = comparison_op(other, a)
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tm.assert_equal(result, result2)
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expected = pd.array([None, None, True], dtype="bool[pyarrow]")
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expected[-1] = getattr(other[-1], op_name)(a[-1])
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tm.assert_extension_array_equal(result, expected)
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def test_comparison_methods_list(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 = comparison_op(a, other)
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# ensure operation is commutative
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result2 = comparison_op(other, a)
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tm.assert_equal(result, result2)
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if dtype.na_value is np.nan:
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if operator.ne == comparison_op:
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expected = np.array([True, True, False])
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else:
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expected = np.array([False, False, False])
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expected[-1] = getattr(other[-1], op_name)(a[-1])
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tm.assert_numpy_array_equal(result, expected)
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else:
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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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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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elif cls is StringArrayNumpySemantics:
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msg = "StringArrayNumpySemantics requires a sequence of strings or NaN"
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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 or cls is StringArrayNumpySemantics:
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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):
|
|
cls(np.array(["a", np.datetime64("NaT", "ns")], dtype=object))
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
cls(np.array(["a", np.timedelta64("NaT", "ns")], dtype=object))
|
|
|
|
|
|
@pytest.mark.parametrize("na", [np.nan, np.float64("nan"), float("nan"), None, pd.NA])
|
|
def test_constructor_nan_like(na):
|
|
expected = pd.arrays.StringArray(np.array(["a", pd.NA]))
|
|
tm.assert_extension_array_equal(
|
|
pd.arrays.StringArray(np.array(["a", na], dtype="object")), expected
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("copy", [True, False])
|
|
def test_from_sequence_no_mutate(copy, cls, dtype):
|
|
nan_arr = np.array(["a", np.nan], dtype=object)
|
|
expected_input = nan_arr.copy()
|
|
na_arr = np.array(["a", pd.NA], dtype=object)
|
|
|
|
result = cls._from_sequence(nan_arr, dtype=dtype, copy=copy)
|
|
|
|
if cls in (ArrowStringArray, ArrowStringArrayNumpySemantics):
|
|
import pyarrow as pa
|
|
|
|
expected = cls(pa.array(na_arr, type=pa.string(), from_pandas=True))
|
|
elif cls is StringArrayNumpySemantics:
|
|
expected = cls(nan_arr)
|
|
else:
|
|
expected = cls(na_arr)
|
|
|
|
tm.assert_extension_array_equal(result, expected)
|
|
tm.assert_numpy_array_equal(nan_arr, expected_input)
|
|
|
|
|
|
def test_astype_int(dtype):
|
|
arr = pd.array(["1", "2", "3"], dtype=dtype)
|
|
result = arr.astype("int64")
|
|
expected = np.array([1, 2, 3], dtype="int64")
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
arr = pd.array(["1", pd.NA, "3"], dtype=dtype)
|
|
if dtype.na_value is np.nan:
|
|
err = ValueError
|
|
msg = "cannot convert float NaN to integer"
|
|
else:
|
|
err = TypeError
|
|
msg = (
|
|
r"int\(\) argument must be a string, a bytes-like "
|
|
r"object or a( real)? number"
|
|
)
|
|
with pytest.raises(err, match=msg):
|
|
arr.astype("int64")
|
|
|
|
|
|
def test_astype_nullable_int(dtype):
|
|
arr = pd.array(["1", pd.NA, "3"], dtype=dtype)
|
|
|
|
result = arr.astype("Int64")
|
|
expected = pd.array([1, pd.NA, 3], dtype="Int64")
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
|
|
def test_astype_float(dtype, any_float_dtype):
|
|
# Don't compare arrays (37974)
|
|
ser = pd.Series(["1.1", pd.NA, "3.3"], dtype=dtype)
|
|
result = ser.astype(any_float_dtype)
|
|
expected = pd.Series([1.1, np.nan, 3.3], dtype=any_float_dtype)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("skipna", [True, False])
|
|
def test_reduce(skipna, dtype):
|
|
arr = pd.Series(["a", "b", "c"], dtype=dtype)
|
|
result = arr.sum(skipna=skipna)
|
|
assert result == "abc"
|
|
|
|
|
|
@pytest.mark.parametrize("skipna", [True, False])
|
|
def test_reduce_missing(skipna, dtype):
|
|
arr = pd.Series([None, "a", None, "b", "c", None], dtype=dtype)
|
|
result = arr.sum(skipna=skipna)
|
|
if skipna:
|
|
assert result == "abc"
|
|
else:
|
|
assert pd.isna(result)
|
|
|
|
|
|
@pytest.mark.parametrize("method", ["min", "max"])
|
|
@pytest.mark.parametrize("skipna", [True, False])
|
|
def test_min_max(method, skipna, dtype):
|
|
arr = pd.Series(["a", "b", "c", None], dtype=dtype)
|
|
result = getattr(arr, method)(skipna=skipna)
|
|
if skipna:
|
|
expected = "a" if method == "min" else "c"
|
|
assert result == expected
|
|
else:
|
|
assert result is arr.dtype.na_value
|
|
|
|
|
|
@pytest.mark.parametrize("method", ["min", "max"])
|
|
@pytest.mark.parametrize("box", [pd.Series, pd.array])
|
|
def test_min_max_numpy(method, box, dtype, request):
|
|
if dtype.storage == "pyarrow" and box is pd.array:
|
|
if box is pd.array:
|
|
reason = "'<=' not supported between instances of 'str' and 'NoneType'"
|
|
else:
|
|
reason = "'ArrowStringArray' object has no attribute 'max'"
|
|
mark = pytest.mark.xfail(raises=TypeError, reason=reason)
|
|
request.applymarker(mark)
|
|
|
|
arr = box(["a", "b", "c", None], dtype=dtype)
|
|
result = getattr(np, method)(arr)
|
|
expected = "a" if method == "min" else "c"
|
|
assert result == expected
|
|
|
|
|
|
def test_fillna_args(dtype):
|
|
# GH 37987
|
|
|
|
arr = pd.array(["a", pd.NA], dtype=dtype)
|
|
|
|
res = arr.fillna(value="b")
|
|
expected = pd.array(["a", "b"], dtype=dtype)
|
|
tm.assert_extension_array_equal(res, expected)
|
|
|
|
res = arr.fillna(value=np.str_("b"))
|
|
expected = pd.array(["a", "b"], dtype=dtype)
|
|
tm.assert_extension_array_equal(res, expected)
|
|
|
|
msg = "Invalid value '1' for dtype 'str"
|
|
with pytest.raises(TypeError, match=msg):
|
|
arr.fillna(value=1)
|
|
|
|
|
|
def test_arrow_array(dtype):
|
|
# protocol added in 0.15.0
|
|
pa = pytest.importorskip("pyarrow")
|
|
import pyarrow.compute as pc
|
|
|
|
data = pd.array(["a", "b", "c"], dtype=dtype)
|
|
arr = pa.array(data)
|
|
expected = pa.array(list(data), type=pa.large_string(), from_pandas=True)
|
|
if dtype.storage == "pyarrow" and pa_version_under12p0:
|
|
expected = pa.chunked_array(expected)
|
|
if dtype.storage == "python":
|
|
expected = pc.cast(expected, pa.string())
|
|
assert arr.equals(expected)
|
|
|
|
|
|
@pytest.mark.filterwarnings("ignore:Passing a BlockManager:DeprecationWarning")
|
|
def test_arrow_roundtrip(dtype, string_storage, using_infer_string):
|
|
# roundtrip possible from arrow 1.0.0
|
|
pa = pytest.importorskip("pyarrow")
|
|
|
|
data = pd.array(["a", "b", None], dtype=dtype)
|
|
df = pd.DataFrame({"a": data})
|
|
table = pa.table(df)
|
|
if dtype.storage == "python":
|
|
assert table.field("a").type == "string"
|
|
else:
|
|
assert table.field("a").type == "large_string"
|
|
with pd.option_context("string_storage", string_storage):
|
|
result = table.to_pandas()
|
|
if dtype.na_value is np.nan and not using_infer_string:
|
|
assert result["a"].dtype == "object"
|
|
else:
|
|
assert isinstance(result["a"].dtype, pd.StringDtype)
|
|
expected = df.astype(pd.StringDtype(string_storage, na_value=dtype.na_value))
|
|
if using_infer_string:
|
|
expected.columns = expected.columns.astype(
|
|
pd.StringDtype(string_storage, na_value=np.nan)
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
# ensure the missing value is represented by NA and not np.nan or None
|
|
assert result.loc[2, "a"] is result["a"].dtype.na_value
|
|
|
|
|
|
@pytest.mark.filterwarnings("ignore:Passing a BlockManager:DeprecationWarning")
|
|
def test_arrow_from_string(using_infer_string):
|
|
# not roundtrip, but starting with pyarrow table without pandas metadata
|
|
pa = pytest.importorskip("pyarrow")
|
|
table = pa.table({"a": pa.array(["a", "b", None], type=pa.string())})
|
|
|
|
result = table.to_pandas()
|
|
|
|
if using_infer_string and not pa_version_under19p0:
|
|
expected = pd.DataFrame({"a": ["a", "b", None]}, dtype="str")
|
|
else:
|
|
expected = pd.DataFrame({"a": ["a", "b", None]}, dtype="object")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.filterwarnings("ignore:Passing a BlockManager:DeprecationWarning")
|
|
def test_arrow_load_from_zero_chunks(dtype, string_storage, using_infer_string):
|
|
# GH-41040
|
|
pa = pytest.importorskip("pyarrow")
|
|
|
|
data = pd.array([], dtype=dtype)
|
|
df = pd.DataFrame({"a": data})
|
|
table = pa.table(df)
|
|
if dtype.storage == "python":
|
|
assert table.field("a").type == "string"
|
|
else:
|
|
assert table.field("a").type == "large_string"
|
|
# Instantiate the same table with no chunks at all
|
|
table = pa.table([pa.chunked_array([], type=pa.string())], schema=table.schema)
|
|
with pd.option_context("string_storage", string_storage):
|
|
result = table.to_pandas()
|
|
|
|
if dtype.na_value is np.nan and not using_string_dtype():
|
|
assert result["a"].dtype == "object"
|
|
else:
|
|
assert isinstance(result["a"].dtype, pd.StringDtype)
|
|
expected = df.astype(pd.StringDtype(string_storage, na_value=dtype.na_value))
|
|
if using_infer_string:
|
|
expected.columns = expected.columns.astype(
|
|
pd.StringDtype(string_storage, na_value=np.nan)
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_value_counts_na(dtype):
|
|
if dtype.na_value is np.nan:
|
|
exp_dtype = "int64"
|
|
elif 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 dtype.na_value is np.nan:
|
|
exp_dtype = np.float64
|
|
elif 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)
|
|
msg = "use_inf_as_na option is deprecated"
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
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_value_counts_sort_false(dtype):
|
|
if dtype.na_value is np.nan:
|
|
exp_dtype = "int64"
|
|
elif dtype.storage == "pyarrow":
|
|
exp_dtype = "int64[pyarrow]"
|
|
else:
|
|
exp_dtype = "Int64"
|
|
ser = pd.Series(["a", "b", "c", "b"], dtype=dtype)
|
|
result = ser.value_counts(sort=False)
|
|
expected = pd.Series([1, 2, 1], index=ser[:3], dtype=exp_dtype, name="count")
|
|
tm.assert_series_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", dtype.na_value, "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)
|
|
|
|
result = s.isin([fixed_now_ts])
|
|
expected = pd.Series([False, False, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_isin_string_array(dtype, dtype2):
|
|
s = pd.Series(["a", "b", None], dtype=dtype)
|
|
|
|
result = s.isin(pd.array(["a", "c"], dtype=dtype2))
|
|
expected = pd.Series([True, False, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s.isin(pd.array(["a", None], dtype=dtype2))
|
|
expected = pd.Series([True, False, True])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_isin_arrow_string_array(dtype):
|
|
pa = pytest.importorskip("pyarrow")
|
|
s = pd.Series(["a", "b", None], dtype=dtype)
|
|
|
|
result = s.isin(pd.array(["a", "c"], dtype=pd.ArrowDtype(pa.string())))
|
|
expected = pd.Series([True, False, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s.isin(pd.array(["a", None], dtype=pd.ArrowDtype(pa.string())))
|
|
expected = pd.Series([True, False, True])
|
|
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 ser.dtype.na_value
|
|
|
|
# for other non-string we should also raise an error
|
|
ser = pd.Series(["a", "b", "c"], dtype=dtype)
|
|
msg = "Invalid value '1' for dtype '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)
|
|
|
|
|
|
@pytest.mark.parametrize("box", [pd.Series, pd.array])
|
|
def test_numpy_array_ufunc(dtype, box):
|
|
arr = box(["a", "bb", "ccc"], dtype=dtype)
|
|
|
|
# custom ufunc that works with string (object) input -> returning numeric
|
|
str_len_ufunc = np.frompyfunc(lambda x: len(x), 1, 1)
|
|
result = str_len_ufunc(arr)
|
|
expected_cls = pd.Series if box is pd.Series else np.array
|
|
# TODO we should infer int64 dtype here?
|
|
expected = expected_cls([1, 2, 3], dtype=object)
|
|
tm.assert_equal(result, expected)
|
|
|
|
# custom ufunc returning strings
|
|
str_multiply_ufunc = np.frompyfunc(lambda x: x * 2, 1, 1)
|
|
result = str_multiply_ufunc(arr)
|
|
expected = box(["aa", "bbbb", "cccccc"], dtype=dtype)
|
|
if dtype.storage == "pyarrow":
|
|
# TODO ArrowStringArray should also preserve the class / dtype
|
|
if box is pd.array:
|
|
expected = np.array(["aa", "bbbb", "cccccc"], dtype=object)
|
|
else:
|
|
# not specifying the dtype because the exact dtype is not yet preserved
|
|
expected = pd.Series(["aa", "bbbb", "cccccc"])
|
|
|
|
tm.assert_equal(result, expected)
|