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231 lines
7.1 KiB
231 lines
7.1 KiB
import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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from pandas.api.types import is_integer
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from pandas.core.arrays import IntegerArray
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from pandas.core.arrays.integer import (
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Int8Dtype,
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Int32Dtype,
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Int64Dtype,
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)
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@pytest.fixture(params=[pd.array, IntegerArray._from_sequence])
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def constructor(request):
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return request.param
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def test_uses_pandas_na():
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a = pd.array([1, None], dtype=Int64Dtype())
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assert a[1] is pd.NA
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def test_from_dtype_from_float(data):
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# construct from our dtype & string dtype
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dtype = data.dtype
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# from float
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expected = pd.Series(data)
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result = pd.Series(data.to_numpy(na_value=np.nan, dtype="float"), dtype=str(dtype))
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tm.assert_series_equal(result, expected)
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# from int / list
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expected = pd.Series(data)
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result = pd.Series(np.array(data).tolist(), dtype=str(dtype))
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tm.assert_series_equal(result, expected)
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# from int / array
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expected = pd.Series(data).dropna().reset_index(drop=True)
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dropped = np.array(data.dropna()).astype(np.dtype(dtype.type))
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result = pd.Series(dropped, dtype=str(dtype))
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tm.assert_series_equal(result, expected)
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def test_conversions(data_missing):
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# astype to object series
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df = pd.DataFrame({"A": data_missing})
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result = df["A"].astype("object")
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expected = pd.Series(np.array([np.nan, 1], dtype=object), name="A")
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tm.assert_series_equal(result, expected)
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# convert to object ndarray
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# we assert that we are exactly equal
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# including type conversions of scalars
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result = df["A"].astype("object").values
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expected = np.array([pd.NA, 1], dtype=object)
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tm.assert_numpy_array_equal(result, expected)
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for r, e in zip(result, expected):
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if pd.isnull(r):
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assert pd.isnull(e)
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elif is_integer(r):
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assert r == e
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assert is_integer(e)
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else:
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assert r == e
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assert type(r) == type(e)
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def test_integer_array_constructor():
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values = np.array([1, 2, 3, 4], dtype="int64")
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mask = np.array([False, False, False, True], dtype="bool")
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result = IntegerArray(values, mask)
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expected = pd.array([1, 2, 3, np.nan], dtype="Int64")
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tm.assert_extension_array_equal(result, expected)
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msg = r".* should be .* numpy array. Use the 'pd.array' function instead"
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with pytest.raises(TypeError, match=msg):
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IntegerArray(values.tolist(), mask)
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with pytest.raises(TypeError, match=msg):
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IntegerArray(values, mask.tolist())
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with pytest.raises(TypeError, match=msg):
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IntegerArray(values.astype(float), mask)
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msg = r"__init__\(\) missing 1 required positional argument: 'mask'"
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with pytest.raises(TypeError, match=msg):
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IntegerArray(values)
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def test_integer_array_constructor_copy():
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values = np.array([1, 2, 3, 4], dtype="int64")
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mask = np.array([False, False, False, True], dtype="bool")
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result = IntegerArray(values, mask)
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assert result._data is values
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assert result._mask is mask
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result = IntegerArray(values, mask, copy=True)
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assert result._data is not values
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assert result._mask is not mask
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@pytest.mark.parametrize(
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"a, b",
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[
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([1, None], [1, np.nan]),
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([None], [np.nan]),
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([None, np.nan], [np.nan, np.nan]),
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([np.nan, np.nan], [np.nan, np.nan]),
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],
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)
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def test_to_integer_array_none_is_nan(a, b):
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result = pd.array(a, dtype="Int64")
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expected = pd.array(b, dtype="Int64")
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize(
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"values",
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[
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["foo", "bar"],
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"foo",
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1,
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1.0,
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pd.date_range("20130101", periods=2),
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np.array(["foo"]),
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[[1, 2], [3, 4]],
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[np.nan, {"a": 1}],
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],
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)
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def test_to_integer_array_error(values):
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# error in converting existing arrays to IntegerArrays
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msg = "|".join(
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[
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r"cannot be converted to an IntegerDtype",
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r"invalid literal for int\(\) with base 10:",
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r"values must be a 1D list-like",
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r"Cannot pass scalar",
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]
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)
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with pytest.raises((ValueError, TypeError), match=msg):
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pd.array(values, dtype="Int64")
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with pytest.raises((ValueError, TypeError), match=msg):
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IntegerArray._from_sequence(values)
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def test_to_integer_array_inferred_dtype(constructor):
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# if values has dtype -> respect it
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result = constructor(np.array([1, 2], dtype="int8"))
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assert result.dtype == Int8Dtype()
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result = constructor(np.array([1, 2], dtype="int32"))
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assert result.dtype == Int32Dtype()
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# if values have no dtype -> always int64
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result = constructor([1, 2])
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assert result.dtype == Int64Dtype()
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def test_to_integer_array_dtype_keyword(constructor):
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result = constructor([1, 2], dtype="Int8")
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assert result.dtype == Int8Dtype()
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# if values has dtype -> override it
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result = constructor(np.array([1, 2], dtype="int8"), dtype="Int32")
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assert result.dtype == Int32Dtype()
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def test_to_integer_array_float():
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result = IntegerArray._from_sequence([1.0, 2.0])
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expected = pd.array([1, 2], dtype="Int64")
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tm.assert_extension_array_equal(result, expected)
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with pytest.raises(TypeError, match="cannot safely cast non-equivalent"):
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IntegerArray._from_sequence([1.5, 2.0])
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# for float dtypes, the itemsize is not preserved
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result = IntegerArray._from_sequence(np.array([1.0, 2.0], dtype="float32"))
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assert result.dtype == Int64Dtype()
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def test_to_integer_array_str():
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result = IntegerArray._from_sequence(["1", "2", None])
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expected = pd.array([1, 2, np.nan], dtype="Int64")
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tm.assert_extension_array_equal(result, expected)
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with pytest.raises(
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ValueError, match=r"invalid literal for int\(\) with base 10: .*"
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):
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IntegerArray._from_sequence(["1", "2", ""])
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with pytest.raises(
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ValueError, match=r"invalid literal for int\(\) with base 10: .*"
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):
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IntegerArray._from_sequence(["1.5", "2.0"])
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@pytest.mark.parametrize(
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"bool_values, int_values, target_dtype, expected_dtype",
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[
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([False, True], [0, 1], Int64Dtype(), Int64Dtype()),
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([False, True], [0, 1], "Int64", Int64Dtype()),
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([False, True, np.nan], [0, 1, np.nan], Int64Dtype(), Int64Dtype()),
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],
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)
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def test_to_integer_array_bool(
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constructor, bool_values, int_values, target_dtype, expected_dtype
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):
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result = constructor(bool_values, dtype=target_dtype)
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assert result.dtype == expected_dtype
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expected = pd.array(int_values, dtype=target_dtype)
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize(
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"values, to_dtype, result_dtype",
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[
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(np.array([1], dtype="int64"), None, Int64Dtype),
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(np.array([1, np.nan]), None, Int64Dtype),
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(np.array([1, np.nan]), "int8", Int8Dtype),
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],
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)
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def test_to_integer_array(values, to_dtype, result_dtype):
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# convert existing arrays to IntegerArrays
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result = IntegerArray._from_sequence(values, dtype=to_dtype)
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assert result.dtype == result_dtype()
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expected = pd.array(values, dtype=result_dtype())
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tm.assert_extension_array_equal(result, expected)
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