193 lines
6.1 KiB
Python
193 lines
6.1 KiB
Python
import numpy as np
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import pytest
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from pandas.compat import IS64
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import pandas as pd
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import pandas._testing as tm
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@pytest.mark.parametrize("ufunc", [np.abs, np.sign])
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# np.sign emits a warning with nans, <https://github.com/numpy/numpy/issues/15127>
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@pytest.mark.filterwarnings("ignore:invalid value encountered in sign")
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def test_ufuncs_single(ufunc):
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a = pd.array([1, 2, -3, np.nan], dtype="Float64")
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result = ufunc(a)
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expected = pd.array(ufunc(a.astype(float)), dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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s = pd.Series(a)
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result = ufunc(s)
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expected = pd.Series(expected)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("ufunc", [np.log, np.exp, np.sin, np.cos, np.sqrt])
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def test_ufuncs_single_float(ufunc):
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a = pd.array([1.0, 0.2, 3.0, np.nan], dtype="Float64")
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with np.errstate(invalid="ignore"):
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result = ufunc(a)
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expected = pd.array(ufunc(a.astype(float)), dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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s = pd.Series(a)
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with np.errstate(invalid="ignore"):
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result = ufunc(s)
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expected = pd.Series(ufunc(s.astype(float)), dtype="Float64")
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("ufunc", [np.add, np.subtract])
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def test_ufuncs_binary_float(ufunc):
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# two FloatingArrays
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a = pd.array([1, 0.2, -3, np.nan], dtype="Float64")
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result = ufunc(a, a)
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expected = pd.array(ufunc(a.astype(float), a.astype(float)), dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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# FloatingArray with numpy array
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arr = np.array([1, 2, 3, 4])
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result = ufunc(a, arr)
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expected = pd.array(ufunc(a.astype(float), arr), dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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result = ufunc(arr, a)
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expected = pd.array(ufunc(arr, a.astype(float)), dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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# FloatingArray with scalar
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result = ufunc(a, 1)
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expected = pd.array(ufunc(a.astype(float), 1), dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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result = ufunc(1, a)
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expected = pd.array(ufunc(1, a.astype(float)), dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize("values", [[0, 1], [0, None]])
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def test_ufunc_reduce_raises(values):
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arr = pd.array(values, dtype="Float64")
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res = np.add.reduce(arr)
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expected = arr.sum(skipna=False)
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tm.assert_almost_equal(res, expected)
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@pytest.mark.skipif(not IS64, reason="GH 36579: fail on 32-bit system")
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@pytest.mark.parametrize(
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"pandasmethname, kwargs",
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[
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("var", {"ddof": 0}),
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("var", {"ddof": 1}),
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("kurtosis", {}),
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("skew", {}),
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("sem", {}),
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],
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)
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def test_stat_method(pandasmethname, kwargs):
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s = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, np.nan, np.nan], dtype="Float64")
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pandasmeth = getattr(s, pandasmethname)
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result = pandasmeth(**kwargs)
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s2 = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype="float64")
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pandasmeth = getattr(s2, pandasmethname)
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expected = pandasmeth(**kwargs)
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assert expected == result
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def test_value_counts_na():
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arr = pd.array([0.1, 0.2, 0.1, pd.NA], dtype="Float64")
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result = arr.value_counts(dropna=False)
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idx = pd.Index([0.1, 0.2, pd.NA], dtype=arr.dtype)
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assert idx.dtype == arr.dtype
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expected = pd.Series([2, 1, 1], index=idx, dtype="Int64")
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tm.assert_series_equal(result, expected)
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result = arr.value_counts(dropna=True)
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expected = pd.Series([2, 1], index=idx[:-1], dtype="Int64")
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tm.assert_series_equal(result, expected)
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def test_value_counts_empty():
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ser = pd.Series([], dtype="Float64")
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result = ser.value_counts()
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idx = pd.Index([], dtype="Float64")
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assert idx.dtype == "Float64"
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expected = pd.Series([], index=idx, dtype="Int64")
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tm.assert_series_equal(result, expected)
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def test_value_counts_with_normalize():
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ser = pd.Series([0.1, 0.2, 0.1, pd.NA], dtype="Float64")
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result = ser.value_counts(normalize=True)
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expected = pd.Series([2, 1], index=ser[:2], dtype="Float64") / 3
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assert expected.index.dtype == ser.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.parametrize("min_count", [0, 4])
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def test_floating_array_sum(skipna, min_count, dtype):
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arr = pd.array([1, 2, 3, None], dtype=dtype)
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result = arr.sum(skipna=skipna, min_count=min_count)
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if skipna and min_count == 0:
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assert result == 6.0
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else:
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assert result is pd.NA
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@pytest.mark.parametrize(
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"values, expected", [([1, 2, 3], 6.0), ([1, 2, 3, None], 6.0), ([None], 0.0)]
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)
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def test_floating_array_numpy_sum(values, expected):
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arr = pd.array(values, dtype="Float64")
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result = np.sum(arr)
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assert result == expected
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@pytest.mark.parametrize("op", ["sum", "min", "max", "prod"])
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def test_preserve_dtypes(op):
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df = pd.DataFrame(
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{
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"A": ["a", "b", "b"],
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"B": [1, None, 3],
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"C": pd.array([0.1, None, 3.0], dtype="Float64"),
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}
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)
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# op
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result = getattr(df.C, op)()
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assert isinstance(result, np.float64)
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# groupby
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result = getattr(df.groupby("A"), op)()
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expected = pd.DataFrame(
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{"B": np.array([1.0, 3.0]), "C": pd.array([0.1, 3], dtype="Float64")},
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index=pd.Index(["a", "b"], name="A"),
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("skipna", [True, False])
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@pytest.mark.parametrize("method", ["min", "max"])
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def test_floating_array_min_max(skipna, method, dtype):
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arr = pd.array([0.0, 1.0, None], dtype=dtype)
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func = getattr(arr, method)
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result = func(skipna=skipna)
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if skipna:
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assert result == (0 if method == "min" else 1)
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else:
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assert result is pd.NA
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@pytest.mark.parametrize("skipna", [True, False])
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@pytest.mark.parametrize("min_count", [0, 9])
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def test_floating_array_prod(skipna, min_count, dtype):
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arr = pd.array([1.0, 2.0, None], dtype=dtype)
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result = arr.prod(skipna=skipna, min_count=min_count)
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if skipna and min_count == 0:
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assert result == 2
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else:
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assert result is pd.NA
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